INVESTIGATION INTO FULLY – ELECTRIC AND HYBRID – ELECTRIC POWERTRAINS FOR ROLLING STOCK

ATHANASIOS IRAKLIS

SUPERVISORS: CHARALAMPOS DEMOULIAS ROB HENSEN, KASPER VAN ZUILEKOM, ERIC VAN BERKUM

FACULTY OF ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT OF ELECTRICAL ENERGY ARISTOTLE UNIVERSITY OF THESSALONIKI

THESSALONIKI, GREECE 2015 Acknowledgements

Foremost, I would like to express my sincere gratitude to Charalampos Demoulias, my supervisor from Aristotle University of Thessaloniki, for supporting my work and providing me with immense knowledge and insightful comments. Also, special thanks to Kasper van Zuilekom and Eric van Berkum, my supervisors from the University of Twente, for providing me with great amount of knowledge, information and support. Their guidance helped me a lot during the research and writing of this thesis. Also, sincere thanks to Rob Hensen for the dream, Ellen Linnenkamp and Rudi Broekhuis for the support, and of course Erik Hoogma for being there for me, everytime I needed help. Thanks for the amazing journey, your patience, your enthusiasm and the high degree of freedom I was generally given during this investigation. I would also like to thank Edwin de Kreij, Rene Cohlst, and Martijn Elias for the assistance they provided at various levels and their helpful comments along the way.

I dedicate this thesis to my brother, Chris Intentionally Blank Page Index Acknowledgements - Chapter 1: 1.1 Brief Introduction 1 1.2 Thesis Objective 2 1.3 Thesis Outline 3 Chapter 2: 2.1 Battery-Powered and Hybrid Operations 5 2.1.1 General Considerations and Goals 5 2.1.2 Previous Developments in HEVs and Energy Storage 7 2.1.3 Control Strategies in Different Hybrid-Electric Configurations 10 2.1.3.1 Series HEV powertrain operating modes 11 2.1.3.2 Parallel HEV powertrain operating modes 12 2.1.3.3 Power-Split HEV powertrain operating modes 13 2.1.3.4 Complex HEV powertrain operating modes 15 2.1.3.5 Full-Hybrids 18 2.2 Energy Storage for EVs and HEVs 19 2.2.1 Electrochemical Batteries (Lithium-Ion) 19 2.2.2 23 2.3 Energy Management And Distribution 25 2.4 Integrated System Optimization 28 2.5 Generic Modeling Structure 31 Chapter 3: 3.1 Modeling methodology 36 3.2 Internal Combustion Engine Modeling 38 3.3 /Generator Modeling 40 3.4 Energy Storage Modeling 45 3.4.1 Electrochemical Battery Modeling 45 3.4.2 Modeling 49 3.4.3 Temperature Estimation of Energy Storage cells 53 3.5 Power Demands Calculation 55 3.5.1 Acceleration Resistance 56 3.5.2 Running Resistance 57 3.5.2.1 Running Resistance in General 57 3.5.2.2 Rolling Resistance 63 3.5.2.3 Aerodynamic Resistance 66 3.5.3 Other Resistances 73 3.6 Power division 75 3.7 Verification of Energy Demands 83 3.7.1 Case I - Gelede Lang (GTL8) Tram 83 3.7.2 Case II - NS Intercity Passenger (VIRM) 98 Chapter 4: 4.1 CASE I - Elimination of a Tram's Overhead Catenary System Through Battery Electrification 102 4.2 CASE II - Hybridization of a Diesel-Electric Configuration 110 Chapter 5: 5.1 Summary - Conclusions 116 5.2 Future Work and Modeling Improvements 117 References 119 Appendix 123 CHAPTER 1

1.1 Brief Introduction

Mass transportation rail systems have historically relied on high voltage DC or AC overhead catenary lines, third-rail lines or primary energy carriers – mostly fossil liquid hydrocarbons - to satisfy their power and energy demands. In Europe, approximately half of the continent’s transit systems run on electricity but still, there are many and running on fossil fuels by using low efficiency internal combustion engines. The International Energy Agency (IEA) recently published information on the percentages that took part in the rail sector for the year 2012, regarding the total energy consumption and where this energy came from. By that year, worldwide, 73% (19 Mtoe) of the total energy used was extracted from oil products by the process of combustion and 27% (7 Mtoe) of it relied on electricity, by using overhead or third-rail systems. In Greece, 100% (0.03 Mtoe) of the total energy demands relied on oil products, although it is known that currently, main train and tram lines rely on electricity, while the percentages for the Netherlands were 17% (0.03 Mtoe) for oil products and 83% (0.15 Mtoe) for electricity. [IEA: 1 toe is equal to 41.868 GJ, 11.63 MWh or 0.99 t of diesel]

Figure 1.1.1: Rail Sector Energy Consumption - 2012, www.iea.org, left: Greece, center: Worldwide, right: Netherlands

However, catenary-free hybrid-electric and fully-electric operations, with maximized energy efficiency, minimized local and global emissions and which eliminate the installation and maintenance costs of overhead and third-rail high-voltage power lines, are fast gaining prominence, especially in developed countries. Better and low cost energy storage devices, with high energy density and high discharging and charging rates, providing greater lifetime and efficiency, are also affecting the break-even point of the transformation of the rail sector as we know it. Hybrid-electric and fully-electric powertrains with on-board energy storage are already considered technologies leading to the direction of reducing fuel consumption and emissions by increasing total system efficiency, not only for on-site combustion systems but also for electric

1 propulsion systems. Power for such operations is extracted either by using a combination of fuel consuming units, such as internal combustion engines/fuel cells, and energy storage devices along with specific power/energy-flow strategies, or by simply charging and discharging an on/off–board energy storage package, complying of one or more devices with different characteristics and operation limits, while fulfilling certain technical, performance and comfort constraints. Meeting maximum performance and efficiency criteria is always of top priority. In general, catenary–free and innovative hybrid solutions could offer various benefits to the railway sector, compared to existing used technologies. They are very promising towards the reduction of the construction costs, lifetime maintenance costs, as well as costs associated with design and approval of catenary systems and pantographs, while reducing the local and global energy/fuel consumption and emissions by using regenerative braking, smart control systems and optimized power/energy–flow concepts for achieving high system efficiency. Furthermore, they could allow more flexibility in rolling stock design because many conditions are relaxed (such as specified headroom and vehicle height) while being less susceptible to severe weather conditions such as snow, blizzard and storms, which may damage overhead or third-rail systems. Many approaches that could allow the elimination of overhead catenary and third-rail lines and reduce the energy/fuel consumption of existing fuel consuming systems have been introduced so far and are already proven technology in the automotive industry and in applications where a wireless high efficiency power supply is needed, thus an on-site energy providing unit is required. One drawback of the current proposed approaches in the automotive sector is the high investment cost because of high system complexity, adoption of expensive components and the lack of optimized vehicle parameters and the power-flow strategy for given driving schedules used worldwide. Regarding the modeling and simulation of such powertrains, it is important to mention that in most – if not all – cases, these new systems have a higher degree of complexity than the traditional approaches. Additional electric motors, special internal combustion engines, energy storage devices, torque converters-splitters, electronically controlled clutches and operation and/or energy management units are added with the intention to improve the system's behavior. For the investigation into such complex systems (sizing of components, defining the power/energy–flow strategy and optimizing the driving performance), where simulation of different driving scenarios may take place while looking at the total power demands, energy demands and components' outputs, special modeling of the vehicle's powertrain and its behavior is required.

1.2 Thesis Objective

The primary objective of this study, is to develop a mathematical/simulation model of fully-electric and hybrid-electric powertrains for investigations into mass transit rolling stock systems within the environment of

2 Matlab/Simulink. The outcome of this assignment could provide an open-source low-complexity system with high degree of freedom, in terms of changeability, capable of calculating the total power and energy demands, estimating the fuel consumption and emissions of fuel consuming units and approximating the behavior of on- board or off-board energy-storage devices that interact with the rest of the powertrain or an existing overhead catenary or third-rail line in different configurations, adopting different power/energy-flow strategies and for different driving scenarios/schedules, while staying within certain operating limits, for given vehicle parameters. The ultimate goal of this investigation is to provide a tool capable of cooperating with optimization algorithms mostly used for optimum sizing of powertrain components and efficiently optimum definition of the power/energy-flow strategies chosen for different drivetrain topologies. Furthermore, the validation of the energy demands calculated by the developed model for given speed profiles, is not of less importance, but also considered of top priority. At last but not least, we are investigating how battery cells with different operating characteristics could or could not be used for the refurbishment of existing light rail, towards the elimination of overhead catenary systems or the reduction of fuel consumption and emissions because of the combustion that takes place in diesel-electric configurations, for the speed profiles used during the verification procedure.

1.3 Thesis Outline

The thesis report is organized in five chapters, whose content summary is presented below:

In chapter 1, we went through a brief introduction to the main topic and the main objective of this study, complying of modeling, verification of the energy demands and investigation into different battery cells for tram and intercity train applications.

In chapter 2, the basic concepts of hybrid- propulsion systems are described and all already known hybrid-electric powertrain configurations with energy storage are briefly discussed. Furthermore, the influence of different power/energy-flow strategies in different operating modes is reviewed. We also go through a discussion of energy management and distribution issues along with an integrated system optimization approach, while we also present the generic modeling structure to be considered for this investigation.

In chapter 3, the modeling approach of this thesis is presented. The basic structure of the mathematical/simulation models adopted is discussed and the mathematical equations describing the behavior of the powertrain

3 components are defined. Also, the energy demands for two different cases are evaluated and the results are discussed.

In chapter 4, we are using the model implemented in Matlab/Simulink to evaluate the performance of different battery cells currently in mass production, that could be used as a power source for a city tram, in order to eliminate the existence of the currently used overhead catenary system. A second case for a passenger train tests the possibility of adopting the same concept for intercity trains as well, or using a battery package to reduce the fuel consumption of a diesel-electric configuration, thus converting it to a series hybrid-electric trainset. We will see how more than 20% in fuel reduction can be achieved by controlling the operating point of the internal combustion engine within the maximum efficiency operating area and for a specified rotational speed.

In chapter 5, we discuss the conclusions of this study, and propose directions for future work and further improvements.

4 CHAPTER 2

2.1 Battery-Powered and Hybrid Operations

2.1.1 General Considerations and Goals Conventional rolling stock vehicles with internal combustion engines ( ICE) show good performance and long operating range by utilizing the high energy-density advantage of fossil fuels. However, conventional vehicles have the disadvantages of poor fuel economy and environmental pollution due to the ongoing combustion process. The main reasons for their poor fuel economy are:

1. Engine fuel efficiency characteristics are mismatched with the real operation requirements, especially in the railway sector, as sometimes they are designed for different driving cycles.

2. Dissipation of vehicle's kinetic energy during braking, especially in urban areas with much more e- braking capacity.

3. Low efficiency of hydraulic and purely mechanical transmission in current vehicles in stop-and-go driving profiles.

Current electric powertrains show great performance but they exhibit increasing infrastructure maintenance costs due to the usage of overhead catenary and third-rail electrification systems. Also, as already mentioned, severe weather conditions often cause problems, decreasing the overall reliability of such systems. Battery-powered Electric Vehicles (EV), on the other hand, have some advantages over conventional and current electric vehicles, such as high energy efficiency and zero environmental pollution. However, the performance, especially the operation range per battery charge, is less competitive than internal combustion engine vehicles, due to the lower energy density of batteries and supercapacitors, although already having charging stations in the rail sector provides great opportunities towards the direction of full electrification with energy storage on-board. On the other hand, Hybrid Electric Vehicles (HEV), which use two power sources – a primary (average) power source and a secondary (peaking) power source – have the advantages of both conventional vehicles and EVs and are proven technology that overcomes the disadvantages described above for ICEs and the limited driving range of EVs with energy storage, and they could be the intermediate stage between currently used technologies and full battery electrification, which is the ultimate goal. The opportunity in different operation modes in a hybrid-electric powertrain creates more flexibility. With proper configuration, tuning and optimized power flow control, applying a specific mode for each operating condition can improve overall performance, efficiency, fuel economy and emissions, keeping the operating range at the level of a conventional/diesel-electric configuration. However, in a practical design, deciding which mode should be used depends on many criteria, such as the physical configuration of the

5 drivetrain - available space -, the powertrain efficiency characteristics, load characteristics, driving cycles and future driver's behavior. Operating a powertrain in its highest efficiency area is very important for the overall efficiency of the vehicle. An internal combustion engine generally has the best efficiency operating region with a wide throttle opening. Operating away from this region will cause the efficiency to suffer. On the other hand, efficiency suffering in an electric motor is not as detrimental when compared to an internal combustion engine that operates away from its optimal region or line. Most of the times, the load power of a vehicle varies randomly in real operation conditions due to frequent acceleration, deceleration, and climbing up and down grades, although it can be pre-known in rail applications, as the driving schedules are quite fixed and the vehicle parameters already defined. In a hybrid- electric vehicle design, for maximizing the overall efficiency of the powertrain, the load power is usually decomposed in two components: one is steady (average) power with a pretty much constant value and can be provided by the average power source, and the other is dynamic power, which has a zero average and can be provided by the peaking power source (batteries, supercapacitors etc.). Decomposition of the demanded power profile is always a matter of huge importance in real applications of any kind. In such designs, one powertrain that favors steady-state operation conditions, such as an internal combustion engine or fuel cells, can be used to supply the average power component, as described above. On the other hand, other powertrains such as electric motors can be used to supply the dynamic power component more efficiently than the internal combustion engine. The total energy output from the dynamic powertrain will be zero in a whole driving profile. This means that the energy source of the dynamic powertrain does not lose energy capacity at the end of the driving cycle, if designed and sized properly. It functions only as a power ''damper''. A final optimized design is a compromise that uses certain efficiency regions of each component for the maximization of overall efficiency in different driving profiles. Moreover, the internal combustion engine or the fuel cells can be much smaller than the ones used in conventional powertrains because the dynamic power is provided by the dynamic power source, and can then operate steadily in their most efficient region. As already mentioned, the dynamic power may be provided by an electric motor powered by electrochemical batteries, super-capacitors, or combinations of those. A flywheel (mechanical energy storage) can also be used for this purpose. The definite advantage of the HEV is to greatly extend the original EV driving range, and to offer rapid refueling of liquid petrol or diesel. The key drawbacks of the HEV are the loss of zero-emission concept and the increased complexity compared to an EV. Nevertheless, the HEV is less polluting and has less fuel consumption than the ICE vehicle while having the same range. These merits are due to the fact that the ICE of the HEV can always operate in its most efficient area, with low emissions and low fuel consumption. Also, the HEV may be purposely operated as an EV in the zero-emission zones and areas where internal combustion engines are prohibited. Due to the variations in HEV configurations, different power control strategies are required to

6 regulate the power flow to or from different components.

These control strategies aim to a number of goals for HEVs. There are four key goals, in general:

1. Maximum fuel economy - fuel efficiency - reduction of fuel consumptions.

2. Best driving performance - smooth and low noise operation.

3. Minimum cost during production and operation - minimum investment cost.

4. Minimum emissions - environmental impact during production & operation.

2.1.2 Previous Developments in HEVs and Energy Storage

 U.S. Department of Energy - Office of Energy Efficiency and : ''Alternative fuel vehicles, including plug-in electric, natural gas, and flexible fuel vehicles reduce US dependence on oil, support the domestic economy and decrease carbon pollution. The Energy Department announced up to $4.5 million in funding to expand the use of alternative fuels through the Clean Cities program, which works with nearly 100 local coalitions and thousands of stakeholders across the country to decrease the use of petroleum in transportation. Electric trains are already big business in the EU, with almost half the trains working there being full electrics. However, a new regenerative braking system developed by MTU could soon be fitted to diesel trains, making the current ICE-driven European trains into hybrid trains.''

Rail Europe in www.hitachirail-eu.com on their newly developed hybrid V-Train: ''We worked in partnership with HSBC and carried out demonstrations with diesel mode trains and electric mode trains. The V-Train 2 was a high speed diesel . We installed the train in a British High Speed Train (HST) power car, the first of our hybrid system in Europe, and we anticipated that it could reduce fuel consumption by 20%. We were very excited to unveil this technology, which will demonstrate energy and emissions savings. This trial unlocked the future potential for the application of rapidly-improving battery hybrid-powered traction technology to future generations of rail vehicles in the UK.''

 Hybrid Trains are the Future: EU to fit KERS Systems to High-speed Rail in www.gas2.org: ''The first train fitted with MTU’s system was a German Classic VT 642 , which was originally powered by a pair of diesel engines. The hybrid train system consists of two 315 kilowatt- rated hybrid power packs and batteries, and the locomotive saw a 25% reduction in fuel consumption and greenhouse gas emissions with the regenerative braking system in place. That is HUGE when considering so many thousands of tons of people and cargo are moved by rail in the EU every day.''

 Primary electric locomotives, such as the Class 74, were converted from existing electric locomotives. The primary is an electric locomotive with a small auxiliary diesel intended only to operate on low-speed or for short distances. The Southern Region of British Railways

7 used these locomotives to cross non-electrified regions and to haul boat trains that used tramways at the ports of Southampton and Weymouth. For economy, the and its generator are smaller than the electric traction system. The southern types were of 1,600 hp or 'Type 3' rating as electrics, but only 600 hp on the diesel engine. Later classes had as much as 2,500 hp on electric power, but still the same diesel engines.

 In 1986, ČKD, a locomotive manufacturer, built a prototype hybrid shunting locomotive termed as the DA 600. The locomotive was powered by a 190 kW diesel engine and four electric motors, with a maximum overall power of 360 kW extracted from batteries. The batteries were recharged while the diesel engine was running, by regenerative braking or from an external electric power source. After tests on a railway test circuit and some minor tunings, the locomotive was lent to a train depot and successfully operated there for ten years. Mass production never started, mainly because of the lack of proper battery manufacturing capacities.

 In 2007, (GE) put a hybrid locomotive on display at Los Angeles Union Station. The locomotive used regenerative braking and a bank of high-capacity batteries that GE was constructing to achieve reduction in fuel consumption and lower emissions than previous conventional diesel locomotives. It was expected to join GE's current line of Evolution Series locomotives by May of 2007.

 In 2004, , a Canadian company, began running pilots in the United States with the ''Green Goat'' shunting locomotive. These diesel-electric hybrid trains were expected to cut emissions by up to 90% and to decrease fuel consumption by up to 60%, when compared to conventional diesel- powered locomotives. The Green Goat locomotives were intended to be used in yards. To make its Green Goat hybrid-electric locomotive, Railpower replaced the large diesel engine of an existing locomotive with a battery pack and a small, highly efficient, diesel engine for recharging. The Green Goat not only used less fuel than a regular , it was also less noisy - making it better suited for operations in congested city centers. Unfortunately, Railpower found that most large railroads had not bought a yard locomotive for years, if not decades. Instead, they were simply downgrading older locomotives that were no longer suitable for hauling trains along the main line. The Green Goat locomotive was more efficient, but yard owners were not interested. [Hybrid Locomotive Maker Loses Steam by Ian Austen on green.blogs.nytimes.com, February 5, 2009]

 Sinara Transport Machines has developed the TEM9H, a hybrid version of its TEM9 series diesel- electric shunting locomotives, at its Lyudinovsky Locomotive Plant. This is a 1,200 , four- axle, locomotive with lithium-ion batteries, supercapacitors and AC motors.

 The city of Savannah, Georgia tested the operation of a W class Melbourne tram in service as a bio- diesel fueled hybrid-electric with an on-board battery storage, in 2008. Regular service of the streetcar

8 started in 2009.

 Since 1936, a fleet of London Underground battery-electric locomotives have been used for engineering work on the Tube system. These locomotives operate by either using a third-rail, or a battery bank which can be recharged on the move.

 A Sunday-service operated on the Stourbridge Town Branch Line for a period of two years, using a flywheel-based energy storage system built by . In 2008, a pair of rail-cars were ordered to provide full service on the branch line from 2009 onwards.

 During 2007, a modified Class 43 power-car ran on the Great Central Railway and then as part of the Network Rail New Measurement Train (a 200 km/h track-recording train). The system developed by Hitachi used a battery-assisted diesel-electric drive system; the hope was that it would demonstrate a cut in emissions by up to 50% and a reduction in fuel consumption of 20%. The modified locomotive, named Hayabusa, was semi-permanently attached to a converted passenger carriage containing the battery bank during the testing period.

 In 2009, it was announced that Hitachi, as preferred contractors for the Intercity Express Programme (IEP) would deploy hybrid technology on the self-powered variants of the IEP trains. These trains would be capable of 200 km/h. Some hybrid-electric trains would also be bi-modal, allowing power to be taken from overhead catenary lines.

 In 2003, JR East started test runs using a NE (New Energy) train, testing the system performance in cold regions. The design had two 65-kilowatt fuel cells and six hydrogen tanks under the floor, with a lithium-ion battery on the roof. The test train was capable of 100 km/h (60 mph) with a range of 50–100 km (30–60 mi) between hydrogen refills. Research was underway into the use of regenerative braking to recharge the test train's batteries, intending to increase the range further. JR had stated that it hoped to introduce the train into scheduled local service during the summer of 2007. This production version became the KiHa E200 diesel/battery rail-car. The prototype locomotive, HD300-901, was delivered from the factory in Fuchū, Tokyo to Tokyo Freight Terminal in 2010. The new locomotive used lithium-ion batteries, and was designed to reduce exhaust emissions by at least 30% to 40% and noise levels by at least 10 dB compared to existing Class DE10 diesel locomotives.

 Earlier hydrogen locomotive technology lent itself to smaller applications, such as the BNSF Port of Los Angeles HH 1205 locomotive developed in Kansas.

 In another project, the South African mining company AMPLATS started changing some of its electric locomotives to hydrogen fuel cell rail-cars. The mini hyd-rail locos refuel much faster than the electric versions can recharge, and have no emissions, crucial to the cramped underground passages in which they work.

9  Small, of course, is on the beginning, and if electric trains, such as the high-speed TGV, were the last major evolutionary step, the hydrogen fuel cell locomotive could very well be the next big step. The benefits are numerous, including zero-emissions and powerful but efficient electric traction. Cost, though, could be a major deciding factor, when you consider that laying down an electric railway costs millions per mile, but hydrogen refueling stations only need to be built at train stops.

2.1.3 Control Strategies in Different Hybrid-Electric Configurations The design of power flow control strategies for HEVs involves different considerations. Some key considerations are summarized below [1] (Fuel Cell Vehicles should also be considered as HEVs but with fuel cells instead of ICEs – the considerations below are similar):

1. Optimal ICE Operating Point: The optimal operating point on the torque – speed profile of the ICE can be based on the maximization of fuel economy, the minimization of emissions, or even a compromise between fuel economy and emissions.

2. Optimal ICE Operating Line: In case the ICE needs to deliver different power demands, the corresponding optimal operating points constitute an optimal operating line. The optimization is based on the minimum fuel consumption which is equivalent to the maximum fuel economy.

3. Optimal ICE Operating Region: The ICE has a preferred operating region on the torque – speed plane, in which the fuel efficiency remains optimum.

4. Minimum ICE Dynamics: The ICE operating speed needs to be regulated in such a way that any fast fluctuations are avoided, thus minimizing the ICE dynamics.

5. Minimum ICE Speed: When the ICE operates at low speeds, the fuel efficiency is very low. The ICE should be cut off when its speed is below a threshold value.

6. Minimum ICE Turn-On Time: The ICE should not be turned on and off frequently; otherwise, it results in additional fuel consumption and emissions. A minimum turn-on time should be set to avoid such drawbacks. This is a very important consideration that causes problems in stop-and-go driving cycles. Such driving profiles are not involved in long journey locomotives, but they do in light rail applications, intercity trains, and switching

10 locomotives.

7. Proper Battery Capacity: The battery capacity needs to be kept at a proper level so that it can provide sufficient power for acceleration and can accept regenerative power during braking or downhill. When the battery capacity is high, the ICE should be turned off or operated idly. When the capacity is low, the ICE should increase its output to charge the battery as fast as possible.

8. Safety Battery Voltage: The battery voltage may be significantly altered during discharging, generator charging or regenerative charging. This battery voltage should not be over-voltage or under-voltage; otherwise, the battery may be permanently damaged.

9. Battery Lifetime/Cycling/Aging: Different battery/super-capacitor cells offer various values of number of cycles before their capacity drops down to 70-80% of the initial maximum capacity. Aging also affects the energy storage as well and it should be considered during the sizing procedure of the package designed for dynamic or full power purposes.

10. A non-technical nevertheless very important consideration has to do with Geographical Policies: In certain cities or areas, the HEV needs to be operated in the pure electric mode. This change-over should be controlled manually and / or automatically.

For each specific hybrid configuration the operating modes are designed to satisfy the above criteria. The general and main operating modes found in existing topologies are briefly described below:

2.1.3.1 Series HEV powertrain operating modes

In the series hybrid system, the power flow control can be illustrated by four operating modes as shown in the figures below (FT = Fuel Tank, DC = DC-bus):

 During start-up, normal driving or acceleration of the series HEV, both the ICE - through the generator (EG) - and the energy-storage (ES) deliver electrical power output to the power converters (PC) which then drives the electric motor (EM) and hence the wheels via the transmission.

 At light load, the ICE output is greater than that required to drive the wheels so that the generated electrical energy is also used to charge the ES until the ES capacity reaches a proper level.

 During braking or deceleration, the electric motor acts as a generator which transforms the kinetic energy of the wheels into electricity, hence charging the battery via the power converters.

11  Also, the ES can be charged by the ICE via the generator and power converters, even when the vehicle comes to a complete stop.

1. Start-up/Normal load/Acceleration/Uphill: 2. Light load/Downhill/Low State-Of-Charge:

FT ICE EG PC FT ICE EG PC

DC PC EM DC PC EM

ES PC ES PC

3. Deceleration/Downhill + battery charging: 4. Standing still and charging the battery pack:

FT ICE EG PC FT ICE EG PC

DC PC EM DC PC EM

ES PC ES PC

Figure 2.1.1: Series HEV powertrain operating modes

2.1.3.2 Parallel HEV powertrain operating modes

In the parallel hybrid system, the power flow control can be illustrated by four operating modes as shown in the figure below:

 During start-up or full-throttle acceleration, both the ICE and electric motor share the required power to propel the vehicle. Typically, the relative distribution between the ICE and the electric motor is 80% - 20%.

 During normal driving, the ICE solely supplies the necessary power to propel the vehicle while the electric motor remains in the off mode.

 During braking or deceleration, the electric motor acts as a generator to charge the ES via the power converters.

 Also, since both the ICE and the electric motor are coupled to the same drive shaft, the ES can be charged by the ICE via the electric motor when the vehicle is at light load.

12 1. Start-up/Acceleration/Uphill(Boost mode): 2. Normal or light load/Low State-Of-Charge:

FT ICE FT ICE

R R

ES PC DC PC EM ES PC DC PC EM

3. Deceleration/Downhill + battery charging: 4. EV mode (for example inside green zones):

FT ICE FT ICE

R R

ES PC DC PC EM ES PC DC PC EM

Figure 2.1.2: Parallel HEV powertrain operating modes

2.1.3.3 Power-Split HEV powertrain operating modes

The series-parallel hybrid system involves the features of series and parallel hybrids. Thus, there are many possible operating modes to carry out its power flow control. Basically, we can identify them into two groups, namely ICE-heavy and Electric-heavy power-splits. The ICE-heavy one denotes that the ICE is more active than the electric motor for series-parallel hybrid propulsion, whereas the Electric-heavy one indicates that the electric motor is more active than the internal combustion engine. (R = Road)

α. ICE-Heavy Power-Split HEV powertrain operating modes

 At start-up, the ES solely provides the necessary power to propel the vehicle while the ICE is in the off mode.

 During full-throttle acceleration, both the ICE and electric motor share the required power to propel the vehicle.

 During normal driving, the ICE solely provides the necessary power to propel the vehicle while the electric motor remains in the off mode.

 During braking or deceleration, the electric motor acts as a generator to charge the ES via the power converters.

 For ES charging during driving, the ICE not only drives the vehicle but also the generator to charge the

13 ES via the power converter.

 When the vehicle is at a standstill, the ICE can keep driving the generator to charge the ES.

1. Start-up 2. Acceleration/High load 3. Normal driving

FT ICE FT ICE FT ICE

EG R EG R EG R

ES PC EM ES PC EM ES PC EM

4. Braking 5. Charging while driving 6. Charging while standing still

FT ICE FT ICE FT ICE

EG R EG R EG R

ES PC EM ES PC EM ES PC EM

Figure 2.1.3: ICE-Heavy Power-Split HEV powertrain operating modes

b. Electric-Heavy Power-Split HEV powertrain operating modes:

 During start-up and driving at light load, the battery solely feeds the electric motor to propel the vehicle while the ICE is in the off mode.

 For both full-throttle acceleration and normal driving, both the ICE and electric motor work together to propel the vehicle. The key difference is that the electrical energy used for full-throttle acceleration comes from both the generator and the batteries whereas that for normal driving is solely from the generator driven by the ICE. A planetary gear is usually employed to split up the ICE output, hence to propel the vehicle and to drive the generator.

 During braking or deceleration, the electric motor acts as a generator to charge the ES via the power converters.

 Also, for battery charging during driving, the ICE not only drives the vehicle but also the generator to

14 charge the ES.

 When the vehicle is at a standstill, the ICE can keep driving the generator to charge the ES.

1. Start-up 2. Acceleration/High load 3. Normal driving

FT ICE FT ICE FT ICE

EG R EG R EG R

ES PC EM ES PC EM ES PC EM

4. Braking 5. Charging while driving 6. Charging while standing still

FT ICE FT ICE FT ICE

EG R EG R EG R

ES PC EM ES PC EM ES PC EM

Figure 2.1.4: Electric-Heavy Power-Split HEV powertrain operating modes

2.1.3.4 Complex HEV powertrain operating modes

The development of complex hybrid control has been focused on the dual-axle propulsion system for HEVs in the automotive industry, and so this sector will refer to automobiles. The general concept can be easily transferred to multi-axle propulsion systems for rail applications. In this system, the front-wheel axle and rear- wheel axle are separately driven. There is no propeller shaft to connect the front and rear wheels, so in enables a more lightweight propulsion system and increases the vehicle packaging flexibility. Moreover, regenerative braking on all four wheels can significantly improve the vehicle fuel efficiency and hence the fuel economy. Two groups are identified, like in power-splits. On the 1 st group the front-wheel axle is propelled by a hybrid powertrain and the rear-wheel axle is driven by an electric motor and on the 2 nd group the front-wheel axle is driven by an electric motor and the rear-wheel axle is propelled by a hybrid powertrain. Six operating modes are illustrated, for both configurations. (FW = Front Wheels, RW = Rear Wheels)

15 α. Complex Front-Hybrid Rear-Electric HEV powertrain operating modes

 During start-up, the ES delivers electrical energy to feed both the front and rear electric motors to individually propel the front and rear axles of the vehicle whereas the ICE is in the off mode.

 For full-throttle acceleration, both the ICE and front electric motor work together to propel the front axle while the rear electric motor also drives the rear axle. Notice that this operating mode involves three propulsion devices (one ICE and two electric motors) to simultaneously propel the vehicle.

 During normal driving and/or ES charging, the ICE output is split up to propel the front axle and to drive the electric motor (which works as a generator) to charge the ES. The corresponding device to mechanically couple the ICE, the front electric motor and the front axle all together is usually based on a planetary gear-set.

 When driving at light load, the battery delivers electrical energy to the front electric motor only to drive the front axle whereas both the ICE and rear electric motor are off.

 During braking or deceleration, both the front and rear electric motors act as generators to charge the ES.

 A unique feature of this dual-axle system is the capability of axle balancing. In case the front wheels slip, the front electric motor works as a generator to absorb the change of ICE output power. Through the ES, this power difference is then used to drive the rear wheels to achieve axle balancing.

1. Start - up 2. Acceleration 3. Normal driving + Charging

FT ICE FW FT ICE FW FT ICE FW

PC EM PC EM PC EM

ES PC EM RW ES PC EM RW ES PC EM RW

Figure 2.1.5 [a]: Complex Front-Hybrid Rear-Electric HEV powertrain operating modes

16 4. Light load 5. Deceleration 6. Axle balancing + Charging

FT ICE FW FT ICE FW FT ICE FW

PC EM PC EM PC FM

ES PC EM RW ES PC EM RW ES PC EM RW

Figure 2.1.5 [b]: Complex Front-Hybrid Rear-Electric HEV powertrain operating modes

b. Complex Front-Electric Rear-Hybrid HEV powertrain operating modes

 During start-up, the battery delivers electrical energy only to the front electric motor which in turn drives the front axle of the vehicle whereas both the ICE and rear electric motor are off. Once the vehicle moves forward, the battery also delivers electrical energy to the rear electric motor which functions to quickly rise up the ICE speed, thus starting the ICE.

 For full-throttle acceleration, the front electric motor drives the front axle while both the ICE and rear electric motor work together to propel the rear axle. So, there are three propulsion devices (one ICE and two electric motors) simultaneously propelling the vehicle.

 During normal driving, the ICE works alone to propel the rear axle of the vehicle.

 During braking or deceleration, both the front and rear electric motors act as generators to charge the ES.

 For battery charging during driving, the ICE output is split up to propel the real axle and to drive the rear electric motor (which works as a generator) to charge the ES.

1. Start - up 2. Starting the ICEngine 3. Acceleration/Uphill

FT ICE RW FT ICE RW FT ICE RW

PC EM PC EM PC EM

ES PC EM FW ES PC EM FW ES PC EM FW

Figure 2.1.6 [a]: Complex Front-Electric Rear-Hybrid HEV powertrain operating modes

17 4. Light load 5. Deceleration/Braking 6. Driving + charging

FT ICE RW FT ICE RW FT ICE RW

PC EM PC EM PC EM

ES PC EM FW ES PC EM FW ES PC EM FW

Figure 2.1.6 [b]: Complex Front-Electric Rear-Hybrid HEV powertrain operating modes [b]

2.1.3.5 Full Hybrids Power-split and complex configuration are sometimes called F ull Η ybrids. Full hybrid is a vehicle that can run by only using the engine, the batteries, or a combination of those two. A large, high-capacity battery pack is required for electric-only operation. These vehicles have a power-split path that allows more flexibility in the drivetrain at some cost in complexity. In general, the operation of such a full hybrid system can be divided into six distinct modes which actually comply the basic power flow strategy mostly used [2]:

1. Electric vehicle mode: The engine is turned off, and the battery package provides all the energy to power the electric motor (or the reverse while regenerative braking). Used as well when the battery State Of Charge (SOC) is high.

2. Cruise mode: The vehicle is cruising, and the engine can meet the road load demand. The power from the engine is split between the mechanical path and the generator. The battery package provides electrical energy to power the motor, whose power is added to that of the internal combustion engine. If the battery state-of- charge is low, part of the power from the generator is directed towards the DC bus to charge the battery.

3. Overdrive mode: A portion of the rotational energy is siphoned off by the main electric motor, operating as a generator, to produce electrical energy. This electrical energy is then used to drive the sun gear (electrically operated epicyclic gear in general) of the planetary gear-set in direction opposite to its usual rotation. The result is the engine rotating slower than the ring gear, at higher torque providing better efficiency.

4. Battery charging mode: Also used for idling, except that in this case the battery state-of-charge is low and requires charging, which is provided by the internal combustion engine and the coupled to it.

18 5. Power boost mode: It is used in cases where the internal combustion engine cannot meet the road load demand. The battery is then used to power the electric motor to provide a boost to the engine output power.

One more mode can be applied and that could lead to very promising results, in terms of fuel economy, when the vehicle is cruising and the internal combustion engine needs to be slowed down, so it can operate in a more efficient point of the fuel consumption map. This very specific mode is described below:

6. Negative split mode: The vehicle is cruising and the battery state-of-charge is high. The battery provides power to both the motor (to provide mechanical power) and to the generator which is coupled to the internal combustion engine and controlled as a motor in the first quadrant. The generator converts this to mechanical energy that it directs towards the engine shaft, slowing it down (although not altering its torque output). The purpose of this engine "lugging" is to increase the fuel economy of the vehicle.

2.2 Energy Storage for EVs and HEVs

2.2.1 Electrochemical Batteries (Lithium-Ion) Lithium-ion batteries vary and the difference lies mainly in the cathode materials. Innovative materials are also appearing in the anode to modify or replace graphite. In the descriptions below each Li-ion system is listed by its full name, chemical definition, abbreviations and short form.

1. Lithium Cobalt Oxide(LiCoO2): The battery consists of a cobalt oxide cathode and a graphite carbon anode. The cathode has a layered structure and during discharge lithium ions move from the anode to the cathode. The flow reverses on charge. The drawback of Li-cobalt is a relatively short life span, low thermal stability and limited load capabilities (specific power). Li-cobalt cannot be charged and discharged at a current higher than its rating. Forcing a fast charge or applying a load higher than its rated causes overheating and undue stress. For optimal fast charge, the manufacturer recommends a C-rate of 0.8C . The mandatory battery protection circuit should limit the charge and discharge rate to a safe level of about 1C.

2. Lithium Manganese Oxide (LiMn2O4): Lithium insertion in manganese spinels was first published in the Materials Research Bulletin in 1983. In 1996, Moli Energy commercialized a Li-ion cell with lithium manganese oxide as a cathode material. The

19 architecture forms a three-dimensional spinel structure that improves ion flow on the electrode, which results in lower internal resistance and improves current handling. A further advantage of spinel is high thermal stability and enhanced safety, but the cycle and calendar life is limited. Low internal cell resistance promotes fast charging and high-current discharging. Li-manganese has a capacity that is roughly one-third lower compared to Li-cobalt but the battery still holds about 50 percent more energy than nickel-based chemistries. Design flexibility allows engineers to maximize the battery for either optimal longevity (life span), maximum load current (specific power) or high capacity (specific energy).

3. Lithium Nickel Manganese Cobalt Oxide (LiNiMnCoO2 or NMC):

Leading battery manufacturers focus on a cathode combination of nickel-manganese-cobalt (NMC). Similar to Li-manganese, these systems can be tailored for high specific energy or high specific power, but not both. A silicon-based anode will have higher specific power but at reduced loading and shorter cycle life. The secret of NMC lies in combining nickel and manganese. An analogy of this is table salt, in which the main ingredients of sodium and chloride are toxic on their own but mixing them serves as seasoning salt and food preserver. Nickel is known for its high specific energy but poor stability; manganese has the benefit of forming a spinel structure to achieve low internal resistance but offers a low specific energy. Combining the metals enhances each other strengths. NMC is the battery of choice for power tools, e-bikes and other electric powertrains. The cathode combination of typically one-third nickel, one-third manganese and one-third cobalt offers a unique blend that also lowers raw material cost due to reduced cobalt content. Other combinations, such as NCM, CMN, CNM, MNC and MCN are also being offered in which the metal content of the cathode deviates from the 1/3-1/3-1/3 formula. Manufacturers keep the exact ratio a well-guarded secret.

4. Lithium Iron Phosphate(LiFePO4): In 1996, the University of Texas (and other contributors) discovered phosphate as cathode material for rechargeable lithium batteries. Li-phosphate offers good electrochemical performance with low resistance. This is made possible with nano-scale phosphate cathode material. The key benefits are high current rating and long cycle life, besides good thermal stability, enhanced safety and tolerance if abused. Li-phosphate is more tolerant to full charge conditions and is less stressed than other lithium-ion systems if kept a high voltage for a pronged time. As trade-off, the lower voltage of 3.2V/cell reduces the specific energy to less than Li-manganese. As with most batteries, cold temperature reduces performance and elevated storage temperature shortens the service life, and Li-phosphate is no exception. Li-phosphate has a higher self-discharge than other Li-ion batteries, which can cause balancing issues with aging.

20 5. Lithium Nickel Cobalt Aluminum Oxide (LiNiCoAlO2): Lithium Nickel Cobalt Aluminum Oxide battery, or NCA, has been around since 1999 for special application and shares similarity with NMC by offering high specific energy and reasonably good specific power and a long life span. Less flattering are safety and cost. NCA is a further development of lithium nickel oxide; adding aluminum gives the chemistry greater stability.

6. Lithium Titanate (Li4Ti5O12): Batteries with lithium titanate anodes have been known since the 1980s. Li-Titanate replaces the graphite in the anode of a typical lithium-ion battery and the material forms into a spinel structure. The cathode is graphite and resembles the architecture of a typical lithium-metal battery. Li-Titanate has a nominal cell voltage of 2.40V, can be fast-charged and delivers a high discharge current of 10C, or 10 times the rated capacity. The cycle count is said to be higher than that of a regular Li-ion. Li-Titanate is safe, has excellent low-temperature discharge characteristics and obtains a capacity of 80 percent at –30 C (–22  F). However, the battery is expensive and at 65Wh/kg the specific energy is low, rivaling that of NiCd. Li-Titanate charges to 2.80V/cell, and the end of discharge is 1.80V/cell. While NCA is the clear winner by storing more capacity than other systems, this only applies to specific energy. In terms of specific power (load characteristics) and thermal stability, Li-Manganese and Li-Phosphate are superior. Li-Titanate may have low capacity but this chemistry outlives most other secondary batteries in terms of life span. It has also the best cold temperature performance. As we move towards electric powertrains, safety and cycle life are becoming more important than capacity alone.

Upcoming Chemistries:

1. Lithium-Sulfur (Li-S):

The Lithium–Sulfur battery (Li–S battery) is a , notable for its high energy density [33]. The low atomic weight of lithium and moderate weight of sulfur means that Li–S batteries are relatively light (about the density of water). Lithium–Sulfur batteries may succeed lithium-ion cells because of their higher energy density and reduced cost from the use of sulfur [34]. Currently the best Li–S batteries offer energy densities on the order of 500 Wh/kg, significantly better than most lithium-ion batteries which are in the 150 to 200 range. Li–S batteries with up to 1,500 charge and discharge cycles have been demonstrated.

2. Aluminum-Air (Al-Air):

Aluminium–air batteries or Al–air batteries produce electricity from the reaction of oxygen in the air

21 with aluminium. They have one of the highest energy densities of all batteries, but they are not widely used because of problems with high anode cost and byproduct removal when using traditional electrolytes and this has restricted their use to mainly military applications. However, an electric vehicle with aluminium batteries has the potential for up to eight times the range of a lithium-ion battery with a significantly lower total weight [35]. Aluminium–air batteries are primary cells; i.e., non-rechargeable. Once the aluminium anode is consumed by its reaction with atmospheric oxygen at a cathode immersed in a water-based electrolyte to form hydrated aluminium oxide, the battery will no longer produce electricity. However, it is possible to mechanically recharge the battery with new aluminium anodes made from recycling the hydrated aluminium oxide. Such recycling would be essential if aluminium–air batteries are to be widely adopted.

3. Aluminum-ion (Al-ion):

Aluminum-ion batteries are conceptually similar to lithium-ion batteries, but possess an aluminum anode instead of a lithium anode. While the theoretical voltage for aluminum-ion batteries is lower than lithium-ion batteries, 2.65V and 4V respectively, the theoretical energy density potential for aluminum-ion batteries is 1060 Wh/kg in comparison to lithium-ion's 406 Wh/kg limit [36]. The large difference in energy density potential is due to the fact that aluminum ions have three valence electrons while lithium ions only have one. Aluminum is also more abundant than lithium, lowering material costs [37]. Aluminum-ion batteries have a relatively short shelf life. The combination of heat, rate of charge, and cycling can dramatically decrease energy capacity. When metal ion batteries are fully discharged, they can no longer be recharged. Ionic electrolyte materials are expensive. Like most batteries, they have a far lower energy density than gasoline [38].

4. Zinc-Air (Zn-Air):

Zinc–air batteries have two major advantages. they weigh less than half the weight of comparable batteries and cost only $140/kWh. During discharge, a mass of zinc particles forms a porous anode, which is saturated with an electrolyte. Oxygen from the air reacts at the cathode and forms hydroxyl ions which migrate

2− into the zinc paste and form zincate (Zn(OH) 4), releasing electrons to travel to the cathode. The zincate decays into zinc oxide and water returns to the electrolyte. The water and hydroxyl from the anode are recycled at the cathode, so the water is not consumed. The reactions produce a theoretical 1.65 , but this is reduced to 1.35– 1.4 V in available cells. Zinc–air batteries have some properties of fuel cells as well as batteries: the zinc is the fuel, the reaction rate can be controlled by varying the air flow, and oxidized zinc/electrolyte paste can be replaced with fresh paste.

22 5. Carbon Fluoride (CFx):

Metal fluorides may be applied as conversion materials in lithium-ion batteries. They also allow for lithium-free batteries with a fluoride-containing electrolyte, a metal anode, and metal fluoride cathode, which reach a much better storage capacity and possess improved safety properties. Instead of the lithium cation, the fluoride anion takes over charge transfer. At the cathode and anode, a metal fluoride is formed or reduced. As several electrons per metal atom can be transferred, this concept allows to reach extraordinarily high energy densities – up to ten times as high as those of conventional lithium-ion batteries [39] [40].

An energy density Ragone Plot of most of the batteries mentioned above is shown below:

Figure 2.2.1: Ragone Plot of some Electrochemical Batteries

2.2.2 Supercapacitors A supercapacitor (SC) (sometimes ultracapacitor, formerly electric double-layer capacitor (EDLC)) is a high-capacity electrochemicalcapacitor with capacitance values up to 10,000 farads at 1.2 that bridge the gap between electrolytic capacitors and rechargeable batteries. They typically store 10 to 100 times more energy per unit volume or mass than electrolytic capacitors, can accept and deliver charge much faster than batteries, and tolerate many more charge and discharge cycles than rechargeable batteries. They are however 10 times larger than conventional batteries for a given charge. The properties of supercapacitors come from the interaction of their internal materials. Especially, the

23 combination of electrode material and type of electrolyte determine the functionality and thermal and electrical characteristics of the capacitors. Electrical energy is stored in supercapacitors via two storage principles: static double-layer capacitance and electrochemical pseudocapacitance; and the distribution of the two types of capacitance depends on the material and structure of the electrodes. There are three types of supercapacitors based on storage principle [41] [42]:

1. Double-layer capacitors (EDLCs) – with activated carbon electrodes or derivatives with much higher electrostatic double-layer capacitance than electrochemical pseudocapacitance.

2. Pseudocapacitors – with transition metal oxide or conducting polymer electrodes with a high electrochemical pseudocapacitance.

3. Hybrid capacitors – with asymmetric electrodes, one of which exhibits mostly electrostatic and the other mostly electrochemical capacitance, such as lithium-ion capacitors

Both electrostatic and electrochemical energy storage in supercapacitors are linear with respect to the stored charge, just as in conventional capacitors. For a given constant current, the voltage between the capacitor terminals drops linearly over time. Such linear voltage gradient differs from rechargeable electrochemical batteries, in which the voltage between the terminals is closer to the charge and discharge curves of the green area shown in Figure 2.2.2. Such behavior also exists in hybrid supercapacitors (Li-ion supercapacitors), giving an advantage over EDLCs.

Figure 2.2.2: Discharging/Charging Difference between Batteries and Supercapacitors

A Ragone Plot of batteries and supercapacitors together is shown below:

24 Figure 2.2.3: Ragone Plot of Batteries and Supercapacitors [43]

2.3 Energy Management And Distribution

With the increase in electrical/electronic content in vehicles, the on-board electric power requirement is likely to increase for non-propulsion and propulsion loads in the near future. This is observed during the past decade, and this increase in power demands is mainly due to the development of various electric and hybrid drivetrains. However, other emerging automotive technologies for internal combustion engine vehicles, have also made considerable contributions to this trend [3]. As indicated, such a large amount of power makes the issue of proper and fine-tuned power management and distribution a much more complicated and difficult problem compared to the energy management of the existing conventional vehicles. In a conventional internal combustion engine vehicle, electric power is simply managed through the control of an generator and a voltage regulator. Early electric and hybrid vehicles included the control of the battery's state-of-charge as an important component of the on-board power management strategy. Nevertheless, the overall power management scheme for these vehicles remains quite simple and straight-forward. However, with the increase in power demands and system complexity, this can no longer be the case. Thus, optimal power and energy management strategy is becoming necessary.

25 The primary function of electric power management is to prioritize real-time power demands for different units and allocate power resources available from the generation and storage devices in an optimized way for maximum vehicle efficiency, maximum fuel economy and performance. The use of energy storage devices in vehicles, such as batteries, supercapacitors, and flywheels, essentially requires the extension of the power management concept into one that covers both power and energy (i.e., time integral of power) perspectives. Good power and energy management strategies can help to reduce the weight, size and cost; improve the performance of the vehicle and improve system reliability during limited resource availability. Here we introduce a generic power/energy management and distribution architecture, which is applicable to a wide variety of vehicles including conventional ICE-powered, electric, hybrid, and fuel cell vehicles. This generic architecture is applicable to ICE, electric, fuel-cell, or any hybrid-type vehicles. For a particular vehicle configuration, not all components or subsystems presented in the figure are necessary, and some minor changes in the system topology may be required. In general, the power management system can be divided into the following six subsystems:

1. Power Generation 2. Energy Storage 3. Power Bus (DC-Bus) 4. Electrical Load 5. Power Electronics 6. Power Management Controller (PMC)

1. Power Generation: The main function of the electric power generation subsystem is to convert chemical, electrochemical, mechanical, and other forms of energy into electrical energy. Examples of primary power sources include ICEs (petrol, diesel, hydrogen and other alternative fuels), other types of engines (Stirling and turbine), and fuel cells. Fuel-cell devices convert electrochemical energy directly into electrical energy, while all other engine types of power sources have to rely on various electric machines to generate electrical energy. It should be denoted that all power sources and generators have their own optimal operating ranges, in terms of power output and efficiency. Operation outside the desirable regions may result in low system performance and efficiency. Therefore, it is a major task for the power management system to control and coordinate the operation of the power generation subsystem to achieve maximum system efficiency, hence fuel economy for hybrid configurations.

26 2. Energy Storage: The main function of the energy storage subsystem is to provide available electric power when the primary power sources have no or insufficient power output, and to store surplus electrical energy generated by the primary power sources or recovered by regenerative braking. The most common energy storage devices are the various types of batteries, ranging from lead-acid, metal-hydride to lithium-ion types. The key consideration for battery selection is specific power (power/weight), specific energy (energy/weight), cost, and cycle lifetime. Other energy storage devices, such as supercapacitors and flywheels, are normally used along with batteries to compensate for the limited battery power capability. While these devices typically have a limited energy capability, they can greatly improve the system power capability to respond to a burst- mode (high power but short duration) power demand. The proper control of the energy storage subsystem presents both a challenge and opportunity for the power and energy management system.

3. Power Bus: Two direct current (DC) power buses are mostly used. One is the high-voltage power bus and the other is the low-voltage power bus. Both load units and power supplies are connected to these power buses through various PCUs and power electronics. This is different from the point-to-point or direct connection between the source and load in traditional ICE vehicles. Usually, the high-voltage power bus provides electrical energy to the electric traction motors, and other high-power loads, while the low- voltage bus supplies energy to low power accessories such as lamps, small motors, and microcontroller units. The use of two or more power buses at different voltages in a vehicle can simultaneously address power and safety requirements. It should be denoted that the ''high'' and ''low'' voltages are in relative terms and depend on the vehicle configuration. There are one or more DC/DC converters linking the two power buses to transfer energy back and forth. In addition, an alternating current (AC) power bus is sometimes included to provide power access for external plug-in appliances or machine tools.

4. Electrical Load: Electrical loads can be divided into two classes: propulsion and non-propulsion loads. The propulsion loads usually include one or more electric machines such as induction or synchronous machines to serve as the traction motors or generators. The propulsion load demands the highest power in a vehicle. Non- propulsion loads include all other electric loads in a vehicle, such as lamps, heaters, solenoids, and small motors for fans and pumps. Some non-propulsion loads, such as power steering and special heaters, actually demand a large amount of electric power. In general, many loads such as starter motors and cold lamps, tend to have a peak power several times higher than its average power consumption. It is important for the power management system to ensure that the power request from all loads are met in a

27 timely manner. In the meantime, it is impractical and cost prohibitive to offer a continuous power capacity many times higher than the average power demand, just to meet momentary peaks in power needs. Thus, how to prioritize load-power requests and allocate limited power resources becomes a major task for the power and energy management system.

5. Power Electronics: Power electronics include all power converters and PCUs (Power Control Units). These power converters and PCUs control the power flows between the power sources, loads, and power buses, following the commands of the central PMC. For example, a bidirectional DC/AC converter (inverter) is used to control the motor/generator machine. Depending on the control signal received, the DC/AC converter can run the electric machine as a generator and serves as either a to output DC current or an inverter to convert the DC bus voltage into three-phase voltage to drive the machine as a motor or a starter. The PCUs are usually power semiconductor load switches, which can communicate with the central PMC via a data network such as controller area network (CAN). One PCU can control one or more loads as shown in the figure.

6. Power Management Controller (PMC): The PMC serves as the central control unit for the power/energy management and distribution system to command, control, and coordinate various components. The PMC communicates with all power converters and PCUs by sending control commands to, and receiving sensor signals and status reports from, these units. It also communicates with the vehicle system controller to interact with other systems of the vehicle. The overall power and energy management algorithm is implemented in the PMC. Even though, most of the times, only one PMC is used, a secondary backup PMC might be necessary for some systems.

2.4 Integrated System Optimization

An integrated system optimization approach for electrical power and energy management and distribution systems is presented in this section. This approach intends to achieve optimal performance of the overall vehicle system rather than that of each individual subsystem. It is required to carefully study all power subsystems including power sources, energy storage devices, and various electrical loads, and develop a well- defined problem statement. Important factors to be considered include determining appropriate objectives, constraints, and inter- relationships between these subsystems. Three fundamental objectives have been identified for electrical power and energy management and distribution systems:

28 1. Achieving maximum energy efficiency -fuel economy for low cost operation.

2. Providing a high level of vehicle performance and reliability, even on extreme weather conditions.

3. Maintaining a low emission and noise level for ''green'' and ''quiet'' operation.

The ultimate goal of system optimization is to achieve maximum energy efficiency (for a predefined or chosen performance and emission level), that is, the efficiency of converting chemical or electrochemical energy into mechanical motion of a vehicle. In conventional ICEs or hybrid vehicles, this is defined as fuel economy (miles per gallon – mpg). Maximum energy efficiency needs to be achieved without compromising vehicle performance aspects, such as driving range, acceleration and comfort/convenience features, and emission levels. The optimization of a power and energy management system can be formulated into a mathematical model, and the operation of each power subsystem or component can be controlled by a set of decision variables. The appropriate measure of system performance can be expressed as a mathematical function of these decision variables, which is called the ''objective function''. The objective function for a particular vehicle power system can be developed by combining all three previously mentioned objectives, with each carrying a certain weight. Any restrictions on the values that can be assigned to the decision variables are called ''constraints''. The target of the optimization problem is then to choose the values of the decision variables so as to maximize (or minimize, as the case may be) the objective function, subject to the specific constraints. The constraints in automotive electric power management systems are imposed by several key factors, including the following [3]:

UNINTERRUPTABLE POWER PRACTICAL COMPONENT CONSTRAINTS DYNAMIC RESOURCE ALLOCATION REQUIREMENT

GREAT PERFORMANCE – RELIABLE ENERGY EFFICIENCY - LOW COST LOW EMISSION AND NOISE LEVEL

SYSTEM STABILITY FUALT DIAGNOSIS & PROGNOSIS POWER QUALITY

Figure 2.4.1: Integrated System Optimization Approach

1. Dynamic Resource Allocation Requirement: The peak and average power demands differ for various types of electric loads in a vehicle, as previously discussed. It is not practical to design a power system that can simultaneously meet the peak power demands of all electric loads. Power and energy management systems should not only optimize the

29 available sources on-board, but also match them properly with the loads. It prioritizes and schedules the load request and allocates power / energy based on need, dynamically.

2. Practical Component Constraints: Components in vehicle power systems usually have practical limitations and constraints. Battery – the most common energy storage device in vehicles – has a limited power and energy capacity and, more importantly, cycle life and sensitivity to temperature. Electric machines are used in vehicle drivetrains both as motors and generators. The power output and efficiency of these electric machines are not fixed but are functions of machine speed, torque and temperature. These limitations and constraints have a direct impact on the optimization of the power management system.

3. Uninterruptible Power Availability Requirement: Some on-board electric loads in a vehicle are mission- and safety-critical. Examples include steer-by- wire, brake-by-wire, and safety restraint subsystems. It is advantageous to have one or more backup batteries in the power system to meet the power demand for these mission critical loads for at least a short time period in case of a primary power source interruption. The backup or secondary battery usually has a limited energy and power capacity to minimize its weight and cost. The charging and discharging cycles of the backup battery should be minimized to maintain a long operational life. It should also be disconnected via a PCU when not needed to maintain its state of charge. The on-board power and energy management system needs to meet these requirements.

4. Power Quality Requirement: It is important to maintain the power quality of power buses to guarantee the safety and proper operation of all electric loads and PCUs. Automotive environments are extremely noisy and subject to various types of transients caused by switching large currents through inductive loads. A worst-case scenario is the so-called ''load-dump event'', where the battery is inadvertently disconnected while still undergoing a charging process by an or generator. Load dump can also occur when a large load is suddenly disconnected either purposely or inadvertently, leading to voltage spikes. If not suppressed, a high-voltage spike would appear on the power bus for a certain time period and potentially endanger all electronic modules connected on the power bus. The bus voltage can also decrease below a desirable range possibly due to the low-voltage battery level at low temperatures (or due to sudden overload). The on-board power and energy management system needs to address these issues.

5. System stability requirement: DC power systems that employ multiple switch-mode power converters are known to be prone to instability because of the high degree of sensitivity to parameter and load variation. In particular, power

30 systems may be susceptible to large signal stability concerns. Large signal stability refers to the ability of the system to transit from one steady-state operating point to another following a disturbance, such as change in power demand, loss of power sources, short circuits, and open circuits. In addition, the power system is affected by interconnection between its components. The stability of power systems depends on several factors, such as switching off power electronic converters, non-linearity of magnetic components, self-protection operation of power electronic circuits, and temperature variations. A well-designed power management system can maintain system stability by managing the loads properly according to the operating conditions of power sources and distribution systems, and ensure that the system always operates around its nominal power.

6. Fault diagnosis and prognosis requirement: With the increasing complexity of electrical/electronic architectures, it is desirable to provide fault diagnosis and prognosis to the automotive electric power management and distribution system. These features allow detection of fault conditions, such as short circuits and open circuits, and graceful degradation of the system performance to avoid hard, fail-stop behaviors. Overall fault detection or tolerance can be accomplished by the power management and distribution system, which monitors and isolates a fault condition, and possibly reconfiguring the system to minimize the impact of the fault condition. In a more sophisticated scheme, historical data on the behaviors of various components can be recorded and analyzed. This information can be used to predict the state or condition of these components or subsystems and can provide early warnings for the components or modules that are degrading but have not yet failed catastrophically.

2.5 Generic Modeling Structure

The model of a complete hybrid-electric powertrain can be split into a number of sub-models representing the different components of the system. A good and useful modeling practice consists of making these sub-models ''autonomous'', so that each sub-model interacts only with the sub-models of the components which are actually linked by a power flow. This approach yields a modular system description, in which each module has clear input and output variables which may be combined with input and output variables of other sub-models to represent a complex configuration. Another advantage of this approach is that it allows various arrangements of the hybrid-electric powertrain components (i.e., battery, engine, motor) while keeping the same basic sub-models. In other words, the same ''library'' of sub-models can be used to represent series, parallel, power-split and complex hybrids. During the design procedure of a powertrain for a specific application, the different configurations with

31 their operating modes, the energy management and distribution algorithms and the operation requirements should all work together to bring out the final optimized sizing of components and the definition of energy flows within the powertrain, in order to meet all the performance and efficiency criteria as well as to overcome the design bounds. For modeling purposes, a basic-generic structure of the powertrain should be established, before going into building the actual simulation model. This will be used as a basis for the detailed model required for investigations into EVs and HEVs. There are five main sub-models describing the interaction of electrical and/or mechanical components within an electric or a hybrid powertrain. Those stand for the five main sub-groups:

1. Energy storage units (ES) 2. Electromechanical units (EM/EG) 3. Fuel consuming units (ICE) 4. Auxiliary units (AUX) 5. Supervisory controller (PMC)

1. The energy storage devices (batteries, supercapacitors) and an external power source (catenary) sometimes connected to the DC-bus are the 1st group. The controllers are connected to the low voltage dc-bus, and also getting electrical and thermal measurements from the energy storage cells and their power converters. The battery controller, the supercapacitors' controller and the general energy storage controller can actually be algorithms implemented in the power management controller (PMC). The purpose of such algorithms is to supervise the power flows within the energy storage units, getting electrical and thermal measurements (sensoring), and executing the final decisions. Final decisions should deal with ON/OFF states or constraints of the energy flows for efficiency and safety purposes.

SENSORING

BATTERY POWER BATTERY CONTROLLER CONVERTER PACKAGE

EXTERNAL ENERGY HIGH POWER LOW VOLTAGE MAIN HIGH STORAGE VOLTAGE CONVERTER/ DC BUS SWITCH VOLTAGE CONTROLLER DC BUS FILTER LINE

SUPER SUPER POWER CAPACITORS CAPACITORS CONVERTER CONTROLLER PACKAGE

SENSORING

Figure 2.5.1: Energy storage units sub-block

32 2. The electric motors used for traction and the electric generators used for the conversion of mechanical to electrical energy supplied to the DC-bus are the 2nd group. In a similar way, all the controllers shown in the figure below can be implemented in the PMC. It is also important to consider inertia of all rotating parts, because it surely affects the acceleration resistance the vehicle needs to overcome during acceleration. The clutches used in this figure represent imaginary switches dealing with the ON/OFF states of the electric motors and the electric generators, and are considered a simple, yet very practical way to pre-define the topology we want to use. For modeling purposes, the traction units (motors) are separated from the power generation units (generators) coupled to ICEs. For demonstration, the figure below refers to electric motors, yet the same structure is applied for electric generators as well.

Figure 2.5.2: Electromechanical units sub-block

3. The fuel consuming units (ICEs, fuel cells) used for traction and/or power generation are the 3rd group. As previously discussed, the same simple technique is also used for the internal combustion engine in HEV and diesel-electric configurations. All the controllers can be embedded in the PMC, inertias should be carefully considered and imaginary clutches should be used for the definition of the ICE's ON/OFF states.

ICE CLUTCH INERTIA INERTIA CONTROLLER

MECHANICAL INPUT INTERNAL SPEED/ INTERNAL COMBUSTION TORQUE THROTTLE COMBUSTION GEAR CLUTCH ENGINE CONTROLLER ENGINE CONTROLLER

Figure 2.5.3: Fuel consuming units (ICE) sub-block

Energy storage devices, electric motors, electric generators and fuel cells require the existence of controlled power converters while the three rotating units, most of the times, require a connection to gears, for torque and speed conversion.

33 4. Auxiliary units form another group, not of the same capacity but of high importance. They affect not only the sizing of components but also the definition of power flows in different operating modes, thus they influence the final result, in terms of efficiency, reliability and performance. Auxiliary units can be either connected through DC/DC or DC/AC converters to the high voltage or low voltage DC buses (depends on the type of the auxiliary unit) or mechanically connected to an electric motor or an internal combustion engine, although mechanical connections are proven ways reducing overall efficiency, as they cannot be easily controlled by PMCs.

5. Furthermore, a very important component of complex systems is the ''supervisory controller'' acting as the main ''brain'' of the vehicle. If a specific schedule and real time are given as inputs, the output can be an immediate decision. Nevertheless, it may sound like a pretty much easy task, yet more things than schedule and relative timing should be given as inputs. A combination of a future journey schedule and acceleration profiles, stopping/refueling times and after-journey state-of-charge of the battery package should be major considerations. The supervisory controller is actually acting as the PMC described in the previous section. It is the intermediate stage between human interaction and system performance. The interaction between discrete and continuous components can cause difficulties in controller design. A discrete controller issuing commands that are incompatible with the state of the continuous system could cause catastrophic results. The solution is to generate continuous controllers and consistent discrete abstractions for the closed loop system. It is proposed to use a hierarchical structure that utilizes a centralized information sharing system. This structure avoids myopic decisions. The top level of this hierarchy is management/supervision, while assuming conventional all lower level controllers. Therefore, the controller structure can be divided into only two levels: supervision and execution. Dealing with the supervising controller level, the dynamics of the system are best described by discrete-event equations. For example, we could define the control algorithm as a simple thermostat on/off strategy: If the battery SOC is greater than a certain level, use battery (motor) only, otherwise use engine only. Obviously such a strategy is very simplistic and makes no effort to maximize the global system efficiency. THROTTLE BRAKE

POWERTRAIN SYSTEM CONTROLLER SENSORING

ICE EM EG TR ES

Figure 2.5.4: Powertrain System Controller

34 However, treating the system states as a series of discrete events, or ''flags'', is a useful tool that we can exploit, and this is the main reason of using imaginary clutches and power switches to work with the PMC unit, not only for power division purposes but also for easily adopting different powertrain configurations during modeling and simulations. The execution level is simply dealing with the execution of commands, by sending proper signals to the power converters, switches and imaginary clutches, inspired by currently used electronically controlled clutches found in the automotive HEV industry and especially on power-split and complex configurations (see Toyota Prius).

35 CHAPTER 3

3.1 Modeling Methodology

Since one of the main objectives of powertrain modeling is to estimate the fuel consumption and emissions in different operating modes, the next section will provide a brief description of the three most common modeling approaches suitable for this application. A conventional powertrain, with a unique power source represented by an internal combustion engine will be considered for simplicity [4].

1. Kinematic approach: The kinematic approach is based on a backward methodology where the input variables are mainly the speed of the vehicle and the inclination of the road [5]. The engine speed can therefore be easily determined from simple kinematics, starting from the wheel rotational speed and the total transmission ratio of the driveline, while the traction force that should be provided to the wheels to propel the vehicle according to the chosen speed profile can be calculated from the main vehicle characteristics (i.e. vehicle mass, aerodynamic drag and rolling resistance, in general). Once both engine torque (or the Brake Mean Effective Pressure, BMEP) and speed have been determined, a ''dimensionless'' black box model of the engine can be used to find the instantaneous fuel consumption and/or emissions. Finally, instantaneous fuel consumption and emissions are integrated over the driving cycle to obtain cumulative data. Obviously, this approach neglects all the dynamic phenomena considering transient conditions as a sequence of stationary states; therefore it is often used only for a first preliminary estimation of the fuel consumption or emissions of a powertrain, although the simulation results can differ significantly from the experimental data due to these simplifying assumptions. Moreover, the backward approach ensures that the driving profile will be exactly followed, but, on the other hand, there are no guarantees that a given vehicle will actually be able to meet the desired speed, since the power demand is directly computed from the speed and it is not checked vs. actual powertrain capabilities [3]. Finally such an approach also neglects the thermal transient occurring after an engine cold start, which is, on the contrary, usually taken into account by most of the type approval driving cycles. On the other hand, it is important to denote that in rail vehicles, engine dynamics are not similar to automotive applications plus the speed profiles can be really pre-known/pre-defined, something that can also be the case for any mass transportation means in general, as the time schedules and routes are fixed. Another important aspect of mass transportation is that it favors real time optimization of the power flow strategies which gives advantage over other applications.

36 2. Quasi-static approach: In the quasi static approach, a driver model (typically a PID) compares the target vehicle speed with the actual speed and generates a power demand profile in order to follow the target vehicle speed profile, by solving the longitudinal vehicle dynamics equation [3] [6]. Once the torque and speed of the engine have been determined, fuel consumption or emissions can then be calculated by means of interpolation of engine maps, as for the kinematic methodology which was described in the previous section. The simulation model can therefore be regarded as a “quasi-static” model, since, although system dynamics are taken into account, the behavior of the main devices (ICE, EM, batteries) is described by means of steady state performance maps. Depending on the simulation targets, on the transient to be simulated and on the powertrain characteristics, a quasi-static approach can be suitable or not. For instance, for the evaluation of the fuel consumption of a vehicle equipped with a conventional powertrain on the NEDC driving cycle, it can provide a reasonable accuracy [7], and as a matter of fact, since load and speed transients are relatively smooth for a conventional powertrain on the NEDC the simplifying assumptions of this method do not deteriorate significantly its prediction capabilities. Similar remarks could also be made as far as NOx emissions are concerned. On the other hand, for the same driving cycle and for the same powertrain, the same approach does not provide satisfactory results when used to predict soot emissions, since for this kind of pollutant the acceleration transients and the related “turbo-lag” phenomena significantly contribute to the cycle cumulative emissions, thus requiring a more detailed engine simulation model, capable to properly capture also the engine transient behavior.

3. Dynamic approach: Finally, in the fully dynamic approach, not only the longitudinal vehicle dynamics equation is solved to determine the engine speed and the torque demand, but also the internal combustion engine behavior during transients is modeled by means of detailed OD or 1D fluid-dynamic models. For instance, for an internal combustion engine, the intake and exhaust systems can be represented as a network of ducts connected by junctions that represent either physical joints between the ducts, such as area changes or volumes, or subsystems such as the engine cylinder. The solution of the equations governing the conservation of mass, momentum and energy of the flow for each element of the network can then obtained using a finite difference technique. In this way, even highly dynamic events, such as abrupt vehicle accelerations during tip-in maneuvers can be properly and reliably simulated with a reasonable accuracy [8]. As already mentioned in chapter 1, the goal of this study is to produce a simple model capable of cooperating with optimization algorithms, so simplicity and low running times for the simulation model can be obtained by adopting the simple, yet not simplified kinematic approach. Extreme dynamics in early stage sizing procedures are not of concern and so the dynamic approach is not of favor. One could use a quasi-static approach

37 which yields no big differences from the kinematic one, but requires greater running times. As long as the driving cycles are pre-known, the time schedules are pre-defined and the powertrain sizing and controls do not allows slip conditions to occur, the kinematic approach can be adopted for our investigation.

3.2 Internal Combustion Engine Modeling

Depending on the application and on the required level of detail different simulation approaches can be used for the internal combustion engine modeling, as depicted in the figure below [4].

Figure 3.2.1: Running times vs Model detail for ICEs

The most complete and detailed methodology is the 3D CFD: it is widely used for modeling flow dynamics in intake and exhaust systems of internal combustion engines, as well as for modeling in-cylinder phenomena such as gas flow through the intake and exhaust valves, direct fuel injection, mixture formation, combustion process etc. It can usually provide only component level details (such as for instance, pressure drops, flow distributions, fuel and air mixing, etc.), but cannot usually provide a system level perspective, since, because the computational time increases with the system volume to be discretized, this approach is usually applied only to a specific engine component, such as for instance a single cylinder or a manifold, although some examples of application of 3D CFD modeling to a whole internal combustion engine, including intake and exhaust systems, have been reported in literature [9]. In order to gain a system level perspective, 1D fluid-dynamic simulation tools are generally used [10] [11], in which the engine intake and exhaust systems are usually modeled as a network of ducts connected by junctions that may represent either physical joints between the ducts or subsystems such as the engine cylinder, and the solution of the equations governing the conservation of mass, momentum and energy of the flow for

38 each element of the network is obtained using a finite difference technique. Typical applications of 1D fluid- dynamic models may include volumetric efficiency, torque and fuel consumption predictions under steady state operating conditions, as well as predictions of engine transient response to throttle tip-in and tip-out maneuvers, turbocharging system response, etc. Beyond these traditional uses, 1D simulations are today expanding in the area of control system modeling towards Software in the Loop (SiL), which is nowadays a popular activity in control system development prior to prototype hardware availability. At this level, 0D black-box models which follow a quasi- static approach based on experimental steady-state maps are currently the preferred options, due to their superior real-time capabilities. This approach is generally suitable for fuel consumption and emission calculations on type approval driving cycles, where transients are quite smooth and can be simulated by means of a sequence of stationary states. However, 0D black box models, which neglect most (if not all) of the engine dynamic phenomena, are definitely unsuitable for the simulation of fast transients, such as for instance for the predictions of engine response to throttle tip-in and tip-out maneuvers, or of turbocharging system response, for which 1D fluid- dynamic models would certainly be more appropriate, but are usually inapplicable because of their high computational time requirements. For this reason the development of the so called “mean value” model seems to be the most valuable solution to combine the low computational requirements of black-box models with the accuracy of 1D models: the purpose of these models is the reduction of the complexity of a detailed 1D approach, while maintaining, at the same time, a physical description of the main phenomena in order to achieve the best compromise between detail level and computational requirements. Basically mean value models simplify both intake and exhaust systems in single equivalent volume where, as in a filling emptying model, the equation of conservation of energy and mass are applied. The separation of the thermodynamic properties between adjacent volumes is then possible thanks to restricting elements which imposes to the adjacent volumes a mass flow rate, which is calculated according to its characteristics and to the boundary conditions. Therefore the overall system behavior, including turbocharger, is still represented although there is no detail about pressure wave dynamics. Thanks to these features, the computational time is significantly reduced, with only limited impoverishment of the model detail, thus achieving an intermediate level between system black-box models and detailed engine models. For the sake of simplicity in estimating fuel consumption and emissions in early design stages of diesel- electric and hybrid-electric configurations we will be using efficiency and emission maps for our internal combustion engines in 0D models like the maps shown in the figure below. Further improvements of internal combustion engine modeling, through adopting the compromise described in the previous paragraph, are mentioned in chapter 5. Mean value modeling considering simple equivalent volumes for the intake and exhaust system can be applied like what is already found in previous developments (see ADVISOR) with backward or

39 forward quasi-static approaches.

Figure 3.2.2: BEMP[bar] vs SPEED[RPM]: Efficiency (left) and BSNOx(g/kWh) (right) of an ICE

3.3 Electric Motor/Generator Modeling

Nowadays the increasing share of HEVs and EVs in the automotive market makes electric machines (and in particular Electric Motors, EM) key components in vehicle powertrain development [12]. Although the wide diffusion of electric machines in several areas of application has led to the development of different technologies (such as for instance AC and DC machines, synchronous vs. asynchronous motors, etc.) for powertrain development purposes electric machines can usually be modeled through a system-level approach similar to the 0D methodology previously described for the internal combustion engines, by means of torque and efficiency maps in which desired values of electrical power or torque are used as control inputs. Rotor inertia is the only dynamic element modeled, as the electrical dynamics are generally much faster. Thus, in most of the cases this quasi static modeling approach shows very good agreement with experimental data, and, as consequence, it is the most widely used technique in HEVs powertrain modeling.

Efficiency Maps: The efficiency map of an electric motor can also be used as an efficiency map of the same used as a generator, although slight differences on the efficiency could occur (mainly for induction motors [IMs], not for permanent magnet motors [PMs]). The mirroring of the efficiency map to negative values of torque is sometimes not perfect, yet it can be shown that the general image of the efficiency map does not carry many differences that could possibly affect the sizing of other components of the powertrain or the power flow strategy implemented in the PMC.

40 Figure 3.3.1: TORQUE[Nm] vs SPEED[rpm] Efficiency Maps: PM Motor (left) and Induction Motor (right)[17]

By developing a greater understanding regarding electric motor efficiency and operating characteristics, you can realize some serious electric energy cost savings. Apart from efficiency, an often overlooked and widely misunderstood characteristic of electric motors is Power Factor (PF). Proper electric motor application and efficiency are important things to know when applying or replacing a motor, although PF can be equally important when operating costs are considered. Recent studies indicate the level of knowledge and implementation of motor system energy-efficiency measures is low. A plethora of information on motor system design, best practices, purchasing, and management is widely available, but few companies have embraced it. Training opportunities and tools to deepen the knowledge base of end-users is necessary to apply motor and motor system efficiency measures. Studies have also shown that a low percentage of facility managers have implemented motor purchasing and maintenance specifications. These factors can be attributed to adoption of plant-level motor system efficiency improvements. After the calculation of the tractive force required to propel the vehicle is done, the efficiency of mechanical and electrical components has to be taken into account. The estimated power can then flow into the

41 power division block, the output of which will be the input to the block dealing with the energy storage devices or the input to the internal combustion engine block, so that fuel consumption and emissions can be approximated. For simplicity, rotating parts and power electronics efficiency can be considered as constant, although it surely depends on torque and speed for mechanical parts or voltage and current for power electronics. For the efficiency of electric motors, electric generators and internal combustion engines there is the dependence on torque and speed in a similar way to what was mentioned for other mechanical parts. Yet, in this case, it is extremely important to know the operating point at every time step, because total system efficiency, sizing of other components(for example energy storage), fuel consumption and emissions in internal combustion engines are highly affected. For this reason, our approach proposes the use of efficiency maps both for electric ''engines'' and combustion ones.

Figure 3.3.2: 3D Efficiency maps of Induction Motor (left) and an ICE (right) in Matlab

Scaling of Efficiency Maps: During the first stages of design and development of electric and hybrid systems, the actual size of components is not pre-known, although when it comes to refurbishment of powertrains, information for all the components used exists and can be given by the manufacturer of each one. For design of new powertrains or re- design of old ones, there is lack of such information and so specific maps of electric motors, electric generators and internal combustion engines cannot be used immediately, as during the design procedure many parameters will change affecting the maximum required power, torque etc.. A scaling process should be used in such cases with default efficiency maps, so that the designer can get an estimation of how he should proceed further with the final sizing of his powertrain and the definition of the power flows as well (see ADVISOR). Examples of the efficiency map of an electric motor and a diesel engine are shown in the previous figure [Figure 3.3.2]. It should be mentioned that especially for diesel-electric trainsets, knowing the whole efficiency map of

42 the diesel engines and the electric generator is not necessary, as the speed of the diesel engine can be controlled through the electric generator and the power output can then be controlled through the throttle opening, so that the performance of the diesel-generator set can meet the highest efficiency values. For initialization purposes, vehicle parameters, such as for instance initial mass of the vehicle, drag coefficient, rolling resistance coefficient, and speed profile over time have to be defined. Then, a script in Matlab (see Appendix) can be used to extract the power vs speed profile of the road load demands and scale the maximums of torque and power vs speed efficiency maps used for the electric motor modeling up the maximum values derived for the initial vehicle parameters and speed profile used for the initialization.

Willans Line Model [ 48 ] [ 51 ]: The Willans line model was originally used to describe an approximate linear relationship between the brake mean effective pressure and the fuel consumption of engines [49]. Rizzoni, Guzzella and Baumann [50] extended this model to describe generalized energy converters, i.e., internal combustion engines and electric machines. Based on a known steady state efficiency map of the referenced machine, the efficiency of a new machine in the same category can be estimated using this scaling approach. Furthermore, it permits an automated exploration in the design space which considers some candidate machines even though the machine itself and/or its efficiency data do not exist in reality. In the Willans line model, the energy conversion efficiency of an energy converter is defined as the ratio between the output and input power. The resulting affine representation is described by two coefficients: the slope, or intrinsic energy conversion efficiency e; and the vertical axis intercept Ploss, describing losses due to air pumping, mechanical friction, magnetic phenomena, etc. This relationship is described for both ICEs and electric machines by the general equation:

Pout = e⋅Pιn − Ploss (3.3.1)

For ICEs, in order to eliminate sizing effects, the engine speed and the torque are substituted by the normalized variables including the mean piston speed and the mean effective pressures as shown below:

S⋅ω C = ice (3.3.2) m −ice π

4⋅π⋅Hv ⋅mf Pma−ice = (3.3.3) V d⋅ωice

4⋅π⋅Tice Pme−ice = (3.3.4) Vd

Hence, the energy conversion efficiency and the input-output relationship can be written in a

43 corresponding format as:

Pme−ice ηice = (3.3.5) Pma−ice and

Pme−ice = eice⋅Pma−ice − Pml−ice (3.3.6)

where

Hv gasoline lower heating value in J/kg,

mf engine mass flow rate of fuel in kg/s (for the existing map),

ωice engine speed in rad/s,

Cm_ice engine mean piston speed in m/s,

Pma-ice engine theoretically available mean effective pressure in Pa,

Pme-ice engine actual mean effective pressure in Pa, S engine stroke in m,

3 Vd engine displacement in m ,

eice engine intrinsic energy conversion efficiency excluding transferring losses in percentage.

For electric machines, applying the normalized variables of the mean rotor speed and the imaginary ''air gap shear stresses'', we obtain:

Cm−em = rr⋅ωem (3.3.7)

Pelec Pma−em = (3.3.8) 2⋅Vr ⋅ωem

Tem Pme−em = (3.3.9) 2⋅V r

Pme−em ηem = (3.3.10) Pma−em and

Pme−em = eem ⋅Pma−em − Pml−em (3.3.11)

where

ωem electric machine rotor speed in rad/s,

Cm_em electric machine mean rotor speed in m/s,

Pma_em electric machine theoretically available air gap shear stress in Pa (imaginary),

44 Pme_em electric machine actual air gap shear stress in Pa (imaginary),

Pelec electrical input in motoring mode (for the existing map),

3 Vr electric machine rotor volume in m ,

eem electric machine intrinsic energy conversion efficiency in percentage,

rr electric machine rotor radius in m.

The EM behaviors in the generating mode (negative EM torque region) is usually different from those in the motoring mode (positive EM torque region), but engineers may consider they are the same, i.e., symmetric about the zero torque line for simplicity [48]. The difference is typically more important for induction motors [3].

3.4 Energy Storage Modeling

3.4.1 Electrochemical Battery Modeling The battery block implements a generic dynamic model parameterized to represent most popular types of rechargeable batteries in Matlab/Simulink. The figures show the equivalent circuit of the battery [13]:

Figure 3.4.1: Electrochemical battery model in Matlab/Simulink

For different types of batteries, different equations during charging and discharging need to be used, with the lithium-ion model being the one we need for our investigations. Lead-acid and nickel-based batteries are not

45 considered chemistries to be used in modern applications because of their low energy content (energy/power density). Nevertheless, the equations used in those three chemistries are listed below.

Charge/Discharge Equations: 1. Lead-Acid Model:

 Discharge model (i* > 0):

Q Q −1 Εxp(s) f (it ,i∗, i, Εxp(s)) = E −K⋅ ⋅i∗−K⋅ ⋅it +Laplace ⋅( ⋅0) (3.4.1) 1 0 Q −it Q −it Sel(s)

 Charge Model (i* < 0):

Q Q −1 Εxp(s) 1 f (it ,i∗, i ,Εxp(s)) = E −K⋅ ⋅i∗−K⋅ ⋅it +Laplace ⋅( ⋅ ) (3.4.2) 2 0 0.1⋅Q +it Q −it Sel(s) s

2. Lithium-Ion Model:

 Discharge Model (i* > 0): Q Q f (it ,i∗, i) = E −K⋅ ⋅i∗−K⋅ ⋅it + A⋅exp(−B⋅it ) (3.4.3) 1 0 Q −it Q −it

 Charge Model (i* < 0): Q Q f (it ,i∗, i) = E −K⋅ ⋅i∗−K⋅ ⋅it + A⋅exp(−B⋅it ) (3.4.4) 2 0 0.1⋅Q +it Q −it

3. Nickel-Cadmium and Nickel-Metal-Hydride Model:

 Discharge Model (i* > 0):

Q Q −1 Εxp(s) f (it ,i∗, i, Εxp(s)) = E −K⋅ ⋅i∗−K⋅ ⋅it +Laplace ⋅( ⋅0) (3.4.5) 1 0 Q −it Q −it Sel(s)

 Charge Model (i*< 0): (3.4.6)

Q Q −1 Εxp(s) 1 f (it,i∗, i ,Εxp(s)) = E −K⋅ ⋅i∗−K⋅ ⋅it +Laplace ⋅( ⋅ ) 2 0 0.1⋅Q +∣it∣ Q −it Sel(s) s

where

EBatt = Nonlinear voltage [V],

E0 = Constant Voltage = Vfull - A [V], Exp(s) = Exponential zone dynamics [V]:

46 The Exp(s) transfer function represents the hysteresis phenomenon for the Lead-Acid, NiCD and NiMH batteries during charge and discharge cycles. The exponential voltage increases when battery is charging, no matter the SOC of the battery. When the battery is discharging, the exponential voltage decreases immediately.

Sel(s) = Represents the battery mode:

Sel(s) = 0 during battery discharge, Sel(s) = 1 during battery charging, K = Polarization resistance [Ohms] or Polarization Constant [V/Ah], i* = Low frequency current dynamics [A] related to voltage response time, i = Battery current [A], it = Extracted capacity [Ah], Q = Maximum battery capacity [Ah], A = Exponential voltage [V] - not Fully-charged Voltage, 1/B = Exponential capacity [Ah−1],

The figures below give an example of the discharging and charging curves over time for a lithium-ion battery of 3.7V Nominal, 4.25V Maximum (@100% SOC), 1 Ah capacity and an internal resistance of 5 mOhms for different charging and discharging C-RATES (0.35C, 0.50C, 0.75C, 1.00C, 1.50C, 2.00C, 3.00C, 5.00C, 7.50C, 10.0C).

Figure 3.4.2: Lithium-ion cell Voltage (V) vs Time (s) for different discharging rates, initial SOC = 100%

Figure 3.4.3: Lithium-ion cell Voltage (V) vs Time (s) for different charging rates, initial SOC = 0.00%

47 The SOC for a fully charged battery is 100% and for an empty battery is 0%. The SOC is calculated as:

∫i (τ)⋅dτ SOC = SOC − ⋅100 (3.4.7) init Q

Battery parameters given by the user in Simulink: 1. Nominal Voltage (V):

The nominal voltage (Vnom) of the battery (volts). The nominal voltage represents the end of the linear zone of the discharge characteristics.

2. Rated Capacity (Ah):

The rated capacity (Qrated) of the battery in ampere-hour. The rated capacity is the minimum effective capacity of the battery.

3. Initial State-Of-Charge (%): The initial State-Of-Charge (SOC) of the battery. 100% indicates a fully charged battery and 0% indicates an empty battery. This parameter is used as an initial condition for the simulation and does not affect the discharge curve.

4. Maximum Capacity (Ah): The maximum theoretical capacity (Q), when a discontinuity occurs in the battery voltage. This value is generally equal to 105% of the rated capacity.

5. Fully charged Voltage (V):

The fully charged voltage (Vfull), for a given discharge current. Note that the fully charged voltage is not the no-load voltage.

6. Nominal Discharge Current (A): The nominal discharge current, for which the discharge curve has been measured. For example, a typical discharge current for a 1.5 Ah NiMH battery is 20% of the rated capacity: (0.2 * 1.5 Ah / 1h = 0.3A).

7. Internal Resistance: The internal resistance of the battery (ohms). The resistance is supposed to be constant during the charge and the discharge cycles and does not vary with the amplitude of the current.

8. Capacity (Ah) @ Nominal Voltage:

The capacity (Qnom) extracted from the battery until the voltage drops under the nominal voltage. This value should be between Qexp and Qmax.

48 9. Exponential zone [Voltage (V), Capacity (Ah)]:

The voltage (Vexp) and the capacity (Qexp) corresponding to the end of the exponential zone. The voltage

should be between Vnom and Vfull. The capacity should be between 0 and Qnom.

Model Assumptions: 1. The internal resistance is supposed constant during the charge and the discharge cycles and doesn't vary with the amplitude of the current. 2. The parameters of the model are deduced from discharge characteristics and assumed to be the same for charging. 3. The capacity of the battery doesn't change with the amplitude of current (no Peukert effect). 4. The model doesn't take the temperature into account. 5. The self-discharge of the battery is not represented. It can be represented by adding a large resistance in parallel with the battery terminals. 6. The battery has no memory effect. 7. The block model assumes cell balancing for strings.

Limitations:

1. The minimum no-load battery voltage is 0 volt and the maximum battery voltage is equal to 2*E0.

2. The minimum capacity of the battery is 0 Ah and the maximum capacity is Qmax.

3.4.2 Supercapacitor Modeling The supercapacitor block implements a generic model parameterized to represent most popular types of supercapacitors in Matlab/Simulink. The figure shows the equivalent circuit of the supercapacitor [14] [15]:

Figure 3.4.4: Supercapacitor model in Matlab/Simulink

49 Charge/Discharge Equations: The supercapacitor output voltage is expressed using a Stern equation as: (3.4.8)

N ⋅Q ⋅d 2⋅N ⋅N ⋅R⋅T Q V = s T + e s ⋅sinh−1 ( T ) − R ⋅i SC ⋅ ⋅ ⋅ ⋅ 2 SC SC Np N e ε ε0 A i F N p⋅Ne⋅Ai ⋅√8⋅R⋅T⋅ε⋅ε0⋅c

with QT =∫ iSC⋅dt (3.4.9)

To represent the self-discharge phenomenon, the supercapacitor electric charge is modified as follows

(when iSC = 0):

QT =∫iself −dis⋅dt (3.4.10)

where

CT⋅a1 iself−dis = if t −toc ≤ t3 (3.4.11) 1 + s⋅RSC ⋅CT

CT ⋅a2 iself−dis = if t3 < t −toc ≤ t4 (3.4.12) 1 + s⋅R SC⋅CT

C T⋅a3 iself−dis = if t −toc > t4 (3.4.13) 1 + s⋅R SC ⋅CT

The constants α1, α2, and α3 are the rates of change of the supercapacitor voltage during time intervals

(toc, t3), (t3, t4), and (t4, t5) respectively, as shown in the figure:

Figure 3.4.5: Estimating constants α1, α2, α3 for a supercapacitor cell [19]

For the equations listen above:

2 Ai Interfacial area between electrodes and electrolyte [m ]

−3 3 c Molar concentration [mol*m ] equal to c = 1/(8*NA*r )

50 F Faraday constant

isc Supercapacitor current [A]

Vsc Supercapacitor voltage [V]

CT Total capacitance [F]

Rsc Total resistance [ohms]

Ne Number of layers of electrodes

NA Avogadro constant

Np Number of parallel supercapacitors

Ns Number of series supercapacitors

QT Electric charge [C] R Ideal gas constant d Molecular radius of ions T Operating temperature [K] ε Permittivity of material

ε0 Permittivity of free space

The figures below give an example of the discharging and charging curves over time for a supercapacitor of 5V Nominal (@100% SOC, 6V Maximum (Surge), 1 F capacity and an internal resistance of 0.1 mOhms for different charging and discharging currents (0.35mA, 0.50mA, 0.75mA, 1.00mA, 1.50mA, 2.00mA, 3.00mA, 5.00mA, 7.50mA, 10.0mA).

Figure 3.4.6: Supercapacitor cell Voltage (V) vs Time (s) for different discharging currents, initial Voltage = 5V

51 Figure 3.4.7: Supercapacitor cell Voltage (V) vs Time (s) for different charging currents, initial Voltage = 0.0 V

Supercapacitor parameters given by the user in Simulink: 1. Rated capacitance (F): The nominal capacitance of the supercapacitor, in farad.

2. Equivalent DC series resistance (Ohms) The internal resistance of the supercapacitor, in ohms.

3. Rated voltage (V): The rated voltage of the supercapacitor, in volts.

4. Initial voltage (V): Specify the initial voltage of the supercapacitor, in volts.

5. Operating temperature (Celsius): The operating temperature of the supercapacitor. The nominal temperature is 25°C.

6. Number of layers: The number of layers related to the Stern model.

7. Molecular of radius (m): The molecular radius related to the Stern model, in meters.

8. Permittivity of electrolyte material (F/m): The permittivity of the electrolyte material, in farad/meter.

The SOC for a fully charged supercapacitor is 100% and for an empty supercapacitor is 0%. The SOC is calculated as: ∫i (τ)⋅dτ SOC = SOCinit − ⋅100 (3.4.14) QT

52 Model Assumptions: 1. Internal resistance is assumed constant during the charge and the discharge cycles. 2. The model does not take into account temperature effect on the electrolyte material. 3. No aging effect is taken into account. 4. Charge redistribution is the same for all values of voltage. 5. The block does not model cell balancing. 6. Current through the supercapacitor is assumed to be continuous.

3.4.3 Temperature Estimation of Energy Storage cells To derive the model it relies heavily on the fundamental of heat transfer. Deriving model for heat transfer is inherently challenging due to the complexity of battery chemistry and composition. Thus, many assumptions and simplification must be made [20]. The heat transfer inside the battery can be divided into three parts, i.e., the heat generated by the cell's internal resistance, the change of entropy that occur in the cell components during discharge and heat transferred to ambient conditions by convection [21] and could be written as equation (3.4.13):

dT 2 I m⋅c ⋅ cell = I ⋅R + T ⋅ΔS⋅ + A⋅h⋅(T −T ) (3.4.15) cell dt cell n⋅F cell amb

Use of this equation assumes that the cell temperature is uniform. Hence a lumped capacitance model must be assumed in order to apply the equation correctly. In heat transfer theory, the validity of this assumption through the use of the Biot number. It is a dimensionless number that compares heat transfer occurring inside a body and at its surface using equation (3.4.14):

h⋅L B = c (3.4.16) i k

The characteristic length can be calculated by the manufacturer's cell specification. For thermal conductivity and heat transfer coefficient, typical values can be taken from literature [22], like k = 3 W/mK. Typical values from literature can be used for the value of the entropy change [23], like ΔS = -30 J/molK. ΔS can be obtained in the function of state of charge in simplified as a second order polynomial as in equation (3.4.15):

ΔS =−179⋅SOC2 + 274.29⋅SOC − 116 (3.4.17)

In the figures below, there are examples of how specific heat capacity, mass, heat transfer coefficient and

53 surface area affect the calculation of temperature over time. Entropy change is neglected for simplicity.

Figure 3.4.8: Temperature (oC) vs Time (s), 1 W/m2/K, 1 m3, 1 kg

Figure 3.4.9: Temperature (oC) vs Time (s), 1 W/m2/K, 1 m3, 1000 J/kg/K

Figure 3.4.10: Temperature (oC) vs Time (s), 1 m3, 1000 J/kg/K, 1 kg

54 Figure 3.4.11: Temperature (oC) vs Time (s), 1 W/m2/K, 1000 J/kg/K, 1 kg

3.5 Power Demands Calculation

To start with the simulation of the power and energy demands of a powertrain, we need to begin with the calculation of the resistances the vehicle needs to overcome for a given speed profile. Speed profiles/driving cycles are different for each application, while they also depend on the purpose of traveling. Driving cycles are produced by different countries and organizations to assess the performance of vehicles in various ways, as for example fuel consumption and polluting emissions. Another use for driving cycles is in vehicle simulations. More specifically, they are used in propulsion system simulations to predict performance of internal combustion engines, transmissions, electric drive systems, batteries, fuel cell systems, and similar components. Some driving cycles are derived theoretically, whereas others are direct measurements of a driving pattern deemed representative [16].

There are two types of driving cycles:

1. Transient driving cycles involve many changes, representing the constant speed changes typical of on- road driving. 2. Modal driving cycles involve protracted periods at constant speeds.

The "Drive-cycle" basically is the representative of the track. Drive cycles are used to reduce the expense of on track tests, time of test and fatigue of the test engineer. The whole idea is to bring the track to the test lab (a chassis dynamo-meter) or to the computer simulation. Two kinds of drive cycle can be made. One is distance dependent (speed vs distance vs altitude) and the other one is time dependent (speed vs time vs gear shift). The distance dependent is the actual replica of the test track whereas time dependent is the compressed version of the actual time taken to conduct the test on track. For the calculation of the power demands in time (power profile on the wheels) required to run a vehicle

55 for a given driving cycle, four resistances need to be calculated, and those are acceleration resistance, rolling resistance, aerodynamic resistance and inclination resistance. Especially for rail vehicles, two more resistances are added, numbering six resistances in total:

1. Acceleration Resistance 2. Resistance depending on number of axles 3. Rolling Resistance 4. Aerodynamic Resistance 5. Inclination Resistance 6. Curving Resistance (Track)

Modeling the calculation of the resistances is a quite easy task as shown below. SP, IP, WP and CP represent the speed, inclination, wind and curving profiles while C1 to C6 can be used for the different resistances already discussed. If the speed profile is given in km/h, constant C = 1/3.6 will then be used to transform it to m/s. FT will bring out the total tractive effort on the wheels. A clock with a decimation of 1 second should be used for better reading of the speed profile.

Figure 3.5.1: Calculation of Total Resistance (N) in Matlab/Simulink

3.5.1 Acceleration Resistance Acceleration resistance depends on the derivative of the speed profile (acceleration profile) given as an input, the total mass of the vehicle and a surplus equivalent mass calculated for rotating parts as a result of inertia: du F = M ⋅ = (m + m ) ⋅a (3.5.1) a dt eq

56 2 2 2 1 if if ⋅ig where m eq = (Iw⋅( ) + Ip⋅ηf ⋅( ) + Ie ⋅ηt⋅( ) ) (3.5.2) r w r w rw

represents the surplus equivalent mass required for the calculation of the acceleration resistance [18], and

2 Iw = moment of inertia of wheels and axles [kg·m ],

2 Ip = moment of inertia of propeller shafts [kg·m ],

2 Ie = moment of inertia of engine + moment of inertia of flywheel and clutch [kg·m ],

hf = mechanical efficiency of the final drive,

ht = mechanical efficiency of the transmission system [hg·hf],

ig = gearbox reduction ratio [ig1 or ig2 or …],

if = final drive reduction ratio, and rw = tire radius [m]

The above formula is an example that can be given for conventional powertrains with internal combustion engines. In different configurations, the formula given above has to take into account the differences occurred because of the topology chosen. For modeling purposes in Matlab/Simulink, it can simply be calculated by using the blocks shown in the figure, where constants C 1 to C6 represent the parameters given above, so that the equation is properly formulated.

Figure 3.5.2: Calculation of surplus equivalent mass (kg) in Matlab/Simulink

3.5.2 Running Resistance

3.5.2.1 Running Resistance in General Running Resistance is calculated by using empirical formulas such as Sauthoff Formula and Davis Equation complying of:

1. Rolling Resistance (no dependence on speed)

2. Resistance depending on the number of axles

57 3. Aerodynamic Resistance w/dependence on v2

In general, running resistance for railway applications can be expressed as:

2 Fr = A + B⋅V + C⋅V (3.5.3) where A,B,C are constants, and V is the vehicle's speed [m/s] but Sauthoff and Davis empirical formulas are often used. Other formulas like the VPT formula, the Dahlaus formula and the Strahl formula can be found in literature [24] and they are briefly discussed below.

1. Sauthoff Formula: The Sauthoff formula solves vehicle resistance calculation for passenger wagons, and its coefficients for formula are determined by the mass of the wagons, a factor relevant with the number of axles as already mentioned, the number of vehicles and a value which stands for the cross-sectional area of the vehicles weighted with their aerodynamic behavior:

2 FTR = 0.001⋅mw ⋅g⋅(1.9 + cb⋅3.6⋅v) + 0.0471⋅(nw + 2.7)⋅Af ⋅(3.6⋅vr) (3.5.4) where mw Total mass of the vehicle [kg], cb Factor for the number of axles, nw Number of wagons,

Af Factor for the cross-sectional area of the vehicles weighted with their aerodynamic behavior [for

2 example Af = 1.45 for A = 10 m often used], and vr Relative speed between air and vehicle [m/s]

2. VPT Formula: In the Netherlands, ProRail developed a system called VPT (Dutch name: Vervoer Per Trein) and formed a train resistance formula which is based on existing Dutch railway equipment [26]. The VPT train resistance formula is given in formula (3.5.5):

2 FTR = (A + N⋅B)⋅(V + ΔV) + M⋅(C + D⋅V) + N⋅E⋅(V + ΔV) (3.5.5)

58 where A = Rear-end aerodynamic coefficient [N·s²/m²], B = Length-dependent air resistance coefficient [N·s²/m²], C = Running resistance [N/kg], D = Speed dependent resistance [N·s/m·kg], E = Internal resistance [N·m/s], N = Number of wagons, M = Train mass in kilograms [kg], V = Train speed [m/s], and ΔV= Wind speed in [m/s]

From this formula, it is easy to recognize that the first part of the resistance represented by the quadratic velocity term and the second part describe the running resistance. The last term, the internal resistance, is often omitted by E = 0. The speed-sensitive portion of the running resistance is also often omitted by D = 0. For rear-end aerodynamic coefficient factor A, it is usually estimated under the condition of wind speed between 10 km/h and 15 km/h. Therefore, in reality, actual running time may even be smaller than calculated if the wind speed is smaller than 10 km/h. This formula also proved that air resistance will play a major role in the overall resistances [26]. Comparing to parabolas format formula (generic), the VPT the formula uses both speed of the train and wind together instead of just the speed of the train, which is a major characteristic of the VPT formula.

3. Dahlaus Formula: In [25], a parabolas resistance formula specialized for traction unit (including multiple units) is given in formula (3.5.6):

2 2 FTR = g⋅mT⋅(a 0 + a1⋅v) + a2 ⋅v + a2r ⋅v r (3.5.6) where

2 2 α0, α1[s/m] and α2 & α2r [Ns /m ] are the coefficient of the formula, mΤ [kg] is the mass of the traction unit, and vr is the relative speed between air and vehicle. In a situation of head wind equals to 15 km/h, it will have vr = v + 4.17 m/s.

59 4. Strahl Formula: For freight wagons, another formula called Strahl formula is used to estimate the vehicle resistance as formula (3.5.7):

(0.007 + c )⋅(3.6⋅v)2 F = 0.001⋅m ⋅g⋅(c + m ) (3.5.7) TR w a 100

where

mw mass of the vehicles [kg],

ca Factor for axle adhesion,

cm Value for air resistance dependent on the kind of wagons.

It is important to say that aerodynamic resistance is of the highest importance as it generally produces greater required tractive effort for the powertrain. For our model we use the equation (extracted by Sauthoff formula), given below:

2 Fd = 0.0471⋅(n w + 2.7)⋅A f⋅(3.6⋅v r) (3.5.8)

where

nw = Number of wagons,

Af = Factor for the cross-sectional area of the vehicles weighted with their aerodynamic behavior [for

2 example Af = 1.45 for A = 10 m often used], and

vr = Relative speed between air and vehicle [m/s].

5. Davis Formula Similar to Sauthoff Formula, Davis Formula can be used to calculate running resistance for trains. The Davis equation has been modified over the years for various rail systems and configurations:

20 K⋅V 2 R = 0.6 + + 0.01⋅V + (3.5.9) u w w⋅n

where

Ru is the resistance in [lb/ton], w is the weight per axle [W/n], n is the number of axles,

60 W is the total car weight on rails [tons], V is the speed in miles per hour and K is a drag coefficient. Values of K are 0.07 for conventional equipment, 0.0935 for containers and 0.16 for trailers on flatcars. Application of Davis Equation to a High-Speed Rail System (Japan Shinkansen Series 200) per Rochard and Schmid [28]:

R = 8.202 + 0.10656⋅V + 0.01193⋅V2 (3.5.10)

where R is the total resistance [kN], and V is the speed of the train [m/s]

Previous experiments mentioned in [24] have shown that usage of different train resistance formulas will show clear differences in tractive force, while minimal deviations in running time. This will lead to the result that by using different formulas, derived power usage will differ a lot while the running time will be almost the same.

Estimation of A,B,C parameters in general running resistance equation: Due to the differences occurred in traction effort when using various formulas to calculate the running resistance, it is proposed that, in any case, the general running resistance equation can be used if its parameters are properly tuned. An example of proper tuning of the general A,B,C running resistance coefficients for measured power profiles is given below. For acceleration and cruising operating modes in various speeds, an estimation of the parameters can be done, assuming that the speed and power profiles are measured relatively to the time step used in the simulation. If A,B,C are tuned correctly different speed profiles can be investigated afterwards. For the examples below, the vehicle's total mass is assumed to be 200 tons. The tuning is also considering correction of the average efficiency factors for rotating parts, power electronics and cables. This way the efficiency of those parts can be described as a function of torque applied and speed.

61 Vehicle Speed (km/h) vs Time (s): ■ Measured ■ Corrected

Power (W) vs Time (s): ■ Measured ■ Simulated

Power (W) vs Time (s): ■ Measured ■ Simulated

Figure 3.5.4: Utrecht-Amsterdam, 1st 600 seconds, Low speed acceleration and high speed cruising example for running resistance parameter estimation

62 3.5.2.2 Rolling Resistance In general, rolling resistance depends on the total mass of the vehicle, the rolling resistance coefficient, which is usually a small number for rail applications, and the inclination of the track, as inclination reduces the vertical load from the vehicle to the wheels.

Fi = f R ⋅m⋅9.81⋅cos(φ) (3.5.11)

where

fR is the rolling resistance coefficient (dimensionless), m = total mass of the vehicle [kg], and, φ = track's inclination [degrees or rad].

◦

Table 3.5.1: Table of rolling resistance coefficient examples [29]

fR b Description 0,0003 to 0,0004 - "Pure rolling resistance" Railroad steel wheel on steel rail 0,0010 to 0,0015 0.1 mm Hardened steel ball bearings on steel 0,0010 to 0,0024 0.5 mm Railroad steel wheel on steel rail. Passenger rail car about 0,0020 0,0019 to 0,0065 - Mine car cast iron wheels on steel rail Production bicycle tires at 120 psi (8.3 bar) and 50 km/h (31 mph), measured on 0,0022 to 0,0050 - rollers 0,0025 - Special Michelin /eco-marathon tires 0,0050 - Dirty tram rails (standard) with straights and curves 0,0045 to 0,0080 - Large truck (Semi) tires 0,0100 - Typical BMX bicycle tires used for solar cars 0,0062 to 0,0150 - Car tire measurements 0,0100 to 0,0150 - Ordinary car tires on concrete 0,0385 to 0,0730 - Stage coach (19th century) on dirt road. Soft snow on road for worst case. 0,3000 - Ordinary car tires on sand

It should be noted that equation expresses the maximum rolling resistance. That is to say, the equation does not apply if there is slip between the wheel and the rails. For wagons, slip is very rare, since the wheels are not driven. On the other hand, slip can more likely occur with the locomotive, especially under starting condition.

The weight of the train is normally given, and g is a constant. On the other hand, fR can be more difficult to establish, since the coefficient can depend on mass, number of axles, axle load and several other variables. In

63 practice, fR is determined through measurements for the wheel in question under different operating conditions. The following is a description of a method for an approximate calculation of the rolling resistance of different train shapes. It is assumed that the rolling resistance coefficient is dependent on the speed of the train.

The general formula for the calculation of fR is given as:

v v 2 f R = C0 + C1⋅( ) + C2⋅( ) (3.5.12) v 0 v0

where

fR is the rolling resistance coefficient in ‰,

C0, C1 and C2 are constants in ‰, v is the speed of the train in m/s,

v0 is a constant, 100 km/h = 27,778 m/s.

The constant C0 can be calculated as:

f SL + mL + f SV + m V C0 = (3.5.13) m tog

where

fSL is the starting value for a locomotive’s rolling resistance (dimensionless),

mL is the locomotive weight in [kg],

mp is the total weight of the wagons in [kg],

mTog is the total weight of the train in [kg],

fSV is an initial value for the rolling resistance of the wagon (dimensionless), and can be calculated as:

FA f SV = CCV + (3.5.14) GA

where

CSV is a constant in ‰,

FA is an axle pressure constant of 100 [N],

GA is the average axle load for the train in [kN].

Since the average axle load is the train’s total load divided with the number of axles (nax):

64 FA⋅nax f SV = CCV + (3.5.15) m tog⋅g

Table 3.5.2: Constants for the calculation of rolling resistance [16]

Four axle locomotive fSL = 2,50 - 3,50 ‰

Six axle locomotive fSL = 3,50 - 4,50 ‰

ICE trainset - motor wagon fSL = 1,30 ‰

ICE trainset - middle wagon fSV = 0,60 ‰

ICE trainset C1 = 0,10 ‰ C2 = 0,30 ‰

Passenger train CSV = 0,40 ‰

C1 = 0,25 ‰ C2 = 0,50 ‰

Goods train CSV = 0,60 ‰

C1 = 0,50 ‰ C2 = 0,60 ‰

Rolling resistance for goods trains : Previously, a method was presented for calculating the rolling resistance for a given train of known composition. This method is fairly comprehensive and it is advantageous to be able to calculate FR more simply.

As was shown in the previous chapter FR depends on factors such as axle load, as well as several constants. The axle load and the weight have the result that a number of wagons will be similar when considering FR. For example, the two-axle wagon will have about the same axle load with the loading is about the same. This is in contrast to the aerodynamic loading, FL, which is very dependent on the shape of the wagon and its placement in the wagon string. This means that it is possible to approximate F L, while a calculation of FL will require more details for the individual wagons.

To a good approximation, the rolling resistance FR can be written as a linear function of the speed:

FR = A + B⋅v (3.5.16)

where A is a constant in [N], that depends on the number of axles, that is, the number of wagons. The locomotive is not considered here. B is a constant in [N·s/m] dependent on the train length. The locomotive is considered here. v is the train speed in [m/s].

In the section below an approximate expression for the rolling resistance for different goods trains is

65 shown. Also shown are descriptions, compositions, as well as the axle load and the speed dependent expression for rolling resistance.

Table 3.5.3: Configuration of homogeneous trains [16] Mean axle Train type Length - m A - N B - N·s/m Description load - kN

Loco + n*Sgis type wagon 21 + n*19,64 195,4 343 + 195*n 15,14 + 1,62*L 3 container/wagon ax t

Loco + n*Sgis type wagon 21 + n*19,64 144,7 231 + 170*n 23,96 + 1,20*L 2 container/wagon ax t

Loco + n*Sgis type wagon 21 + n*19,64 93,94 344 + 146*n 32,89 + 0,78*L 1 container/wagon ax t

Loco + n*Sgis type wagon 21 + n*19,64 43,21 344 + 121*n 344 + 121*n unloaded ax ax

Loco + n*Kbs type wagon 21 + n*13,96 196,4 2881 + 195*n 25,10 + 1,14*L 2 container/wagon ax t

Loco + n*Kbs type wagon 21 + n*13,96 128,9 2881 + 165*n 33,36 + 0,75*L 1 container/wagon ax t

Loco + n*Kbs type wagon 21 + n*13,96 61,40 2881 + 130*n 41,62 + 0,36*L unloaded ax t

Loco + n*Fals type wagon 21 + n*12,54 196,4 344 + 195*n -4,38 + 2,55*L fully loaded ax t

Loco + n*Fals type wagon 21 + n*12,54 128,9 344 + 162*n 14,01 + 1,67*L half loaded ax t

Loco + n*Fals type wagon 21 + n*12,54 61,40 344 + 130*n 32,71 + 0,82*L unloaded ax t

3.5.2.3 Aerodynamic Resistance The purpose of this section is to describe the air resistance of a freight train. The air resistance is a function of the train area, form and speed, according to the following equation (3.5.17):

66 ρ 2 F = ⋅c ⋅A ⋅v (3.5.17) L 2 L Norm

where

FL is the total air resistance,

2 2 ANorm is the normal-frontal area in m (ANorm assumed 10 m ), v in the train speed in m/s, ρ is the air density in kg/m3,

cL is the drag coefficient (dimensionless).

The coefficient cL given for in equation (3.5.17) is for the entire train. It is possible to divide c L, so that the individual coefficient can be calculated for the individual portions of the train. The value of c L is calculated from the contributions from the locomotive and the string of cars:

cLtot = cLlok + ΣcLv (3.5.18)

where

cLtot is the air resistance coefficient for the entire train,

cLlok is the air resistance coefficient for the locomotive,

cLv is the air resistance coefficient for the following string of cars as a unit.

Table 3.5.4: Coefficients given for the calculation of cLtot [16]

Electric locomotives Air Resistance Coefficient cLlok Four axles, normal shape 0,80 Four axles aerodynamic shape 0,45 Six axles, normal shape 1,10 Six axles aerodynamic shape 0,55 Diesel locomotives Four axle 0,60 Six axles 1,10 Middle axles 1,00

Passenger cars Air Resistance Coefficient cLv General 0,15 26,4 m (Standard German passenger wagon) 0,11

67 In the calculation of cLv , the first and last wagons are calculated separately from the rest of the string of cars. This is because the air resistance for these wagons is larger than that for the wagons in the middle. The values of cL have been measured and depend on the specific locomotive/wagon. It should be noted, that the values in the tables are given such that extra contributions for the first and last wagons are included with the locomotive. When using the values in the tables, all the wagons can be assumed to be intermediate wagons. The equation above is for the calculation of homogeneous wagon strings. This limits the validity, though such trains are seen for goods transport. This is not a problem for passenger trains, where a non-homogeneous wagon string would be an exception. Goods trains can, however be very inhomogeneous, and so a modification of equation (3.5.18) is necessary.

The principle in the redefinition is to take the cL- value for the wagon in question, and add a contribution for the extra area that will give extra air resistance. As an example, two goods wagons can be mentioned - a flat and a high. If the high wagon is coupled in front of the low, there will not be any extra area, and the air resistance coefficient can therefore be calculated normally. On the other hand, if the low wagon is coupled in front of the high, a portion of the high goods wagon’s frontal area will not be covered by the flat wagon. The non-covered area there gives rise to extra air resistance and must be included. In the calculation of the total c Lv for the two wagons, the two cLm-values are added together, since a contribution for that portion of the frontal area which is not covered by the flat must be included. The contribution consists of a value c Lf as well as a portion α for the increase in the frontal area.

That is to say:

cLtot = ΣcLm + a⋅cLf (3.5.19)

where

cLtot is the total air resistance coefficient,

cLm is the air resistance coefficient for a wagon in the middle of the train,

cLf is the air resistance coefficient for the wagon as in the front of the train, α is an area ratio that is obtained when a wagon is followed by another wagon with greater frontal area.

For operation with non-homogeneous wagon strings, that is with wagons of different heights, cLv for the train is not equal to cLm times the number of wagons. Since all wagons do not have the same height, and therefore, not the same frontal areal, the wagon string cannot be calculated as a coherent unit.

68 Table 3.5.5: Constants for the calculation of aerodynamic resistance from freight wagons [16]

Goods Wagons cLm cLf GLS 205 type wagon, closed doors 0.092 0.900 GLS 205 type wagon, open doors 0.100 0.967 ES 040 type wagon, empty, open 0.249 0.679 ES 040 type wagon, loaded 0.119 0.673 ED 090 type wagon, empty, open 0.178 0.760 ED 090 type wagon, loaded 0.043 0.844 FAD 168 type wagon, empty, open 0.228 1.081 FAD 168 type wagon, loaded 0.115 0.983 EAOS 106 type wagon, empty, open 0.409 0.730 EAOS 106 type wagon, loaded 0.141 0.769 KBS 442 type wagon: empty, without stakes 0.116 0.496 empty, with stakes 0.159 0.697 loaded with, 2 - 20 foot containers 0.153 0.715 SGIS 716 type wagon: empty, without stakes 0.165 0.601 empty, with stakes 0.236 0.686 loaded 1 - 20 foot container in 0.452 0.885 middle loaded 2 - 20foot container in middle 0.276 0.850 loaded 1 - 20 foot container in each 0.392 0.866 end. loaded 3 - 20 foot containers 0.218 0.866

Air resistance as a function of train length - homogeneous trains:

As an extension of the previous chapter, where the calculation of c L was generally described, the

2 dependence of cL*A on train length will now be shown. Note since a standard reference area of 10 m was chosen, the air resistance coefficient is simply one tenth of the values on the y-axis of the c L*A plots. An overview of the different type of train arrangements is shown in the section below.

69 Table 3.5.6: Equations for calculating the air resistance of different homogeneous trains as a function of train

length, Lt, in meters [16]

2 Wagon type and arrangement cL * Anorm - m Description

-2 Κbs type wagon no stakes 9,26 + Lt*8,310*10

-2 Κbs type wagon with stakes 8,59 + Lt*11,47*10

-2 Gls type wagon 9,40 + Lt*7,610*10

-2 Sgis type wagon, empty no stakes 9,24 + Lt*8,400*10

-2 Sgis type wagon, 3x20 ft containers 8,67 + Lt*11,10*10

-2 Sgis type wagon, 2x20 middle 8,05 + Lt*14,05*10

-2 Sgis type wagon, 2x20 ends 6,81 + Lt*19,96*10

-2 Sgis type wagon, 1x20 middle 6,17 + Lt*23,01*10

-2 Fad type wagon, loaded 9,07 + Lt*9,170*10

-2 Fad type wagon, empty 7,19 + Lt*18,18*10

-2 Eaos type wagon, loaded 8,89 + Lt*10,04*10

-2 Eaos type wagon, empty 4,88 + Lt*29,13*10

-2 Es type wagon, loaded 8,62 + Lt*11,33*10

70 -2 Es type wagon, empty 6,02 + Lt*23,71*10

Air Resistance as a function of length - non-homogeneous trains: Previously, trains were considered that consisted of the same shape of wagon. It is now of interest to investigate how a freight train is arranged. The question is what is the significance of open and closed cars located next to each other, for example. Or what is the significance of where containers are located on a wagon, or what is the difference of resistance between homogeneous and non-homogeneous trains. The locomotive and wagons are the same as in the previous chapter. A summary of non-homogeneous trains is found in section below. As mentioned, the calculations for non-homogeneous trains are more complicated than for homogeneous.

Table 3.5.7: Equations for calculating the aerodynamic resistance of non-homogeneous trains [16]

Wagon type and arrangement Air Resistance, cL * Anorm Description

-2 Sgis type wagon, 2x20 middle/3x20 8,35 + Lt*12,58*10

-2 Sgis type wagon, 2x20 end/3x30 7,74 + Lt*15,53*10

Sgis type wagon, empty no stakes/1x20 4,63 + L *30,33*10-2 middle t

-2 Sgis type wagon, empty no stakes/3x20 5,95 + Lt*21,06*10

Sgis type wagon, 2x20 middle/empty no 5,69 + L *25,27*10-2 stakes t

Sgis type wagon, 2x20 end/ empty no 4,99 + L *28.64*10-2 stakes t

-2 Sgis type wagon, 1x20 middle/3x20 7,42 + Lt*17,06*10

-2 Sgis type wagon, 2x20 end/1x20 middle 6,49 + Lt*21,49*10

71 Sgis type wagon, 2x20 middle/1x20 7,02 + L *18,53*10-2 middle t

-2 Gls/Κbs type wagon 8,17 + Lt*13,49*10

Air Resistance for Passenger Trains and Simplified Goods Trains: The calculations for passenger trains are basically the same as for goods trains. Here, the wagons are basically all the same shape, so the difference in heights does not enter into consideration. The air resistance coefficient can be calculated by the formula given below:

ρ 2 F = ⋅(c + cL1 + (n − 2)⋅c + c )⋅A ⋅v (3.5.20) Ltot 2 Lloco Lm ln norm

where

FLtot is the total aerodynamic resistance of the train

cLloco is the drag coefficient for the locomotive

cL1 is the drag coefficient for the first wagon after the locomotive

cLm is the drag coefficient contribution for the intermediate wagons

cLn is the drag coefficient for the last wagon in the train

Recommended values for an IC train with a type 103 locomotive [31], typical of intercity and regional locomotive drawn trains:

cLloco = 0,30

cL1 = 0,23

cLm = 0,14

cLn = 0,30

The method can also be used for goods trains, though not as detailed as the method presented in previous chapters. The value of cLm is said to range from 0,15 to 0,3 for goods trains. For a high-speed train, the following formula is used:

ρ F = ⋅(c + n⋅c )⋅Anorm⋅v2 (3.5.21) Ltot 2 L0 Lm

72 where cL0 = 2*0,2 = 0,4 and cLm = 0,095. The rolling resistance coefficient is said to lie between 0,001 and 0,003.

3.5.3 Other Resistances

1. Inclination Resistance: depending on the total mass of the vehicle and the inclination profile over distance.

Fi = m⋅9.81⋅sin(φ) (3.5.22)

where m = total mass of the vehicle [kg], and φ = track's inclination [degrees or rad]

2. Curving Resistance (Empirical Formula): depending on the total mass of the vehicle and the curving profile over distance. It can be neglected for vehicles running on almost straight tracks (for example when R ~ 500 m).

6.116 F = ⋅m (3.5.23) c R

where R = the track's curvature [m], and m = total mass of the vehicle [kg]

3. Other operating resistance: For a train operating on a flat straight stretch of track, there are some resistances in addition to air and rolling resistance that could be considered. Normally, though, it is these two resistances that dominate. For completeness, a short discussion is presented of two other possible resistances. They are brake disk resistance, FBS and impulse resistance, FIMP.

Brake disk resistance: When a train has mechanical brakes, there will unavoidably be a heating of the brake pads. This is caused by the friction against the wheels, which is the essential element of the braking process. To prevent overheating of the brake pads, locomotives and wagons are often built such that some airflow can cool the brake

73 pads. In [30] the following equation is given for the calculation of the brake disc resistance, FBS:

v v 2 FBS = nBS ⋅(C3⋅ + C4 ⋅( ) ) (3.5.24) v0 v0

where

FBS is the brake disk resistance for all brake discs on the train [N],

nBS is the number of brake discs in the train (normally four per axle),

C3 and C4 are constants in N; C3 = 4,33 N and C4 = 3,16 N, v is the train speed in [m/s],

v0 is a speed constant = 27,778 [m/s].

Impulse resistance: One could consider the so-called air impulse resistance, FIMP as a form of air resistance. It occurs in connection with cooling and ventilation units in locomotives and passenger wagons. For locomotives, it is primarily the cooling fan and air intake for cooling of the engine. For passenger wagons, it is the ventilation and heating/cooling equipments. The airflow occurring here is accelerated due to the motion of the train. The air impulse resistance can be calculated from the following Equation :

FIMP = ρ⋅Qluft⋅( v + Δv) (3.5.25)

where

FIMP is the air impulse resistance in [N]n ρ is the air density - normally 1,20 kg/m3,

3 Qluft is the airflow emanating from the train in m /s. An estimate for a goods train locomotive is on the order of 0,1 m3/s, v is the train speed in [m/s],

v0 is a speed constant = 27,778 [m/s].

Slip Condition: Finally, to estimate slip conditions of a rail vehicle, the formula given below can be used to calculate the maximum tractive force that can be applied on a wheel just before the wheel slips:

7.5 F = ( + 0.161)⋅N⋅cos(φ) (3.5.26) MAX 44+v

74 where

FMAX is the maximum tractive force [N] that can be applied on the wheel, v is the vehicle's speed in [km/h], N is the vertical load applied on the wheel in [N], and φ is the inclination of the track in [deg].

For modeling purposes, the figure below will simply give us the maximum force that can be delivered to the wheel, before slipping occurs in Newtons per Newtons of vertical load. Here, SP = Speed Profile [km/h] and C = φ [deg].

Figure 3.5.4: Estimation of maximum tractive force on the wheel before slip, FM = FMAX

3.6 Power division

As previously mentioned in chapter 2, the three main sub-models of the total powertrain model (see Generic Modeling Structure) need to be interconnected, so that the energy flows can be defined. For that reason, we are using imaginary switches, representing the on/off states of power converters or possible electronically controlled clutches used in HEVs. This way, we can control the power flows and define the topology we want to use for any investigation without having to change the already existed structure of the model, something that would require higher system complexity. Also, by adopting this simple technique the model remains user-friendly in terms of changeability and further modeling improvements, without going into quasi-static or dynamic approaches, as kinematic approach we are using favors such a power division technique. Apart from the ON/OFF STATES, some parameters for the power division in general, need to be defined, not only for the power division between mechanical components but also for cases where multiple power source units are used. Supercapacitors used in parallel with batteries for increasing batteries' lifetime could be such a case, while fuel cells providing an average power output along with batteries or supercapacitors being the dynamic power sources could be another one. There are 10 main parameters required for the power division block:

1. Maximum power output

75 2. Minimum power output 3. Maximum power input for energy storage 4. Response time 5. Power output percentage 6. Power input percentage for energy storage 7. Power output rising rate 8. Power output falling rate 9. Power input rising rate for energy storage 10. Power input falling rate for energy storage

Furthermore, certain constraints affect the power flows and should be considered in any investigation. Those are:

1. Energy storage maximum charging cell current 2. Energy storage maximum discharging cell current 3. Energy storage maximum cell voltage 4. Energy storage minimum cell voltage 5. Energy storage maximum cell temperature 6. Energy storage minimum cell temperature 7. Maximum power provided by the catenary (for example on a charging point) 8. Maximum power provided by the diesel engine in HEV configurations

For modeling purposes the output power definition of a component can be described by using the blocks shown in the figure below:

Figure 3.6.1: Power division example

76 where

Pin = power input of the power division block,

Pout = power output of the power division block sent as a reference to the component's modeling block,

Vcc = cell voltage for the case of an energy storage unit,

C1 = power percentage, one of the parameters of power division,

C2 = maximum charging cell current for energy storage units.

Blocks S1 and S2 represent current constraints the user would like to embed inside the algorithms controlling the power converters of the energy storage units. In cases where no power converter is user (for example on a battery-only electric powertrain) then safety values for SOC error from 100% and 0% should be on 0%. Then the minimum and maximum SOC for the energy storage units should be defined by changing other parameters (for example by increasing the number of cells – capacity).

-Block S1 = discharging cell current constraint for SOC reaching values greater than the minimum safety SOC given by the user.

100 −SOC I out = ⋅Idischarge −max if SOC < SOCmin (3.6.1) SOCmin

3.6.2: Block S1

-Block S2 = charging cell current constraint for SOC reaching values lower than the safety SOC given by the user for a CC-CV charging method.

SOC −100 I = ⋅I if SOC > SOC (3.6.2) out 100 charge −max safety

77 3.6.3: Block S2

The power division between two different traction or power providing units can simply follow the equation:

Pin2 = Pin1 − Pout1 (3.6.3) where

st Pin1 = power input of the power division block for the component chosen 1 ,

nd Pin2 = power input of the power division block for the component chosen 2 ,

st Pout1 = power output of the power division block sent as a reference to the 1 component's modeling block. An example is shown in the figure below:

Figure 3.6.4: Two components in series power division

78 The next figures provide some examples of batteries and supercapacitors used together. The purpose is to give a first impression of how the SOC of two different modules is defined, when some parameters of the power division block are changed. The vehicle's mass is set at 10 tons and default running resistance coefficients were used. The speed profile in km/h over ime used for this example is shown below. The rest of the figures show the state-of-charge (SOC) of batteries and supercapacitors in % over time (s) for the 30 seconds simulated.

Figure 3.6.5: Speed profile for power division example, Speed (km/h) vs Time (s)

Figure 3.6.6: [Left]: Batteries-only SOC starting at 100% (fully-charged) – Min SOC: 97.25%, Final SOC: 97.65%, [Right]: Supercapacitors-only SOC starting at 100% (fully-charged) – Min SOC: 83%, Final SOC: 88%

79 Figure 3.6.7: [Left]: Batteries' SOC starting at 10% - Batteries will not accept 100% of the power demands during acceleration and braking, supercapacitors will provide or accept a portion of the power demands - Min SOC: 7.25%, Final SOC: 7.75%, [Right]: Supercapacitors' SOC starting at 100% - Supercapacitors are not used during cruising - Min SOC: 92.75%, Final SOC: 95%

Figure 3.6.8: [Left]: Batteries' SOC starting at 10% with minimum SOC set at 9% - Supercapacitors will provide the energy required if the batteries have reached the minimum SOC set by the user for safety reason, [Right]: Supercapacitors' SOC starting at 100% - Providing the energy needed when the battery's OFF

80 Figure 3.6.9: [Left]: Batteries' SOC starting at 100% - Power division set at 50%-50% between batteries and supercapacitors – Min SOC: 98.85%, Final SOC: 99.225%, [Right]: Supercapacitors' SOC starting at 100% - Power division set at 50%-50% between batteries and supercapacitors – Min SOC: 91.75%, Final SOC: 94.5%

Figure 3.6.10: [Left]: Batteries' SOC starting at 100% - Batteries are set OFF for negative values of power (braking) - Min SOC: 98.4%, Final SOC 98.4%, [Right]: Supercapacitors' SOC starting from 100% - Batteries are set OFF for negative values of power (braking) – Min SOC: 94.15%, Final SOC: 99% (Almost zero average energy – acting as a buffering device)

81 The above figures represent only a portion of the power flow freedom and the constraints set by the user, that can exist in the developed model, yet it is shown that there are many ways to deal with different devices/power sources. A catenary system along the track, charging points on main stations, diesel generator sets, batteries and supercapacitors can co-exist. Especially for batteries and supercapacitors, constraints can be set on maximum and minimum SOC and also on maximum charging and discharging currents apart from the limits set for the power division strategy described previously. Also, ON/OFF states can be defined for five different driving modes. For example, this way a scenario of batteries/supercapacitors buffering a diesel generator or supercapacitors buffering a battery pack can be investigated, as shown in Figure 3.6.10.

82 3.7 Verification of Energy Demands

3.7.1 Case I - Gelede Tram Lang (GTL8) Tram The eight-axle Gelede Tram Lang (GTL8) is a tram model of Hague's municipal transport company HTM . The "8" in the name stands for the 8 drive axles, with eight electric motors of 45 kW each. GTL (3000 series) is 28.6 meters long and was the longest tram in the Netherlands in the 80's. The average bogie distance is about 7.2 meter, instead of the usual center-to-center distance of about 6 meters. Important characteristics are described below:

Table 3.7.1: Characteristics of Gelede Tram Lang (GTL8) [44] Product Gelede Tram Lang (GTL) Type Series 1 & 2 Number 100 + 47 3001 t/m 3100 Series 3101 t/m 3147 Manufacturer BN (mechanical part), ACEC(traction motors), HOLEC (electrical part) Carrier HTM 1981 - 1984 Year Built 1990 - 1991 1981: 3001 t/m 3027 1982: 3028 t/m 3060 1983: 3061 t/m 3083 Employed 1984: 3084 t/m 3100 1992: 3101 t/m 3110 1993: 3111 t/m 3147 2011: 3003, 3007, 3051 2013: 3005, 3018, 3020 Out of service 3023, 3027, 3029, 3030 3031, 3034, 3035, 3036 3039, 3040, 3041, 3096 Back (train) 3-Piece Axles 8 Gauge 1,435 mm Series 1: 37 tons Mass Series 2: 38 tons Series 1: 28.6 m Length over buffers Series 2: 29.0 m

83 Width 2.35 m Height 3.19 m Speed limit 70 km/h Speed service 50 km/h Floor height 860 mm Doors 5 doors Series 1: 71 Number of seats Series 2: 76 Series 1: 118 Number of pitches Series 2: 112 Power System Catenary Power 8 x 45 = 360 kW

To validate the energy demands of this case, 12 speed profiles and power measurements were considered for the 2nd series of the GTL8 [45]. An average efficiency correction factor (AECF – 1: Sauthoff Formula, AECF – 2: Davis Equation) was used to correct the system's average efficiency including: 1. Correction for the efficiency maps of the 45kW DC motors used in GTL8 (the model used an efficiency map of 50kW AC induction motor – 92.5% maximum efficiency). 2. Average efficiency of the two DC/DC converters (traction choppers). 3. Average efficiency of mechanical rotating parts (axles, bearings etc.). 4. Losses on cables. 5. Correction for the actual total mass of the vehicle during the time of measurements, as 40.000 kgs (40 tons) where considered for all speed profiles. The installation's schematic and the results are shown below (■ Measured ■ Simulated):

Figure 3.7.1: Gelede Tram Lang (GTL8) installation's schematic

84 Figure 3.7.2: Speed profile 1 [01 – APRIL_RUN01] – AECF-1: 41.25%, AECF-2: 44.15%

85 Figure 3.7.3: Speed profile 2 [03 – APRIL_RUN01] – AECF-1: 41.50%, AECF-2: 44.15%

86 Figure 3.7.4: Speed profile 3 [06 – APRIL_RUN01] – AECF-1: 42.25%, AECF-2: 45.15%

87 Figure 3.7.5: Speed profile 4 [07 – APRIL_RUN01] – AECF-1: 41.25%, AECF-2: 44.20%

88 Figure 3.7.6: Speed profile 5 [09 – APRIL_RUN01] – AECF-1: 38.25%, AECF-2: 42.75%

89 Figure 3.7.7: Speed profile 6 [26 – MARCH_RUN02] – AECF-1: 42.15%, AECF-2: 44.80%

90 Figure 3.7.8: Speed profile 7 [29 – MARCH_RUN03] – AECF-1: 42.15%, AECF-2: 44.80%

91 Figure 3.7.9: Speed profile 8 [01 – APRIL_RUN01_1] – AECF-1: 40.65%, AECF-2: 43.15%

92 Figure 3.7.10: Speed profile 9 [01 – APRIL_RUN04] – AECF-1: 42.25%, AECF-2: 45.20%

93 Figure 3.7.11: Speed profile 10 [29 – MARCH_RUN01] – AECF-1: 42.75%, AECF-2: 45.20%

94 Figure 3.7.12: Speed profile 11 [30 – MARCH_RUN01] – AECF-1: 39.75%, AECF-2: 41.75%

95 Figure 3.7.13: Speed profile 12 [07 – APRIL_RUN02] – AECF-1: 41.50%, AECF-2: 45.15%

96 Table 3.7.2: Average Efficiency Correction Factors (AECF) RUN Energy (Wh) AECF – 1 (%) AECF – 2 (%) Speed profile 1 [01 – APRIL_RUN01] 180000 41.25 44.15 Speed profile 2 [03 – APRIL_RUN01] 115000 41.5 44.15 Speed profile 3 [06 – APRIL_RUN01] 170000 42.25 45.15 Speed profile 4 [07 – APRIL_RUN01] 37500 41.25 44.2 Speed profile 5 [09 – APRIL_RUN01] 125000 38.25 42.75 Speed profile 6 [26 – MARCH_RUN02] 32500 42.15 44.8 Speed profile 7 [29 – MARCH_RUN03] 65000 42.15 44.8 Speed profile 8 [01 – APRIL_RUN01_1] 85000 40.65 43.15 Speed profile 9 [01 – APRIL_RUN04] 34000 42.25 45.2 Speed profile 10 [29 – MARCH_RUN01] 210000 42.75 45.2 Speed profile 11 [30 – MARCH_RUN01] 31000 39.75 41.75 Speed profile 12 [07 – APRIL_RUN02] 22000 41.5 45.15 MEAN VALUE: 41.30 (+1.5%,-3%) 44.20 (+1%,-2.5%)

AECF – 1 was used for Sauthoff's formula and AECF – 2 was used for Davis' equation, both described previously. A mean value of 41.30% with relative differences of +1.5%, -3% was extracted for Sauthoff's formula and a mean value of 44.20% with relative differences of +1%, -2.5% was extracted for Davis' equation. Both results show good estimation of the energy profiles over time for the given speed profiles and power profiles measured. Nevertheless, accurate measurement of the speed profiles with position or speed sensors will play an important role on the verification of the power profiles over time (especially the calculation of the peaks in power), as the method used in this case involved GPS location and accelerometer combination, which is not very accurate for simulation time steps of 1 and 0.1 second.

97 3.7.2 Case II - NS Intercity Passenger Train (VIRM) The NS DD-VIRM, full name Dubbeldeks Interregiomaterieel or Verlengd InterRegio Matrieel, Electric Multiple Units (EMUs) trains were built by De Dietrich or Talbot between 1994 and 2009. They are operated by the Nederlandse Spoorwegen in the Netherlands. VIRM is a double-decker inter regional EMU train for passenger transport. A VIRM Series 2 consists of four or six cars. The VIRM Series 3 consists of 6 cars. Important characteristics are described below:

Table 3.7.3: Characteristics of DD-VIRM [46] NS Class 8600/8700/9400/9500 In service 1994 - present Manufacturer De Dietrich Ferroviaire Number built 178 6 cars (8600/8700) Formation 4 cars (9400/9500) 8600/8700 - 129 (1st), 442 (2nd) Capacity 9400/9500 - 61 (1st), 330 (2nd) Operator NS Travel Specifications Maximum speed 160 km/h (99 mph) 2,388 kW (3,202 hp) (8600/8700) Power output 1,592 kW (2,135 hp) (9400/9500) 1.5 kV DC Catenary; all units have extra space for equipment to operate on 25 kV Electric system(s) AC Catenary Current collection method 1,435 mm (4 ft 81⁄2 in) standard gauge

For this case, 2 speed profiles are considered (from Amsterdam to Utrecht and from Utrecht to Amsterdam). The AECF used in both cases is +9% and so the results are shown together in Figure 3.7.16. Coefficients are the same with the previous case, total vehicle mass is considered to be 250.000 kgs (250 tons). The speed profiles were again measured in such a way that proper estimation of the power profiles over time cannot be easily extracted. Accurate measurement of the speed profiles through position or speed sensors is still an issue. Nevertheless, the energy profiles over time can be estimated by using the AECF, as in the previous case. 0.1 second was used as a time step. The results are shown below.

98 ■ Mechanical [Measured] ■ Electrical [Measured]

■ Measured Energy ■ Simulated Energy

Figure 3.7.14: Amsterdam-Utrecht, 1st 1300 seconds, Sauthoff: Pass (AECF: +9%)

99 Figure 3.7.15: Utrecht-Amsterdam, 1st 600 seconds, Sauthoff: Pass, Davis: Fail (AECF: +9%)

100 Figure 3.7.16: Speed profile for NS VIRM [Utrecht-Amsterdam, Amsterdam-Utrecht] (AECF: +9%)

101 CHAPTER 4

4.1 CASE I - Elimination of a Tram's Overhead Catenary System Through Battery Electrification

In this case, we will be testing the adoption of a battery package towards the reduction of the energy consumption and the elimination of the overhead catenary system used in a tram vehicle. The speed profile is taken from chapter 3.8.1, representing one of the many speed profiles used for the verification of the energy demands of the eight-axle Gelede Tram Lang (GTL8), and is shown below:

Figure 4.1.1: Speed profile for CASE I (km/h) over time (s)

The main concept is to recharge the battery package every time the vehicle returns to its main station. This way, we can eliminate the overhead catenary system and only use one charging point (the main station). We are testing this concept for different battery sizes and chemistries shown in the table below:

Table 4.1.1: Battery cells for CASE I

Capacity N. Voltage Resistance D C- C C- GED Cycles (N) N Type (Ah) (V) (Ohm) RATE RATE (Wh/kg) 100% DoD LiFePO4 1 20 3,20 0,0025 3,0 0,8 85,35 1500 Prismatic LiFePO4 2 40 3,20 0,0025 3,0 0,8 85,35 1500 Prismatic LiFePO4 3 60 3,20 0,0020 3,0 0,8 76,85 1500 Prismatic

102 LiFePO4 4 100 3,20 0,0018 3,0 0,8 100,00 1500 Prismatic LiFePO4 5 200 3,20 0,0010 3,0 1,0 112,25 1500 Prismatic LiFePO4 6 1,10 3,20 0,2550 3,0 1,0 121,35 1500 Pouch LiFePO4 7 10 3,20 0,0100 5,0 1,0 128,00 1500 Pouch LiFePO4 8 15 3,20 0,0040 2,0 1,0 126,35 1500 Pouch LiFePO4 9 20 3,20 0,0040 2,0 1,0 125,00 1500 Pouch LiFePO4 10 1,50 3,20 0,0380 3,5 2,0 117,00 1500 18650 LiFePO4 11 1,10 3,20 0,0100 27,5 3,5 88,00 1500 18650 LiFePO4 12 3,30 3,20 0,0300 3,0 1,0 117,35 1500 26650 LiFePO4 13 4,50 3,20 0,0100 20 1,0 82,00 1500 32900P LiFePO4 14 10 3,20 0,0080 3,0 3,0 96,95 1500 38120S LiFePO4 15 15 3,20 0,0040 5,0 3,0 100,00 1500 40152SE LiNiMnCo 16 2,00 3,70 0,0700 1,0 1,0 185,00 1500 18650 LiNiMnCo 17 4,00 3,70 0,0910 1,0 1,0 163,15 1500 26650 LiNiMnCo 18 10 3,70 0,0080 1,0 1,0 154,16 1500 Pouch LiNiMnCo 19 40 3,70 0,0015 1,5 0,5 110,00 1500 Pouch LiNiMnCo 20 50 3,70 0,0030 0,2 1,0 182,25 1500 Pouch Li-Titanate 21 0,05 2,40 0,3800 20 20 42400 2000 10200 Li-Titanate 22 0,50 2,40 0,0400 15 10 120,00 2000 14500 Li-Titanate 23 1,10 2,40 0,0170 10 5,0 66,00 2000 18650 Li-Titanate 24 1,30 2,40 0,0200 10 5,0 78,00 2000 18650

103 LiNiCoAl 25 3,40 3,60 0,0500 2,0 0,5 243,00 500 18650 LiNiCoAl 26 3,10 3,60 0,0500 1,5 0,3 245,00 500 18650 LiNiCoAl 27 2,90 3,60 0,0500 3,5 0,5 207,00 500 18650 LiNiMnCo 28 2,80 3,70 0,0700 2 1,0 207,00 1500 18650 LiNiMnCo 29 2,20 3,70 0,0700 4,5 1,0 185,00 1500 18650 Li-Titanate 30 4,20 2,30 0,0025 10 12 65,00 2000 Prismatic Li-Titanate 31 20 2,30 0,0005 10 12 90,00 2000 Prismatic Li-Titanate 32 11 2,30 0,0020 10 10 74,00 2000 Prismatic Li-Titanate 33 50 2,30 0,0004 6,0 6,0 72,00 2000 Pouch Li-Sulfur 34 2,50 2,15 0,0250 2,0 0,2 350,00 500 Pouch Li-Sulfur 35 95 2,05 0,0080 1,0 2,0 400,00 500 Pouch LiNiMnCo 36 10 3,70 0,0080 1,0 1,0 154,16 1500 Pouch

For the 36 cells shown in the table above, the results are shown below:

Figure 4.1.2: Battery Capacity (Wh)

104 Figure 4.1.3: Installation Cost (€ - Battery: 500€/kWh, DC/DC Converter: 250€/kW)

Figure 4.1.4: Battery Weight (kg)

Figure 4.1.5: Battery Volume (m3)

105 Figure 4.1.6: Charging Power (W)

Figure 4.1.7: Charging Energy (Wh)

Figure 4.1.8: Regenerated Braking Energy/Used Energy (%)

106 Figure 4.1.9: Charging Energy/Energy from Catenary (%)

Figure 4.1.10: Battery Depth-of-Discharge [DoD] (%)

Figure 4.1.11: Battery Maximum Temperature (oC)

107 Figure 4.1.12: Maximum Simulated Current/Maximum Datasheet Current (%) - Discharge

Figure 4.1.13: Maximum Simulated Current/Maximum Datasheet Current (%) - Recharge

Figure 4.1.14: Maximum Battery Losses during Discharge (W)

108 Figure 4.1.15: Maximum Battery Losses during Recharge (W)

Figure 4.1.16: Average Battery Losses (W)

Figure 4.1.17: Battery Cycles (N)

109 The figures above show various aspects of adopting a battery package to replace the overhead catenary system used for this case, and recharging it back to 100% SOC every time the vehicle returns to its main station. Most importantly, the results show a significant reduction in energy consumption due to better system efficiency and regenerative braking taking place during deceleration periods. An average of 40% reduction in energy consumption can be achieved by almost all of the 36 battery cells used for this scenario, as 40+% of regenerative braking exists. Only cell number 21 shows a minimum 20% reduction in energy consumption from the grid. The final choice between different types of battery cells depends on design limitations of weight and volume, as well as the lifetime of the battery pack (depending on depth of discharge and temperature) and return on investment due to elimination of installation and/or maintenance costs of the catenary system.

4.2 CASE II - Hybridization of a Diesel-Electric Configuration

In the second case, we will be testing the adoption of a battery package towards the reduction of the fuel consumption of a diesel-electric intercity configuration. The speed profile is taken from chapter 3.8.2, where it was used for the verification of the energy demands of the DD-VIRM trainset, and it is shown below:

Figure 4.2.1: Speed profile for CASE II (km/h)

Here, the main concept is to first run with the battery package only, from Utrecht to Amsterdam and back (1st trip). During the 2nd trip we switch to the diesel engine and we operate it on 2000 RPMs and 45% throttle, giving us a constant efficiency of 35%. If more power is needed the battery kicks in. If less power is needed, the difference between the demand and the diesel generator's output power is delivered to the battery. This operating points has been tuned so that the state-of-charge of the battery package reaches 100% at the end of the 2 nd trip. Again, we are testing this concept for different battery sizes and chemistries shown in the table below:

110 Table 4.2.1: Battery cells for CASE II

Capacity N. Voltage Resistance D C- C C- GED Cycles (N) N Type (Ah) (V) (Ohm) RATE RATE (Wh/kg) 100% DoD LiFePO4 1 20 3,20 0,0025 3,0 0,8 85,35 1500 Prismatic LiFePO4 2 40 3,20 0,0025 3,0 0,8 85,35 1500 Prismatic LiFePO4 3 60 3,20 0,0020 3,0 0,8 76,85 1500 Prismatic LiFePO4 4 100 3,20 0,0018 3,0 0,8 100,00 1500 Prismatic LiFePO4 5 200 3,20 0,0010 3,0 1,0 112,25 1500 Prismatic LiFePO4 6 1,10 3,20 0,2550 3,0 1,0 121,35 1500 Pouch LiFePO4 7 10 3,20 0,0100 5,0 1,0 128,00 1500 Pouch LiFePO4 8 15 3,20 0,0040 2,0 1,0 126,35 1500 Pouch LiFePO4 9 1,50 3,20 0,0380 3,5 2,0 117,00 1500 18650 LiFePO4 10 1,10 3,20 0,0100 27,5 3,5 88,00 1500 18650 LiNiMnCo 11 2,00 3,70 0,0700 1,0 1,0 185,00 1500 18650 Li-Titanate 12 1,10 2,40 0,0170 10 5,0 66,00 2000 18650 LiNiCoAl 13 3,40 3,60 0,0500 2,0 0,5 243,00 500 18650 Li-Titanate 14 20 2,30 0,0005 10 12 90,00 2000 Prismatic Li-Sulfur 15 2,50 2,15 0,0250 2,0 0,2 350,00 500 Pouch

For the 15 cells shown in the table above, the results are shown below:

111 Figure 4.2.2: Battery Capacity (Wh)

Figure 4.2.3: Installation Cost (€ - Battery: 500€/kWh, DC/DC Converter: 250€/kW)

Figure 4.2.4: Battery Depth-of-Discharge [DoD] (%)

112 Figure 4.2.5: Battery Maximum Temperature (oC)

Figure 4.2.6: Average Battery Losses (W) – 1st Trip

Figure 4.2.7: Average Battery Losses (W) – 2nd Trip

113 Figure 4.2.8: Average Battery Losses – 1st Trip/Average Battery Losses - 2nd Trip (%)

Figure 4.2.9: Regenerated Energy/Used Energy (%)

Figure 4.2.10: Fuel Consumption H-E/Fuel Consumption D-E (%)

114 Figure 4.2.11: Battery Cycles (N)

In this case we will see that, due to changes on the speed profile for intercity trains, the regenerative braking consists only an average of 20% of the total used energy. Nevertheless, this percentage leads to an average of 30% reduction in fuel consumption, in comparison to the diesel-electric configuration without a battery package on-board, while reducing the maximum power output of the internal combustion engine to 50%. That amount of reduction in fuel consumption can surely lead to significant reduction of CO2 and NOx emissions, although this is not always the case as it is shown on Figure 3.2.2. The operating point of the internal combustion engine may be set carefully for reduction of both fuel consumption and harmful emissions. Again, the final choice between different battery cells depends on many factors as the battery's temperature and DoD, installation costs and return on investment, due to reduction in fuel costs.

115 CHAPTER 5

5.1 Summary - Conclusions

Energy efficiency is growing into a major problem for engineers, as oil's and other fossil fuels' prices are increasing day by day, along with the demand for lower local and global emissions, produced by human activities and which are threatening human life as we know it. Better ways of energy production and consumption need to be investigated. Especially for the railway sector, battery/supercapacitor-powered and hybrid-electric vehicles are very promising technologies towards the reduction of energy/fuel consumption, the reduction of emissions and the elimination of unnecessary catenary systems with high installation and maintenance costs, as well as with bad aesthetics, especially inside cities. For investigations into such fully-electric and hybrid-electric powertrains, it is necessary for the designers to be able to calculate and simulate the power and energy demands of the vehicle along with the performance of crucial components, which require proper sizing and power division, in order to get the installation/investment costs as low as possible and the lifetime of those components at propel values, especially when it comes to refurbishment of rolling stock. (1) Thus, the development of a simulation model in Matlab/Simulink was the first goal of this work. Especially for battery/supercapacitor-powered vehicles, regenerative braking is very important for proper sizing of the power sources and it needs to be simulated/estimated, something that will be missed if no simulation tools are adopted (see [45]) is such investigations, a fact increasing the importance of having this first goal. (2) Adopting a kinematic approach with 0D (dimensionless) blocks, without the need of PWM frequencies, dynamic modeling of components and closed loop structures found in quasi-static and dynamic approaches, we can reduce the simulation times and cooperate with optimization algorithms efficiently (0.1 second time step was used for all the simulations of this report – hours of speed profiles were simulated in seconds). (3) For user-friendliness reasons, it was also important to accompany the simulation model with a graphics user interface (GUI), so that the designer can easily gain access to all the parameters required for sizing and power division purposes and so such a GUI was developed within Matlab/Simulink by using the GUI editor (GUIDE) (see Appendix). (4) Having a simulation model and a GUI developed in Matlab/Simulink led us to the second goal of this work, the verification of the energy demands for different speed profiles and for two cases, the eight-axle Gelede Tram Lang (GTL8) and the 4-car double-deck VIRM trainset. For both cases, speed and power measurements were taken from data acquisition of previous projects in Strukton Rolling Stock and NS in the Netherlands. An Average Efficiency Correction Factor was used in both cases to determine the correction in the system's average efficiency, so that the energy profiles measured and simulated are the same. The verification results show that proper tuning of the coefficients used in running resistance

116 calculations, the vehicle's total mass during the measurements and the average efficiency of rotating parts, power electronics and cables, can lead to a very good estimation of the vehicle's energy demands for proper sizing of batteries and supercapacitors. It is of high importance that the speed profiles and the power profiles are measured with high accuracy so that conclusions can be extracted for the power demands as well. This way, we can have a better image of the power sources' size required for the vehicle's propulsion. (5) The final goal of this investigation was to check if the adoption of different battery cells can lead us to better energy efficiency (less energy/fuel required), for a fully-electric tram's normal speed profile and a diesel- electric train's one. For the first case, 36 different cells were used and the results clearly show that due to regenerative braking, an average of 40% reduction in energy consumption from the grid can be achieved, while eliminating the overhead catenary system used in this case, offering batteries a great advantage over the existing technology. Only charging points are then needed on the main stations. (6) For the second case, 15 ouf of the 36 battery cells were simulated and the results show that, this time, only a 20% of regenerative braking can be achieved due to the different speed profiles used in intercity trains in the Netherlands, but an important 30% can be obtained in the reduction of fuel consumption due to greater average efficiency of the internal combustion engine (this time the ICE is operating at only one operating points, below 50% of its maximum power at 2000 RPMs). Such a reduction in fuel consumption is very promising for the future of rolling stock and the gradial transformation of diesel-electric powertrains into highly efficient no- local-emissions fully-electric ones. For the two speed profiles used in chapter 4, we can see that the potential of using energy storage devices on board is great. The design, fabrication and testing of such modules is highly crucial for the establishment of such efficient system in rolling stock during the next five years. As the manufacturing costs of batteries and supercapacitors decrease, as the energy density, charging and discharging rates and lifetime increase and as battery management systems are getting better and better, designers may lay hands on energy storage for a better future, with no emissions and no dependence on liquid hydrocarbons.

5.2 Future Work and Modeling Improvements

1. For given speed profiles (and measured power profiles) that generally represent driving schedules for city trams and intercity passenger trains, testing of real time aging due to cycling for different battery chemistries (NMC, NCA, Li-S, Li-Titanate) for a small battery pack with the power profiles scaled down. Temperature should be kept constant at 23-25oC (in general below 30oC). 2. Cost analysis for the refurbishment of rolling stock towards: 1. Elimination of overhead catenary maintenance costs and adoption of charging points/stations (substations required).

117 2. Elimination of overhead catenary installation costs and adoption of charging points/stations (substations required) 3. Reduction of fuel costs with an increase on battery installation cost for prices of 2020. 3. Thermal finite element analysis (electrolyte temperature estimation) for prismatic, pouch and cylindrical cells of different battery chemistries (different specific heat capacities, thermal masses, total area and placement inside the battery pack). Check differences between air cooling and non-conductive liquid cooling. 4. Real time temperature measurements to accommodate (3) and lifetime measurements without temperature being kept constant at 23-25oC. 5. Development of a battery library for the GUI developed in this work. 6. Efficiency maps of ICEs and EMs gathering and Willans Line Model comparisons for different types (SI, CI engines, IM, PM, SR motors). 7. From kinematic to quasi-static approach for more detailed modeling without optimization purposes. 8. Proper estimation of emission rates through the development of an exhaust block for the simulation model. 9. Tuning of resistance calculation coefficients for certain types of mass transit systems. 10. Speed profile optimization for battery adoption towards minimum investment and maximum regenerative braking over used energy ratio. 11. Optimization algorithms for sizing and power flow strategies in complex topologies (hybrid-electric or dual-source fully-electric). 12. Lifetime measurements for hybrid battery-supercapacitor energy storage. 13. Lithium-ion supercapacitor (hybrid supercapacitor) block in developed model plus testing - verification. 14. Lithium-Sulfur (Li-S) battery block in developed model plus testing – verification. 15. Temperature dependence on VOC, SOC and temperature dependence on internal resistance of batteries and supercapacitors.

118 References

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121 [51] ETH Zurich, Department of Mechanical and Process Engineering, Institute for Dynamic Systems and Control, Vehicle Propulsion Systems Course, Lecture Slides – Electric Motors: Modeling of Conversion Efficiency

122 Appendix

A. Characteristics of Lithium-Ion Battery Chemistries:

Table A.1: Characteristics of Lithium Cobalt Oxide [32]

Lithium Cobalt Oxide: LiCoO2 Cathode (~60% Co), Graphite Anode Short form: LCO or Li-Cobalt since 1991

Voltage, nominal 3.60V

Specific energy 150–200Wh/kg. Specialty cells provide up to 240Wh/kg. (capacity)

0.7–1C, charges to 4.20V (most cells); 3h charge typical. Charge current above 1C Charge (C-rate) shortens battery life.

Discharge (C-rate) 1 C; 2.50V cut off. Discharge current above 1C shortens battery life.

Cycle life 500–1000, related to depth of discharge, load, temperature.

Thermal runaway 150°C. Full charge promotes thermal runaway.

Applications Mobile phones, tablets, laptops, cameras.

Very high specific energy, limited specific power. Cobalt is expensive. Serves as Comments Energy Cell. Market share has stabilized.

Table A.2: Characteristics of Lithium Manganese Oxide [32]

Lithium Manganese Oxide: LiMn2O4 Cathode, Graphite Anode Short form: LMO or Li-Manganese (spinel structure) since 1996

Voltage, nominal 3.70V (some may be rated 3.80V)

Specific energy 100–150Wh/kg (capacity)

Charge (C-rate) 0.7–1C typical, 3C maximum, charges to 4.20V (most cells).

Discharge (C-rate) 1C recommended; 10C possible, 30C pulse (5s), 2.50V cut-off.

123 Cycle life 300–700 (related to depth of discharge, temperature).

Thermal runaway 250°C typical. High charge promotes thermal runaway.

Applications Power tools, medical devices, electric powertrains.

High power but less capacity; safer than Li-cobalt; commonly mixed with NMC Comments to improve performance.

Table A.3: Characteristics of Lithium Nickel Manganese Cobalt Oxide (NMC) [32]

Lithium Nickel Manganese Cobalt Oxide: LiNiMnCoO2 Cathode, Graphite Anode Short form: NMC (NCM, CMN, CNM, MNC, MCN similar with different metal combinations) since 2008

Voltage, nominal 3.60V, 3.70V

Specific energy 150–220Wh/kg (capacity)

0.7–1C, charges to 4.20V, tendency to 4.30V; 3h charge. Rapid charge causes Charge (C-rate) stress.

Discharge (C-rate) 1C recommended; 2C continuous possible; 2.50V cut-off.

Cycle life 1000–2000 (related to depth of discharge, temperature).

Thermal runaway 210°C typical. High charge promotes thermal runaway.

Applications E-bikes, medical devices, EVs, industrial.

Provides high capacity and high power. Serves as Hybrid Cell. Favorite chemistry Comments for many uses; market share is increasing.

Table A.4: Characteristics of Lithium Iron Phosphate [32]

Lithium Iron Phosphate: LiFePO4 Cathode, Graphite Anode Short form: LFP or Li-Phosphate since 1996

Voltage, nominal 3.20V, 3.20V

Specific energy 90–120Wh/kg

124 (capacity)

Charge (C-rate) 1C typical, charges to 3.65V; 3h charge time.

1C recommended; 20–25C continuous; 40A pulse (2s); 2.50V cut-off (lower that Discharge (C-rate) 2V causes damage).

Cycle life 1000–2000 (related to depth of discharge, temperature).

Thermal runaway 270°C Very safe battery even if fully charged.

Applications Portable and stationary needing high load currents and endurance.

Very flat voltage discharge curve but low capacity. One of safest Comments Li-Ions. Used for special markets. Elevated self-discharge.

Table A.5: Characteristics of Lithium Nickel Cobalt Aluminum Oxide [32]

Lithium Nickel Cobalt Aluminum Oxide: LiNiCoAlO2 Cathode (~9% Co), Graphite Anode Short form: NCA or Li-Aluminum since 1999

Voltage, nominal 3.60V

Specific energy 200-260Wh/kg; 300Wh/kg predictable (capacity)

0.7C, charges to 4.20V (most cells), 3h charge typical, fast charge possible with Charge (C-rate) some cells.

Discharge (C-rate) 1C continuous; 3.00V cut-off.

Cycle life 500 (related to depth of discharge, temperature).

Thermal runaway 150°C typical, High charge promotes thermal runaway.

Applications Medical devices, industrial, electric powertrains.

Comments Shares similarities with Li-cobalt. Serves as Energy Cell.

125 Table A.6: Characteristics of Lithium Titanate [32]

Lithium Titanate: Graphite Cathode; Li4Ti5O12 (titanate) Anode Short form: LTO or Li-Titanate since 2008

Voltage, nominal 2.40V

Specific energy 70–80Wh/kg (capacity)

Charge (C-rate) 1C typical; 5C maximum, charges to 2.85V.

Discharge (C-rate) 10C continuous, 30C 5s pulse; 1.80V cut-off on LCO/LTO.

Cycle life 3,000–7,000

Thermal runaway One of safest Li-ion batteries.

Applications UPS, electric powertrains.

Long life, fast charge, wide temperature range but low specific energy and Comments expensive. Among safest Li-ion batteries.

Table A.7: Characteristics of Lithium-Sulfur Specific energy 500 Wh/kg demonstrated Energy density 350 Wh/l Charge/discharge C/5 nominal efficiency Cycle durability Disputed Cell voltage varies non-linearly in the range 2.5–1.7V during discharge; batteries Nominal cell voltage often packaged for 3V

Table A.8: Characteristics of Aluminum-Air Specific energy 1300 (practical), 6000/8000 (theoretical) Wh/kg Energy density N/A Specific power 200 W/kg Nominal cell voltage 1.2 V

Table A.9: Characteristics of Zinc-Air Specific energy 470 (practical),1370 (theoretical) Wh/kg

126 (1.692, 4.932 MJ/kg) 1480-9780 Wh/l Energy density (5.328–35.21 MJ/l) Specific power 100 W/kg Nominal cell voltage 1.65 V

127 B. Matlab Initialization Script:

Assign given speed profile from GUI's workspace to base workspace, store in KM and evaluate if speed has always a positive value. If not, all negative values should be zero. assignin('base','KMH',KMH); KM = evalin('base','KMH.signals.values'); for i = (1:TIME/evalin('base','Ts')+1) SPEED_PROF(i) = KM(i); if (SPEED_PROF(i) < 0) SPEED_PROF(i) = 0; end end assignin('base','SPEED_PROF',SPEED_PROF); Assign initial power profile from GUI's workspace to base workspace, store in PO, sort PO, and then correct KMH to sorted PO's index. assignin('base','PR',PR); PO = evalin('base','PR.signals.values'); [PRinit,ind] = sort(abs(PO)); KMH_init = KM(ind,:); Find base speed of KMH (for maximum sorted PO's value) and transform from km/h to RPM. for i = 1:(TIME/evalin('base','Ts')+1) if (PRinit(i) == PRinit(TIME/evalin('base','Ts')+1)) BASE_KMH = KMH_init(i); BASE_RPM = BASE_KMH*60/(3.6*2*pi* ... evalin('base','WHEEL_RAD')); break; end end Assign found base speed in RPM from GUI's workspace to base workspace and sort KMH. assignin('base','BASE_RPM',BASE_RPM); KMHinit = sort(KMH_init); Store maximum speed (km/h) in MAX_KMH and maximum power in PRINIT parameters. MAX_KMH = KMHinit(TIME/evalin('base','Ts')+1); PRINIT = Prinit(TIME/evalin('base','Ts')+1)* ... evalin('base','MASS_INIT'); Assign PRINIT from GUI's workspace to base workspace and evaluate if MAX_KMH is greater than

128 BASE_KMH. If not, add +1. assignin('base','PRINIT',PRINIT); if (MAX_KMH == BASE_KMH) MAX_KMH = MAX_KMH+1; end Transform MAX_KMH to MAX_RPM and assign it from GUI's workspace to base workspace. MAX_RPM = MAX_KMH*60/(3.6*2*pi*evalin('base', ... 'WHEEL_RAD')); assignin('base','MAX_RPM' ,MAX_RPM) ; Final power vs speed curve on the wheels will have MAX_PPERKG for axis Y and RPM_BREAKP for axis X. Assign both axis Y and axis X from GUI's workspace to base workspace. Axis Y is in W/kg and axis X is in RPM. MAX_PPERKG = [0 PRINIT/evalin('base','MASS_INIT') ... PRINIT/evalin('base','MASS_INIT')]; assignin('base','MAX_PPERKG',MAX_PPERKG); RPM_BREAKP = [0 BASE_RPM MAX_RPM]; assignin('base','RPM_BREAKP',RPM_BREAKP);

After the initialization process, the torque and speed inputs of the 0D black box of the electric motor/motors are given separately for speeds before and after the base speed of the power vs speed profile of the road load demands calculated by the script given above. The sub-model describing the whole procedure are given below. T1 and S1 are MAXIMUM TORQUE and SPEED before base speed, T2 and S2 are MAXIMUM TORQUE and SPEED after the base speed for the electric motor/generator.

Figure A.1: TORQUE[Nm] (left) & SPEED[RPM] (right) inputs for the 3D efficiency maps

129 C. Extraction of Battery Parameters From Data Sheets:

This section gives an example of detailed parameters extracted from the Panasonic NiMH-HHR650D battery data sheet:

From the specification tables, we obtain the rated capacity and the internal resistance. The other detailed parameters are deduced from the Typical Discharge Characteristics plot:

Table A.10: Panasonic NiMH-HHR650D battery characteristics

Parameter Value Rated capacity 6.5 Ah Internal Resistance 2 mΩ Nominal Voltage (a) 1.18 V Maximum Capacity (b) 7 Ah (5.38h * 1.3A)

130 Parameter Value Fully Charged voltage (c) 1.39 V Nominal Discharge Current (d) 1.3 A Capacity @ Nominal Voltage (a) 6.25 Ah Exponential Voltage (e) 1.28 V Exponential Capacity (e) 1.3 Ah

131 D. Graphics User Interface:

In the section we will briefly present the graphics user interface (GUI) developed to accompany the simulation model for easier access to the required parameters. The GUI was designed in Matlab's GUIDE (graphical user interface design environment) and complies of different dataset blocks shown below:

Figure A.2: Speed and power profiles initialization block

Figure A.3: Energy storage electrical parameters block

132 Figure A.4: Energy storage thermal parameters block

Figure A.5: Other general parameters block

Figure A.6: Energy storage maximum performance parameters block

133 Figure A.7: Power flow strategy parameters block

Figure A.8: Response times (left) and power sources ON/OFF states for different operating modes (right)

134 Figure A.9: Energy storage ON/OFF information block

Figure A.10: GUI

135