Analysis and Design of the Powertrain and Development of an Energy

1 Analysis and Design of the Powertrain and Development of an Energy Management Strategy for InMotion IM01 Hybrid Race Car I.Papaliouras, P.Beviz, A.Pliatskas, E.Stamatopoulos, E.Papanikolaou, S.A.Krishna, S.Velayutham, C.Vichas, K.H.F.E.Emam, V.Sridhar, B.D.Cano and E.A.Ross PDEng Automotive Systems Design, Stan Ackermans Institute Eindhoven University of Technology, Eindhoven, The Netherlands. Email: [email protected] and J.J.H.Paulides Electromechanics and Power Electronics Group, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands. Email: [email protected]

Abstract—This paper describes the analysis and design of an achieve high performance, races such as Le Mans and com- energy efficient powertrain configuration for the InMotion IM01 panies such as Audi, Porsche and Nissan have silenced that race car. It is a Hybrid Electric Vehicle (HEV) with several scepticism. In that context, the InMotion racing team has an- novel technologies, aiming to participate in the 24-hour Le Mans nounced and started the production of a high-performance low- race, competing in the Garage 56 category. InMotion, the main consumption hybrid supercar (IM01) that is set to participate developer of the IM01, is a multidisciplinary project-oriented in the Le Mans race in 2017. group supported by Eindhoven University of Technology (TU/e). The main goal of the InMotion team is to achieve better fuel The InMotion student group consists of students from TU/e efficiency and performance than that of their competitors in the and Fontys University of Applied Sciences, as well as experts LMP1 category of the Le Mans race, namely, the Audi R18 e-tron in the field of automotive domain. They have a close collab- (2014) on Circuit de la Sarthe. The Automotive Systems Design oration with the two universities and automotive companies (ASD) generation 2014-2016 are involved in the development [1]. In this context, trainees of the Automotive System Design of the series powertrain architecture of IM01 super car. This (ASD) PDEng program in TU/e were assigned to develop paper presents an optimal HEV powertrain analysis, including a powertrain model and an energy management strategy for the description of different components, focused on using the series hybrid powertrain topology and taking into account the their race car. The findings of this research are presented in design restrictions from InMotion and the constraints of the this paper. There are certain safety regulations imposed on the Garage 56 category. We recommend improvements to the Internal contestants and the vehicle must comply with the performance Combustion Engine (ICE), the Energy Storage System (ESS) type and reliability criteria, but in general, it is an opportunity to selection and introduce a component sizing algorithm. Several test novel technological achievements. Energy Management Strategies (EMS) are investigated within this The work carried out by the ASD group focuses on configur- study; a rule-based controller and an Equivalent Consumption Minimization Strategy (ECMS) are implemented and validated ing a series hybrid powertrain for IM01, modelling and sizing using a Simulink forward model of the HEV. A scaled model of the components, and simulating the performance of the of the drive cycle is proposed using a test bench to observe the vehicle for different drive cycles using two energy management behavior of the electric motor on Circuit de la Sarthe. strategies. The proposed sized powertrain is simulated on a drive cycle that is based on the La Sarthe circuit track Keywords—Hybrid Electric Race Car, InMotion, Garage 56, properties. The speed and acceleration profiles, and the power Energy Management Strategy, Series Powertrain, ECMS, Le Mans demand profile have been modified to match the current powertrain configuration, and different driving strategies have been developed to increase the efficiency. Furthermore, two Energy Management Strategies (EMS), Equivalent Consumption Min- I.INTRODUCTION imization Strategy (ECMS)[2][3]and a rule-based strategy, are YBRID vehicle technology is continuously evolving by implemented and compared. H introducing innovative technologies into the automotive During the development of the powertrain of IM01 race car, industry. The main goals of the ongoing research are to achieve several restrictions have been taken into consideration. These the best combination of performance and efficiency, reduce constraints are mainly Garage 56 safety regulations, as well the emissions and create an environment-friendly means of as performance requirements that must be met, based on the transportation. Despite skepticism that hybrid vehicles cannot main opponent’s performance, i.e. Audi R18. Figure1 shows 2 the preliminary performance results of IM01 compared to Audi II.HEV POWERTRAIN ANALYSIS R18. There are also restrictions imposed by InMotion, regard- By the term hybrid electric vehicle, this paper refers to the ing the selection of components. The main requirements that vehicle that features two or more different types of energy are derived from these restrictions and are initially considered sources [5]. The two main power sources are the prime mover as follows: and the energy storage system, which can consist of various • The curb weight of the vehicle, including the fuel, must components. In most cases, the prime mover is an Internal not exceed 900kg. Combustion Engine (ICE), or a Fuel Cell (FC) and the energy • The topology will be that of a series powertrain to ensure storage system consists either of batteries, or ultra-capacitors, easier assembly [4]. or a flywheel, or a combination. • Some of the components that are being used are the same as in the IM/e (Fully electric race car (2015) of A. Topologies and Advantages/Disadvantages InMotion) vehicle [1]. The powertrain is the sum of the components that are • The maximum voltage on the electric circuits of the generating and delivering energy to the road surface for the vehicle must not exceed that of 1kV . propulsion of the vehicle. These components mainly include • The fastest lap time of Audi R18 is 3min 22.57s. the prime mover, the transmission, the wheels, the generator, IM01 must achieve a lap time equal or less than the the electric motors, the power electronics for the power con- aforementioned. version and the energy storage system. There are three main • The number of laps Audi R18 completed in 2014 is types of HEV powertrain topologies: series, parallel and series- 379laps. That number is based on the total distance parallel. covered by Audi in 2014 in 24 hours. In order to achieve In the parallel configuration, there is a mechanical coupling over and above 379laps the average speed of the IM01 between the internal combustion engine and the wheels, as well should be at-least equal to 215.25km/h. as between the electric motors and the wheels. This translates • The average fuel consumption during the 24-hour race of into the wheels being propelled by these two sources either Audi R18 is 32l/100km. The average fuel consumption individually or simultaneously. The benefits of this layout are of IM01 must be at least equal or smaller. the low energy conversion losses, since the power from the prime mover is directly delivered to the driving wheels, the lower fuel consumption and the increased efficiency, and the fact that only two propulsion devices are needed, namely the prime mover and the electric motor [6][7]. On the other hand, the connection of the prime mover to the wheels requires the presence of a gear mechanism. This, in combination with the complex transmission results in mechanical losses as well as a complex control strategy [4]. A simplified architecture of the parallel configuration is shown in figure2.

FUEL TANK ENGINE

Mech

WHEEL . .

Fig. 1: The initial performance results of IM01 Vs. Audi R18 Coupler 2014

DC/DC I/C MOTOR / ESS DC BUS CONVERTER UNIT GENERATOR

A brief literature review of the existing powertrain topologies and components used in HEVs is introduced in sectionII, AUXILLIARIES as well as the decision process followed to select the optimum type of each of the powertrain components. The mathematical modelling of these components is described in section III in Fig. 2: Parallel powertrain topology order to reach a complete forward model of the powertrain using the parameters of the selected components. Two EMS strategies are implemented, compared and tested using the In the series configuration (figure3), the mechanical power developed forward model to validate the race requirements in output of the internal combustion engine is converted to terms of fuel efficiency. A lab testing methodology is presented electrical energy through the generator. This energy either in sectionIV that describes a procedure for scaling and charges the Energy Storage System (ESS) or propels the testing the drive cycle using a lower power test bench. The wheels through the electric motors. The motors can also results of the model simulation and a comparison of different be used for regenerative braking. It is a simple powertrain control strategies are listed in sectionV. Finally, we present configuration, in terms of mechanical connection, control and a conclusion of what has been achieved and recommendations energy management, that is based upon the principle of using for future work. the engine (ICE or FC) as a range extender for the energy 3 storage system on board [7]. The independence of the engine engine and the generator are outside the scope of this speed from the vehicle load and speed allows the engine to be paper. operated under its most efficient conditions. Additionally, it 1) Engine – Generator Unit (EGU): In this study, different enables the use of a lightmass high speed internal combustion options for the engine are considered to define the best engine [6]. When developing a series powertrain, however, we selection for the IM01 race car. Simulation tests and a quality take into consideration that the energy generated by the engine comparison between the different options provide the ideal has to go through two additional components, the generator engine proposal according to the requirements of the project. and the motor, thus increasing the losses and decreasing the The criteria and parameters according to which the selection efficiency of the powertrain [4]. Furthermore, the propulsion is done, are: devices need to be sized according to the maximum sustained power, resulting in a more expensive and heavy powertrain • Innovation configuration [7]. • Specific power Apart from these main configurations, a combination of • Efficiency series–parallel as well as other complex hybrid powertrain con- • Safety figurations have been developed that feature the advantages of • Mechanical Complexity the basic powertrain architectures, but have shown drawbacks The most common engine types used in race cars that in terms of complexity and cost [7]. participate in the Le Mans race are the reciprocating engines. In this paper, we use the series hybrid topology to perform Diesel and petrol engines are the most dominant engines an analysis and a sizing of the components, as well as to selected by the contestants. Furthermore, during the previous develop an energy management strategy. This work is based decade, cars with rotary engines have also achieved great upon the selection done by InMotion and the ASD generation results in the race. In the selection of the engine for IM01, of 2011 [4], where a preliminary study on a powertrain con- the following types are taken into consideration: figuration was performed and an energy management strategy • Reciprocating engines was developed. We focus on the efficiency of IM01 hybrid • Rotary engines race car, while the previous work from ASD has focused on • Micro turbines its performance. • Fuel cells The steps of the selection algorithm for the EGU are B. Component Description for the Series Topology and depicted in figure4, and this procedure consists of four Power Flow Analysis main stages. First, based on the drive cycle [4], we calculate The analysis of the powertrain topology and the race the power demand for one lap, and subtract the possible requirements are used to design the flow of power during regenerative braking power from the overall power need. The the various modes of operation between the components. A estimated remaining power is to be supplied by the EGU, representative diagram is depicted on figure3. The different and in this case the remaining average power is 400kW . The modes of operation for IM01 are the following [7]: second step includes considering various EGU options, their • ESS-only mode: The engine is switched off and the efficiencies and their specific power, and calculating from these vehicle is powered by the ESS solely. This mode is data the total mass of the different EGU options. Various applicable in the case that the configured ESS has high options are depicted in tableI. energy and power density. The mass of the engine is then used as an input parameter • Engine-only mode: The vehicle is powered by the inter- for the drive cycle simulation. The simulation estimates the nal combustion engine only. performance of the vehicle, in terms of number of laps during • Combined mode: The power demand is met by a combi- the 24-hour long race, and the efficiency, based on the fuel nation of both the power sources, as determined by the consumption per lap. In this way the various engine options energy management strategy. can then be compared, in terms of racing performance and fuel • Power split mode: The engine power is split to propel efficiency. the vehicle and charge the ESS at the same time. The engine mass, however, may influence the power de- • Regenerative braking mode: During this mode, the fuel mand. In order to see whether this is the case, the power de- to the engine is cut off. The motors are recovering energy mand was recalculated for the heaviest configuration, namely that is stored in the ESS during braking. After the ESS the diesel reciprocating engine. The difference in required is fully charged or the regenerative currents exceed its power is 10kW . Resizing the engine to this power demand rated value, the energy from regeneration goes to the and running the drive cycle simulation again, we find that for generator (which temporarily acts as motor) attached to this new power demand the energy consumption differs by less the engine. This converts the engine into an ”energy than 0.1%. The number of laps is unaffected. Thus, the mass dump”. In our implementation, we switch off the engine of the EGU does not have a significant impact on the results in the case that the ESS is fully charged during the and no iterations need to be made. regenerative braking mode. This aims to zero the engine In order to choose a type of engine which satisfies the fuel consumption. We do not model the generator to project’s requirements, tableII summarizes the advantages and operate as a motor, since the transient behaviour of the disadvantages of the different types based on the simulations 4

FUEL TANK AUXILLIARIES

Pfuel PAUX

P PGENDC PM/GDC PM/G Pwheel PENG GENERATOR / GEN DC GENERATOR / ENGINE INVERTER INVERTER WHEEL MOTOR BUS MOTOR

PESSDC

DC/DC CONVERTER

PESS

ESS

Fig. 3: Series powertrain topology with power ﬂow

TABLE I: Engine data based on sweet spot performance Engine Specific Efficiency Generator power included (W/kg) Audi R18 e- 1640 0.40 Yes tron (2014) Diesel reciprocating [8] Rebellion 3725 0.34 Yes R-One (2015) Petrol reciprocating [9][10] Mazda 2867 0.25 Yes 787B Petrol rotary [11] Jaguar 2000 0.25 No CX-75 Gas Fig. 4: ICE type selection algorithm turbine [12][13] Toyota Mi- 1401 0.37 No rai Fuel cell is lighter and thus more attractive for a race car. Furthermore, [14][15][16] the petrol engine outbalances the diesel one in performance. This advantage is in fact an extra degree of freedom that will be used in the design process to increase the vehicle’s fuel as described above and literature research. For a full report, efficiency. This is performed through investigating different see [17]. strategies that result in a smaller number of laps but also less Considering InMotion’s vision of winning the race in terms fuel being consumed. of energy efficiency, the reciprocating engines seem to be the From the options mentioned in tableII, the reciprocating best option. Comparing between diesel and petrol, the last one petrol engine features the second lowest total mass with 258kg, 5

TABLE II: Advantages and disadvantages of different Engines TABLE IV: BCAP 3400 Ultra-Capacitor Specification [19] types Property Value Diesel Petrol Rotary Gas Fuel Cell Capacitance 3400 F turbine Good Nominal Voltage 2.85 V performance, Lowest Innovative, Absolute Maximum Current 2000 A Low Advantages energy Innovative High Innovative energy Specific Power 6.7 kW/kg consumption performance Consump- Stored Energy 3.84 Wh tion mass 0.52 kg Heaviest, Questionable Highest High Lowest Safety, Volume 0.3991 L Disadvantages Conventional energy Energy performance, Low consumption consumption ESR 350 Ohm Conventional performance achieves the second best number of laps with 463laps in 24 vehicle, we assume that the stored energy is dissipated within hours and has the second lowest energy consumption with two braking instances. The aforementioned requirements can 62.4kW h/lap, thus making it the optimal choice for the be fulfilled by the use of batteries, ultra-capacitors or a com- engine. bination of both. Therefore, we perform an ESS hybridization 2) Energy Storage System (ESS): Based on the power flow analysis [20] focusing on the mass of the ESS to choose analysis and the backward modelling of the powertrain, we the optimal combination of battery and ultra-capacitors. The calculate the power that can be regenerated and stored in the Hybridization Level (HL) is on a scale of 0 to 1. energy storage system. The requirements that the ESS should Where meet are: 0: The total power is provided only by battery pack • Retrieving the maximum power during the regenerative 1: The total power is provided only by ultra- braking and delivering the power in combined mode, so capacitors that the currents are within the ESS specifications. This The mass of the energy storage system is the primary factor amounts to 344kW in regenerative braking mode and to determine that only ultra-capacitors will be used in IM01. 129kW in combined mode. In figure5, the mass and volume of the ESS according to • Storing the maximum energy during regenerative brak- the level of hybridization are depicted. The case of HL = 1, ing (247W h) i.e. using only ultra-capacitors, results in the lowest mass and • Not exceeding 390kg, considering the combined mass volume values for the energy storage system. This is mainly of the ESS and EGU due to the high specific power of the ultra-capacitors, when The most commonly used energy storage systems are batter- compared to batteries. In this race, the high power demand can ies, ultra-capacitors and flywheels. The flywheels have been be provided solely by a combination of ultra-capacitors, and rejected as a design constraint by InMotion, so only batteries the energy levels that need to be stored during the regenerative and ultra-capacitors are considered for the ESS choice. The braking mode match the ESS specification of specific energy. selection is based on the mass to power ration as a main constraint. The type of battery chosen for analysis is the 200 100 XALT Superior lithium ion cell [18]. For the ultra-capacitors, Volume the Maxwell Technologies BCAP 3400 [19] is used. The Mass specifications of the selected battery and ultra-capacitor are shown in tables III andIV respectively. 150 80

TABLE III: XALT Superior Li Ion cells Specification [18] 100 60 Property Value Mass [kg] Nominal Voltage 3.7 V Volume [L] Specific Energy 153 Wh/kg Specific Power 2.6 kW/kg 50 40 Capacity 40 Ah Charge Rate 12C Discharge Rate 60C 0 20 0 0.2 0.4 0.6 0.8 1 mass 0.97 kg ESS Hybridization Level Volumetric Energy Density 350 Wh/L Fig. 5: mass and volume of ESS according to HL To limit the size of the ESS within reasonable boundaries so as not to have a negative effect on the performance of the 6

3) Power conversion systems: The power electronics used in afterwards the energy saving strategies are explained and a series hybrid topology typically include rectifiers, inverters proposed combination is presented. and DC/DC converters. The inverter is used to convert the The process of sizing the components is iterative, as the DC voltage on the side of the DC-link to AC voltage fed to resulting sizes influence the drive cycles itself. We initially set the electric motors during motoring mode. During regenerative arbitrary values for the size of the components, based on ed- braking the motor acts as a generator, switching the inverter ucated assumptions, and reiterate through the same procedure block to operate as a rectifier so that the regenerated energy to achieve an output in the algorithm that will have minimal can be stored into the energy storage system, that operates on deviation from initial inputs. The algorithm for determining the DC voltage. specifications of the components, depicted in figure6, involves In HEV powertrain, there are numerous different DC voltage the following steps: levels: the DC-bus voltage level, the high voltage on the ESS side and the low voltage for the auxiliary systems. The DC/DC converters are used to convert the input voltage to a desired output voltage level and are usually efficient. In this study, the different converters are not modelled in detail. In order to simulate the power flow of the IM01 powertrain, we assume that the power converters have a constant efficiency of 95%. 4) Electric motor: The electric motor is the heart of the powertrain system of a hybrid vehicle, responsible for both the propulsion and the regeneration of energy from braking. The induction motors are the most commonly used in automotive applications, due to their low cost, robustness and reliability [21]. In recent years though, the permanent magnet synchronous motor has slowly taken over the induction motors due to the following advantages, as seen as in [22]: • 40% reduced mass and volume • 15% reduced peak inverter current • 25% increased torque density In this project, the motor used in the simulation is the PMSM YASA 750, designed and built by YASA Motors, sponsor of InMotion. The specifications of the motor are presented in tableV:

TABLE V: YASA 750 Speciﬁcation [23] Parameters Value Peak Power @ 700V @65°C 200 kW Continuous Power >75 kW Continuous Torque 400 Nm Maximum Torque @ 450V @65°C 790 Nm Peak Efﬁciency >95% Maximum Speed 3250 rpm mass 33 kg Fig. 6: Component sizing algorithm

1) The first step is to choose which design and driving strate- C. Component sizing gies to apply. Six such strategies have been determined, The power train components are sized based on the drive each meant to increase energy efficiency, and are detailed cycle energy demands. The EGU is sized exactly such that it below. provides, during a lap, the energy the drive cycle demands. 2) The second step is to make an estimation of the initial com- The ESS is sized such that it meets the power and energy ponent size, namely the EGU, ESS and DC/DC converter. demands, and the DC/DC converter is sized according to the 3) The third step is to simulate the drive cycle. The result is the power it needs to handle. The drive cycle is based on the energy balance and the power of the various components. available model [4], however both performance (lap time) and 4) The fourth step is to find the required EGU power. We fuel efficiency calculations were added for the purpose of this consider that the energy stored in the ESS should be equal paper. In this section first the sizing procedure is explained; to the energy drawn from it. Based on this restriction, the 7

algorithm calculates the EGU power, through an iterative v) High temperature superconducting machines (HTS) process. The main advantage is that these motors have roughly 5) The fifth step is to find the required ESS mass. The ESS three times higher specific power and twice the lower configuration is determined based on the energy balance volumetric power, including cooling systems [24]. This over time; both power and energy demands are taken into means that the mass and volume of the motors and consideration. generator can be reduced tremendously. Furthermore, their 6) The final step of the algorithm is to determine whether to efficiency is higher than that of normal electric motors. reiterate, starting from the third step, or not. It mainly de- Using HTS machines is certainly not an easy decision. pends on whether the mass of the newly found component The coils need to be cooled down to approximately 77K. sizes differs significantly from the initial estimated mass However, once cooled, the motors will barely generate any or not. If the iteration is not deemed necessary, then the heat at all. If the choice is made to apply HTS technology, configuration of the components is final for the applied set it is recommended to cool the motors before the race, of strategies as determined in the first step. insulate them very well, and have a small on-board liquid nitrogen supply to overcome the remaining warming up The first step of the sizing algorithm, as mentioned before, of the motors. is choosing the set of strategies that we will follow. These When applied to IM01 for the four motors as well as the strategies each aim in enhancing the efficiency of the vehicle; generator, mass savings of 60 to 120kg can be expected, in some of the cases there is a small racing performance depending on the vehicle’s configuration. If, for instance, penalty. They are the following: the choice is made to use only two motors instead of four, i) Reduce mechanical braking force there is less mass that can be saved. The applied braking force directly affects the energy that Using HTS machines mostly benefits the performance, can be recovered during the regenerative braking mode. while maintaining the same fuel consumption. Therefore, This is based on the fact that only a portion of the to again increase efficiency, the above methods will have braking force is applied by the motors, and the rest of to be applied to a greater extent in conjunction with the the energy that is not being recovered is dissipated in the HTS motors. In the simulations, HTS is implemented as service brakes. If we limit the braking force applied by a mass reduction and increase in electric motor efficiency the driver and we increase the duration of the braking, only. the motor can provide a larger part of the braking power vi) Use an active aerodynamics system and hence, recover more energy. Instead of hard braking, The purpose of using an active aerodynamics strategy is we suggest a strategy of soft braking, with a combined to adapt the aerodynamics properties of the vehicle to its applied brake force of 3800N. This value does not include power need. We assume that the IM01 incorporates an the air drag and other resistive forces. This strategy can actuator that regulates the angle of the rear wing. Both be implemented with a brake controller that will allocate the down-force and drag of the vehicle are proportional brake force between the motors and service brakes, and to the angle of the wing. The actuator operates under the a feedback mechanism in the brake pedal or an indicator following principles [25]: on the steering wheel that will guide the driver in every - V ehicle speed: The angle of the rear wing braking occasion. is inversely proportional to the speed of the ii) Increase the regenerative motor power vehicle, in order to reduce the drag force at high Oversizing the motor will increase the recuperated energy speeds. and will also result in larger, heavier motors, heavier ESS - Steering angle: During cornering, the angle and DC/DC converter(s). This will lead to a heavier vehi- of the rear wing is increased, regardless of the cle and the trade-off between the mass and performance speed, to produce more down-force thereby in- needs to be re-investigated. creasing the traction and stability of the vehicle. iii) Limit in the top speed - Braking: During braking, the angle is set to its Limiting the top speed will reduce the air drag force and maximum in order to increase the drag force and consequently, the power consumption from the motors. the traction, as a result of increased downforce. However, this strategy will result in a smaller number of laps and a reduced efficiency of the YASA750 motors To simulate this technique in our model, we assume that (since they are more efficient when operating at higher the frontal area of IM01 varies linearly with the speed speeds). in a range of 1.41m2 to 1.54m2. By altering the frontal iv) Reduce the traction motor power area, we simulate the change in the rear wing angle. We A simple way to reduce the energy consumed is to supply also make an assumption that the drag and lift coefficient less power to the motors. In this way, a limit is imposed change linearly with the frontal area. When braking, we on how much energy is used over time. This leads to assume the maximum value for the drag coefficient. It is reduced acceleration and consequently, high speeds are then used to calculate the forces acting on the vehicle, more difficult to reach. The same reservations regarding and in the specific drive cycle this instance occurs while motor efficiency as mentioned in the previous strategy, cornering. TableVI indicates the advantage of using an will also apply to this solution. active aerodynamics system, instead of having constant 8

aerodynamic properties. The aerodynamic properties (drag TABLE VII: Estimated mass of IM01 powertrain coefficient, aerodynamic ratio, frontal area) of Mazda RX- 792P GTP are taken as a reference in order to compare Components Specification mass [Kg] the active aerodynamic system we simulated with a no ICE 304 kW 123.7 active aerodynamic chassis [26]. Generator 292 kW 67.9 ESS 66 44.59 (inc. Ultra-capacitors Cooling & TABLE VI: Effect of applying active aerodynamics Packaging) Lap time −5% Motors YASA Motors 132 (inc. Number of Laps 6% Inverters) Average Power at Wheels −7% Drivetrain Misc. Cables, fluids 10 from the Powertrain etc. Total Energy consumption −12% DC/DC converter 350 kW 23.5 at Wheels Chassis Monocoque 315 Body Unsprung components Driver - 75 Component selection & powertrain configuration Σ vehicle mass 791.7 After simulating various combinations of the aforementioned strategies and applying the component sizing algorithm, the optimal configuration for the IM01 powertrain is concluded If the above sizing procedure is applied to a design with in table VII based on the final derived drive cycle. The only active aerodynamics, the resulting required EGU power is final power demand is depicted on figure7. The realized determined to be 535kW . The simulations for this case yield ESS configuration according to the vehicle specifications is a lap time of 185s, with a fuel consumption of 148l/hr. By included in table VIII. It should be noted that while researching applying the strategies as mentioned, the EGU needs to be the optimal configuration, only rule-based control was applied. 292kW . The lap time is increased to 209s, however, the fuel consumption was reduced to to 81l/hr. This clearly indicates Hard Braking Soft Braking the influence of increasing brake time and reducing traction 300 power on achieving these results.

250 TABLE VIII: ESS conﬁguration [soft braking case] 200 Property Value

Velocity [km/hr] 150 Total number of Capacitors 66 Capacitors in series 66 0 50 100 150 200 Time [s] Capacitors in parallel 1 400 Maximum voltage of capacitor bank 188 V 200 Maximum charging current 2000 A Maximum discharging current 2000 A 0 Maximum power 376 kW Energy capacity 253.2 Wh

Power [kW] −200 mass (excluding cooling and packaging) 34.32 kg −400 Volume 26.34 L 0 50 100 150 200 Time [s] Time to fully charge / discharge 4.84 s Fig. 7: Power Demand III.MODELLING For the configuration presented in table VII, the soft braking A complete model of the powertrain components (figure3) strategy, with 3826N applied brake force, a 43% reduction of is described in this section. The mathematical model of each maximum traction motor power and an active aerodynamics of the components is designed using Matlab/Simulink based mechanism are used. Though the motors are not used to their on the preliminary calculations from the components sizing full capabilities when accelerating, their size is not reduced algorithm as system parameters. The integrated model of the so as to retain regenerative braking capability. In effect, this powertrain, including a model for the road-tyre interaction, is means that the motors are sized for regenerative braking, rather used to simulate the power demands based on the designed than for acceleration. Although beneficial, HTS is not used, as drive cycle as an input to the model. Thus this modelling the upsides were too little to justify the risks. process aims to validate the performance of the vehicle. In 9 this section, the models of the EGU, electric motor and the Turbine: The turbine acquires energy by expanding the ultra-capacitors are described briefly and an overview of the gases from a higher pressure and temperature to a lower integrated model is presented. pressure and temperature. The turbine then delivers the energy to the turbocharger shaft. The mathematical model [27] is derived for the turbine flow in equation4. A. EGU model pem 1) Turbocharger: The turbocharger mechanically couples a m˙ t = √ (4) compressor and a turbine. The turbine recovers some of the Re · Tem energy lost in the engine thermodynamic cycle to drive the Where compressor and increase the intake gas density to provide mt: mass of air through the turbine fuel economy and high power output. The turbocharger model pem: pressure in the exhaust manifold consists of two sub-models: a compressor massflow-efficiency Tem: temperature in the exhaust manifold model and a turbine massflow-efficiency model. Re: exhaust gas constant The mechanical connection between the compressor and The turbine efficiency is given in equation5. turbine results in mechanical losses due to the shaft friction. 2! The imbalance of the produced and consumed power gives an BSR − BSRmax acceleration to the shaft. The dynamic response is modeled η(BSR) = ηt,max · 1 − (5) BSRmax using Newton’s second law of motion: Where ˙ ˙ ! 1 Wt Wc BSR ω˙tc = · − − Mfric(ωtc) (1) : Blade Speed Ratio Jtc ωtc ωtc η(BSR): turbine efficiency ηt,max: maximum turbine efficiency Where Then, the turbine efficiency is used to determine the turbine ω˙tc: angular velocity of turbocharger power [28]. Jtc : inertia of the turbocharger’s shaft 2) Internal Combustion Engine: The ICE should respect Mfric: friction coefficient of the shaft the dimensional limitations that have been mentioned in Wt: turbine energy Tab. VII,i.e. maximum mass < 123.7Kg (peak power/mass Wc: compressor energy of 3). This ICE should handle the mass of air provided by the When ωtc = 0, singularity is circumvented by specifying a turbocharger, as it is defined by the throttle valve position. In shaft friction term Mfric(ωtc) in the dynamic equation to avoid order to fulfill the aforementioned component requirements, a uncertainties in the turbocharger performance. 3.0 L, V6 petrol engine has been selected with its geometry Compressor: The compressor, which is powered by the and thermodynamics properties summarized in Tab.IX,X turbocharger shaft, compresses the gas from a lower to a higher pressure and temperature, thereby increasing its temperature. A mathematical model for the compressor flow [27] is described TABLE IX: ICE geometry properties in equation2. Parameter Value p /p Number of cylinders 6 m˙ =m ˙ · 01 ref (2) c c,corr T /T Bore 0.086 m 01 ref Stroke 0.085 m Where: Rod length to crank radius 3.5 mc: mass of air through the compressor ratio mc,corr: corrected mass of air Compression ratio 9 p01: inlet pressure Clearance volume 0.0617 L pref : reference pressure Displacement volume 0.4937 L T01: inlet temperature Tref : reference temperature During the modelling process, two main assumptions are The compressor efficiency is given in equation3 made. The first one is that the air to fuel mass ratio is considered to be 14.5 during the whole process, by maintaining T dθ Q1 Q3 dθ the air to fuel equivalence ratio (λ) equal to 1. ηc = ηc,max − · · (3) dω Q3 Q2 dω ma AF = = λ · Ls (6) Where mf

Q1−3: matrix parameters where θ: optimal ﬂow AF : air to fuel mass ratio ω: optimal speed ma: mass of air The consumed power is simply the input power divided by mf : mass of fuel the compressor efﬁciency [28]. λ: air to fuel equivalence ratio 10

TABLE X: ICE thermodynamics properties TABLE XI: Rated and maximum values of the ICE Parameter Value Parameter Value Mechanical efficiency 98% Speed range 0-15000 rpm κ 1.4 Nominal speed 9700 rpm −4 Specific heat constant vol- 7.1750 ·10 kJ/gK Nominal nfuel 38% ume (cv) Nominal BSFC 223 g/kWh Specific heat constant pres- 5.1250 ·10−4 kJ/gK Nominal BMEP 1300 kPa sure (cp) Nominal Teng 300 Nm Heating value of fuel (Qlhv) 43 kJ/g Nominal Peng 304 kW Heat efficiency of fuel 90 % Maximum Peng 371.1 kW Stoichiometric air to fuel 14.5 mass ratio 400 Engine Torque Engine Power Ls: stoichiometric air to fuel mass ratio 350 The second assumption is regarding the modelling of the thermodynamics of the ICE. Once a petrol engine is used, an 300 ideal Otto cycle is modelled in order to further examine the thermodynamic behaviour of the ICE. However, in reality the Otto cycle will not be ideal due to heat and mechanical losses, 250 and leakage between cylinder and piston areas. For this reason, the engine work (Weng) produced per cycle is decreased by a 200 factor of 2/3.

Once the geometry and the thermodynamics properties of Torque [Nm] − Power [kW] the ICE have been speciﬁed, the next step is to determine the 150 engine torque (Teng) and power (Peng), the Brake Speciﬁc Fuel Consumption (BSFC) and the Brake Mean Effective Pressure 100 (BMEP) per engine cycle. Taking into consideration that the 5000 6000 7000 8000 9000 10000 11000 12000 13000 operational speed range of the ICE varies from 0 to 15000rpm Engine Speed [rpm] and that the ”sweet spot” (i.e. nominal speed) is at 9700rpm, Fig. 8: Engine torque, power and speed characteristic curves the following equations are used to calculate the respective quantities.

Weng 217 Teng = (7) 300 4π 350 223 n 1 Peng = Weng · Ncyl · · (8) 250 60 2 230 m˙ BSFC = f (9) 200 Peng 250 300

4π · Teng 150 BMEP = (10) Torque [Nm] Ncyl · Vd

1 100 350 nfuel = (11) 350 Qlhv·BSFC

Where 50 5000 6000 7000 8000 9000 10000 11000 12000 13000 Ncyl: number of cylinder Engine Speed [rpm] n: engine speed (rpm) m˙f : fuel mass flow rate Fig. 9: BSFC (in g/kWh) map of the 3.0 L, V6 petrol engine Vd: engine volume displacement nfuel: engine fuel efficiency TableXI summarizes the respective results of the ICE operation, while also in figures8,9 the torque-speed characteristic 3) Generator model: The generator is coupled with the ICE curves and engine efficiency map are provided. in the powertrain described in figure3. Permanent magnet 11 synchronous machines have the highest power density and B: rotor damping coefficient efficiency in mid-range speeds [29] , making them ideal for Tfr: frictional torque power generation in our powertrain. Due to the series topology J: rotor inertia of the powertrain, the ICE is only used to drive the generator The derived PMSM model is used to design the FOC and therefore the operating point of the two components is strategy. The aim of the FOC is to control the magnetic field independent of the speed range of the IM01 race car. The and torque by controlling the d and q components of the generator modeled with the same nominal operating point of stator currents. The implementation of the technique is carried the ICE in order to eliminate the need for a gear raito. out using two current controllers and one speed controller in The Permanent Magnet Synchronous Generator (PMSG) is cascade configuration as shown in figure 10. modelled by transforming the 3-phase machine into 2-phase machine in the d-q system using Clark’s and Park’s transfor- mation [30]. The differential equations used to describe PMSG are linearized for stability and controller design purposes. The dynamic equations of the PMSG are shown in equations 12 to 15. di V − R · i − ω · (L · i + λ ) q = q s q e d d m (12) dt Lq di V − R · i + ω · L · i d = d s d e q q (13) dt Lq Fig. 10: Field Oriented Control (FOC) for PMSM machine 3 T = · P · λ · i + (L − L ) · i · i (14) e 2 m q d q d q Tuning the two controllers is carried out using the method ωe = P · ωr (15) proposed in [31] in table XII. The currents and voltages are saturated to limit the motor to the maximum speed and torque. Lq and Ld are assumed to be equal. Table XII indicates the equations used to obtain the constant parameters for the three PI controllers. Where ωe: electrical angular velocity αcurrent = 2π · fs/10 (17) ωr: mechanical angular velocity Vq,Vd: d-q axis voltages αspeed = αcurrent/10 (18) iq,id: d-q axis currents Lq,Ld: d-q axis inductance Where αcurrent & αspeed are the controller’s bandwidth and Rs: stator resistance fs is the switching frequency of the inverter (equal to the λm: permanent magnet flux of the rotor sampling frequency of the system). P : number of pole pairs Te: electromechanical torque TABLE XII: Parameters of the tuned PI controllers in Cascade The control of the PMSG is done by using Field Oriented configuration Control (FOC) (subsection III-B). The output of the rectifier gives a DC voltage with floating, causing losses and Controllers Proportional Integral disturbances to the powertrain system. This can be avoided Constant Constant by either controlling the DC-link voltage or by using DC- Speed αspeed · J αcurrent · B DC converter. In this paper, DC-link voltage is assumed to be q-axis Current αcurrent · Lq αcurrent · Rs constant without investigating its dynamic behaviour and ways d-axis Current αcurrent · Ld αcurrent · Rs of resolving the problems that might occur.

B. Motor model C. Ultra - capacitor model The modelling of the Permanent Magnet Synchronous Mo- tor (PMSM) are same as those of the PMSG (section III-A3).In The ultra capacitor pack is modelled as an equivalent ideal addition, the output mechanical torque of the motor is de- capacitor with a resistor in series. scribed by the equation 16 Q(t) E(t) = (19) J · ω˙m = Te − B · ωm − Tfr − Tload (16) C

Where Nparallel C = · Ccell (20) ωm: mechanical angular velocity Nseries 12

IV. EXPERIMENTAL SETUP Nseries RESR = · Rcell (21) Hardware testing is carried out at the laboratory using Nparallel the available UQM75 [33] PMSM machine (testing machine) as a prototype of the selected YASA 750 machine and an induction machine (loading machine) that replicates the road E(t) = i(t) · RESR + v(t) (22) load forces. Using Model-in-the-Loop (MiL) simulations, the selected YASA 750 machine is modelled and simulated with the control algorithm. The developed model for the YASA Q(t) SoC = (23) 750 motor is adapted to the UQM75 motor in the lab setup by Qmax using Triphase ®toolbox for real-time interaction between the physical hardware setup and the software controller model. In order to test the derived cycle of the Le Mans circuit using Qmax = C · Nseries · Ecell (24) this setup, scaling the desired speeds of the YASA750 to the UQM machine is necessary; scaling the road load from the Where environment is also required. C: pack capacitance R : equivalent pack series resistance ESR TABLE XIII: UQM 75 Specifications [33] Ccell:cell capacitance Rcell: cell series resistance Specification Value Ecell: nominal cell voltage Continuous shaft power 45 kW N : number of cells in series series Pcont Nparallel: number of cells in parallel Peak shaft power Ps,max 75 kW v(t): terminal voltage Maximum speed ns,max 8000 rpm Q(t): capacitor charge Shaft nominal speed at peak 3000 rpm i(t): current torque and power n E(t) s,corner : internal capacitor voltage Number of poles P 18 SoC: state of charge - capacitor pack Continuous (average) shaft 150 Nm torque τs,cont Peak shaft torque τs,max 240 Nm D. Forward model of powertrain Efficiency at maximum 94 % The mathematical model of the powertrain components shaft power and continuous is derived, described previously, to investigate the system torque ηEM,a response and to validate the component sizing process. Models Efficiency at maximum 90 % of different components are either created, modified or reused shaft power and maximum from the existing EV toolbox [32]. The basic layout of the speed ηEM,b forward model, which includes all the software and hardware components as well as the environment interactions and the Scaling is performed using the Pi-theorem [34]. The pro- driver commands, is depicted on figure 11. posed method for obtaining scaling factors and determining dynamic similarity of systems involves the formation of an equivalent system representation using dimensionless vari- ables. To study this dynamic similarity of the motors, the steady-state torque-speed curves of each motor are plotted as shown in figure 12. Steady-state is chosen because transient effects of each motor are minor compared to their steady-state performance during typical drive cycles. The scaling is carried out using equations 25& 26. The results of the scaling technique are validated by plotting both the YASA750 and the scaled UQM torque speed curves in the dimensionless domain as depicted in figure 13.

Pmax,Y ASA · Tmax,UQM ωUQM,scaled = ωY ASA · (25) Tmax,Y ASA · Pmax,UQM

Fig. 11: Schematic of the forward model Tmax,UQM TUQM,scaled = TY ASA · (26) Tmax,UQM 13

800 UQM75 Continuous Output @300 VDC input 700 YASA750 Continuous Output @800 VDC input

600

500

400

Torque [Nm] 300

200

100 Fig. 14: Kistler Setup 0 0 1000 2000 3000 4000 5000 6000 7000 8000 Speed [rpm] the mathematical model of the machine, in order to tune the Fig. 12: Speed Torque curves for UQM75 & YASA750 controller in Simulink. To make the modeling process more accurate, several measurements are carried out to determine the machine parameters such as ﬂux linkage constant, torque 1 constant, number of poles, phase resistance and inductance YASA 750 using no-load test, back EMF and passive load test. Some Scaled UQM 75 parameters such as the inertia, and damping coefﬁcient are 0.8 also determined using the roll down test. These parameters are used in the modelling process.

max 0.6 A. Deriving load machine reference points from the drive cycle The scaled hard braking drive cycle velocity for the UQM machine using the Pi theorem is shown in ﬁgure 15. This 0.4 velocity proﬁle is used as the input for the UQM machine.

Torque/Torque In the real world scenario, the electric motor overcomes the resistive forces and reaches a desired speed requested by the 0.2 driver. So, the driver acts as the speed controller, while the output of the speed controller is the desired torque controlled using the throttle pedal. The developed cascade controller 0 model of the PMSM motor can be used to control the motor 0 0.5 1 1.5 2 Speed *Torque/Power on the test bench with reference input as the Vscaled[km/hr] max max from the drive cycle in [rad/s]. To simulate the drive cycle, Fig. 13: Dimensionless Speed Torque curves for scaled the load acting on the motor has to be calculated during UQM75 & YASA750 acceleration and braking. The resistive forces acting on the vehicle are given by equations 27 to 29. Since the IM01 uses active aerodynamics, it results in a variable down force that affects the rolling resistance Froll. Both the UQM machine and loading machine are mounted 1 F = · ρ · C · A · V 2 (27) on the Kistler setup at the laboratory. Figure 14 shows a Airdrag 2 d f description of the setup and its hardware components. The Kistler setup is a test bench consisting of the loading ma- F = f · (m · g · cos θ + R · F ) (28) chine, torque sensor, cooling circuit, power analyzer, two host roll r df Airdrag computers to control the test motor (UQM75) and the loading sides separately with separate controllers. The loading machine Fslope = m · g · sin θ (29) is controlled via a PC connected to the setup in a torque control mode. The setup is also connected to a real time Where target inverter (Triphase) which drives the UQM75 machine FAirdrag: air drag resistance force through the Simulink model implemented on the host-PC. Fslope: force due to the gradient of the road The controller for the UQM75 machine is built by using Cd: co-efﬁcient of air drag 14

Af : frontal Area V : velocity 450 V Rdf : ratio of air drag force used to increase the uqm 400 V rolling resistance yasa ρ: ambient air density 350 fr: coefﬁcient of rolling resistance θ: gradient of the road surface 300 m: mass of the IM01 car g: acceleration due to gravity 250 The total resistive forces acting on the vehicle are given by the equation 30. Velocity[km/hr] 200

Froadload = Froll + FAirdrag + Fslope (30) 150

100 Fmotor = Ftotal + Froadload (31) 0 50 100 150 200 Time [s] Ftotal = m · a · δ (32) Fig. 15: Velocity profile for different motors Where δ: inertial coefficient of the vehicle a: acceleration of the vehicle Where The mass of the vehicle (m) and the frontal area (Af ) are Fmech: mechanical braking force scaled with respect to torque to mass ratio of a single YASA Fbraking: regenerative braking force motor, resulting in a scaling factor of 1/13.33 for the UQM The maximum braking force that can be applied on the tyre machine. is calculated, then amount of energy to be recovered during 1) Acceleration: To reach the desired velocity, the motors braking from the electric motor is fixed. The remaining amount in the car produce forces which overcome the road load of energy is dissipated by the mechanical brakes. For the forces and result in desired acceleration of the car. The total test case, the amount of power to be recovered is given by force acting on the vehicle during acceleration is then given equations 35 and 36. by the equation 31, where Fmotor is the force produced by the motor. So the load torque to be applied on the loading Fregen = −m · a · δ − Fmech − Froadload (35) machine is computed by calculating the road load forces and P = F · v (36) the required forces to reach the desired acceleration under the regen regen assumption that the motor in the race car delivers the maximum The torque applied on the load machine during braking is torque throughout the race. The load torque that has to be Fregen · Rwheel. The loading machine acts on the motoring supplied by the load machine during acceleration is calculated mode in the direction of rotation of the UQM motor to simulate by Fmotorload · Rwheel, where Froadload is computed for the the momentum of the vehicle while braking. The loading scaled velocity profile of the drive cycle for the UQM motor, machine is set on the torque control mode to follow the scaled as shown in figure 15. We observe that the scaled velocities are reference trajectory as shown in figure 16. The loading torque higher than the original drive cycle velocities, since the UQM during braking is limited to - 80 Nm due to the limitations of motor has a higher speed capability than the YASA motor. the setup. 2) Deceleration: During deceleration in the real world scenario, the vehicle has a momentum which is reduced by V. RESULTS applying braking forces in addition to the resistive forces. The braking forces are applied through the mechanical and the We have performed several simulations to compare both regenerative braking mechanisms for the IM01. The balance the rule based and ECMS control strategies in combination between the amount of the mechanical and the regenerative with the soft and hard braking techniques applied to each braking is calculated based on the limits of regeneration, control strategy, since the aim of this analysis is to give a calculated for a specific energy storage system. The load recommendation for the final powetrain to be assebmled on machine during braking, instead of opposing the movement, the InMotion race car. Furthermore, the reduced traction power acts as a motor by supplying loading torque proportional to and the active aerodynamics strategies are utilised for the the energy to be recovered during the regenerative braking as aforementioned reasons, and contribute in the final powertrain mentioned in equation 33. configuration. Throughout these simulations, the selected powertrain components are inserted as system parameters and the F = F + F (33) estimated drive cycle is used. total roadload braking Audi R−18 2014 has recorded a fuel efficiency of Fbraking = Fmech + Fregen (34) 32l/100km with a lap time of 202.6s and 379laps. The 15

250 TABLE XIV: Comparison of theoretical results for the In- Motion Vehicle in Le Mans applying Rule-based strategy and 200 ECMS strategies

150 Soft Braking Hard Braking Rule ECMS Rule ECMS 100 Based Based Number 402 403 426 427 50 of laps

Loading Torque [Nm] Fuel con- 34.6 31.1 41.58 39.3 0 sumption [l/100km] -50 Rate of 75 71.8 103.5 98.3 Loading Torque -100 fuel con- 0 20 40 60 80 100 120 140 160 180 200 sumption Time [s] [l/hr] Fig. 16: Load torque acting on the UQM machine Lap time 209.3 209.3 195.6 195.6 [s] Distance 212.5 220.4 164 174 [km/ results of the simulations, listed in table XIV, prove that the tank] IM01 outperforms Audi R−18 by following the powertrain specifications recommended in this paper. The two control strategies show satisfactory results. However, ECMS achieves better results compared to the rule based strategy with respect VI.CONCLUSION &RECOMMENDATIONS to race requirements in terms of fuel efficiency, lap time and number of laps. This study covers the design and analysis of a complete Figure 17 depicts the operational points of the ICE following powertrain for the IM01 as well as the energy management the soft braking strategy and applying ECMS as an energy strategy that determines the power flow. A methodology to management strategy; the ICE operates at the highest efficiency select the type and size of each of the powertrain components region on the engine efficiency map. This proves that ECMS is developed. This methodology takes into account the drive shows an adaptive behaviour according to the given drive cycle, cycle of the Le Mans 24-hour race and race requirements set by while the rule-based strategy maintains a constant operational the InMotion team. In order to provide a deeper insight into the point throughout the drive cycle. design decisions, the trending energy management strategies of HEVs are investigated. Two strategies, a rule-based controller Points of ICE operation selected by ECMS and an ECMS algorithm, are implemented and compared with

0.22 respect to satisfying the race requirements.

0.24

0.31 0.23

300 0.3 0.33

0.32 0.34 After sizing the powertrain components, a complete model

0.27

0.21 0.22 0.25

0.26 0.32 0.34 of the powertrain is modeled using Simulink using the de- 0.29 0.28 rived drive cycle of the race route. The two control strate- 0.24

250 0.31

0.27 gies are tested separately using this model. The simulations 0.33 0.23 showed success of the proposed powertrain speciﬁcations. 0.26 IM01 achieves 7s reduced lap time and 2.1l/100km less fuel

200 consumption, and completes the race covering 25 laps more. 0.3

0.3 0.3

0.22 0.22

0.25 0.25 0.25

0.29 0.29

0.32

0.34 0.24 The authors of this paper would like to point out to some 0.29 0.28 0.28 0.31 0.23 0.27 0.32 0.33 0.26 150 0.24 recommendations for future work on this tpoic: ICE torque [Nm] 0.28 0.31 0.21

0.23 0.27 • The developed Simulink model has limitations as the 0.3 0.26 0.29 0.2 scope of this study is to give an estimate of the size of 100 0.21 0.22 0.28 0.2 0.25 0.27 0.25 ICE/Generator efficiency0.19 the powertrain components. 0.24 0.18 0.19 0.24 0.26 ICE0.22 operating points 0.23 0.17 0.18 0.23 ICE maximum torque • We recommend an investigation on the transient re- 0.17 0.16 0.16 0.22 0.21E line 0.21 sponse of the engine and the development of a more 700 800 900 1000 1100 1200 1300 1400 1500 accurate model of the inverter and rectiﬁer. ICE speed [rad/sec] • One more point of improvement is that the series topol- Fig. 17: Points of ICE operation selected by ECMS ogy is a requirement from the InMotion team, however other powertrain topologies should be considered in further research. 16

ACKNOWLEDGMENT [22] M. Burwell and J. Goss, “Performance/cost comparison of induction- motor & permanent-magnet-motor in a hybrid electric car,” (Tokyo), The authors would like to thank the InMotion team for their International Copper Association- Copper Alliance, July 2013. continuous collaboration and provision of useful information, [23] YASA Motors, “Yasa 750 axial flux electric motor.” Dr. J.J.H. Paulides for his valuable support and guidance and http://www.yasamotors.com/wp-content/uploads/2014/07/ Ing R.S. van Veen for helping with the lab setup. Datasheet-YASA-750 en-ID-15637.pdf, 2015. [24] P. J. Masson, G. V. Brown, D. S. Soban, and C. A. Luongo, “Hts machines as enabling technology for all-electric airborne vehicles,” REFERENCES Superconductor Science and Technology, vol. 20, no. 8, p. 748, 2007. [1] InMotion Automotive Technology, “Inmotion automotive technology.” [25] B. J. J. van Uden, “Proof of Concept of an Integrated Automotive http://inmotion.tue.nl/, May 2015. 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