Urban Bus Fleet Conversion to Hybrid Fuel Cell Optimal Powertrains

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Procedia - Social and Behavioral Sciences 111 ( 2014 ) 692 – 701

EWGT2013 – 16th Meeting of the EURO Working Group on Transportation Urban bus fleet conversion to hybrid fuel cell optimal powertrains

Paulo Meloa, João Ribaua,*, Carla Silvaa

a IDMEC, Instituto Superior Técnico, Universidade Técnica de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal

Abstract

Electric, hybrid and plug-in hybrid drive trains are being considered as the future for road transportation. Private fleets such as taxis, buses, mail distribution or even state vehicles are usually firstly considered to introduce alternative technologies due to its smaller scale and routing knowledge. Usually no study of the optimal fleet conversion is made and only available alternative technologies, sometimes, oversized are considered. In this paper, a current conventional bus urban fleet is regarded to analyze the possibility to substitute the conventional vehicles by a more efficient fleet equipped with a battery and a hydrogen fuel cell. A methaheuristic method will be used with a vehicle simulation software to perform the optimal components selection of the hybrid electric vehicle (HEV) and plug-in hybrid electric vehicle (PHEV) powertrain. This model analyses an urban bus service requirements and regards the power and energy demand to the vehicle, to build a model able to do the optimal size and interaction of components, namely the tractive electric motor, electric battery, and fuel cell. The components are optimized with the objective to minimize cost and fuel consumption of the vehicle. Real measured driving cycles in Oporto city (Porto), Portugal, and the official European Transient driving Cycle, (ETC), are regarded to test the model. The results aim to give a more comprehensive knowledge for fleet conversion research/demonstration projects.

©© 20132013 The Authors. Published Published by by Elsevier Elsevier Ltd. Ltd. SelectionSelection and/or peer-reviewpeer-review under under responsibility responsibility of ofScientific Scientific Committee Committee.

Keywords: Fleet conversion; Fuel cell; Optimization ; Real driving cycles; Urban bus.

1. Introduction

Presently, fossil fuels are at the center of global climate changes originating negative environmental impacts. Studies have shown that the world primary energy demand is rising and the transport sector accounts for 97% of this rising, from which road transportation is accountable for 80% of energy consumption and 93% of CO2 emissions (Eurostat, 2012). In Portugal, fossil fuel dependency accounts for 76% of the total primary energy. In 2010, road transportation was responsible for about 30% of CO2 emissions and 37% of the total energy consumption (DGEG, 2012). In order to reduce the dependence of fossil fuels in road transportation and consequently reduce emissions, the study of alternative vehicle technologies, more efficient, became extremely important. Some of the alternative road vehicle technologies are the hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), pure electric vehicles (EV) and fuel cell vehicles (FCV). These alternative technologies explore the electrification of the vehicle. This strategy enables the improvement of urban air quality (no local or reduced emissions), the diversification of primary energy sources (electricity/hydrogen can be generated from a wider range of sources, not necessarily from fossil origin), and allows the use of technologies that may improve energy efficiency. A problem with the EV’s it’s the small range (distance autonomy). To solve this, the HEVs can have their range extended by using energy provided by a combustion engine such or by a fuel cell, in addition with an energy storage system (usually a battery). Additionally, the HEV can have the possibility to receive energy by plugging in electricity from the grid (PHEV). Some

* Corresponding author. Tel.: + 351 21 841 9554 E-mail address: [email protected]

1877-0428 © 2013 The Authors. Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Scientific Committee doi: 10.1016/j.sbspro.2014.01.103 Paulo Melo et al. / Procedia - Social and Behavioral Sciences 111 ( 2014 ) 692 – 701 693 studies were developed to test the consumptions and emissions of these type of vehicles and its influence in light-duty fleets (Silva, Ross, and Farias (2009); Ribau, Silva, and Farias (2010); Ribau, Silva, Brito, and Martins (2012a); Baptista, Tomás, and Silva (2010); Silva (2011); Baptista, Silva, Farias, and Heywood (2012)). Fuel cell and plug-in hybrid public transit buses can take advantage of well-defined duty cycles and a fixed fuel and maintenance infrastructure that facilitates the working schedule and refueling of the bus. Buses also allow more space for propulsion system and fuel storage. In addition, buses are highly visible by the community; and it can generate feedback and create interest for “greener” methods of transportation. An example of the implementation of fuel cell buses was explicit in the Project Clean Urban Transport for Europe (CUTE). When hybrid vehicle companies build their vehicles, no efforts are made to optimize the sizing of the components and they can be oversized. The optimization of the sizing of the components can reduce the vehicle cost, fuel consumption and emissions while ensuring the vehicle performance. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV (and HEV) powertrain components presents a significant area of research. Fuel cell PHEVs (FC- PHEV) are still scarce in the literature even more if real driving cycles are to be considered in a multi-optimization problem: fuel consumption and drivetrain cost minimization. Light-duty vehicles are generally the main object of the optimization, however bus optimization regarding performance and efficiency improvement can be found in Gao, Jin, and Lu (2008). Private fleet taxi sizing can be found in Ribau and Silva (2011) and Ribau, Sousa, and Silva (2012b). The Table B1 in appendix B summarizes some work in this area. The objective of this paper is to develop an algorithm to optimize the sizing of the components of a fuel cell hybrid vehicle based on the Mercedes-Benz Citaro fuel cell bus, using a genetic algorithm (GA) associated with a vehicle simulation software, and compare results with the same type of bus but with different drive train, namely: diesel engine conventional bus (ICEV bus), a direct FC bus (DFCV bus) and a fuel cell hybrid vehicle (FC-HEV). A real driving cycle measured in the city of Oporto is used (Porto), as well as the reference Europe Transient Driving Cycle for heavy-duty vehicles (ETC). Optimal configurations are discussed and proposed for this distinct driving cycles. This paper focuses on the fuel use in the bus and also its impact in the vehicle powertrain cost. One implicit assumption in these analyses is that PHEV charging is coordinated with power system operations, and charging points are strategically located within the bus company to ensure proper electricity charging. Another assumption is that hydrogen stations are available, also, strategically located within the bus company, and using an efficient hydrogen production pathway.

2. Methodology

A hybrid and a plug-in hybrid bus are considered for optimization and compared to a reference conventional diesel bus and fuel cell powered buses. In order to evaluate the designed hybrid vehicles a simulation software, ADVISOR (ADvanced VehIcle SimulatOR), developed in MatLab/Simulink, created by the U.S. Department of Energy's National Renewable Energy Laboratory's (NREL), was used linked with the optimization algorithm.

2.1. The vehicles - Conventional and fuel cell buses

Four different types of passenger bus are considered. A conventional internal combustion engine powered bus (ICEV bus), a direct fuel cell powered bus (DFCV bus), and a battery hybrid fuel cell bus (FC-HEV bus) are the three reference vehicles, and its specifications are based on the HYFLEET:CUTE project (2009) (Clean Urban Transport for Europe), and in Gonçalves, Bravo, Baptista, Silva, and Farias (2009) (Table 1). The FC-HEV bus and afterwards the plug-in version of the hybrid bus are optimized regarding its fuel cell, battery and electric motor (Fig. 1). Note that the DFCV powertrain do not have battery since the fuel cell power is directly delivered to the controller and electric motor. The FC-PHEV is similar the hybrid bus, however it has the ability of its battery to be charged from an electricity outlet. The Plug-in is designed to use a charge depleting strategy for the battery (CD mode), draining only electricity. The battery is discharged until it reaches a minimum state-of-charge (SOC), of 40%. When this SOC level is reached, a charge sustaining strategy (CS mode) is engaged and the SOC is maintained. The fuel cell is used to help propulsion and to provide additional energy in order to maintain the battery SOC (similarly to the FC-HEV). In Table 1 a cost is attributed to the vehicle´s powertrain. This cost considers the electric motor and controller, the fuel converter and the battery for the respective vehicles. Additionally, one replacement for the fuel cell and the battery is accounted during the vehicles lifetime.

Fig. 1. Basic configuration of a fuel cell series hybrid powertrain. 694 Paulo Melo et al. / Procedia - Social and Behavioral Sciences 111 ( 2014 ) 692 – 701

Table 1. Reference bus main characteristics.

ICEV bus DFCV bus FC-HEV bus Nominal Power (kW @ rpm) 210 @ 2200 -- -- Diesel engine Max. Torque (N.m @ rpm) 1120 @ 1200-1800 -- --

AC Electric Nominal Power (kW @ rpm) -- 200 @ 2100 240 @ 2100 motor Max. Torque (N.m @ rpm) -- 1050 @ 800 1250 @ 800 Fuel Cell Nominal Power (kW) -- 250 (30 power dump) 120 Lithium battery Max. Power (kW) / Energy capacity (kWh) -- -- 180 / 26.9 Auxiliaries (W) 9000 mechanical 17000 electrical 17000 electrical Volume (length/height/width) (m) 12.11 / 3.12 / 2.55 12.11 / 3.67 / 2.55 12.11 / 3.40 / 2.55 Curb weight (kg) 11460 14200 13200 powertrain “virtual cost” (see section 2.2) 8500 34748 67382

2.2. Hybrid vehicle components

In order to perform the vehicle powertrain design optimization different components were taken as possible hypothesis: 4 different proton exchange membrane fuel cell models (FC), 4 electric motors (MC, including controller), and 8 batteries (BAT) were available in this study, and are presented in Tables 2 and 3.

Table 2. FC models. (source: Baptista et al. (2011), NREL, Hydrogenics, HYFLEET:CUTE (2009), Gonçalves, Bravo, Baptista, Silva, and Farias (2009)) and MC models. (source:HYFLEET:CUTE (2009), Gonçalves, Bravo, Baptista, Silva, and Farias (2009), UQM). aInduction. bPermanent Magnet

FC_1 FC_2 FC_3 FC_4 MC_1 MC_2 MC_3 MC_4 Nominal power (kW) 32 50 33 250 200a 240a 76b 104b System weight (kg) 170 223 162 1470 171 200 57 102

Table 3. Battery models. (source:KOKAM, SAFT) aLithium. b Nickel-metal hydride

BAT_1 BAT_2 BAT_3 BAT_4 BAT_5 BAT_6 BAT_7 BAT_8 Nominal voltage (V) 3.7a 3.6a 3.6a 3.6a 12b 6b 6b 12b Low/High limit voltage (V) 2.7/4.2 2.5/4 2.5/4 2.5/4 10.3/14 10.3/14 10.3/14 10.3/14 Energy capacity (Ah) / weight (kg) 40/1 7/0.37 20/0.8 30/1.1 100/18.6 200/18.6 68/9 34/9

The ADVISOR has a scaling function that sizes the component regarding its nominal power. A scaling range of 1 to 8 is used (1 represents the original component as in Table 2). In Annex A, a power scaling evolution is shown. The battery model doesn’t consider scaling; however the number of modules was varied in vehicle design, from 50 to 250 modules. A cost for each component was estimated and used to attribute a “virtual” cost to the designed vehicle. The costs estimated (Equations 1 to 4) were based in several cost analysis studies which assumed large volume production scale (Pistoia (2010), Kromer and Heywood (2007), Delorme, Pagerit, Sharer, and Rousseau (2009), Satyapal (2009), Ahluwalia, Wang, Kwon, and Rousseau (2011), DOE (2012), IPHE). These estimations include the power of the component (kW), battery´s energy capacity (kWh), and component´s mass (kg). FCcost ($) P (159××= Power mass)/( +33) (1) MCcost ($) P (20××= Power mass + .0)/( 25) (2) BAT _ Lithiumcost ($)= Energycapacity (368×× | ln(Power/ Energycapacity |) +177) (3) BAT _ Nimhcost ($) = 8.0 Energycapacity (368×× | ln(Power/ Energycapacity |) +177) (4) Where Li and Nimh regard to the battery chemistry, Lithium or Nickel-metal hydride. Paulo Melo et al. / Procedia - Social and Behavioral Sciences 111 ( 2014 ) 692 – 701 695

2.3. Driving cycles

A real driving cycle was used from which data was measured within the city of Oporto (Porto) metropolitan area, by using a speed sensor, a GPS system equipped with a barometric altimeter and data recovery from the OBD (On-Board Diagnostic) vehicle interface (Gonçalves, Bravo, Baptista, Silva, and Farias (2009)). Additionally, an official driving cycle was also used, the ETC (European Transient Cycle) for heavy duty vehicles (DieselNET). Both cycles are present in Table 4 and Figure 2. Despite being completely different, these cycles were chosen to test the algorithm and draw preliminary conclusions.

Table 4. Driving cycle characteristics.

2 Driving cycle Time (s) Distance (km) Average speed (km/h) Average acceleration (m/s ) # stops Idle time (s) Porto 2138 7.67 12.91 0.52 29 621 ETC_heavy 1799 29.48 58.97 0.20 4 75

Fig. 2. Speed (left) and road grade (right) profile of the official driving cycle ETC (red) and real driving cycle Porto (blue).

3. Vehicle Optimization

The object of optimization is the main powertrain components sizing (fuel cell, electric motor, and battery accordingly to Tables 2 and 3), and the objective is to minimize the cost of the vehicle and its hydrogen fuel consumption.

3.1. Optimization objectives - Cost and fuel consumption

Assuming that the vehicle body, transmission, and auxiliary systems, are kept the same for the different vehicle designs, the objective function, aiming cost minimization, focuses on direct comparison between the different component choices (see Equations 1 to 5). Note that the fuel cell and the battery accounts with one replacement each during vehicle´s lifetime. → cos t cos t ++ cos t Min x=$ f ($) 2( FC MC BAT )2 (5) Alike to cost objective, the fuel optimization focuses on direct comparisons between different vehicle designs, however the proposed algorithm aims to compare and find the designed vehicle that minimizes the hydrogen consumption. For the FC- PHEV version the fuel consumption is calculated in charge sustaining, thereby not influenced by the use of the electric energy in pure electric mode. The fuel consumption is directly obtained by the simulation software ADVISOR. Both FC-HEV and FC-PHEV buses must fulfil minimum performance requirements: top speed of 80 km/h, acceleration time from 0-100 km/h in 12 seconds (an average between the reference vehicles as it can be seen in results), a charge depleting (PHEVs only) mode using electricity only, a charge sustaining mode of 5% range around the CS level, and all-electric-range (AER) of 33 km. This distance is aproximately ¼ of the estimated daily distance of the urban bus (Jorge Carvalho (2011)).

3.2. Optimization algorithm

The optimization was performed by using a genetic algorithm (GA). A GA is a stochastic global search and optimization method, and its creation was inspired in natural biological evolution, based on Darwin’s theory of survival of the fittest, which applies the principle of survival of the fittest preliminary solutions to produce successively better approximations to a solution. Several literatures refer to this method (Chipperfield, Fleming, Pohlheim, and Fonseca (1994), Rao (2009)). A modularized Genetic and Evolutionary Algorithm Toolbox(GAtbx) for MATLAB software was used for the optimization in this study (Chipperfield, Fleming, Pohlheim, and Fonseca (1994)). A genetic algorithm starts by building a random population of individuals which is a set of possible solutions to the optimization problem. The GA chromosome structure is composed by the individuals that contain information, genes, 696 Paulo Melo et al. / Procedia - Social and Behavioral Sciences 111 ( 2014 ) 692 – 701

representing the respective components and all the design variables to be optimized (see Table 5). Each possible gene combination forms an individual which can be seen as a candidate solution regarding to a designed FC-HEV or FC-PHEV bus. The set of individuals is intended as a population. The FC model, FC power scale, MC model, MC power scale, BAT model, and battery module number define the 6 genes composing the chromosome (see Table 5, and Tables 2 and 3), and any combination of these genes can be regarded to compose a vehicle.

Table 5. Genes used to assembly a vehicle.

FC model FC power scale MC model MC power scale BAT model BAT modules FC_1 to FC_4 0.5 - 3 MC_1 to MC_4 0.5 - 3 BAT_1 to BAT_8 50 - 250

Each assembled vehicle (or each individual) from the generated population; to be evaluated is simulated performing a specific driving cycle, in ADVISOR. A ranking profile is assigned to each individual of the population, where better fitness is assigned to individuals with lower cost or lower fuel consumption, depending on the case study. Accordingly to the individual´s fitness and the generation gap rate (0.8), the selection of the individuals for crossover is performed using stochastic universal sampling (default routine). Afterwards, a crossover routine is applied (crossover rate 0.8) where genes are selected, and from each pair of best ranked individuals in the current generation two individual child individuals are formed. Single point crossover routine (where part of the first parent is copied and the rest is taken in the same order as in the second parent) is used. Next a mutation process occurs (mutation rate of 1/(variables per individual)), changing a gene value, and adding diversity to a generated population. At this point the offspring population is completed. After being evaluated (similarly to the evaluation process explained behind) the offspring individuals are reinserted into original population maintaining the best fitted individuals, by replacing the least fitted. These processes are repeated till a maximum number of generations are reached. In this study the population is composed by 20 individuals performing 300 generations.

4. Results and Discussion

4.1. Reference buses

In order to be compared with the optimized vehicles, the reference buses (Table 1) were simulated in equal conditions. The curb weight of the vehicle was used in simulations (plus one driver of 70kg), and a charge sustaining SOC level of 40% was regarded for the hybrid bus. The simulation results are presented in Table 6 and Figure 3. Comparing the reference vehicles, the DFCV achieved 12-15% less energy consumption (per km) than the conventional ICEV bus, in ETC and Porto driving cycle respectively. The hybridization of the vehicle with the addition of a battery (FC- HEV) achieved less 20-33% of energy consumption (per km) in ETC and Porto driving cycle respectively in comparison with the ICEV bus. It can be seen that the addition of a battery, allowing a more controlled energy flow and decreased power losses, leads to 10-21% energy savings (per km) in ETC and Porto driving cycle respectively when comparing the FC-HEV to DFCV.

Table 6. Results from simulations of the reference vehicles, in ETC and Porto driving cycles. Diesel fuel is used in ICEV, and hydrogen (23.36 g/l, 120 kJ/g) is used for DFCV and FC-HEV buses.

Fuel Consumption – ETC Fuel Consumption - Porto Performance (L/100km) (g) (MJ/km) (L/100km) (g) (MJ/km) Max. Speed (km/h) Time (s) 0-50km/h ICEV 29.0 7255.5 10.5 72.8 4734.0 26.4 88.1 10.2 DFCV 430.8 2280.9 9.2 1053.4 1434.1 22.4 93.4 16.4 FC-HEV 389.2 2064.8 8.3 820.3 1130.8 17.6 114.8 9.1

When comparing the driving cycles clearly the Porto is on average a more demanding driving cycle. ETC has higher speeds, but the Porto driving cycle has variable road grade and more acceleration demands, since it has also a higher number of stops. Despite Porto driving cycle has higher average acceleration, the ETC has the highest maximum acceleration and highest maximum speed, and consequently it has a few peaks of higher power demand. Although these high demand characteristics exist in ETC, they are scarce, and therefore it maintains the ETC as a driving cycle responsible for less energy consumption in average, however it can influence the optimization process. The conventional bus has its fuel consumption reduced by 60% in ETC when compared to its Porto simulation. The DFCV and the FC-HEV also had their fuel consumption reduced in 59% and 52% respectively, in ETC driving cycle. This also indicates that the use of a battery allows the vehicle to be less affected by driving cycle transient demands, which are very severe to the internal combustion engine and to the fuel Paulo Melo et al. / Procedia - Social and Behavioral Sciences 111 ( 2014 ) 692 – 701 697 cell (however less than in the diesel engine). Note that the battery can also supply extra power to the vehicle, therefore compensating the weight increasing in power/acceleration.

4.2. Vehicle optimization

The hybrid bus optimization results are presented in Table 7 (and Table 8 for the FC-PHEV). As verified in the reference vehicles, the official ETC driving cycle imply, in general, lower energy consumption. Note that cost and energy consumption can be concurrent objectives, therefore it can be expected that the energy consumption decreasing requirement should influence a raise in the cost of the vehicle. Two key points in decreasing the energy consumption relay mainly in reducing the weight of the vehicle and choosing more efficient powertrain. More efficient components and with higher specific power are more expensive, then increasing the cost to the vehicle. This effect, will present sudden increases in cost even for a little reduction in fuel consumption. However, since there is a battery capable to “absorb” some of the fuel cell losses and to better control the energy flow in the powertrain, results point to a larger battery in order to achieve better fuel efficiencies. In this sense, larger batteries will tend to increase the cost and the weight of the vehicle.

Table 7. FC-HEV bus optimization results aiming cost and fuel minimization in official (ETC) and real driving cycle (Porto). (Cost in thousand dollars and total voltage (V) in series assembly)

FC MC BAT Vehicle Cost H Consumption Max. Speed Time (s) Objective 2 (kW) (kW) (kW) (kWh) CS mass (kg) (1000$) (MJ/km) (km/h) 0-50km/h Min Cost 125 243 457 36 0.49 13883 84.9 8.2 148.6 7.1 ETC Min Fuel 83 288 824 267 0.75 16742 271.9 7.5 161.0 7.2 Min Cost 82 227 163 9 0.47 12730 39.3 17.2 99.6 12.0 Porto Min Fuel 25 256 945 306 0.75 17140 298.9 11.2 155.0 8.4

Table 8. FC-PHEV bus optimization results aiming cost and fuel minimization in official (ETC) and real driving cycle (Porto). (Cost in thousand dollars and total voltage (V) in series assembly)

BAT H Electric Vehicle 2 Max. FC MC Cost Consumption consumption AER Time (s) Objective mass Speed (kW) (kW) (kW) (kWh) CS (1000$) in CS in CD (km) 0-50km/h (kg) (km/h) (MJ/km) (MJ/km) Min Cost 116 254 929 74 0.37 14622 150.9 8.4 5.1 33.0 158.4 7.1 ETC Min Fuel 86 281 729 237 0.75 16284 243.4 7.7 5.2 40.9 162.4 7.2 Min Cost 42 209 427 139 0.30 14590 141.9 18.6 10.5 33.1 136.7 8.8 Porto Min Fuel 25 256 945 306 0.67 17140 298.9 11.2 10.9 33.0 155.0 8.4

Although the ETC driving cycle is responsible in average for a lower energy demand, as can be seen also by the consumption of fuel (when compared with Porto), the cost of the vehicle may not have the same relation. The ETC driving cycle is characterized by an average power demand lower than in Porto, however it has a few peaks of higher power demand. Although this results in lower energy consumption, the components must fulfil those high power demands. Therefore, even if such high power is not needed frequently through the driving cycle, the optimization algorithm enforces the choice of components capable to deliver such power. In average the ETC vehicles have more nominal power at the wheel than the vehicles optimized for Porto driving cycle. The fact that ETC has higher power peaks, but in average lower energy demand, can influence the choice of the power of the components. A lower power fuel cell can be the best choice in terms of fuel economy, if the combined battery and fuel cell power is enough for the peak power demands. Regarding the plug-in option (FC-PHEV) the same discussion above can be developed. In Table 8 the optimized FC-PHEV buses are shown. Note that the minimum AER constraint was followed severely in cost minimization. Although the AER could be more extensive, the high cost of the batteries will lead to very high costs for the vehicle. As it can be seen for the fuel minimization, the battery is responsible for the largest share in the cost of the vehicle (more than 70%). In Figure 3 a resume of the optimization results is presented. One of the optimization characteristics is that the objective value is aimed disregarding any sensitive analysis. Probably this has to be considered in future work. In cost minimization of the FC-HEV the best achieved results were 8.2 MJ/km and 17.2 MJ/km at a cost of around 85 and 40 thousand dollars for ETC and Porto driving cycles respectively. In fuel minimization the best achieved results for the FC-HEV in ETC and Porto driving cycles respectively, were 7.5 MJ/km and 11.2 MJ/km at a cost of around 272 and 299 thousand dollars. It is interesting to note that when CS level resulting from optimization is near 40% (the value considered for the reference hybrid 698 Paulo Melo et al. / Procedia - Social and Behavioral Sciences 111 ( 2014 ) 692 – 701

bus) the battery component is smaller than a higher CS level near 70%. This means that the 40% level is more appropriate for production buses, resulting in less costly buses with acceptable H2 fuel consumption reductions.

Fig. 3. Resume of optimization results for different types of vehicles and different optimization objectives, in ETC driving cycle (left) and Porto (right): cost (1000$), fuel energy consumption (MJ/km) (electricity consumption is also presented as MJ/km for PHEVs).

The optimized FC-HEVs presented lower fuel consumption in comparison to the reference vehicles in all cases: less 1.2- 37% for the reference FC-HEV, less 12-50% regarding the DFCV, and less 22-54% regarding the ICEV (cost and fuel minimization respectively). Additionally, the cost optimized FC-HEV for Porto achieved also less 41% of cost in comparison to the reference HEV. Figure 4 shows the results of optimized buses of ETC driving cycle when performing in Porto driving cycle. Again is clear that the Porto is a more energy demanding driving cycle. The optimized bus aiming the ETC objectives for cost minimization achieved similar fuel consumption in Porto. This suggests that the ETC driving cycle, can be used as a reasonable approach in problems of cost minimization for buses. However for fuel consumption improvement, optimization aiming a specific driving cycle (and purpose) is indispensable.

Fig. 4. Comparison between fuel consumption (MJ/km) of the FC-HEVs optimized for ETC (ETC original) regarding cost and fuel minimization, the FC- HEVs optimized for Porto (Porto original), and the buses optimized for ETC simulated in Porto driving cycle.

5. Summary

A fuel cell hybrid passenger bus, including a plug-in version, was optimized regarding cost and hydrogen fuel consumption minimization, by using a genetic algorithm and ADVISOR simulation package. Two distinct driving cycles were used: an official and a real measured driving cycle. Comparing to the conventional diesel bus it is possible to reduce the energy consumption (per km) by 12-15% using direct fuel cell and 20-33% hybridising the powertrain with a battery. Further reduction in fuel consumption using optimization techniques is achievable, lowering the consumption by 3% and with less 42% of the cost regarding the reference HEV. Higher fuel efficiency improvements can be achievable, decreasing the consumption by 37% but at a cost increase of 343% due to the increase of the battery. Although the official driving cycle ETC presented less energy demand, its high power peaks (higher than in Porto) lead to the requirement of high nominal power components. The plug-in version presented one of the highest costs for the vehicle mainly due to the battery cost. This result questions the feasibility of the plug-in version in a bus vehicle since for a reasonable AER in a daily operation it requires a Paulo Melo et al. / Procedia - Social and Behavioral Sciences 111 ( 2014 ) 692 – 701 699 very large battery (both high energy and high power), leading to great costs. Nevertheless, the battery showed to globally improve the powertrain efficiency. The cost optimized bus aiming the ETC driving cycle achieved similar fuel consumption in Porto cycle, suggesting that the ETC driving cycle can be used as a reasonable approach in cost issues regarding bus vehicle evaluations. However a more driving cycle focused optimization showed to have major importance in fuel consumption minimization

Acknowledgements

The second author is supported by a PhD grant financed by Fundação para a Ciência e a Tecnologia (FCT), reference SFRH/ BD/ 68569/ 2010. The research is part of the National research project ESIBITS- Evaluation of the Sustainability of Industrial Biohydrogen production by microalgae, and Integration in taxi/bus Transport Systems, funded also by FCT, trough National funds, reference EXPL/EMS-ENE/1078/2012.

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Appendix A. ADVISOR scaling factors relation to the respective components

The following figures present the scaling factors relation to each considered fuel cell and electric motor model (see Tables 2 and 3).

0,34 2250 0,32 2000 0,30 1750 0,28 1500 0,26 1250 0,24 1000 0,22 750 0,20 Nominal Power (kW) 500 Specific Power (kW/kg) Power Specific 0,18 250 0,16 0 12345678 12345678 FC_1 FC_2 FC_3 FC_4 FC_1 FC_2 FC_3 FC_4

Fig. A1. Scaling factor of fuel cell available models: specific power (left) and nominal power (right)

1,4 2250 2000 1,3 1750 1500 1250 1,2 1000 750 1,1 Nominal Power (kW) 500

Specific Power (kW/kg) Power Specific 250 1,0 0 12345678 12345678 MC_1 MC_2 MC_3 MC_4 MC_1 MC_2 MC_3 MC_4

Fig. A2. Scaling factor of electric motor available models: specific power (left) and nominal power (right) Paulo Melo et al. / Procedia - Social and Behavioral Sciences 111 ( 2014 ) 692 – 701 701

Appendix B. Overview of literature studies

The following table summarizes the most relevant scrutinized literature studies and highlights the aspects that are covered within this research.

Table B1. Overview of literature studies. EMS regards to energy management strategy optimization. aMin Fuel regards to fuel consumption minimization, Min eq. Fuel to fuel equivalent minimization. bThe vehicle optimized is a passenger bus. (DP- Dynamic Programming, DIRECT- Dividing Rectangles algorithm, ECM-Equivalent Consumption Method, Par- Parallel configuration, PSO- Particle Swarm Optimization, SA- Simulated Annealing, Ser- Series configuration, UC-Ultracapacitor)

Component Vehicle Objectivea Optimization method EMS Driving cycles Reference sizing HEV Ser Min Fuel global; DP Yes No NEDC, FUDS Brahma A. et al. (2000) PHEV Par Min Fuel global; DIRECT Yes No UDDS / HWFET Rousseau, A. et al. (2007) real-time, predictive, global; FUDS / FHDS / ECE / NEDC / HEV Par Min Fuel Yes No Musardo C. et al. (2005) ECM, DP EUDC / JP1015 FC-HEV Min Fuel real-time; Fuzzy + GA, DP Yes No LA92 Ravey A. et al. (2011) FC-HEV UC Min Fuel real-time; PSO Yes No INRETS / ESKISEHIR real Caux S. et al. (2011) FC-HEV Min Fuel real-time; Fuzzy logic Yes No ECE 15, Beijing real Gao D. et al. (2008)b UC+BAT b HEV Par Min Fuel global; GA, DIRECT, PSO, SA No Yes HWFET / FTP75 Gao, W. et al. (2007) FC-HEV UC Min drivetrain cost global; GA, PSO No Yes NEDC / FTP75 Hegazy, O. et al. (2010) Min drivetrain cost and Montazeri-Gh M. et al. HEV Par global; GA Yes Yes TEH-CAR / FTP / ECE-EUDC emissions (2006) FC-HEV Min drivetrain cost global; GA + DP Yes Yes ECE / LA92 Ravey A. et al. (2012) FC-HEV Max Net Present Value global; Pattern search Yes Yes EPA Kansas real Kwon J. et al. (2011) HEV Par b Min Fuel global; PSO Yes Yes UDDS / Montreal Pie-/IX 139 real Desai C. et al. (2010) PHEV Ser/Par Min operation cost real-time; Stochastic DP Yes Yes NHTS random/Markov chain Moura S. et al. (2009) PHEV Ser Min drivetrain cost global; ECM Yes Yes Artemis / NED / real Shankar R. et al. (2012)