1Analysis and Design of the Powertrain andDevelopment of an Energy Management Strategy for

InMotion IM01 Hybrid Race CarI.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.RossPDEng Automotive Systems Design, Stan Ackermans Institute

Eindhoven University of Technology, Eindhoven, The Netherlands.Email: SAI-ASD-2014@tue.nl

and J.J.H.PaulidesElectromechanics and Power Electronics Group, Department of Electrical Engineering,

Eindhoven University of Technology, Eindhoven, The Netherlands.Email: J.J.H.Paulides@tue.nl

AbstractThis paper describes the analysis and design of anenergy efficient powertrain configuration for the InMotion IM01race car. It is a Hybrid Electric Vehicle (HEV) with severalnovel technologies, aiming to participate in the 24-hour Le Mansrace, competing in the Garage 56 category. InMotion, the maindeveloper of the IM01, is a multidisciplinary project-orientedgroup supported by Eindhoven University of Technology (TU/e).The main goal of the InMotion team is to achieve better fuelefficiency and performance than that of their competitors in theLMP1 category of the Le Mans race, namely, the Audi R18 e-tron(2014) on Circuit de la Sarthe. The Automotive Systems Design(ASD) generation 2014-2016 are involved in the developmentof the series powertrain architecture of IM01 super car. Thispaper presents an optimal HEV powertrain analysis, includingthe description of different components, focused on using theseries hybrid powertrain topology and taking into account thedesign restrictions from InMotion and the constraints of theGarage 56 category. We recommend improvements to the InternalCombustion Engine (ICE), the Energy Storage System (ESS) typeselection and introduce a component sizing algorithm. SeveralEnergy Management Strategies (EMS) are investigated within thisstudy; a rule-based controller and an Equivalent ConsumptionMinimization Strategy (ECMS) are implemented and validatedusing a Simulink forward model of the HEV. A scaled modelof the drive cycle is proposed using a test bench to observe thebehavior of the electric motor on Circuit de la Sarthe.

KeywordsHybrid Electric Race Car, InMotion, Garage 56,Energy Management Strategy, Series Powertrain, ECMS, Le Mans

I. INTRODUCTION

HYBRID vehicle technology is continuously evolving byintroducing innovative technologies into the automotiveindustry. The main goals of the ongoing research are to achievethe best combination of performance and efficiency, reducethe emissions and create an environment-friendly means oftransportation. Despite skepticism that hybrid vehicles cannot

achieve high performance, races such as Le Mans and com-panies such as Audi, Porsche and Nissan have silenced thatscepticism. In that context, the InMotion racing team has an-nounced and started the production of a high-performance low-consumption hybrid supercar (IM01) that is set to participatein the Le Mans race in 2017.

The InMotion student group consists of students from TU/eand Fontys University of Applied Sciences, as well as expertsin the field of automotive domain. They have a close collab-oration with the two universities and automotive companies[1]. In this context, trainees of the Automotive System Design(ASD) PDEng program in TU/e were assigned to developa powertrain model and an energy management strategy fortheir race car. The findings of this research are presented inthis paper. There are certain safety regulations imposed on thecontestants and the vehicle must comply with the performanceand reliability criteria, but in general, it is an opportunity totest novel technological achievements.

The work carried out by the ASD group focuses on configur-ing a series hybrid powertrain for IM01, modelling and sizingof the components, and simulating the performance of thevehicle for different drive cycles using two energy managementstrategies. The proposed sized powertrain is simulated ona drive cycle that is based on the La Sarthe circuit trackproperties. The speed and acceleration profiles, and the powerdemand profile have been modified to match the current pow-ertrain configuration, and different driving strategies have beendeveloped to increase the efficiency. Furthermore, two EnergyManagement Strategies (EMS), Equivalent Consumption Min-imization Strategy (ECMS)[2][3]and a rule-based strategy, areimplemented and compared.

During the development of the powertrain of IM01 race car,several restrictions have been taken into consideration. Theseconstraints are mainly Garage 56 safety regulations, as wellas performance requirements that must be met, based on themain opponents performance, i.e. Audi R18. Figure 1 shows

2the preliminary performance results of IM01 compared to AudiR18. There are also restrictions imposed by InMotion, regard-ing the selection of components. The main requirements thatare derived from these restrictions and are initially consideredas follows:

The curb weight of the vehicle, including the fuel, mustnot exceed 900kg.

The topology will be that of a series powertrain to ensureeasier assembly [4].

Some of the components that are being used are thesame as in the IM/e (Fully electric race car (2015) ofInMotion) vehicle [1].

The maximum voltage on the electric circuits of thevehicle must not exceed that of 1kV .

The fastest lap time of Audi R18 is 3min 22.57s.IM01 must achieve a lap time equal or less than theaforementioned.

The number of laps Audi R18 completed in 2014 is379laps. That number is based on the total distancecovered by Audi in 2014 in 24 hours. In order to achieveover and above 379laps the average speed of the IM01should be at-least equal to 215.25km/h.

The average fuel consumption during the 24-hour race ofAudi R18 is 32l/100km. The average fuel consumptionof IM01 must be at least equal or smaller.

Fig. 1: The initial performance results of IM01 Vs. Audi R182014

A brief literature review of the existing powertrain topolo-gies and components used in HEVs is introduced in section II,as well as the decision process followed to select the optimumtype of each of the powertrain components. The mathematicalmodelling of these components is described in section III inorder to reach a complete forward model of the powertrainusing the parameters of the selected components. Two EMSstrategies are implemented, compared and tested using thedeveloped forward model to validate the race requirements interms of fuel efficiency. A lab testing methodology is presentedin section IV that describes a procedure for scaling andtesting the drive cycle using a lower power test bench. Theresults of the model simulation and a comparison of differentcontrol strategies are listed in section V. Finally, we presenta conclusion of what has been achieved and recommendationsfor future work.

II. HEV POWERTRAIN ANALYSISBy the term hybrid electric vehicle, this paper refers to the

vehicle that features two or more different types of energysources [5]. The two main power sources are the prime moverand the energy storage system, which can consist of variouscomponents. In most cases, the prime mover is an InternalCombustion Engine (ICE), or a Fuel Cell (FC) and the energystorage system consists either of batteries, or ultra-capacitors,or a flywheel, or a combination.

A. Topologies and Advantages/DisadvantagesThe powertrain is the sum of the components that are

generating and delivering energy to the road surface for thepropulsion of the vehicle. These components mainly includethe prime mover, the transmission, the wheels, the generator,the electric motors, the power electronics for the power con-version and the energy storage system. There are three maintypes of HEV powertrain topologies: series, parallel and series-parallel.

In the parallel configuration, there is a mechanical couplingbetween the internal combustion engine and the wheels, as wellas between the electric motors and the wheels. This translatesinto the wheels being propelled by these two sources eitherindividually or simultaneously. The benefits of this layout arethe low energy conversion losses, since the power from theprime mover is directly delivered to the driving wheels, thelower fuel consumption and the increased efficiency, and thefact that only two propulsion devices are needed, namely theprime mover and the electric motor [6] [7]. On the other hand,the connection of the prime mover to the wheels requires thepresence of a gear mechanism. This, in combination with thecomplex transmission results in mechanical losses as well as acomplex control strategy [4]. A simplified architecture of theparallel configuration is shown in figure 2.

ENGINE

ESS

WH

EEL

MOTOR / GENERATOR

DC/DC CONVERTER

DC BUS

Me

ch. C

ou

ple

r

FUEL TANK

AUXILLIARIES

I/C UNIT

Fig. 2: Parallel powertrain topology

In the series configuration (figure 3), the mechanical poweroutput of the internal combustion engine is converted toelectrical energy through the generator. This energy eithercharges the Energy Storage System (ESS) or propels thewheels through the electric motors. The motors can alsobe used for regenerative braking. It is a simple powertrainconfiguration, in terms of mechanical connection, control andenergy management, that is based upon the principle of usingthe engine (ICE or FC) as a range extender for the energy

3storage system on board [7]. The independence of the enginespeed from the vehicle load and speed allows the engine to beoperated under its most efficient conditions. Additionally, itenables the use of a lightmass high speed internal combustionengine [6]. When developing a series powertrain, however, wetake into consideration that the energy generated by the enginehas to go through two additional components, the generatorand the motor, thus increasing the losses and decreasing theefficiency of the powertrain [4]. Furthermore, the propulsiondevices need to be sized according to the maximum sustainedpower, resulting in a more expensive and heavy powertrainconfiguration [7].

Apart from these main configurations, a combination ofseriesparallel as well as other complex hybrid powertrain con-figurations have been developed that feature the advantages ofthe basic powertrain architectures, but have shown drawbacksin terms of complexity and cost [7].

In this paper, we use the series hybrid topology to performan analysis and a sizing of the components, as well as todevelop an energy management strategy. This work is basedupon the selection done by InMotion and the ASD generationof 2011 [4], where a preliminary study on a powertrain con-figuration was performed and an energy management strategywas developed. We focus on the efficiency of IM01 hybridrace car, while the previous work from ASD has focused onits performance.

B. Component Description for the Series Topology andPower Flow Analysis

The analysis of the powertrain topology and the racerequirements are used to design the flow of power duringthe various modes of operation between the components. Arepresentative diagram is depicted on figure 3. The differentmodes of operation for IM01 are the following [7]: ESS-only mode: The engine is switched off and the

vehicle is powered by the ESS solely. This mode isapplicable in the case that the configured ESS has highenergy and power density.

Engine-only mode: The vehicle is powered by the inter-nal combustion engine only.

Combined mode: The power demand is met by a combi-nation of both the power sources, as determined by theenergy management strategy.

Power split mode: The engine power is split to propelthe vehicle and charge the ESS at the same time.

Regenerative braking mode: During this mode, the fuelto the engine is cut off. The motors are recovering energythat is stored in the ESS during braking. After the ESSis fully charged or the regenerative currents exceed itsrated value, the energy from regeneration goes to thegenerator (which temporarily acts as motor) attached tothe engine. This converts the engine into an energydump. In our implementation, we switch off the enginein the case that the ESS is fully charged during theregenerative braking mode. This aims to zero the enginefuel consumption. We do not model the generator tooperate as a motor, since the transient behaviour of the

engine and the generator are outside the scope of thispaper.

1) Engine Generator Unit (EGU): In this study, differentoptions for the engine are considered to define the bestselection for the IM01 race car. Simulation tests and a qualitycomparison between the different options provide the idealengine proposal according to the requirements of the project.The criteria and parameters according to which the selectionis done, are: Innovation Specific power Efficiency Safety Mechanical ComplexityThe most common engine types used in race cars that

participate in the Le Mans race are the reciprocating engines.Diesel and petrol engines are the most dominant enginesselected by the contestants. Furthermore, during the previousdecade, cars with rotary engines have also achieved greatresults in the race. In the selection of the engine for IM01,the following types are taken into consideration: Reciprocating engines Rotary engines Micro turbines Fuel cellsThe steps of the selection algorithm for the EGU are

depicted in figure 4, and this procedure consists of fourmain stages. First, based on the drive cycle [4], we calculatethe power demand for one lap, and subtract the possibleregenerative braking power from the overall power need. Theestimated remaining power is to be supplied by the EGU,and in this case the remaining average power is 400kW . Thesecond step includes considering various EGU options, theirefficiencies and their specific power, and calculating from thesedata the total mass of the different EGU options. Variousoptions are depicted in table I.

The mass of the engine is then used as an input parameterfor the drive cycle simulation. The simulation estimates theperformance of the vehicle, in terms of number of laps duringthe 24-hour long race, and the efficiency, based on the fuelconsumption per lap. In this way the various engine optionscan then be compared, in terms of racing performance and fuelefficiency.

The engine mass, however, may influence the power de-mand. In order to see whether this is the case, the power de-mand was recalculated for the heaviest configuration, namelythe diesel reciprocating engine. The difference in requiredpower is 10kW . Resizing the engine to this power demandand running the drive cycle simulation again, we find that forthis new power demand the energy consumption differs by lessthan 0.1%. The number of laps is unaffected. Thus, the massof the EGU does not have a significant impact on the resultsand no iterations need to be made.

In order to choose a type of engine which satisfies theprojects requirements, table II summarizes the advantages anddisadvantages of the different types based on the simulations

4ENGINE WHEELPENG PGEN

INVERTER

DC/DC CONVERTER

DC BUS

PGENDCINVERTER

PwheelPM/GPM/GDC

PAUX

PESS

PESSDC

FUEL TANK

Pfuel

AUXILLIARIES

ESS

GENERATOR / MOTOR

GENERATOR / MOTOR

Fig. 3: Series powertrain topology with power flow

TABLE I: Engine data based on sweet spot performance

Engine Specificpower(W/kg)

Efficiency Generatorincluded

Audi R18 e-tron (2014)Diesel re-ciprocating[8]

1640 0.40 Yes

RebellionR-One(2015)Petrol re-ciprocating[9][10]

3725 0.34 Yes

Mazda787B Petrolrotary [11]

2867 0.25 Yes

JaguarCX-75 Gasturbine[12][13]

2000 0.25 No

Toyota Mi-rai Fuel cell[14][15][16]

1401 0.37 No

as described above and literature research. For a full report,see [17].

Considering InMotions vision of winning the race in termsof energy efficiency, the reciprocating engines seem to be thebest option. Comparing between diesel and petrol, the last one

Fig. 4: ICE type selection algorithm

is lighter and thus more attractive for a race car. Furthermore,the petrol engine outbalances the diesel one in performance.This advantage is in fact an extra degree of freedom that willbe used in the design process to increase the vehicles fuelefficiency. This is performed through investigating differentstrategies that result in a smaller number of laps but also lessfuel being consumed.

From the options mentioned in table II, the reciprocatingpetrol engine features the second lowest total mass with 258kg,

5TABLE II: Advantages and disadvantages of different Enginestypes

Diesel Petrol Rotary Gasturbine

Fuel Cell

AdvantagesLowestenergyconsumption

Goodperformance,LowenergyConsump-tion

InnovativeInnovative,Highperformance

Innovative

Disadvantages

Heaviest,Lowestperformance,Conventional

ConventionalHighestenergyconsumption

HighEnergyconsumption

QuestionableSafety,Lowperformance

achieves the second best number of laps with 463laps in 24hours and has the second lowest energy consumption with62.4kWh/lap, thus making it the optimal choice for theengine.2) Energy Storage System (ESS): Based on the power flow

analysis and the backward modelling of the powertrain, wecalculate the power that can be regenerated and stored in theenergy storage system. The requirements that the ESS shouldmeet are: Retrieving the maximum power during the regenerative

braking and delivering the power in combined mode, sothat the currents are within the ESS specifications. Thisamounts to 344kW in regenerative braking mode and129kW in combined mode.

Storing the maximum energy during regenerative brak-ing (247Wh)

Not exceeding 390kg, considering the combined massof the ESS and EGU

The most commonly used energy storage systems are batter-ies, ultra-capacitors and flywheels. The flywheels have beenrejected as a design constraint by InMotion, so only batteriesand ultra-capacitors are considered for the ESS choice. Theselection is based on the mass to power ration as a mainconstraint. The type of battery chosen for analysis is theXALT Superior lithium ion cell [18]. For the ultra-capacitors,the Maxwell Technologies BCAP 3400 [19] is used. Thespecifications of the selected battery and ultra-capacitor areshown in tables III and IV respectively.

TABLE III: XALT Superior Li Ion cells Specification [18]

Property ValueNominal Voltage 3.7 VSpecific Energy 153 Wh/kgSpecific Power 2.6 kW/kgCapacity 40 AhCharge Rate 12CDischarge Rate 60Cmass 0.97 kgVolumetric Energy Density 350 Wh/L

To limit the size of the ESS within reasonable boundariesso as not to have a negative effect on the performance of the

TABLE IV: BCAP 3400 Ultra-Capacitor Specification [19]

Property ValueCapacitance 3400 FNominal Voltage 2.85 VAbsolute Maximum Current 2000 ASpecific Power 6.7 kW/kgStored Energy 3.84 Whmass 0.52 kgVolume 0.3991 LESR 350 Ohm

vehicle, we assume that the stored energy is dissipated withintwo braking instances. The aforementioned requirements canbe fulfilled by the use of batteries, ultra-capacitors or a com-bination of both. Therefore, we perform an ESS hybridizationanalysis [20] focusing on the mass of the ESS to choosethe optimal combination of battery and ultra-capacitors. TheHybridization Level (HL) is on a scale of 0 to 1.Where

0: The total power is provided only by battery pack1: The total power is provided only by ultra-capacitors

The mass of the energy storage system is the primary factorto determine that only ultra-capacitors will be used in IM01.In figure 5, the mass and volume of the ESS according tothe level of hybridization are depicted. The case of HL = 1,i.e. using only ultra-capacitors, results in the lowest mass andvolume values for the energy storage system. This is mainlydue to the high specific power of the ultra-capacitors, whencompared to batteries. In this race, the high power demand canbe provided solely by a combination of ultra-capacitors, andthe energy levels that need to be stored during the regenerativebraking mode match the ESS specification of specific energy.

0 0.2 0.4 0.6 0.8 10

50

100

150

200

ESS Hybridization Level

Mas

s [kg

]

0 0.2 0.4 0.6 0.8 120

40

60

80

100

Vol

ume

[L]

VolumeMass

Fig. 5: mass and volume of ESS according to HL

63) Power conversion systems: The power electronics used ina series hybrid topology typically include rectifiers, invertersand DC/DC converters. The inverter is used to convert theDC voltage on the side of the DC-link to AC voltage fed tothe electric motors during motoring mode. During regenerativebraking the motor acts as a generator, switching the inverterblock to operate as a rectifier so that the regenerated energycan be stored into the energy storage system, that operates onDC voltage.

In HEV powertrain, there are numerous different DC voltagelevels: the DC-bus voltage level, the high voltage on the ESSside and the low voltage for the auxiliary systems. The DC/DCconverters are used to convert the input voltage to a desiredoutput voltage level and are usually efficient.

In this study, the different converters are not modelled indetail. In order to simulate the power flow of the IM01 pow-ertrain, we assume that the power converters have a constantefficiency of 95%.4) Electric motor: The electric motor is the heart of the

powertrain system of a hybrid vehicle, responsible for boththe propulsion and the regeneration of energy from braking.The induction motors are the most commonly used in au-tomotive applications, due to their low cost, robustness andreliability [21]. In recent years though, the permanent magnetsynchronous motor has slowly taken over the induction motorsdue 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 PMSMYASA 750, designed and built by YASA Motors, sponsor ofInMotion. The specifications of the motor are presented intable V :

TABLE V: YASA 750 Specification [23]

Parameters ValuePeak Power @ 700V @65C 200 kWContinuous Power >75 kWContinuous Torque 400 NmMaximum Torque @ 450V @65C 790 NmPeak Efficiency >95%Maximum Speed 3250 rpmmass 33 kg

C. Component sizingThe power train components are sized based on the drive

cycle energy demands. The EGU is sized exactly such that itprovides, during a lap, the energy the drive cycle demands.The ESS is sized such that it meets the power and energydemands, and the DC/DC converter is sized according to thepower it needs to handle. The drive cycle is based on theavailable model [4], however both performance (lap time) andfuel efficiency calculations were added for the purpose of thispaper. In this section first the sizing procedure is explained;

afterwards the energy saving strategies are explained andproposed combination is presented.

The process of sizing the components is iterative, as theresulting sizes influence the drive cycles itself. We initially setarbitrary values for the size of the components, based on ed-ucated assumptions, and reiterate through the same procedureto achieve an output in the algorithm that will have minimaldeviation from initial inputs. The algorithm for determining thespecifications of the components, depicted in figure 6, involvesthe following steps:

Fig. 6: Component sizing algorithm

1) The first step is to choose which design and driving strate-gies to apply. Six such strategies have been determined,each meant to increase energy efficiency, and are detailedbelow.

2) The second step is to make an estimation of the initial com-ponent size, namely the EGU, ESS and DC/DC converter.

3) The third step is to simulate the drive cycle. The result is theenergy balance and the power of the various components.

4) The fourth step is to find the required EGU power. Weconsider that the energy stored in the ESS should be equalto the energy drawn from it. Based on this restriction, the

7algorithm calculates the EGU power, through an iterativeprocess.

5) The fifth step is to find the required ESS mass. The ESSconfiguration is determined based on the energy balanceover time; both power and energy demands are taken intoconsideration.

6) The final step of the algorithm is to determine whether toreiterate, starting from the third step, or not. It mainly de-pends on whether the mass of the newly found componentsizes differs significantly from the initial estimated massor not. If the iteration is not deemed necessary, then theconfiguration of the components is final for the applied setof strategies as determined in the first step.

The first step of the sizing algorithm, as mentioned before,is choosing the set of strategies that we will follow. Thesestrategies each aim in enhancing the efficiency of the vehicle;in some of the cases there is a small racing performancepenalty. They are the following:

i) Reduce mechanical braking forceThe applied braking force directly affects the energy thatcan be recovered during the regenerative braking mode.This is based on the fact that only a portion of thebraking force is applied by the motors, and the rest ofthe energy that is not being recovered is dissipated in theservice brakes. If we limit the braking force applied bythe driver and we increase the duration of the braking,the motor can provide a larger part of the braking powerand hence, recover more energy. Instead of hard braking,we suggest a strategy of soft braking, with a combinedapplied brake force of 3800N . This value does not includethe air drag and other resistive forces. This strategy canbe implemented with a brake controller that will allocatebrake force between the motors and service brakes, anda feedback mechanism in the brake pedal or an indicatoron the steering wheel that will guide the driver in everybraking occasion.

ii) Increase the regenerative motor powerOversizing the motor will increase the recuperated energyand will also result in larger, heavier motors, heavier ESSand DC/DC converter(s). This will lead to a heavier vehi-cle and the trade-off between the mass and performanceneeds to be re-investigated.

iii) Limit in the top speedLimiting the top speed will reduce the air drag force andconsequently, the power consumption from the motors.However, this strategy will result in a smaller number oflaps and a reduced efficiency of the YASA750 motors(since they are more efficient when operating at higherspeeds).

iv) Reduce the traction motor powerA simple way to reduce the energy consumed is to supplyless power to the motors. In this way, a limit is imposedon how much energy is used over time. This leads toreduced acceleration and consequently, high speeds aremore difficult to reach. The same reservations regardingmotor efficiency as mentioned in the previous strategy,will also apply to this solution.

v) High temperature superconducting machines (HTS)The main advantage is that these motors have roughlythree times higher specific power and twice the lowervolumetric power, including cooling systems [24]. Thismeans that the mass and volume of the motors andgenerator can be reduced tremendously. Furthermore, theirefficiency is higher than that of normal electric motors.Using HTS machines is certainly not an easy decision.The coils need to be cooled down to approximately 77K.However, once cooled, the motors will barely generate anyheat at all. If the choice is made to apply HTS technology,it is recommended to cool the motors before the race,insulate them very well, and have a small on-board liquidnitrogen supply to overcome the remaining warming upof the motors.When applied to IM01 for the four motors as well as thegenerator, mass savings of 60 to 120kg can be expected,depending on the vehicles configuration. If, for instance,the choice is made to use only two motors instead of four,there is less mass that can be saved.Using HTS machines mostly benefits the performance,while maintaining the same fuel consumption. Therefore,to again increase efficiency, the above methods will haveto be applied to a greater extent in conjunction with theHTS motors. In the simulations, HTS is implemented asa mass reduction and increase in electric motor efficiencyonly.

vi) Use an active aerodynamics systemThe purpose of using an active aerodynamics strategy isto adapt the aerodynamics properties of the vehicle to itspower need. We assume that the IM01 incorporates anactuator that regulates the angle of the rear wing. Boththe down-force and drag of the vehicle are proportionalto the angle of the wing. The actuator operates under thefollowing principles [25]:

- V ehicle speed: The angle of the rear wingis inversely proportional to the speed of thevehicle, in order to reduce the drag force at highspeeds.

- Steering angle: During cornering, the angleof the rear wing is increased, regardless of thespeed, to produce more down-force thereby in-creasing the traction and stability of the vehicle.

- Braking: During braking, the angle is set to itsmaximum in order to increase the drag force andthe traction, as a result of increased downforce.

To simulate this technique in our model, we assume thatthe frontal area of IM01 varies linearly with the speedin a range of 1.41m2 to 1.54m2. By altering the frontalarea, we simulate the change in the rear wing angle. Wealso make an assumption that the drag and lift coefficientchange linearly with the frontal area. When braking, weassume the maximum value for the drag coefficient. It isthen used to calculate the forces acting on the vehicle,and in the specific drive cycle this instance occurs whilecornering. Table VI indicates the advantage of using anactive aerodynamics system, instead of having constant

8aerodynamic properties. The aerodynamic properties (dragcoefficient, aerodynamic ratio, frontal area) of Mazda RX-792P GTP are taken as a reference in order to comparethe active aerodynamic system we simulated with a noactive aerodynamic chassis [26].

TABLE VI: Effect of applying active aerodynamics

Lap time 5%Number of Laps 6%Average Power at Wheelsfrom the Powertrain

7%

Total Energy consumptionat Wheels

12%

Component selection & powertrain configurationAfter simulating various combinations of the aforemen-

tioned strategies and applying the component sizing algorithm,the optimal configuration for the IM01 powertrain is concludedin table VII based on the final derived drive cycle. Thefinal power demand is depicted on figure 7. The realizedESS configuration according to the vehicle specifications isincluded in table VIII. It should be noted that while researchingthe optimal configuration, only rule-based control was applied.

0 50 100 150 200

150

200

250

300

Time [s]

Vel

ocity

[km/

hr]

0 50 100 150 200400

200

0

200

400

Time [s]

Pow

er [k

W]

Hard Braking Soft Braking

Fig. 7: Power Demand

For the configuration presented in table VII, the soft brakingstrategy, with 3826N applied brake force, a 43% reduction ofmaximum traction motor power and an active aerodynamicsmechanism are used. Though the motors are not used to theirfull capabilities when accelerating, their size is not reducedso as to retain regenerative braking capability. In effect, thismeans that the motors are sized for regenerative braking, ratherthan for acceleration. Although beneficial, HTS is not used, asthe upsides were too little to justify the risks.

TABLE VII: Estimated mass of IM01 powertrain

Components Specification mass [Kg]ICE 304 kW 123.7Generator 292 kW 67.9ESS 66

Ultra-capacitors44.59 (inc.Cooling &Packaging)

Motors YASA Motors 132 (inc.Inverters)

Drivetrain Misc. Cables, fluidsetc.

10

DC/DC converter 350 kW 23.5Chassis Monocoque

Body Unsprungcomponents

315

Driver - 75 vehicle mass 791.7

If the above sizing procedure is applied to a design withonly active aerodynamics, the resulting required EGU power isdetermined to be 535kW . The simulations for this case yielda lap time of 185s, with a fuel consumption of 148l/hr. Byapplying the strategies as mentioned, the EGU needs to be292kW . The lap time is increased to 209s, however, the fuelconsumption was reduced to to 81l/hr. This clearly indicatesthe influence of increasing brake time and reducing tractionpower on achieving these results.

TABLE VIII: ESS configuration [soft braking case]

Property ValueTotal number of Capacitors 66Capacitors in series 66Capacitors in parallel 1Maximum voltage of capacitor bank 188 VMaximum charging current 2000 AMaximum discharging current 2000 AMaximum power 376 kWEnergy capacity 253.2 Whmass (excluding cooling and packaging) 34.32 kgVolume 26.34 LTime to fully charge / discharge 4.84 s

III. MODELLINGA complete model of the powertrain components (figure 3)

is described in this section. The mathematical model of eachof the components is designed using Matlab/Simulink basedon the preliminary calculations from the components sizingalgorithm as system parameters. The integrated model of thepowertrain, including a model for the road-tyre interaction, isused to simulate the power demands based on the designeddrive cycle as an input to the model. Thus this modellingprocess aims to validate the performance of the vehicle. In

9this section, the models of the EGU, electric motor and theultra-capacitors are described briefly and an overview of theintegrated model is presented.

A. EGU model1) Turbocharger: The turbocharger mechanically couples a

compressor and a turbine. The turbine recovers some of theenergy lost in the engine thermodynamic cycle to drive thecompressor and increase the intake gas density to providefuel economy and high power output. The turbocharger modelconsists of two sub-models: a compressor massflow-efficiencymodel and a turbine massflow-efficiency model.

The mechanical connection between the compressor andturbine results in mechanical losses due to the shaft friction.The imbalance of the produced and consumed power gives anacceleration to the shaft. The dynamic response is modeledusing Newtons second law of motion:

tc =1

Jtc(Wttc WctcMfric(tc)

)(1)

Wheretc: angular velocity of turbochargerJtc : inertia of the turbochargers shaftMfric: friction coefficient of the shaftWt: turbine energyWc: compressor energy

When tc = 0, singularity is circumvented by specifying ashaft friction term Mfric(tc) in the dynamic equation to avoiduncertainties in the turbocharger performance.Compressor: The compressor, which is powered by the

turbocharger shaft, compresses the gas from a lower to a higherpressure and temperature, thereby increasing its temperature. Amathematical model for the compressor flow [27] is describedin equation 2.

mc = mc,corr p01/prefT01/Tref

(2)

Where:mc: mass of air through the compressormc,corr: corrected mass of airp01: inlet pressurepref : reference pressureT01: inlet temperatureTref : reference temperature

The compressor efficiency is given in equation 3

c = c,max [d

d

]T[Q1 Q3Q3 Q2

][d

d

](3)

WhereQ13: matrix parameters: optimal flow: optimal speed

The consumed power is simply the input power divided bythe compressor efficiency [28].

Turbine: The turbine acquires energy by expanding thegases from a higher pressure and temperature to a lowerpressure and temperature. The turbine then delivers the energyto the turbocharger shaft. The mathematical model [27] isderived for the turbine flow in equation 4.

mt =pemRe Tem

(4)

Wheremt: mass of air through the turbinepem: pressure in the exhaust manifoldTem: temperature in the exhaust manifoldRe: exhaust gas constant

The turbine efficiency is given in equation 5.

(BSR) = t,max (

1(BSRBSRmax

BSRmax

)2)(5)

WhereBSR: Blade Speed Ratio(BSR): turbine efficiencyt,max: maximum turbine efficiency

Then, the turbine efficiency is used to determine the turbinepower [28].2) Internal Combustion Engine: The ICE should respect

the dimensional limitations that have been mentioned inTab. VII,i.e. maximum mass < 123.7Kg (peak power/massof 3). This ICE should handle the mass of air provided by theturbocharger, as it is defined by the throttle valve position. Inorder to fulfill the aforementioned component requirements, a3.0 L, V6 petrol engine has been selected with its geometryand thermodynamics properties summarized in Tab. IX, X

TABLE IX: ICE geometry properties

Parameter ValueNumber of cylinders 6Bore 0.086 mStroke 0.085 mRod length to crank radiusratio

3.5

Compression ratio 9Clearance volume 0.0617 LDisplacement volume 0.4937 L

During the modelling process, two main assumptions aremade. The first one is that the air to fuel mass ratio isconsidered to be 14.5 during the whole process, by maintainingthe air to fuel equivalence ratio () equal to 1.

AF =mamf

= Ls (6)

whereAF : air to fuel mass ratioma: mass of airmf : mass of fuel: air to fuel equivalence ratio

10

TABLE X: ICE thermodynamics properties

Parameter ValueMechanical efficiency 98% 1.4Specific heat constant vol-ume (cv)

7.1750 104 kJ/gK

Specific heat constant pres-sure (cp)

5.1250 104 kJ/gK

Heating value of fuel (Qlhv) 43 kJ/gHeat efficiency of fuel 90 %Stoichiometric air to fuelmass ratio

14.5

Ls: stoichiometric air to fuel mass ratioThe second assumption is regarding the modelling of the

thermodynamics of the ICE. Once a petrol engine is used, anideal Otto cycle is modelled in order to further examine thethermodynamic behaviour of the ICE. However, in reality theOtto cycle will not be ideal due to heat and mechanical losses,and leakage between cylinder and piston areas. For this reason,the engine work (Weng) produced per cycle is decreased by afactor of 2/3.

Once the geometry and the thermodynamics properties ofthe ICE have been specified, the next step is to determine theengine torque (Teng) and power (Peng), the Brake Specific FuelConsumption (BSFC) and the Brake Mean Effective Pressure(BMEP) per engine cycle. Taking into consideration that theoperational speed range of the ICE varies from 0 to 15000rpmand that the sweet spot (i.e. nominal speed) is at 9700rpm,the following equations are used to calculate the respectivequantities.

Teng =Weng

4pi(7)

Peng = Weng Ncyl n60 1

2(8)

BSFC =mfPeng

(9)

BMEP =4pi TengNcyl Vd (10)

nfuel =1

QlhvBSFC(11)

WhereNcyl: number of cylindern: engine speed (rpm)mf : fuel mass flow rateVd: engine volume displacementnfuel: engine fuel efficiency

Table XI summarizes the respective results of the ICE oper-ation, while also in figures 8, 9 the torque-speed characteristiccurves and engine efficiency map are provided.

TABLE XI: Rated and maximum values of the ICE

Parameter ValueSpeed range 0-15000 rpmNominal speed 9700 rpmNominal nfuel 38%Nominal BSFC 223 g/kWhNominal BMEP 1300 kPaNominal Teng 300 NmNominal Peng 304 kWMaximum Peng 371.1 kW

5000 6000 7000 8000 9000 10000 11000 12000 13000100

150

200

250

300

350

400

Engine Speed [rpm]

Torq

ue [N

m]

Powe

r [kW

]

Engine TorqueEngine Power

Fig. 8: Engine torque, power and speed characteristic curves

5000 6000 7000 8000 9000 10000 11000 12000 1300050

100

150

200

250

300 217

223

230

250 300

350

350350

Engine Speed [rpm]

Torq

ue [N

m]

Fig. 9: BSFC (in g/kWh) map of the 3.0 L, V6 petrol engine

3) Generator model: The generator is coupled with the ICEin the powertrain described in figure 3. Permanent magnet

11

synchronous machines have the highest power density andefficiency in mid-range speeds [29] , making them ideal forpower generation in our powertrain. Due to the series topologyof the powertrain, the ICE is only used to drive the generatorand therefore the operating point of the two components isindependent of the speed range of the IM01 race car. Thegenerator modeled with the same nominal operating point ofthe ICE in order to eliminate the need for a gear raito.

The Permanent Magnet Synchronous Generator (PMSG) ismodelled by transforming the 3-phase machine into 2-phasemachine in the d-q system using Clarks and Parks transfor-mation [30]. The differential equations used to describe PMSGare linearized for stability and controller design purposes. Thedynamic equations of the PMSG are shown in equations 12 to15.

diqdt

=Vq Rs iq e (Ld id + m)

Lq(12)

diddt

=Vd Rs id + e Lq iq

Lq(13)

Te =3

2 P m iq + (Ld Lq) id iq (14)

e = P r (15)Lq and Ld are assumed to be equal.

Wheree: electrical angular velocityr: mechanical angular velocityVq ,Vd: d-q axis voltagesiq ,id: d-q axis currentsLq ,Ld: d-q axis inductanceRs: stator resistancem: permanent magnet flux of the rotorP : number of pole pairsTe: electromechanical torque

The control of the PMSG is done by using Field OrientedControl (FOC) (subsection III-B). The output of the rec-tifier gives a DC voltage with floating, causing losses anddisturbances to the powertrain system. This can be avoidedby either controlling the DC-link voltage or by using DC-DC converter. In this paper, DC-link voltage is assumed to beconstant without investigating its dynamic behaviour and waysof resolving the problems that might occur.

B. Motor modelThe modelling of the Permanent Magnet Synchronous Mo-

tor (PMSM) are same as those of the PMSG (section III-A3).Inaddition, the output mechanical torque of the motor is de-scribed by the equation 16

J m = Te B m Tfr Tload (16)Where

m: mechanical angular velocity

B: rotor damping coefficientTfr: frictional torqueJ : rotor inertia

The derived PMSM model is used to design the FOCstrategy. The aim of the FOC is to control the magnetic fieldand torque by controlling the d and q components of thestator currents. The implementation of the technique is carriedout using two current controllers and one speed controller incascade configuration as shown in figure 10.

Fig. 10: Field Oriented Control (FOC) for PMSM machine

Tuning the two controllers is carried out using the methodproposed in [31] in table XII. The currents and voltages aresaturated to limit the motor to the maximum speed and torque.Table XII indicates the equations used to obtain the constantparameters for the three PI controllers.

current = 2pi fs/10 (17)speed = current/10 (18)

Where current & speed are the controllers bandwidth andfs is the switching frequency of the inverter (equal to thesampling frequency of the system).

TABLE XII: Parameters of the tuned PI controllers in Cascadeconfiguration

Controllers ProportionalConstant

IntegralConstant

Speed speed J current Bq-axis Current current Lq current Rsd-axis Current current Ld current Rs

C. Ultra - capacitor modelThe ultra capacitor pack is modelled as an equivalent ideal

capacitor with a resistor in series.

E(t) =Q(t)

C(19)

C =NparallelNseries

Ccell (20)

12

RESR =NseriesNparallel

Rcell (21)

E(t) = i(t) RESR + v(t) (22)

SoC =Q(t)

Qmax(23)

Qmax = C Nseries Ecell (24)

WhereC: pack capacitanceRESR: equivalent pack series resistanceCcell:cell capacitanceRcell: cell series resistanceEcell: nominal cell voltageNseries: number of cells in seriesNparallel: number of cells in parallelv(t): terminal voltageQ(t): capacitor chargei(t): currentE(t): internal capacitor voltageSoC: state of charge - capacitor pack

D. Forward model of powertrain

The mathematical model of the powertrain componentsis derived, described previously, to investigate the systemresponse and to validate the component sizing process. Modelsof different components are either created, modified or reusedfrom the existing EV toolbox [32]. The basic layout of theforward model, which includes all the software and hardwarecomponents as well as the environment interactions and thedriver commands, is depicted on figure 11.

Fig. 11: Schematic of the forward model

IV. EXPERIMENTAL SETUP

Hardware testing is carried out at the laboratory usingthe available UQM75 [33] PMSM machine (testing machine)as a prototype of the selected YASA 750 machine and aninduction machine (loading machine) that replicates the roadload forces. Using Model-in-the-Loop (MiL) simulations, theselected YASA 750 machine is modelled and simulated withthe control algorithm. The developed model for the YASA750 motor is adapted to the UQM75 motor in the lab setup byusing Triphase toolbox for real-time interaction between thephysical hardware setup and the software controller model. Inorder to test the derived cycle of the Le Mans circuit usingthis setup, scaling the desired speeds of the YASA750 to theUQM machine is necessary; scaling the road load from theenvironment is also required.

TABLE XIII: UQM 75 Specifications [33]

Specification ValueContinuous shaft powerPcont

45 kW

Peak shaft power Ps,max 75 kWMaximum speed ns,max 8000 rpmShaft nominal speed at peaktorque and power ns,corner

3000 rpm

Number of poles P 18Continuous (average) shafttorque s,cont

150 Nm

Peak shaft torque s,max 240 NmEfficiency at maximumshaft power and continuoustorque EM,a

94 %

Efficiency at maximumshaft power and maximumspeed EM,b

90 %

Scaling is performed using the Pi-theorem [34]. The pro-posed method for obtaining scaling factors and determiningdynamic similarity of systems involves the formation of anequivalent system representation using dimensionless vari-ables. To study this dynamic similarity of the motors, thesteady-state torque-speed curves of each motor are plotted asshown in figure 12. Steady-state is chosen because transienteffects of each motor are minor compared to their steady-stateperformance during typical drive cycles. The scaling is carriedout using equations 25 & 26. The results of the scalingtechnique are validated by plotting both the YASA750 and thescaled UQM torque speed curves in the dimensionless domainas depicted in figure 13.

UQM,scaled = Y ASA Pmax,Y ASA Tmax,UQMTmax,Y ASA Pmax,UQM (25)

TUQM,scaled = TY ASA Tmax,UQMTmax,UQM

(26)

13

0 1000 2000 3000 4000 5000 6000 7000 80000

100

200

300

400

500

600

700

800

Speed [rpm]

Torq

ue [N

m]

UQM75 Continuous Output @300 VDC inputYASA750 Continuous Output @800 VDC input

Fig. 12: Speed Torque curves for UQM75 & YASA750

0 0.5 1 1.5 20

0.2

0.4

0.6

0.8

1

Speedmax

*Torque/Powermax

Torq

ue/T

orqu

e max

YASA 750Scaled UQM 75

Fig. 13: Dimensionless Speed Torque curves for scaledUQM75 & YASA750

Both the UQM machine and loading machine are mountedon the Kistler setup at the laboratory. Figure 14 shows adescription of the setup and its hardware components. TheKistler setup is a test bench consisting of the loading ma-chine, torque sensor, cooling circuit, power analyzer, two hostcomputers to control the test motor (UQM75) and the loadingsides separately with separate controllers. The loading machineis controlled via a PC connected to the setup in a torquecontrol mode. The setup is also connected to a real timetarget inverter (Triphase) which drives the UQM75 machinethrough the Simulink model implemented on the host-PC.The controller for the UQM75 machine is built by using

Fig. 14: Kistler Setup

the mathematical model of the machine, in order to tune thecontroller in Simulink. To make the modeling process moreaccurate, several measurements are carried out to determinethe machine parameters such as flux linkage constant, torqueconstant, number of poles, phase resistance and inductanceusing no-load test, back EMF and passive load test. Someparameters such as the inertia, and damping coefficient arealso determined using the roll down test. These parametersare used in the modelling process.

A. Deriving load machine reference points from the drive cycleThe scaled hard braking drive cycle velocity for the UQM

machine using the Pi theorem is shown in figure 15. Thisvelocity profile is used as the input for the UQM machine.In the real world scenario, the electric motor overcomes theresistive forces and reaches a desired speed requested by thedriver. So, the driver acts as the speed controller, while theoutput of the speed controller is the desired torque controlledusing the throttle pedal. The developed cascade controllermodel of the PMSM motor can be used to control the motoron the test bench with reference input as the Vscaled[km/hr]from the drive cycle in [rad/s]. To simulate the drive cycle,the load acting on the motor has to be calculated duringacceleration and braking. The resistive forces acting on thevehicle are given by equations 27 to 29. Since the IM01 usesactive aerodynamics, it results in a variable down force thataffects the rolling resistance Froll.

FAirdrag =1

2 Cd Af V 2 (27)

Froll = fr (m g cos +Rdf FAirdrag) (28)

Fslope = m g sin (29)Where

FAirdrag: air drag resistance forceFslope: force due to the gradient of the roadCd: co-efficient of air drag

14

Af : frontal AreaV : velocityRdf : ratio of air drag force used to increase therolling resistance: ambient air densityfr: coefficient of rolling resistance: gradient of the road surfacem: mass of the IM01 carg: acceleration due to gravity

The total resistive forces acting on the vehicle are given bythe equation 30.

Froadload = Froll + FAirdrag + Fslope (30)

Fmotor = Ftotal + Froadload (31)

Ftotal = m a (32)Where

: inertial coefficient of the vehiclea: acceleration of the vehicle

The mass of the vehicle (m) and the frontal area (Af ) arescaled with respect to torque to mass ratio of a single YASAmotor, resulting in a scaling factor of 1/13.33 for the UQMmachine.1) Acceleration: To reach the desired velocity, the motors

in the car produce forces which overcome the road loadforces and result in desired acceleration of the car. The totalforce acting on the vehicle during acceleration is then givenby the equation 31, where Fmotor is the force produced bythe motor. So the load torque to be applied on the loadingmachine is computed by calculating the road load forces andthe required forces to reach the desired acceleration under theassumption that the motor in the race car delivers the maximumtorque throughout the race. The load torque that has to besupplied by the load machine during acceleration is calculatedby Fmotorload Rwheel, where Froadload is computed for thescaled velocity profile of the drive cycle for the UQM motor,as shown in figure 15. We observe that the scaled velocities arehigher than the original drive cycle velocities, since the UQMmotor has a higher speed capability than the YASA motor.2) Deceleration: During deceleration in the real world sce-

nario, the vehicle has a momentum which is reduced byapplying braking forces in addition to the resistive forces.The braking forces are applied through the mechanical and theregenerative braking mechanisms for the IM01. The balancebetween the amount of the mechanical and the regenerativebraking is calculated based on the limits of regeneration,calculated for a specific energy storage system. The loadmachine during braking, instead of opposing the movement,acts as a motor by supplying loading torque proportional tothe energy to be recovered during the regenerative braking asmentioned in equation 33.

Ftotal = Froadload + Fbraking (33)

Fbraking = Fmech + Fregen (34)

Time [s]0 50 100 150 200

Vel

ocity

[km/

hr]

100

150

200

250

300

350

400

450V

uqmVyasa

Fig. 15: Velocity profile for different motors

WhereFmech: mechanical braking forceFbraking: regenerative braking force

The maximum braking force that can be applied on the tyreis calculated, then amount of energy to be recovered duringbraking from the electric motor is fixed. The remaining amountof energy is dissipated by the mechanical brakes. For thetest case, the amount of power to be recovered is given byequations 35 and 36.

Fregen = m a Fmech Froadload (35)Pregen = Fregen v (36)

The torque applied on the load machine during braking isFregen Rwheel. The loading machine acts on the motoringmode in the direction of rotation of the UQM motor to simulatethe momentum of the vehicle while braking. The loadingmachine is set on the torque control mode to follow the scaledreference trajectory as shown in figure 16. The loading torqueduring braking is limited to - 80 Nm due to the limitations ofthe setup.

V. RESULTSWe have performed several simulations to compare both

the rule based and ECMS control strategies in combinationwith the soft and hard braking techniques applied to eachcontrol strategy, since the aim of this analysis is to give arecommendation for the final powetrain to be assebmled onthe InMotion race car. Furthermore, the reduced traction powerand the active aerodynamics strategies are utilised for theaforementioned reasons, and contribute in the final powertrainconfiguration. Throughout these simulations, the selected pow-ertrain components are inserted as system parameters and theestimated drive cycle is used.

Audi R18 2014 has recorded a fuel efficiency of32l/100km with a lap time of 202.6s and 379laps. The

15

Time [s]0 20 40 60 80 100 120 140 160 180 200

Load

ing

Torq

ue [N

m]

-100

-50

0

50

100

150

200

250

Loading Torque

Fig. 16: Load torque acting on the UQM machine

results of the simulations, listed in table XIV, prove that theIM01 outperforms Audi R18 by following the powertrainspecifications recommended in this paper. The two controlstrategies show satisfactory results. However, ECMS achievesbetter results compared to the rule based strategy with respectto race requirements in terms of fuel efficiency, lap time andnumber of laps.

Figure 17 depicts the operational points of the ICE followingthe soft braking strategy and applying ECMS as an energymanagement strategy; the ICE operates at the highest efficiencyregion on the engine efficiency map. This proves that ECMSshows an adaptive behaviour according to the given drive cycle,while the rule-based strategy maintains a constant operationalpoint throughout the drive cycle.

0.160.160.170.170.180.180.190.190.2

0.2

0.21

0.21

0.21

0.21

0.21

0.22

0.22

0.22

0.22

0.22

0.22

0.22

0.23

0.23

0.230.23

0.23

0.23

0.24

0.24

0.240.24

0.24

0.24

0.25

0.25

0.25 0.25

0.25

0.25

0.26

0.26

0.26

0.26

0.26

0.27

0.27

0.27

0.27

0.27

0.28

0.28

0.28

0.28

0.28

0.29

0.29

0.29

0.29

0.29

0.3

0.3

0.3

0.3

0.3

0.31

0.31

0.31

0.31

0.32

0.32

0.32

0.32

0.33

0.33

0.33

0.34

0.34

0.34

Points of ICE operation selected by ECMS

ICE speed [rad/sec]

ICE

torq

ue [N

m]

700 800 900 1000 1100 1200 1300 1400 1500

100

150

200

250

300

ICE/Generator efficiencyICE operating pointsICE maximum torqueE line

Fig. 17: Points of ICE operation selected by ECMS

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

Soft Braking Hard BrakingRule

BasedECMS Rule

BasedECMS

Numberof laps

402 403 426 427

Fuel con-sumption[l/100km]

34.6 31.1 41.58 39.3

Rate offuel con-sumption[l/hr]

75 71.8 103.5 98.3

Lap time[s]

209.3 209.3 195.6 195.6

Distance[km/tank]

212.5 220.4 164 174

VI. CONCLUSION & RECOMMENDATIONS

This study covers the design and analysis of a completepowertrain for the IM01 as well as the energy managementstrategy that determines the power flow. A methodology toselect the type and size of each of the powertrain componentsis developed. This methodology takes into account the drivecycle of the Le Mans 24-hour race and race requirements set bythe InMotion team. In order to provide a deeper insight into thedesign decisions, the trending energy management strategies ofHEVs are investigated. Two strategies, a rule-based controllerand an ECMS algorithm, are implemented and compared withrespect to satisfying the race requirements.

After sizing the powertrain components, a complete modelof the powertrain is modeled using Simulink using the de-rived drive cycle of the race route. The two control strate-gies are tested separately using this model. The simulationsshowed success of the proposed powertrain specifications.IM01 achieves 7s reduced lap time and 2.1l/100km less fuelconsumption, and completes the race covering 25 laps more.

The authors of this paper would like to point out to somerecommendations for future work on this tpoic:

The developed Simulink model has limitations as thescope of this study is to give an estimate of the size ofthe powertrain components.

We recommend an investigation on the transient re-sponse of the engine and the development of a moreaccurate model of the inverter and rectifier.

One more point of improvement is that the series topol-ogy is a requirement from the InMotion team, howeverother powertrain topologies should be considered infurther research.

16

ACKNOWLEDGMENTThe authors would like to thank the InMotion team for their

continuous collaboration and provision of useful information,Dr. J.J.H. Paulides for his valuable support and guidance andIng R.S. van Veen for helping with the lab setup.

REFERENCES

[1] InMotion Automotive Technology, Inmotion automotive technology.http://inmotion.tue.nl/, May 2015.

[2] B. de Jager, T. van Keulen, and J. Kessels in Optimal Control of HybridVehicles, pp. 5167, Springer-Verlag London, 2013.

[3] L. Guzzella and A. Sciarretta, Vehicle Propulsion Systems: Introductionto Modeling and Optimization. Springer-Verlag Berlin Heidelberg,2013.

[4] J. Jacob, J. Colin, H. Montemayor, D. Sepac, H. Trinh, S. Voorderhake,P. Zidkova, J. Paulides, A. Borisaljevic, and E. Lomonova, Inmotionhybrid racecar: F1 performance with lemans endurance, COMPEL -The international journal for computation and mathematics in electricaland electronic engineering, vol. 34, no. 1, pp. 210233, 2015.

[5] E. Lo, Review on the configurations of hybrid electric vehicles, inPower Electronics Systems and Applications, 2009. PESA 2009. 3rdInternational Conference on, pp. 14, May 2009.

[6] B. M. David Crolla, Vehicle Powertrain Systems: Integration andOptimization. Wiley, 2012.

[7] C. Chan, The state of the art of electric and hybrid vehicles,Proceedings of the IEEE, vol. 90, pp. 247275, Feb 2002.

[8] Audi, Audi r18 e-tron quattro. https://www.audi.co.uk/audi-innovation/audi-motorsport/audi-r18-etron-quattro.html, May2015.

[9] Advanced Engine Research Ltd., Clean sheet families. http://www.aerltd.com/clean-sheet-families/, May 2015.

[10] Advanced Engine Research Ltd., P60: The nextgeneration lmp1 powerplant. http://www.aerltd.com/p60-the-next-generation-lmp1-powerplant/, May 2015.

[11] R. Shimizu, T. Tadokoro, T. Nakanishi, and J. Funamoto, Mazda 4-rotor rotary engine for the le mans 24-hour endurance race, Tech. Rep.920309, Mazda Motor Corp, Detroit, Michigan, February 1992.

[12] G. C. Congress, Jaguar introduces c-x75 gas micro-turbine extendedrange electric vehicle concept. http://www.greencarcongress.com/2010/09/cx75-20100930.html, May 2015.

[13] J. Mortimer, Micro turbines seek macro market, Professional Engi-neering, 2011.

[14] G. C. Congress, Toyota fcv mirai launches in la; initial tfcsspecs; 57, 500or499 lease; leaning on prius analogy. http://www.greencarcongress.com/2014/11/20141118-mirai.html, May 2015.

[15] U. of Delaware, Vehicle simulation. http://www.me.udel.edu/research groups/prasad/research/vs.html, May 2015.

[16] T. Hua, R. Ahluwalia, J.-K. Peng, M. Kromer, S. Lasher, K. McKenney,K. Law, and J. Sinha, Technical assessment of compressed hydrogenstorage tank systems for automotive applications, International Journalof Hydrogen Energy, vol. 36, no. 4, pp. 3037 3049, 2011.

[17] E. A. Ross and E. Papanikolaou, Design decision document regardingice selection, May 2015.

[18] XALT Energy, XALT 40 Ah High Power (HP) Superior Lithium IonCell, 2015.

[19] Maxwell Technologies, K2 ULTRACAPACITORS - 2.85V/3400F, 2015.[20] R. Sadoun, N. Rizoug, P. Bartholomeus, B. Barbedette, and

P. Le Moigne, Optimal sizing of hybrid supply for electric vehicleusing li-ion battery and supercapacitor.

[21] S. Rajagopalan, T. G. Habetler, C. Kral, and F. Pirker, Monitoring anddiagnostics for electric drivetrain components in hevs, in SAE TechnicalPaper, SAE International, 04 2006.

[22] M. Burwell and J. Goss, Performance/cost comparison of induction-motor & permanent-magnet-motor in a hybrid electric car, (Tokyo),International Copper Association- Copper Alliance, July 2013.

[23] YASA Motors, Yasa 750 axial flux electric motor.http://www.yasamotors.com/wp-content/uploads/2014/07/Datasheet-YASA-750 en-ID-15637.pdf, 2015.

[24] P. J. Masson, G. V. Brown, D. S. Soban, and C. A. Luongo, Htsmachines as enabling technology for all-electric airborne vehicles,Superconductor Science and Technology, vol. 20, no. 8, p. 748, 2007.

[25] B. J. J. van Uden, Proof of Concept of an Integrated AutomotiveArchitecture, Masters thesis, Eindhoven University of TechnologyTU/e, the Netherlands, 2013.

[26] A. Mass, Aerodynamic design of the IM01, with analyses of activeground effect, Masters thesis, Eindhoven University of TechnologyTU/e, the Netherlands, 2013.

[27] L. Eriksson and L. Nielsen, Modeling and Control of Engines andDrivelines. Wiley, 2014.

[28] M. Sivertsson and L. Eriksson, Modeling for optimal control : Avalidated diesel-electric powertrain model, in Proceedings of the 55thConference on Simulation and Modelling (SIMS 55), Modelling, Sim-ulation and Optimization, Linkoping: Linkoping University ElectronicPress.

[29] T. Finken, M. Felden, and K. Hameyer, Comparison and design ofdifferent electrical machine types regarding their applicability in hybridelectrical vehicles, in Electrical Machines, 2008. ICEM 2008. 18thInternational Conference on, pp. 15, Sept 2008.

[30] R. Krishnan, Electric Motor Drives Modeling, Analysis, And Control.Prentice Hall, 2001.

[31] D. V. Munoz, Design, simulation and implementation of a pmsm drivesystem, Masters thesis, Chalmers University of Technology, 2011.

[32] D. Maksimovic and R. Zane, Power Electronics for Electric DriveVehicles ECEN 5017 EV Simulink Toolbox. http://web.eecs.utk.edu/dcostine/ECE482/Spring2015/materials/, Spring 2015.

[33] UQM technologies, Powerphase 75 for electric, hybrid electric, andfuel cell powered vehicles. http://www.uqm.com, May 2015.

[34] M. D. Petersheim and S. N. Brennan, Scaling of hybrid-electricvehicle powertrain components for hardware-in-the-loop simulation,Mechatronics, vol. 19, no. 7, pp. 1078 1090, 2009. Special Issue onHardware-in-the-loop simulation.

ASD PDEng 2014-2016 trainees (Automotive Systems Design ProfessionalDoctorate of Engineering) are the fourth generation of the program. The pro-gram is organised by four departments of Eindhoven University of Technologyin the context of the 3TU.School for Technological Design, Stan AckermansInstitute.

The trainees focus on the multidisciplinary design aspects of project-basedresearch and engineering in high tech automotive systems. The goal of theprojects is to apply a systems approach to problems around mobility and fuelefficient automotive systems, including communication systems and electricaldriving.

IntroductionHEV powertrain analysisTopologies and Advantages/DisadvantagesComponent Description for the Series Topology and Power Flow AnalysisEngine Generator Unit (EGU)Energy Storage System (ESS)Power conversion systemsElectric motor

Component sizing

ModellingEGU modelTurbochargerInternal Combustion EngineGenerator model

Motor modelUltra - capacitor modelForward model of powertrain

Experimental SetupDeriving load machine reference points from the drive cycleAccelerationDeceleration

ResultsConclusion & RecommendationsReferencesBiographiesASD PDEng 2014-2016 trainees