Electrical-Loss Analysis of Power-Split Hybrid Electric Vehicles

energies

Article Electrical-Loss Analysis of Power-Split Hybrid Electric Vehicles

Andrea Bonﬁglio 1 ID , Damiano Lanzarotto 1, Mario Marchesoni 1, Massimiliano Passalacqua 1, Renato Procopio 1,* and Matteo Repetto 2

1 Department of Electrical, Electronic, Tlc Engineering and Naval Architecture (DITEN), University of Genoa, via all’Opera Pia 11a, 16145 Genova, Italy; a.bonﬁ[email protected] (A.B.); [email protected] (D.L.); [email protected] (M.M.); [email protected] (M.P.) 2 Department of Mechanical, Energy, Management and Transportation Engineering (DIME), University of Genova, via all’Opera Pia 15, 16145 Genova, Italy; [email protected] * Correspondence: [email protected]; Tel.: +39-010-353-2730

Received: 28 September 2017; Accepted: 12 December 2017; Published: 15 December 2017

Abstract: The growing development of hybrid electric vehicles (HEVs) has seen the spread of architectures with transmission based on planetary gear train, realized thanks to two electric machines. This architecture, by continuously regulating the transmission ratio, allows the internal combustion engine (ICE) to work in optimal conditions. On the one hand, the average ICE efficiency is increased thanks to better loading situations, while, on the other hand, electrical losses are introduced due to the power circulation between the two electrical machines mentioned above. The aim of this study is then to accurately evaluate electrical losses and the average ICE efficiency in various operating conditions and over different road missions. The models used in this study are presented for both the Continuously Variable Transmission (CVT) architecture and the Discontinuously Variable Transmission (DVT) architecture. In addition, efficiency maps of the main components are shown. Finally, the simulation results are presented to point out strengths and weaknesses of the CVT architecture.

Keywords: hybrid electric vehicle; power-split device; electric machine; energy efﬁciency; continuously variable transmission; hybrid synergy drive

1. Introduction The increasing spread of medium size HEVs in the market has been led by CVT architectures equipped with a power-split device (PSD) able to continuously vary the transmission ratio thanks to the synergy with two motor-generators. This architecture is sketched in Figure1, the main components are: internal combustion engine (ICE), two motor-generators (MG1 and MG2), planetary gear train, i.e., Sun gear (S), Ring gear (R), Carrier gear (C), and the battery (B). In HEVs, the alternative to this structure is given by a discontinuously variable transmission (DVT) equal to that used in conventional vehicles, typically with six different values of transmission ratio. This architecture is sketched in Figure2 (note that only one motor-generator is used). The optimal energy management of the two transmission is fundamental to provide drive comfort and reduce fuel consumption. In scientiﬁc literature, one can ﬁnd various studies on the implementation of optimal control logics, regarding the two architectures described. In [1], an optimized control logic for the power split device (PSD) based architecture is proposed. In [2], a particular energy management system (EMS) is studied for the DVT architecture. Both the CVT structure and the DVT structure are considered for the optimization logic described in [3]. Many variables affect the optimization of the entire hybrid powertrain; in particular, the driving style can

Energies 2017, 10, 2142; doi:10.3390/en10122142 www.mdpi.com/journal/energies Energies 2017, 10, 2142 2 of 16

EnergiesEnergies 2017 2017, 10, 10, 2142, 2142 2 2of of 17 17 play a crucial role, as analyzed in [4]. In [5,6], more sophisticated methods based on neural networks playplay a a crucial crucial role, role, as as analyzed analyzed in in [4]. [4]. In In [5,6], [5,6], more more sophisticated sophisticated methods methods based based on on neural neural networks networks and fuzzy logic are proposed. andand fuzzy fuzzy logic logic are are proposed. proposed.

MMecchanicalecchanical link link S MG S MG11 ElectricalElectrical link link

BB ICEICE CC MG MG22

RR DifferentialDifferential Motor speed Motor speed geargear reductionreduction gear gear

FigureFigureFigure 1. 1. 1. CVTCVT CVT architecture. architecture.

MMecchanicalecchanical link link BB EElectricallectrical link link

IICECE DVTDVT MMGG DifferentialDifferential gear gear

Figure 2. DVT architecture. FigureFigure 2. 2. DVT DVT architecture. architecture.

Thus,Thus,Thus, it is thenit it is is importantthen then important important to study to to study optimalstudy optimal optimal control co co techniques,ntrolntrol techniques, techniques, but to but but evaluate to to evaluate evaluate the energy the the energy energy virtuosity virtuosity of CVT based vehicles, analyzing electrical losses is essential. In [7–9], experimental tests of CVTvirtuositybased of vehicles, CVT based analyzing vehicles, analyzing electrical electrical losses is losses essential. is essential. In [7 In–9 ],[7–9], experimental experimental tests tests were were conducted with the aim of determining the efficiency of electric drives used in the HEVs conductedwere conducted with the aimwith of the determining aim of determining the efficiency the ef officiency electric of drives electric used drives in theused HEVs in the available HEVs on available on the market today. Knowing this parameter is a key step in designing control logic aiming the marketavailable today. on the Knowing market today. this parameter Knowing this is a parameter key step inis a designing key step in control designing logic control aiming logic at aiming optimizing atat optimizingoptimizing thesethese compcomponentsonents [10].[10]. Indeed,Indeed, thethe CVTCVT architecturearchitecture allowsallows anan optimaloptimal energyenergy these components [10]. Indeed, the CVT architecture allows an optimal energy management of the managementmanagement of of the the ICE ICE, ,but but at at the the cost cost of of certain certain of of electrical electrical losses losses which which are are absent absent in in the the DVT DVT ICE, butarchitecture.architecture. at the cost Thus, Thus, of certain one one can can of find electricalfind various various losses works works which on on the the are PSD PSD absent based based in vehicles thevehiclesDVT in in architecture.scientific scientific literature, literature, Thus, one can findhowever,however, various theythey works dodo not onnot dealthedeal PSD withwithbased aa combincombin vehicleseded evaluationevaluation in scientific ofof electrical literature,electrical losseslosses however, andand ICEICE they efficiencyefficiency do not deal withimprovement aimprovement combined evaluationderiving deriving from from of the electricalthe CVT CVT system. system. losses and ICE efficiency improvement deriving from the CVT system.TheThe main main aim aim of of this this paper paper is is to to quantify quantify these these losses losses in in every every possible possible operating operating condition condition and and Thecomparecompare main them them aim with ofwith this the the paper ICE ICE energy energy is to quantifysavings savings obtainable obtainable these losses by by continuously incontinuously every possible regulating regulating operating the the transmission transmission condition and ratio. Firstly, the PSD model is presented; secondly, the electrical losses model is shown; and, finally, compareratio. them Firstly, with the PSD the ICE modelenergy is presented; savings secondly, obtainable the electrical by continuously losses model regulating is shown; the and, transmission finally, the EMS is described. In addition, a brief description is reported of the MATLAB/Simulink (2017b, ratio.the Firstly, EMS theis described.PSD model In addition, is presented; a brief secondly, description the is electricalreported of losses the MATLAB/Simulink model is shown; and,(2017b, finally, Mathworks,Mathworks, Natick, Natick, MA, MA, USA) USA) DVT DVT and and CVT CVT powertrain powertrain models. models. the EMS is described. In addition, a brief description is reported of the MATLAB/Simulink (2017b, ParticularParticular attentionattention isis paidpaid duringduring thethe resultsresults analysisanalysis notnot onlyonly toto thethe electrical-losselectrical-loss Mathworks, Natick, MA, USA) DVT and CVT powertrain models. quantificationquantification but but also also to to the the ICE ICE management management in in both both architectures. architectures. Particular attention is paid during the results analysis not only to the electrical-loss quantification but also to the ICE management in both architectures. In fact, to compare the energy efficiency of the two architectures a combined evaluation of electrical losses and ICE efficiency is of primary importance. Energies 2017, 10, 2142 3 of 17

In fact, to compare the energy efficiency of the two architectures a combined evaluation of Energieselectrical2017, 10, losses 2142 and ICE efficiency is of primary importance. 3 of 16

2. Model 2. Model 2.1. Power Split Device Energy Model 2.1. Power Split Device Energy Model To mathematically describe the PSD, the energy convention depicted in Figure 3 has been Tochosen. mathematically describe the PSD, the energy convention depicted in Figure3 has been chosen.

PSm Mecchanical link

S MG1 Electrical link

PICE Pe ICE C PMG,m2

MG2

P'w Pw R Differential P Rm Motor speed gear reduction gear

FigureFigure 3. 3.CVT CVT energy convention. convention.

In FigureIn Figure3, the 3, powerthe power flows flows are are positive positive in in the the directiondirection shown shown by by th thee arrows. arrows. The The analysis analysis carriedcarried out inout this in this study, study, due due to to the the limited limited influence influence of the the inertial inertial terms terms of ofthe the planetary planetary gear geartrain train components on the overall efficiency, does not consider these terms. Thus, the equations shown components on the overall efficiency, does not consider these terms. Thus, the equations shown below below are only algebraic equations and are to be considered as a stationary model. are only algebraic equations and are to be considered as a stationary model. The variable meanings are explained in Table 1. The variable meanings are explained in Table1. Table 1. Variable meanings. Table 1. Variable meanings. Symbol Meaning Symbol Meaning

MG1 motor-generator 1 T MG2 torque Symbol Meaning SymbolMG2 Meaning MG2 motor-generator 2 i Differential gear ratio MG1 motor-generator 1 TηMG2 MG2 torque Pe Electrical Power diff Differential gear efficiency MG2 motor-generator 2 i Differential gear ratio Pe PICE ElectricalICE Powerpower ηdij f f Motor speedDifferential reduction gear gear efficiency ratio PICE ICE power η j Motor speed reduction gear ratio PL1 MG1 Electrical loss msrg motor speed reduction gear efficiency PL1 MG1 Electrical loss ηmsrg motor speed reduction gear efficiency P MG2 Electrical loss ω wheel rotational speed PL2 L2 MG2 Electrical loss ωww wheel rotational speed ω PSm PSm SunSun gear gear mechanical mechanical power power ωSS sun sungear gear rotational rotational speed speed P Ring gear mechanical power ω ring gear rotational speed Rm P Ring gear mechanical power ωR ring gear rotational speed P Rm MG mechanical power ω R ICE rotational speed MG2,m 2 ωICE PMG,m2 DriveMG shaft2 mechanical mechanical power ICE ICE rotational speed P0 τ Willis constant w power τo P'w Drive shaft mechanical power o Willis constant Pw Wheel power k Sun–Ring gear ratio Pw Wheel power k Sun–Ring gear ratio TS Sun gear torque TICE ICE torque TR TS RingSun gear gear torque torque TηImCE PowerICE split torque device efficiency Tw Wheel torque η TR Ring gear torque m Power split device efficiency

It is ﬁrst necessary to identify the relationship between the different velocity of the components of the PSD; Willis formula can be applied (valid if the planetary rings are merged into a single one externally toothed together): ωR − ωICE = τo (1) ωS − ωICE Energies 2017, 10, 2142 4 of 16 where: zS τo = (2) zR

While zS and zR represent the sun gear and ring-gear-tooth number, zR is conventionally a negative number because the teeth are internal. By conveniently combining:  T ηm = T + TR  ICE S PICEηm = PSm + PRm (3)  1 − 1 ω = ω − 1 ω τo ICE S τo R

One obtains: TS = −τoTR (4)

From which: 1 − 1 τo TICE = TS (5) ηm Based on the three following equations, once the value of k has been chosen, it is possible to acquire a relationship between the mechanical ring gear power and the mechanical sun gear power:  T = −τ T  S o R PSm = ωSTS (6)  P = ω T = − ωR T Rm R R τo S

Hence: PSm = −τok (7) PRm where: ω k = S (8) ωR By analyzing the vehicle energy scheme, it can be immediately understood that the possible configurations in terms of power flows are various; to describe them all, this work would be overburdened. The energy configurations considered in this paper are the ones practically exploited during the vehicle usage. In addition, regenerative braking and pure electric traction are not considered here because the resulting configurations do not have any degree of freedom allowing optimization study. Note that the input to the systems are: ωw, Pw(Tw), k Case 1 (MG1 motor, MG2 generator): k < 0, Pe < 0, PSm < 0, PL1 < 0, PL2 < 0 Combining the following equations: ( Pe = PL1 + PSm 0 (9) PRm + PL1 + Pe = P w

One has: 0 TRωR + TSωS = P w − PL1 − PL2 (10) After some passages:

j 0 − ωwTS + kjωwTS = P w − PL1 − PL2 (11) τo

Finally: Pw/ηdi f f − PL1 − PL2 TS = PL1, PL2 < 0 (12) jω k − 1 w τo Energies 2017, 10, 2142 5 of 16

Furthermore: 0 P w = Pw/ηdi f f (13) Hence: PL2 + Pe PL1 + PL2 + PSm TMG2 = = (14) jiωw jiωw

Case 2 (MG1 generator, MG2 motor): k > 0, Pe > 0, PSm > 0, PL1 > 0, PL2 > 0 Combining the following equations: ( Pe = PSm − PL1 0 (15) Pe − PL2 + PRm = P w

Repeating the same calculations as above, one has:

Pw/ηdi f f + PL1 + PL2 TS = (16) jω k − 1 w τo

PSm − PL1 − PL2 TMG2 = (17) jiωw

Case 3 (MG1 generator—only from a mechanical point of view, MG2 generator):

k > 0, Pe < 0, 0 < PSm < PL1, PL1 > 0, PL1 < 0

This particular conﬁguration is rather frequent and it occurs in low sun gear velocity situations; in this case, the mechanical power delivered by the sun gear is not enough to overcome electrical losses in MG1, hence MG2 has to provide the remaining power. Thus: ( PSm − Pe = PL1 0 (18) PRm + PL2 + Pe = P w

Pw/ηdi f f + PL1 − PL2 TS = PL1 > 0, PL2 < 0 (19) jω k − 1 w τo

PSm − PL1 + PL2 TMG2 = (20) jiωw Since the torque of the two motor generators is determined in each conﬁguration, and their speed is known thanks to the Willis formula, once the ratio k is found (goal of the optimization process, see Section 2.3), the electrical losses can be computed (as shown in Section 2.2), and all the other variables of interest can easily be obtained.

−=  PPPSm e L1  ++= (18) PPPP'RmL2 e w P/η +− P P =>

Since the torque of the two motor generators is determined in each configuration, and their speed is known thanks to the Willis formula, once the ratio k is found (goal of the optimization process, see Section 2.3), the electrical losses can be computed (as shown in Section 2.2), and all the other variables of interest can easily be obtained.

2.2. Electrical Losses and Internal Combustion Engine Modeling The aim of this paper being to evaluate losses in the CVT system to implement a control logic able to consider these losses, similar to that proposed in [10], the modeling of electric machines and their converters is of primary importance. As regards electrical machines, electrical losses were modeled exploiting the results presented in [11]. These loss data are available for a specific size permanent magnet synchronous motor, in order to use these results for machines of different size (torque, speed), and losses were put in per unit, as suggested in [12]. The losses in the inverters were estimated starting from the results shown in [13], which were interpolated in accordance to that proposed in [14] and finally put in per unit. Energies 2017By, 10 doing, 2142 so, these maps can be used in the simulation environment for machines of different6 of 16 (but comparable) size. Hence, a look up table was created for each contour map; these look up tables are formed by Hence,almost 10,000 a look points. up table To wasobtain created a halfway for each loss contour value, linear map; theseinterpolation look up istables used. areThis formed was by almostimplemented 10,000 points. thanks To obtainto available a halfway interpolatio loss value,n blocks linear in Matlab/Simulink interpolation is environment. used. This was implemented thanks toThe available electrical interpolation losses of each blocks electric in drive Matlab/Simulink are presented in environment. per unit in Figure 4. The electricalPlease note losses that the of speed each electricvalue 1 is drive not for are the presented maximum in speed per unit but for in Figurethe base4. speed instead.

FigureFigure 4. 4.Electric Electric drivedrive losses in in per per unit unit (p.u). (p.u).

Please note that the speed value 1 is not for the maximum speed but for the base speed instead. To evaluate the ICE efﬁciency, the spark ignition engine presented in [15] was considered; the fuel consumption data available in [15] were elaborated to obtain the efﬁciency map. This contour map is shown in Figure5.

2.3. CVT Powertrain Efficiency Optimization The goal of this study is to obtain for each couple (wheel speed and wheel torque) the optimal transmission ratio k able to minimize fuel consumption. This process is not a mere search for the k value which maximizes the ICE efficiency, but it is a broader study aiming at the maximization of the entire powertrain. In fact, given the same power delivered to the wheels, the electrical losses must be compensated by a higher ICE power generation with its consequent consumption increase. To carry out this optimization process, a MATLAB/Simulink model in accordance to that proposed at Sections 2.1 and 2.2 was created. A sufficiently numerous finite set of couples (wheel speed and wheel torque) was created to consider the variable k a continuous kopt = kopt(ωw; Tw). The PSD model and the ICE model were necessary to perform the optimization. Therefore, a combination of different value of ωw and Tw were provided as input to the model, together with all the possible k values to evaluate the fuel consumption in every operating condition. The k value, corresponding to the minimum value of fuel consumption for that particular operating condition, was then selected as a kopt value. The logical scheme of this process is represented in Figure6. Thanks to the code implemented, the optimal transmission ratio function of wheel speed and wheel torque was found. In particular, due to the model implemented, this function is a “collection” of steady state points. The optimization result is shown in Figure7. The ICE work points deriving from the optimization process are shown in Figure8a. In Figure8b, the ICE work points declared by Toyota are shown [16]. It is easy to notice how the optimization result is the same as that declared by Toyota, and it basically corresponds to the ICE efficiency maximization; in other terms, the electrical losses do not count in the optimization process (although they are not Energies 2017, 10, 2142 7 of 17

To evaluate the ICE efficiency, the spark ignition engine presented in [15] was considered; the fuel consumption data available in [15] were elaborated to obtain the efficiency map. This contour map is shown in Figure 5.

EnergiesEnergies2017, 201710, 2142, 10, 2142 7 of 177 of 16

To evaluate the ICE efficiency, the spark ignition engine presented in [15] was considered; the negligible),fuel consumption if compared data to available the losses in [15] in the wereICE elaborpowerated generation. to obtain the Please efficiency note map. that This this contour conclusion couldmap not is have shown been in Figure drawn 5. a priori.

Figure 5. ICE efficiency contour map.

2.3. CVT Powertrain Efficiency Optimization The goal of this study is to obtain for each couple (wheel speed and wheel torque) the optimal transmission ratio k able to minimize fuel consumption. This process is not a mere search for the k value which maximizes the ICE efficiency, but it is a broader study aiming at the maximization of the entire powertrain. In fact, given the same power delivered to the wheels, the electrical losses must be compensated by a higher ICE power generation with its consequent consumption increase. To carry out this optimization process, a MATLAB/Simulink model in accordance to that proposed at Sections 2.1 and 2.2 was created. A sufficiently numerous finite set of couples (wheel speed and wheel torque) was created to consider the variable k a continuous kopt = kopt(ωw; Tw). The PSD model and the ICE model were necessary to perform the optimization. Therefore, a combination of different value of ωw and Tw were provided as input to the model, together with all the possible k values to evaluate the fuel consumption in every operating condition. The k value, corresponding to the minimum value of fuel consumption for that particular operating condition, was then selected as a kopt value. The logical FigureFigure 5. 5.ICE ICE efﬁciencyefficiency contour contour map. map. scheme of this process is represented in Figure 6. 2.3. CVT Powertrain Efficiency Optimization TICE TwThe goal of this study is to obtain for each couple (wheelfuel speed and wheel torque)k,T ()theω optimal PSD ICE opt w w transmissionω ratio k able to minimize fuel consumption. This process is not a mere search for the k w Optimization value which maximizesmodel the ICE efficiency, butmodel it is a broader study aiming at the maximization of the entirek powertrain. In fact, given theω same power delivered to the wheels, the electrical losses must be ICE compensated by a higher ICE power generation with its consequent consumptionk increase. To carry out this optimization process, a MATLAB/Simulink model in accordance to that proposed at Sections 2.1 and 2.2 was created. A sufficiently numerous finite set of couples (wheel speed and wheel torque) was created to consider the variable k a continuous kopt = kopt(ωw; Tw). The PSD model and the ICE model were necessary to perform the optimization. Therefore, a combination of different value of ωw Energies 2017, 10, 2142 FigureFigure 6. 6.CVT CVToptimization optimization conceptual conceptual scheme. scheme. 8 of 17 and Tw were provided as input to the model, together with all the possible k values to evaluate the fuel consumptionThanks to the in code every implemented, operating condition. the optimal The ktr value,ansmission corresponding ratio function to the of minimum wheel speed value and of fuelwheel consumption torque was forfound. that Inparticular particular, operating due to thecondition, model implemented,was then selected this asfunction a kopt value. is a “collection” The logical schemeof steady of state this processpoints. The is represented optimization in Figureresult is 6. shown in Figure 7.

TICE Tw fuel k,T()ω PSD ICE opt w w ω Optimization w model model k ω ICE k

Figure 6. CVT optimization conceptual scheme.

Thanks to the code implemented, the optimal transmission ratio function of wheel speed and wheel torque was found. In particular, due to the model implemented, this function is a “collection” of steady state points. The optimization result is shown in Figure 7.

FigureFigure 7. k7.steady k steady state state function function for each each operating operating condition— condition—CVTCVT. .

The ICE work points deriving from the optimization process are shown in Figure 8a. In Figure 8b, the ICE work points declared by Toyota are shown [16]. It is easy to notice how the optimization result is the same as that declared by Toyota, and it basically corresponds to the ICE efficiency maximization; in other terms, the electrical losses do not count in the optimization process (although they are not negligible), if compared to the losses in the ICE power generation. Please note that this conclusion could not have been drawn a priori.

(a) (b)

2.4. DVT Powertrain Effciency Optimazation The DVT powertrain optimization is a simpler process compared to the previous one; indeed, there is only one variable to optimize, i.e., the ICE efficiency (equivalent to the fuel consumption). It is sufficient to find for each couple (wheel speed and wheel torque) the transmission ratio able to satisfy the ICE range of functioning and to maximize its efficiency. The result of this optimization process is shown in Figure 9.

Energies 2017, 10, 2142 8 of 17

Figure 7. k steady state function for each operating condition—CVT.

The ICE work points deriving from the optimization process are shown in Figure 8a. In Figure 8b, the ICE work points declared by Toyota are shown [16]. It is easy to notice how the optimization result is the same as that declared by Toyota, and it basically corresponds to the ICE efficiency maximization; in other terms, the electrical losses do not count in the optimization process (although Energiesthey2017, are10, 2142not negligible), if compared to the losses in the ICE power generation. Please note that this8 of 16 conclusion could not have been drawn a priori.

(a) (b)

Figure 8. (a) ICE optimal work points; and (b) Toyota ICE work points (Reprint with permission [16]; Figure 8. (a) ICE optimal work points; and (b) Toyota ICE work points (Reprint with permission [16]; Copyright 2012, VDI-Verlag). Copyright 2012, VDI-Verlag). 2.4. DVT Powertrain Effciency Optimazation 2.4. DVT Powertrain Effciency Optimazation The DVT powertrain optimization is a simpler process compared to the previous one; indeed, ThethereDVT is onlypowertrain one variable optimization to optimize, isi.e., a simplerthe ICE efficiency process compared(equivalent to the the fuel previous consumption). one; indeed, It there isis only sufficient one variableto find for to each optimize, couple i.e., (wheel the ICE speeefficiencyd and wheel (equivalent torque) the to transmission the fuel consumption). ratio able to It is sufficientsatisfy to findthe ICE for eachrange couple of functioning (wheel speedand to andmaximize wheel its torque) efficiency. the transmissionThe result of this ratio optimization able to satisfy the ICEprocessrange is of shown functioning in Figure and 9. to maximize its efficiency. The result of this optimization process is shownEnergies in Figure 2017, 109,. 2142 9 of 17

FigureFigure 9. 9.Optimal Optimal transmissiontransmission ratio— ratio—DVTDVT. .

2.5. MATLAB/Simulink Model 2.5. MATLAB/Simulink Model 2.5.1. Power Split-Device Vehicle MATLAB Simulink Model 2.5.1. Power Split-Device Vehicle MATLAB Simulink Model To quantify the variable of interest, the implementation of the two powertrain models in ToMATLAB/Simulink quantify the variable was necessary. of interest, In Figure the 10, implementation the CVT powertrain of model the two is shown powertrain while the models DVT in MATLAB/Simulinkpowertrain model was is shown necessary. in Figure In 11. Figure The battery 10, the andCVT its controlpowertrain system model were neglected is shown because while the DVT theypowertrain do not intervene model is in shown the computation in Figure 11 of. Thethe electrical battery andlosses its due control to the system realization were of neglected the becausetransmission they do notratio intervene k. Indeed,in the the role computation played by this of componen the electricalt is the losses same due in the to two the realizationarchitectures; of the transmissionthe same ratiocontrolk. Indeed,logics can the be role applied played and by the this benefits component deriving is thefrom same regenerative in the two braking architectures; and electric traction are identical in both configurations. Implementing control logic for the storage system management and its accurate modeling are important, for instance, to determine fuel consumption, emission evaluation, etc. It is not necessary in this study but would be to evaluate the electrical losses associated with the realization of the transmission ratio (in the CVT architecture) and to determine the average ICE efficiency. The red blocks in Figure 10 represent the model of physical components, the light blue block computes the resistant torque and the orange blocks implement the control logic. The complete model is necessary to simulate the powertrain response to the various road missions with respect to the reference speed and road slope. On the contrary, the light blue block is not necessary to calculate electrical losses and the ICE efficiency; therefore, the inputs in this case are the vehicle speed and the wheel power directly, which are provided as ramp signals to compute the desired parameters in each operating condition.

Energies 2017, 10, 2142 9 of 16 the same control logics can be applied and the benefits deriving from regenerative braking and electric traction are identical in both configurations. Implementing control logic for the storage system management and its accurate modeling are important, for instance, to determine fuel consumption, emission evaluation, etc. It is not necessary in this study but would be to evaluate the electrical losses associated with the realization of the transmission ratio (in the CVT architecture) and to determine the average ICE efficiency. The red blocks in Figure 10 represent the model of physical components, the light blue block computes the resistant torque and the orange blocks implement the control logic. The complete model is necessary to simulate the powertrain response to the various road missions with respect to the reference speed and road slope. On the contrary, the light blue block is not necessary to calculate electrical losses and the ICE efficiency; therefore, the inputs in this case are the vehicle speed and the wheel power directly, which are provided as ramp signals to compute the desired parameters in each operatingEnergiesEnergies condition. 20172017,, 1010,, 21422142 1010 ofof 1717

FigureFigureFigure 10. 10.10.CVT CVTCVTMATLAB/Simulink MATLAB/SimulinkMATLAB/Simulink model.model. model.

FigureFigureFigure 11. 11.11.DVT DVTDVTMATLAB/Simulink MATLAB/SimulinkMATLAB/Simulink model.model. model.

2.5.2.2.5.2. DVTDVT MATLAB/SimulinkMATLAB/Simulink ModelModel 2.5.2. DVT MATLAB/Simulink Model SimilarSimilar toto thatthat implementedimplemented forfor thethe CVTCVT architecture,architecture, oneone modelmodel forfor thethe DVTDVT powertrainpowertrain waswas Similarcreated.created. The toThe that samesame implemented considerationsconsiderations for previouslypreviously the CVT mentioned,mentioned,architecture, asas farfar one asas thethe model rolerole of forof eacheach the blockblockDVT isispowertrain concerned,concerned, was created.remainremain The valid.valid. same considerations previously mentioned, as far as the role of each block is concerned, remain valid. 2.6.2.6. VehicleVehicle FeaturesFeatures 2.6. Vehicle Features TheThe vehiclevehicle featuresfeatures areare reportedreported inin TableTable 2;2; ththee parametersparameters commoncommon toto thethe twotwo architecturesarchitectures areare Thecharacterizedcharacterized vehicle features byby thethe samesame are reportedvaluesvalues toto allowallow in Table aa fairfair2; thecomparison.comparison. parameters common to the two architectures are characterized by the same values to allow a fair comparison. TableTable 2.2. VehicleVehicle features.features.

VehicleVehicle FeatureFeature CVTCVT DVTDVT VehicleVehicle massmass (g)(g) 1450 1450 1450 1450 RollingRolling coefficientcoefficient 0.01 0.01 0.01 0.01 DragDrag coefficientcoefficient 0.25 0.25 0.25 0.25 VehicleVehicle frontfront areaarea (m(m22)) 2.32.3 2.32.3 WheelWheel radiusradius (m)(m) 0.3 0.3 0.3 0.3 DifferentialDifferential geargear ratioratio 3.45 3.45 10.8 10.8 DifferentialDifferential efficiencyefficiency 0.97 0.97 0.97 0.97 GearGear efficiencyefficiency 0.95 0.95 0.95 0.95 AirAir densitydensity (kg/m(kg/m33)) 1.221.22 1.221.22 ICEICE powerpower (kW)(kW) 72 72 72 72 ICEICE maximummaximum torquetorque (Nm)(Nm) 142 142 142 142 MGMG22 maximummaximum torquetorque (Nm)(Nm) 163 163 163 163 MGMG22 basebase speedspeed (rpm)(rpm) 3000 3000 3000 3000 MGMG22 maximummaximum speedspeed (rpm)(rpm) 17,000 17,000 17,000 17,000

Energies 2017, 10, 2142 10 of 16

Table 2. Vehicle features.

Vehicle Feature CVT DVT Vehicle mass (g) 1450 1450 Rolling coefficient 0.01 0.01 Drag coefficient 0.25 0.25 Vehicle front area (m2) 2.3 2.3 Wheel radius (m) 0.3 0.3 Differential gear ratio 3.45 10.8 Differential efficiency 0.97 0.97 Gear efficiency 0.95 0.95 Air density (kg/m3) 1.22 1.22 ICE power (kW) 72 72 ICE maximum torque (Nm) 142 142 MG2 maximum torque (Nm) 163 163 MG2 base speed (rpm) 3000 3000 MG2 maximum speed (rpm) 17,000 17,000 MG1 maximum torque (Nm) 43 - MG1 base speed (rpm) 5000 - MG1 maximum speed (rpm) 10,000 - DC-link voltage (V) 650 650

2.7. Road Mission Features As previously described, the electrical-loss evaluation is ﬁrstly carried out considering every possible operating condition of the powertrain. However, some operating conditions are much more important and more frequent than others during the real usage of the vehicle, hence the importance of the simulation of the powertrain response over actual road missions. The vehicle speed and road slope data (inputs to the vehicle models) were experimentally collected via GPS, in particular these missions were distinguished in: urban mission, extra urban mission and a highway mission. In addition, simulations were also carried out on three American type approval tests (HWFET, US06, and UDD6). The main characteristics of these three missions are listed in Table3.

Table 3. Vehicle features.

Road Mission Average Speed (km/h) Maximum Speed (km/h) Length (km) Elevation (m) Maximum Road Slope (%) US06 78 130 13 - - UDDS 31 90 12 - - HWFET 78 97 16.5 - - Urban 24 57 11.4 20 negligible Extra-urban 45 80 36 300 9 Highway 104 130 22 - -

Detailed results are only reported for real missions; however, the powertrain efﬁciency is evaluated on the totality of the described missions.

3. Results

3.1. Electrical Losses in Power-Split Device

3.1.1. Electrical Losses in Power-Split Device over Different Operating Condition Once the optimization is performed, it is possible to obtain the electrical losses in operating conditions necessary to realize the desired transmission ratio k. Note that only the operation of the two electric drives during the transmission ratio realization is analyzed. The operation of the two machines during regenerative braking and electric traction is not relevant to this study, as it is that of the battery as mentioned above. Energies 2017, 10, 2142 11 of 16

Therefore, the electrical losses considered in this study are only associated with the CVT architecture because of the electrical power circulation between the two motor-generators necessary to realize the transmission ratio, thus these losses are completely absent in the DVT powertrain. In Figure 12a, the electrical losses are plotted again for each operating condition; it is also useful to compare these losses with the power generated by the ICE. In Figure 12b, the same losses are shown in percentage with respect to the power delivered by the ICE in the same work point. One can easily notice how relevant these losses are; in addition, most of the powertrain operating conditions are placedEnergies in areas 2017, characterized10, 2142 by losses higher than 10%. 12 of 17 Energies 2017, 10, 2142 12 of 17

(a) (b) (a) (b) Figure 12. (a) Electrical losses (kW) as a function of wheel power and vehicle speed; and (b) electrical Figure 12. (a) Electrical losses (kW) as a function of wheel power and vehicle speed; and (b) electrical Figurelosses (%)12. (ina) percentageElectrical losses with (kW)respect as toa functionICE power, of wheel as a function power andof wh vehicleeel power speed; and and vehicle (b) electrical speed. losseslosses (%) in(%) percentage in percentage with with respect respect to toICE ICEpower, power, asas a function of of wh wheeleel power power and and vehicle vehicle speed. speed. 3.1.2. Electrical Losses in Power-Split Device over Different Real-Road Missions 3.1.2.3.1.2. Electrical Electrical Losses Losses in Power-Splitin Power-Split Device Device over over DifferentDifferent Real-Road Real-Road Missions Missions With the aim of quantifying the impact of these losses during real operating conditions, it is WithusefulWith the to locate aimthe aim ofthe quantifyingof work quantifying points theof the the impact impactdifferent ofof missions. thesethese losses The during urban during missionreal real operating operating work points conditions, conditions, are shown it is it is usefulusefulin to Figure locate to locate 13 the on workthe a zoomed work points points area of theof thethe different differentelectrical missions. missions.-loss contour The The urbanmap.urban Figure mission 14 work workshows points pointsthe extra are are shown urban shown in in Figure 13 on a zoomed area of the electrical-loss contour map. Figure 14 shows the extra urban Figuremission 13 on awork zoomed points, area while of theFigure electrical-loss 15 shows th contoure ones associated map. Figure with 14the shows highway the mission. extra urban mission mission work points, while Figure 15 shows the ones associated with the highway mission. work points, while Figure 15 shows the ones associated with the highway mission.

Figure 13. Urban mission work points on the electrical-loss (%) contour map. FigureFigure 13. 13.Urban Urban mission mission work work pointspoints on the electrical-loss electrical-loss (%) (%) contour contour map. map.

Energies 2017, 10, 2142 12 of 16 Energies 2017, 10, 2142 13 of 17

FigureFigure 14. Extra-urban14. Extra-urban mission mission work work pointspoints on the electrical-loss electrical-loss (%) (%) contour contour map. map.

Figure 15. Highway mission work points on the electrical-loss (%) contour map. FigureFigure 15. Highway15. Highway mission mission work work points points on the electrical-loss electrical-loss (%) (%) contour contour map. map.

As simulations pointed out, electrical losses have a considerable effect on the powertrain Asefficiency. simulations The results pointed obtained out, electrical for the missions losses have considered a considerable show that effect electrical onthe losses powertrain are nearly efficiency. 13% The resultsof the total obtained energy for delivered the missions by the consideredICE and reach show 21%that for highway electrical missions. losses are nearly 13% of the total energy delivered by the ICE and reach 21% for highway missions. 3.2. ICE Efficiency 3.2. ICE Efficiency 3.2.1. ICE Efficiency over Different Operating Condition 3.2.1. ICE Efficiency over Different Operating Condition

It is shown in Section 3.1 that the amount of electrical losses in the CVT architecture can reach 20% of the ICE generated power. However, this structure allows the engine to work more efﬁciently by continuously varying the transmission ratio. Thus, to perform a comprehensive energy evaluation of Energies 2017, 10, 2142 14 of 17

It is shown in Section 3.1 that the amount of electrical losses in the CVT architecture can reach Energies20%2017 of, 10the, 2142 ICE generated power. However, this structure allows the engine to work more efficiently13 of 16 by continuously varying the transmission ratio. Thus, to perform a comprehensive energy evaluation of both powertrain typologies, an in-depth study of the ICE efficiency during real operating bothconditions powertrain in typologies,both cases is an crucial. in-depth study of the ICE efficiency during real operating conditions in both casesIn is Figure crucial. 16, the global ICE efficiency is shown as a function of vehicle speed and wheel power. InThis Figure contour 16 ,map the globaldoes notICE plotefficiency the ICE efficiency is shown as as a afunction function of ofitsvehicle speed and speed torque and but wheel rather power. This contourplots the map efficiency does notas plota function the ICE ofefficiency road variab asles; a function this result ofits can speed onlyand be torqueobtained but once rather the plots transmission ratio is known for every operating condition. Hence, the relationship between ICE the efficiency as a function of road variables; this result can only be obtained once the transmission variables (torque and speed) and road variables (vehicle speed and wheel power) is unique. ratio is knownIn other for terms, every these operating plots were condition. obtained Hence, starting the from relationship the same betweenICE efficiencyICE variablescontour map (torque and speed)(Figure and5) applying road variables the control (vehicle logics presented speed and in wheelSections power) 2.3 and is 2.4. unique.

(a) (b)

Figure 16. ICE efficiency [%] contour map as a function of road variables: (a) CVT; and (b) DVT. Figure 16. ICE efficiency [%] contour map as a function of road variables: (a) CVT; and (b) DVT. In Figure 16b, one can notice six areas characterized by efficiency values higher than 34.5%. InThese other areas terms, correspond these plotsto the weresix transmission obtained ratios starting available from thefor the same DVTICE architecture.efficiency It contourcan also map (Figurebe 5immediately) applying theseen control in Figure logics 16a presentedthat the efficiency in Sections area 2.3of valuesand 2.4 higher. than to 34.5% is much Inwider Figure and 16it b,extends one can even notice for high six areasvehicle characterized speed. In particular, by efficiency Figure values16a, as previously higher than described, 34.5%. These areasis correspond another way toof looking the six at transmission Figure 8a. In fact, ratios only available the work forpoints the onDVT that redarchitecture. line are those It entirely can also be immediatelyplotted here seen with in Figurerespect 16to aroad that variables. the efficiency area of values higher than to 34.5% is much wider and it3.2.2. extends ICE Efficiency even for highover vehicleDifferent speed. Real Road In particular, Mission Figure 16a, as previously described, is another way of looking at Figure8a. In fact, only the work points on that red line are those entirely plotted here withSimilar respect to to Section road variables.3.1.2, the ICE efficiency was evaluated over real road missions; this analysis is indeed extremely important because it allows us to draw conclusions on CVT structure energy 3.2.2.virtuosity.ICE Efficiency Specifically, over Differentit shows whether Real Road the ICE Mission efficiency increase can compensate for the electrical losses. SimilarIn Figures to Section 17–19, 3.1.2 the ,vehicle the ICE workefficiency points are was placed evaluated on a zoomed over real arearoad of the missions; two ICE efficiency this analysis is indeedcontour extremely maps. What important was previously because mentioned it allows usis thus to draw confirmed: conclusions the CVT on architectureCVT structure in fact energy virtuosity.enables the Specifically, ICE to work it at showshigher efficiency whether values. the ICE In particular,efficiency this increase benefit is can more compensate relevant during for the electricalhigh-speed losses. missions thanks to the overdrive. In Figures 17–19, the vehicle work points are placed on a zoomed area of the two ICE efficiency contour maps. What was previously mentioned is thus confirmed: the CVT architecture in fact enables the ICE to work at higher efficiency values. In particular, this benefit is more relevant during high-speed missions thanks to the overdrive.

Energies 2017, 10, 2142 14 of 16 Energies 2017, 10, 2142 15 of 17 Energies 2017, 10, 2142 15 of 17 Energies 2017, 10, 2142 15 of 17

(a) (b) (a) (b) (a) (b) Figure 17. Urban mission work points: (a) CVT; and (b) DVT. FigureFigure 17. 17.Urban Urban mission mission work points: points: (a ()a CVT) CVT; and; and (b) (DVTb) DVT. . Figure 17. Urban mission work points: (a) CVT; and (b) DVT.

(a) (b) (a) (b) (a) (b) Figure 18. Extra-urban mission work points: (a) CVT; and (b) DVT. Figure 18. Extra-urban mission work points: (a) CVT; and (b) DVT. FigureFigure 18. 18.Extra-urban Extra-urban mission mission work work points: points: (a) (CVTa) CVT; and; and (b) ( bDVT) DVT. .

(a) (b) (a) (b) (a) (b) Figure 19. Highway mission work points: (a) CVT; and (b) DVT. Figure 19. Highway mission work points: (a) CVT; and (b) DVT. Figure 19. Highway mission work points: (a) CVT; and (b) DVT. Figure 19. a CVT b DVT 3.3. Powertrain Global EfficiencyHighway mission work points: ( ) ; and ( ) . 3.3. Powertrain Global Efficiency 3.3. Powertrain Global Efficiency

Energies 2017, 10, 2142 15 of 16

3.3. Powertrain Global Efficiency To quantify the combined effect of electrical losses due to the transmission ratio realization and the ICE efficiency increase, the powertrain global efficiency, defined by the following equation, is computed over all the missions presented:

Positive energy delivered to the wheels η = (21) powertrain Primary energy ( f uel)

This value accounts for the ICE efficiency, the electrical losses and the mechanical efficiency of the transmission and differential gear. The powertrain efficiency values of the different missions are listed in Table4.

Table 4. Powertrain efﬁciency over different road missions.

Road Missions CVT DVT US06 26.0% 28.1% UDDS 23.4% 24.9% HWFET 23.3% 24.9% Urban 22.4% 23.0% Extra-urban 23.5% 24.8% Highway 23.9% 25.5%

4. Conclusions The PSD energy model and losses in the electric drives are modeled in this paper. Subsequently, the MATLAB/Simulink model for the CVT and DVT architectures are described. The CVT electrical-loss contour map is then presented. To estimate the impact of the two different transmissions on the ICE operating conditions, contour maps of the ICE as a function of road variables are obtained for both the parallel configurations by knowing the transmission ratio associated for each work point (acquired by the fuel consumption minimization process). Finally, three real road missions were simulated to locate the work points on the different contour maps, evaluating strengths and weaknesses of both architectures. The results obtained confirmed what was previously speculated: the CVT configuration allows the engine to work at higher efficiency rate, but at the cost of consistently high electrical losses. Again, the CVT architecture is characterized by an 8% ICE efficiency increase in the urban mission (28.6% vs. 26.4%), 9% efficiency increase in the extra-urban mission and 16% in the highway mission. Nevertheless, this improvement is not sufficient to compensate for the large amount of electrical losses generated by the continuously variable transmission on the three missions: 13%, 15% and 21% of the ICE generated energy. Although the CVT based hybrid electric vehicles are widely spread in the medium size car market sector, they do not prove to be the best energy solution. Further developments of this study will include the experimental validation of the results achieved via simulations by means of prototyping in collaboration automotive companies.

Acknowledgments: The authors would like to wholeheartedly thank Alessandro Pini Prato for his suggestions, assistance, and encouragement throughout this work. Author Contributions: Damiano Lanzarotto and Massimiliano Passalacqua created the models. Renato Procopio and Mario Marchesoni provided useful information about the permanent magnet synchronous motors and revised the manuscript while Matteo Repetto provided useful data on the ICE. Massimiliano Passalacqua, Damiano Lanzarotto and Andrea Bonfiglio implemented the models in the simulator, performed the simulations and elaborated the results. Damiano Lanzarotto and Massimiliano Passalacqua wrote the paper. Conflicts of Interest: The authors declare no conflict of interest. Energies 2017, 10, 2142 16 of 16

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