Optimization and Application for Hydraulic Electric Hybrid Vehicle

energies

Article Optimization and Application for Hydraulic Electric Hybrid Vehicle

Hsiu-Ying Hwang 1, Tian-Syung Lan 2,* and Jia-Shiun Chen 1

1 Department of Vehicle Engineering, National Taipei University of Technology, Taipei 10608, Taiwan; [email protected] (H.-Y.H.); [email protected] (J.-S.C.) 2 College of Mechatronic Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China * Correspondence: [email protected]

Received: 27 November 2019; Accepted: 7 January 2020; Published: 9 January 2020

Abstract: Targeting the application of medium and heavy vehicles, a hydraulic electric hybrid vehicle (HEHV) was designed, and its energy management control strategy is discussed in this paper. Matlab/Simulink was applied to establish the pure electric vehicle and HEHV models, and backward simulation was adopted for the simulation, to get the variation of torque and battery state of charge (SOC) through New York City Cycle of the US Environmental Protection Agency (EPA NYCC). Based on the simulation, the energy management strategy was designed. In this research, the rule-based control strategy was implemented as the energy distribution management strategy ﬁrst, and then the genetic algorithm was utilized to conduct global optimization strategy analysis. The results from the genetic algorithm were employed to modify the rule-based control strategy to improve the electricity economic performance of the vehicle. The simulation results show that the electricity economic performance of the designed hydraulic hybrid vehicle was improved by 36.51% compared to that of a pure electric vehicle. The performance of energy consumption after genetic algorithm optimization was improved by 43.65%.

Keywords: hydraulic hybrid vehicle; NYCC driving cycle; optimization; genetic algorithm

1. Introduction The increasing demand for fossil fuels in different fields since the Industrial Revolution has led to increasing global CO2 emission and worsening global warming. Among all CO2 emission, the emission of means of transportation is only second to the industry. Now, the passenger vehicles all develop toward alternative energy, whereas the medium and heavy vehicles for goods transportation are still using gasoline or diesel engines as the main power source. With global warming and increasing stringent laws and regulations, they will definitely develop toward the same clean energy as the passenger vehicles. According to Navigant Research, the market survey company, hydraulic hybrid vehicles seldom known and underestimated in significance will gain a position in the heavy-duty truck market, and even can be expected to apply to the next generation of vehicles. Therefore, hydraulic electric hybrid vehicles (HEHV) will be the first choice for medium vehicles, heavy vehicles, and common carriers. With the DSHplus software simulation, Sokar [1] compared the fuel economy of the hydraulic transmission vehicles and hydraulic hybrid vehicles in urban and highway driving cycles. Chen [2] compared the energy consumption of different hydraulic hybrid configurations, and it showed the HEHV could have better energy efficiency over the pure EV system. The energy optimization can be divided to hardware optimization and control strategy optimization. As for hardware optimization, Ramakrishnan et al. [3] proposed the study on influence of system parameters in hydraulic system on the overall system power and established the series hydraulic hybrid power

Energies 2020, 13, 322; doi:10.3390/en13020322 www.mdpi.com/journal/energies Energies 2020, 13, 322 2 of 17 vehicle with LMS AMESim software. Change of size of accumulator and hydraulic motor/pump and internal pressure greatly improves the output power of the whole system, which also reduces the fuel consumption and pollution of the hydraulic hybrid vehicles. The energy control strategy can be divided into two categories [4]: (1) rule-based strategy and (2) optimization-based strategy. For optimization strategy, Lu et al. [5] introduced the weighted-sum method and no-preference method to solve the multi-objective optimization problem of plug-in electric vehicles, and it was validated with ADVISOR software. Zeng et al. [6] proposed a different strategy, Equivalent Consumption Minimum Strategy (ECMS), to solve the optimization problem of PHEV, and the Simplified-ECMS strategy could effectively shorten the calculation time. Wang et al. [7] applied the Dynamic Programming for PHEV and received an approximately 20% improvement in fuel economy. The rule-based control, featuring a smaller amount of calculation, is adopted by many studies, to design the energy management strategy. Yu et al. [8] developed a simulation model and rule-based control strategy for extended-range electric vehicle (E-REV) and showed that a small engine can be used to reduce the weight of vehicle and batteries of E-REV. Gao et al. [9] proposed two control strategies, thermostat and power follower. With dynamic programming, it showed that the thermostat control strategy optimized the operation of the internal combustion engine, and the power follower control strategy minimizes the battery-charging and -discharging operations. Konev et al. [10] developed a control strategy for series hybrid vehicle. The control strategy was to ensure gradual operation of the motor along the steady-state Optimal Operating Points Line (OOP-Line) in the engine speed–torque map, which could improve the efficiency of series hybrid vehicle. Liu et al. [11] developed a control strategy for a series hybrid vehicle which included two parts, constant SOC control, and driving-range optimization. Comparing to thermostat control strategy, the constant SOC control could have a longer driving range. Li et al. [12] proposed a fuzzy logic energy-management system, using the battery working state, which ensured that the engine would operate in the vicinity of its maximum fuel-efficiency region. The rule-based design is fast and easy and can be readily applied to real vehicle-control strategy. However, the rule-based control strategy is simple, so it cannot provide optimal power management to HEV in real time. Therefore, an optimization algorithm is required for rule-based control to improve the energy efficiency. Ho and Klong [13] introduced an optimization algorithm for series plug-in hybrid electric vehicles by utilizing the genetic algorithm (GA), which could determine the optimal driving patterns offline. Xu et al. [14] developed a fuzzy control strategy for parallel hybrid electric vehicle. The control strategy was adjusted with GA. It was verified that GA could effectively improve the efficiency of the engine and fuel consumption. Kaur et al. [15] proposed a control strategy to control the speed of a hybrid electric vehicle. The controller, which was using GA, could improve fuel economy and reduce pollution. Hu and Zhao [16] applied an adaptive based hybrid genetic algorithm to optimize the energy efficiency of parallel hybrid electric vehicles and presented the effectiveness of the hybrid genetic algorithm. Therefore, global optimization, together with rule-based control method, are selected in this paper for medium and heavy vehicles in fixed driving route, to adjust the rule-based control strategy and improve the electricity economic performance of vehicles. The optimization approach selected in this paper is genetic algorithm (GA). With global optimization ability and probability optimization approach, GA can automatically obtain and instruct the optimized search space and adaptively adjust the search direction without the need of clear rules.

2. Modeling In this study, Matlab/Simulink serves as the main simulation program, and backward simulation is used to establish the model. In order to compare the diﬀerence between an HEHV and a pure electric vehicle, subsystem models of the electric system are established, including models of electric motor, generator, and lithium ion battery. The subsystem models of hydraulic system include variable hydraulic motor/pump and accumulator models. The whole vehicle model can be divided into following subsystem models: (1) driver model; (2) vehicle dynamic model; (3) tyre and drive model; Energies 2020, 13, 322 3 of 17 Energies 2020, 13, x FOR PEER REVIEW 3 of 17

drive model; (4) power component element; and (5) energy storage component model. Driving cycle (4) power component element; and (5) energy storage component model. Driving cycle of the EPA of the EPA NYCC is employed in this study to get the vehicle driving force, and then gear ratio of the NYCC is employed in this study to get the vehicle driving force, and then gear ratio of the transmission transmission system is adopted to calculate the torque and speed needed for the motor. In HEHV, system is adopted to calculate the torque and speed needed for the motor. In HEHV, the electric motor the electric motor does not function as the regenerative brake; rather the hydraulic pump is used for does not function as the regenerative brake; rather the hydraulic pump is used for energy recovery. energy recovery. This is introduced in the following. This is introduced in the following.

2.1. Driver Model The EPAEPA NYCCNYCC driving driving cycle cycle for for testing, testing, as as shown shown in Figurein Figure1, is employed1, is employed in this in model. this model. The total The drivingtotal driving time istime 599 is s. 599 The s. stop The timestop accountstime accounts for 35.08% for 35.08% of the of total the driving total driving time. Thetime. maximum The maximum speed andspeed the and average the average speed arespeed 44.6 are and 44.6 11.4 and km 11.4/h, respectively.km/h, respectively. vehicle velocity [km/hr] vehicle velocity

Figure 1. UnitedUnited States States Environmental Environmental Protection Protection Ag Agencyency New New York York City City Cycle Cycle (US (US EPA EPA NYCC) driving cycle. 2.2. Vehicle Dynamic Model 2.2. Vehicle Dynamic Model A vehiclevehicle dynamicdynamic model model is appliedis applied to respondto respond to the to driving the driving tractive tractive eﬀort andeffort resistance and resistance needed for the simulation vehicle. The resistance included rolling resistance (Rr), aerodynamic resistance (Ra), needed for the simulation vehicle. The resistance included rolling resistance (Rr), aerodynamic grading resistance (Rc), and acceleration resistance Rs. The tractive eﬀort for driving needed by the resistance (Ra), grading resistance (Rc), and acceleration resistance Rs. The tractive effort for driving vehicleneeded canby the be vehicle obtained can with be obtained a vehicle with dynamic a vehicle model, dynamic which model, can be which represented can be by represented Equation (1).by TheEquation detailed (1). calculationThe detailed of calculation resistance will of resistance be introduced will be in introduced the following: in the following:

= F+t = R+r + R+a + Rc + Rs (1)(1)

2.2.1. Rolling Resistance 2.2.1. Rolling Resistance During vehicle traveling, interaction force is produced in both radial and axial directions in the During vehicle traveling, interaction force is produced in both radial and axial directions in the area where the wheels make contact with the ground, and there is also deformation between the tyre area where the wheels make contact with the ground, and there is also deformation between the tyre and the ground. The deformation process will be accompanied by a certain energy loss, regardless of and the ground. The deformation process will be accompanied by a certain energy loss, regardless of whether or not it is in tyre or ground. This energy loss is the cause of rolling resistance during wheel whether or not it is in tyre or ground. This energy loss is the cause of rolling resistance during wheel turning. The rolling resistance can be represented by Equation (2), where µr is the rolling resistance turning. The rolling resistance can be represented by Equation (2), where μr is the rolling resistance coeﬃcient, and W is the vehicle weight: coefficient, and W is the vehicle weight:

Rr ==Rr,A ++ Rr,B == µr ∙W (2) , , · (2) 2.2.2. Aerodynamic Resistance

The aerodynamic resistance can be represented by Equation (3) as follows, where CD is the aerodynamic resistance coeﬃcient, ρ is the air density, Af is the front area of the vehicle, v is the vehicle speed, and vw is the wind speed.

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ρ 2 Ra = CD A (v vw) (3) · 2 · f · −

2.2.3. Grading Resistance During climbing, grading resistance is produced due to the inﬂuence of the vehicle weight. During downhill, this resistance becomes the driving force instead. It can be represented by Equation (4), where θ is the slope angle: Rc = Wsin(θ) (4)

2.2.4. Acceleration Resistance The vehicle driving state covers the acceleration and deceleration for most of the time, except on highways, where it is ﬁxed-speed driving. The required force for acceleration can be represented by Equation (5), where a is the acceleration, and g is the gravity acceleration: a Rs = W (5) · g

2.3. Tyre and Drive Model

Vehicle dynamics is used to analyze the vehicle tyre model. The angular speed (ωdrive) and the torque (Tdrive) of its transmission shaft can be represented by Equations (6) and (7), where GR is the ﬁnal transmission gear ratio, r is the tyre radius, ηfd is the transmission eﬃciency, and Ftire is the tyre force.

60 ω = GR v (6) drive ·2π r· · η f d T = F r (7) drive tire· ·GR

2.4. Electric Motor Model A 150 kW permanent magnetic motor was applied in this study. An efficient map of the motor was reproduced from Autonomie simulation software. In simulation, the motor efficiency can be obtained from a 2D look-up table through the efficiency curve shown in Figure2, based on the motor torqueEnergies and2020, speed. 13, x FOR PEER REVIEW 5 of 17 motor torque [Nm] torque motor

FigureFigure 2. 2.E Efficiencyﬃciency of of electric electric motor. motor.

2.5. Variable Hydraulic Motor/Pump Model Axial slope plate plunger type of hydraulic motor/pump is applied in this study, and its efficiency is obtained through a look-up table, as shown in Figure 3. The fluid flow rate (QP/M) and output torque (TP/M) are calculated according to Equations (8) and (9), where DP/M is the maximum hydraulic motor displacement, N is the hydraulic motor speed, Sp is the plate angular position, ηvP/M is the volumetric efficiency, ΔPP/M is the pressure difference at the entry and exit, and ηtP/M is the mechanical efficiency.

/ =//(1000/) (8)

/ =(ΔP///)/63 (9)

Figure 3. Efficiency of hydraulic motor/pump [1].

2.6. Battery Model The battery used in this study was a lithium ion battery. An RC circuit design was applied, as shown in Equation (10), where Vt is the battery terminal voltage, Voc is the battery open-circuit voltage, Ibat is the output current, and Rint is the internal resistance.

Energies 2020, 13, x FOR PEER REVIEW 5 of 17 motor torque [Nm] torque motor

Energies 2020, 13, 322 5 of 17 Figure 2. Efficiency of electric motor.

2.5.2.5. Variable Variable Hydraulic Hydraulic Motor/Pump Motor/Pump Model Model AxialAxial slope plateplate plunger plunger type type of hydraulicof hydraulic motor motor/pump/pump is applied is applied in this in study, this andstudy, its e ffiandciency its efficiencyis obtained is throughobtained a look-upthrough table,a look-up as shown table, in as Figure shown3. The in Figure fluid flow 3. The rate fluid ( QP /Mflow) and rate output (QP/M torque) and output(TP/M) aretorque calculated (TP/M) are according calculated to Equations according (8) to and Equations (9), where (8) DandP/M (9),is the where maximum DP/M is hydraulic the maximum motor hydraulicdisplacement, motorN displacement,is the hydraulic N motoris the speed,hydraulicSp is motor the plate speed, angular Sp is the position, plate angularηvP/M is theposition, volumetric ηvP/M iseffi theciency, volumetric∆PP/M isefficiency, the pressure ΔPP/M diff iserence the pressure at the entry difference and exit, at and the ηentrytP/M is and the mechanicalexit, and ηtP/M effi ciency.is the mechanical efficiency. QP/M = DP/MNSp/(1000ηvP/M) (8) / =//(1000/) (8) T = (S ∆P D η )/63 (9) P//M =(PΔP/P/M/P/M/tP/)/63M (9)

FigureFigure 3. 3. EfficiencyEﬃciency of of hydrau hydrauliclic motor/pump motor/pump [1]. [1].

2.6.2.6. Battery Battery Model Model TheThe battery battery used used in in this this study study was was a a lithium lithium ion ion battery. battery. An An RC RC circuit circuit design design was was applied, applied, as as shown in Equation (10), where Vt is the battery terminal voltage, Voc is the battery open-circuit voltage, shown in Equation (10), where Vt is the battery terminal voltage, Voc is the battery open-circuit Ibat is the output current, and Rint is the internal resistance. voltage, Ibat is the output current, and Rint is the internal resistance.

Vt = Voc I R (10) − bat· int

Since the terminal voltage and the current can be measured, output of battery power Pbat can be received from Equation (11). P = I Voc (11) bat bat· Equation (12) can be obtained by substituting Equation (10) to Equation (11).

0.5 2 Vt Vt 4 R P − − · int· bat Ibat = (12) 2Rint

The battery SOC is expressed by the capacity ampere hour. Since SOC changes with the current charging and discharging, SOC can be obtained from Equation (13), where SOCint is the initial value of the battery. R t Ibatdt SOC = SOC 0 (13) int − Ah Energies 2020, 13, 322 6 of 17

2.7. Accumulator Model For the accumulator model, the inﬂuence due to temperature change was not considered in this study, so the gas-state change is set to be adiabatic process (rapid change, n = 1.4). The relationship between the pressure and volume is shown in Equations (14) and (15), during actual expansion and compression of gas. PVn = C (14) where P is pressure, V is volume of container area, and C is a constant value.

n n n P0V0 = P1V1 = P2V2 = C (15) where P0 is initial pressure, P1 is the maximum activate pressure of accumulator, P2 is the minimum activate pressure of accumulator, V0 is the total volume of accumulator, V1 is the volume of air in accumulator when the pressure is P1, and V2 is the volume of air in accumulator when the pressure is P2. The boundary movement work, Wb, of the accumulator can be expressed by Equation (16).

Z 2 P1 Wb = PdV = P1V1 ln (16) 1 P2

The inlet/outlet ﬂuid, Vf, of the accumulator can be expressed by Equation (17).

V f = V10 V20 −( 1 1 ) 1 ( 1 ) 1 1 n 1 n P0 n P1 n (17) = (V0 V1) (V0 V2) = V2 V1 = P0 n V0 = V0 1 − − − − P2 − P1 P1 P2 −

The accumulator SOC is expressed by the volume. Since the SOC changes with the volume ﬂow rate, the accumulator SOC can be expressed by Equation (18).

R t Qdt 0 SOC = SOCint (18) − V f

2.8. Vehicle Configurations Two vehicles configurations were applied in this study for energy-efficiency comparison. The electric vehicle (EV) configuration is shown in Figure4, and the HEHV configuration is presented inEnergies Figure 20205., 13, x FOR PEER REVIEW 7 of 17

FigureFigure 4.4. Electric vehicle (EV)(EV) conﬁguration.configuration.

Figure 5. Hydraulic electric hybrid vehicle (HEHV) configuration.

3. Optimization Control Strategy In this study, genetic algorithm (GA) was applied as the optimization function. The rule-based control was taken as the energy management strategy of HEHV first, and the simulation result was compared with the pure-electric-vehicle model. Then, the selected optimization approach was implemented for global optimization. From those results, together with a rule-based control approach, the optimal electricity economic performance was obtained. The global optimization calculation was made by genetic algorithm. The objective of GA optimization was to minimize electricity consumption, and the objective function was set to be the reciprocal of the lithium ion battery’s state of charge, SOL Li, as shown in Equation (19). The setting of objective function in GA could correspond to the fitness function, as shown in Equation (20). Parameters of GA set in this study are shown in Table 1. cost = min (1/∑(SOC Li)) (19) Fitness = 1/cost (20)

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Energies 2020, 13, 322 7 of 17 Figure 4. Electric vehicle (EV) configuration.

Figure 5. Hydraulic electric hybrid vehicle (HEHV) conﬁguration.configuration.

3. Optimization Control Strategy In this study, geneticgenetic algorithmalgorithm (GA)(GA) waswas appliedapplied asas thethe optimizationoptimization function.function. TheThe rule-basedrule-based control was was taken taken as as the the energy energy ma managementnagement strategy strategy of HEHV of HEHV first, ﬁrst, and andthe simulation the simulation result result was wascompared compared with with the thepure-electric-vehicle pure-electric-vehicle model. model. Then, Then, the the selected selected optimization optimization approach approach was implementedimplemented forfor global global optimization. optimization. From From those those results, results, together toge withther a rule-basedwith a rule-based control approach, control theapproach, optimal the electricity optimal economicelectricity performanceeconomic performance was obtained. was obtained. The globalglobal optimizationoptimization calculationcalculation waswas mademade byby geneticgenetic algorithm.algorithm. The The objective of GA optimization was toto minimizeminimize electricityelectricity consumption,consumption, and thethe objectiveobjective function was set to be the reciprocal ofof thethe lithiumlithium ion battery’s state ofof charge,charge, SOLSOL Li,Li, asas shownshown inin EquationEquation (19).(19). TheThe settingsetting of objective function in GA could correspond to the ﬁtnessfitness function, as shown in Equation (20). Parameters of GA setset inin thisthis studystudy areare shownshown inin TableTable1 .1. X costcost= =min min (1 (1// ∑((SOCSOC Li))Li)) (19) (19) Fitness = 1/cost (20) Fitness = 1/cost (20)

Table 1. Parameter settings of genetic algorithm (GA).

Gene Length 20 Bits (10 Bits for Both Acceleration and Accumulator SOC) Group number 50 Algebra 40 Mating rate 0.9 Mutation probability 0.01

Table 1. Parameter settings of genetic algorithm (GA).

Gene Length 20 Bits (10 Bits for Both Acceleration and Accumulator SOC) Group number 50 Algebra 40 Mating rate 0.9 Mutation probability 0.01

Two design variables (vehicle acceleration and accumulator SOC) were applied to judge the time to use the hydraulic system in control strategy. The thresholds of vehicle acceleration and accumulator SOC were set as selected variables x and y for optimization, respectively. If the vehicle acceleration was higher than the acceleration threshold and the accumulator SOC was higher than the accumulator threshold, the hydraulic pump provided the required power for vehicle acceleration. If the vehicle acceleration was lower than the acceleration threshold and the accumulator SOC was lower than the accumulator threshold, the electric motor provided the required acceleration power. If the vehicle acceleration was higher than the acceleration threshold andEnergies the2020 accumulator, 13, 322 SOC was lower than the accumulator threshold, the electric motor provided8 of 17 the major portion of required acceleration power. Some other detailed judgements of applying hydraulicin Figure 6pump. To prevent and the the overall calculation control of variablesflow are shx ownand yinfrom Figure exceeding 6. To prevent the maximum the calculation component of variablesscope, the x settingand y constraintsfrom exceeding of the the variables maximum are shown component in the constraintscope, the Equationssetting constraints (21) and (22).of the variables are shown in the constraint Equations (21) and (22). 0 < x 1(vehicle acceleration constraint) (21) 0 < ≤≤ 1 (vehicle acceleration constraint) (21) 0 0 ≤ y ≤ 0.4 (accumulator constraint)0.4(accumulator constraint) (22)(22) ≤ ≤

FigureFigure 6. 6. ControlControl flow ﬂow of of genetic genetic algorithm. algorithm.

TheThe fitness fitness function waswas adaptedadapted to to judge judge whether whether the the solution solution of GA of wasGA suitablewas suitable for the for overall the overallresponse response of the hydraulicof the hydraulic system. Valuessystem. ofValues acceleration of acceleration threshold thresholdx and accumulator x and accumulator threshold y thresholdwere recorded y were each recorded time the each GA time was simulated.the GA was After simulated. the algorithm After the completed algorithm the completed iteration set the of iterationsimulation, set itsof fitnesssimulation, performance its fitness was performanc looked up toe ensurewas looked the value up ofto fitness ensure function the value was reasonable.of fitness functionThe number was reasonable. of mutations The and nu whethermber of themutations optimization and whether was convergence the optimization were checked was convergence during the wereoperation checked of GA.during In this the study,operation the convergenceof GA. In this of study, GA was the judged convergence by the diof ffGAerence was of judged fitness by values the differencebetween theof fitness final four values generations. between Ifthe each final di fffourerence generations. was smaller If each than 1%,difference the optimization was smaller reached than the convergence. From the solution of optimal fitness value, the recorded variables x and y were selected as the optimal set threshold. This set of variables could be implemented in rule-based control algorithm for real-time simulation and improve the energy consumption. With the implement of genetic algorithm, the rule-based control algorithm for real-time simulation could achieve the energy performance close to optimization. The thresholds of vehicle acceleration and accumulator SOC calculated from the genetic algorithm were 0.9 and 0.1, respectively. The diagram of control strategy was modified, as shown in Figure7. Energies 2020, 13, x FOR PEER REVIEW 9 of 17

1%, the optimization reached the convergence. From the solution of optimal fitness value, the recorded variables x and y were selected as the optimal set threshold. This set of variables could be implemented in rule-based control algorithm for real-time simulation and improve the energy consumption. With the implement of genetic algorithm, the rule-based control algorithm for real-time simulation could achieve the energy performance close to optimization. EnergiesThe2020 thresholds, 13, 322 of vehicle acceleration and accumulator SOC calculated from the genetic algorithm9 of 17 were 0.9 and 0.1, respectively. The diagram of control strategy was modified, as shown in Figure 7.

FigureFigure 7. 7. UpdatedUpdated control control strategy strategy from from genetic genetic algorithm. algorithm.

4. Results 4. Results TheThe vehicle vehicle parameters parameters of of the the si simulatedmulated vehicle vehicle are are presented presented in in Table 22.. TheThe massmass ofof vehiclevehicle includesincludes the the gross gross weight, weight, which which is is 7200 7200 kg, and 20 passengers, which is 1600 kg.

Table 2. Vehicle parameters. Table 2. Vehicle parameters.

ParameterParameter SymbolSymbol UnitUnit Value Value VehicleVehicle mass mass WW kgkg 8800 8800 WheelbaseWheelbase cmcm 378 378 WheelWheel track track cmcm 168 168 A 2 FrontFront area area f mm 5.4 5.4 Aerodynamic drag coeﬃcient CD 0.28 Aerodynamic drag coefficient 0.28 Rolling resistance coeﬃcient µr 0.008 Rolling resistance coefficient 0.008 Tire radius r m 0.334 Tire radius r m 0.334 Air density ρ kg/m3 1.225 Air density ρ kg/m 1.225 Gravitational acceleration g m/s2 9.81 s FinalGravitational reduction gear acceleration ratio GRg m/ 9.81 11.5 HydraulicFinal systemreduction weight gear ratio GR kg 11.5 200 Hydraulic system weight kg 200

InIn this section,section, simulation simulation results results of of the the pure pure electric electric vehicle vehicle and HEHVand HEHV are compared, are compared, and that and of thatthe HEHVof the with HEHV optimized with optimized energy management energy management strategy is explored. strategy Withis explored. the energy With consumption the energy of consumptionthe NYCC driving of the cycle NYCC as the driving analysis cycle basis, as the the diﬀ erenceanalysis of basis, component the difference performance of iscomponent discussed. performanceFirstly, the pure is electricdiscussed. vehicle Firstly, was establishedthe pure basedelectric on vehicle the set subsystemwas established model, andbased it wason takenthe set as subsystemthe basic model. model, Then and the it HEHVwas taken model as was the established basic model. based Then on thethe hydraulicHEHV model components was established (hydraulic basedmotor /onpump the hydraulic and accumulator), components and (hydraulic the rule-based motor/pump control and strategy accumulator), was applied and forthe therule-based energy controlmanagement strategy of the was power applied system. for Finally,the energy the rule-based management control of strategy the power was improvedsystem. basedFinally, on the the results from the genetic algorithm, to get the HEHV with optimization energy management strategy.

4.1. EV vs. HEHV (Rule-Based) This section compares the difference between the EV and HEHV and presents the causes of the differences. The operating points of the EV electric motor are presented in Figure8, and those of HEHV electric motor are show in Figure9. It is obvious that the HEHV electric motor does not work at Energies 2020, 13, x FOR PEER REVIEW 10 of 17 Energies 2020, 13, x FOR PEER REVIEW 10 of 17 rule-based control strategy was improved based on the results from the genetic algorithm, to get the HEHVrule-based with controloptimization strategy energy was improved management based strategy. on the results from the genetic algorithm, to get the HEHV with optimization energy management strategy. 4.1. EV vs. HEHV (Rule-Based) 4.1. EV vs. HEHV (Rule-Based) Energies 2020This, 13 ,section 322 compares the difference between the EV and HEHV and presents the causes of the10 of 17 differences.This section The operating compares points the difference of the EV between electr icthe motor EV and are HEHV presented and presents in Figure the 8, causes and those of the of HEHVdifferences. electric The motor operating are show points in ofFigure the EV 9. electrIt is icobvious motor arethat presented the HEHV in electricFigure 8,motor and thosedoes ofnot heavyworkHEHV load at and heavyelectric low load motor speed, and are low so show it speed, was in replaced soFigure it was 9. with replIt isaced aobvious hydraulic with thata hydraulicmotor the HEHV/pump. motor/pump. electric Therefore, motor Therefore, thedoes operating not the efficiencyoperatingwork pointsat heavyefficiency concentrate load pointsand low onconcentrate speed, the high-e so iton wasffi theciency repl highaced-efficiency region, with a and hydraulic region, the HEHV and motor/pump. the features HEHV Therefore, features better electricitybetter the operating efficiency points concentrate on the high-efficiency region, and the HEHV features better economicelectricity performance economic performance than the pure than electric the pure vehicle. electric vehicle. electricity economic performance than the pure electric vehicle.

FigureFigureFigure 8. 8.8.Electric-motor Electric-motorElectric-motor operating pointspoints points of of ofEV. EV. EV.

Figure 9. Electric motor operating points of HEHV (rule-based). FigureFigure 9. 9.Electric Electric motor motor operatingoperating points of of HEHV HEHV (rule-based). (rule-based). In orderIn order to better to better understand understand the the reason reason that that the the HEHV HEHV has has a bettera better economic economic performance performance than In order to better understand the reason that the HEHV has a better economic performance than the pure electric vehicle, the power of electric motors is compared. As shown in Figure 10, the the purethan electricthe pure vehicle, electric vehicle, the power the ofpower electric of electr motorsic motors is compared. is compared. As shown As shown in Figure in Figure 10, the10, the power power of HEHV electric motor is smaller than that of pure electric vehicle. Figure 11 shows the of HEHVpower electric of HEHV motor electric is smaller motor thanis smaller that of than pure that electric of pure vehicle. electric Figure vehicle. 11 shows Figure the 11 comparisonshows the of comparison of battery SOC, where the electricity economic performance of HEHV is greatly improved. Energiesbatterycomparison 2020SOC, 13,, wherex FORof battery PEER the electricitySOCREVIEW, where economic the electricity performance economic performance of HEHV is of greatly HEHV is improved. greatly improved.11 of 17

FigureFigure 10. 10. ElectricElectric motor motor power power comparison comparison of of EV EV and and HEHV. HEHV.

Figure 11. Battery SOC comparison of EV and HEHV.

Table 3 shows the electricity economic performance of the EV and HEHV (rule-based). Since a hydraulic motor functions as the drive at the HEHV’s low speed, the use of an electric motor in the low-efficiency zone is reduced, and the electricity is optimized. The electricity economy of the HEHV has 36.5% improvement over that of EV.

Table 3. Comparison of electricity economic performance between the EV and HEHV (rule-based).

Energy Consumption Electricity Economy (kWh

(kWh) /km) Electric Vehicle (EV) 0.63 0.334 Hydraulic Electric Hybrid Vehicle, HEHV 0.40 0.212 (Rule-Based) Percent difference +36.5% +36.5%

4.2. HEHV (Rule-Based) vs. HEHV (GA) This research had taken the genetic algorithm (GA), together with rule-based control, to perform global optimization, and it got the optimal electricity economic performance. In this section, the HEHV with original rule-based control is compared with the HEHV with modified rule-based control based on the genetic algorithm optimization. The distribution of operating points of the HEHV (rule-based) and HEHV (GA) electric motors is presented in Figures 12 and 13, respectively. The distribution suggests that the operating points of the electric motor after being modified for optimization concentrate more on the high-efficiency region.

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Energies 2020, 13, 322 11 of 17 Figure 10. Electric motor power comparison of EV and HEHV.

FigureFigure 11. 11.Battery BatterySOC SOC comparison of of EV EV and and HEHV. HEHV.

TableTable3 shows 3 shows the electricitythe electricity economic economic performance performance of the EV EV and and HEHV HEHV (rule-based). (rule-based). Since Since a a hydraulichydraulic motor motor functions functions as as the the drive drive at at the the HEHV’sHEHV’s low low speed, speed, the the use use of ofan anelectric electric motor motor in the in the low-elow-efficiencyﬃciency zone zone is reduced, is reduced, and and the electricitythe electric isity optimized. is optimized. The The electricity electricity economy economy of of the the HEHV HEHV has 36.5% improvement over that of EV. has 36.5% improvement over that of EV. Table 3. Comparison of electricity economic performance between the EV and HEHV (rule-based). Table 3. Comparison of electricity economic performance between the EV and HEHV (rule-based). Energy Consumption Electricity Economy (kWh

Energy Consumption(kWh) (kWh) Electricity Economy/km) (kWh /km) Electric Vehicle (EV) 0.63 0.334 Electric Vehicle (EV) 0.63 0.334 Hydraulic Electric Hybrid Vehicle, HEHV Hydraulic Electric Hybrid Vehicle, 0.40 0.212 (Rule-Based) 0.40 0.212 HEHV (Rule-Based) Percent difference +36.5% +36.5% Percent difference +36.5% +36.5% 4.2. HEHV (Rule-Based) vs. HEHV (GA) 4.2. HEHV (Rule-Based) vs. HEHV (GA) This research had taken the genetic algorithm (GA), together with rule-based control, to Thisperform research global hadoptimization, taken the and genetic it got algorithm the optimal (GA), electricity together economic with performance. rule-based control, In this section, to perform globalthe optimization, HEHV with original and it got rule-based the optimal control electricity is compared economic with performance.the HEHV with In modified this section, rule-based the HEHV with originalcontrol based rule-based on the control genetic is algorithm compared optimization with the HEHV. The withdistribution modified of rule-basedoperating points control of basedthe on HEHV (rule-based) and HEHV (GA) electric motors is presented in Figures 12 and 13, respectively. the genetic algorithm optimization. The distribution of operating points of the HEHV (rule-based) and The distribution suggests that the operating points of the electric motor after being modified for HEHV (GA) electric motors is presented in Figures 12 and 13, respectively. The distribution suggests optimization concentrate more on the high-efficiency region. that the operating points of the electric motor after being modified for optimization concentrate more

Energieson the 2020 high-e, 13, x ﬃFORciency PEER region.REVIEW 12 of 17

FigureFigure 12. 12.EfficiencyEﬃciency points points of the of the HEHV HEHV (rule-based) (rule-based) electric electric motor. motor.

Figure 13. Efficiency points of the HEHV (GA) electric motor.

To understand the motor-use state, the power is compared in this paper, as shown in Figure 14. The usage rate of the electric motor after optimization is less than the original rule-based control strategy, so that better electricity economic performance is reached.

Figure 14. Power comparison between the HEHV (rule-based) and HEHV (GA) electric motors.

The reason why the electric-motor-usage rate after optimization is less can be explained with the help of a comparison of hydraulic motor power, as shown in Figure 15. It is found that the HEHV after optimization uses more hydraulic energy than the original control strategy.

EnergiesEnergies 2020 2020, ,13 13, ,x x FOR FOR PEER PEER REVIEW REVIEW 1212 of of 17 17

Energies 2020, 13, 322 12 of 17 FigureFigure 12. 12. Efficiency Efficiency points points of of the the HEHV HEHV (rule-based) (rule-based) electric electric motor. motor.

FigureFigureFigure 13. 13. 13. Efficiency EfficiencyEﬃciency points points points of of of the the the HE HE HEHVHVHV (GA) (GA) (GA) electric electric motor. motor.

ToTo understand understand thethe motor-usemotor-use state,state, thethe powerpower isis coco comparedmparedmpared inin thisthis paper,paper, as as shown shown in in Figure Figure 14.1414.. TheThe usageusage raterate ofof the the electric electric motor motor afterafter optimization optimization isis lessless thanthan thethe original original rule-basedrule-based controlcontrol strategy,strategy, so so that that better better electricity electricityelectricity economic economiceconomic performance performanceperformance isis reached.reached.

FigureFigureFigure 14. 14. Power PowerPower comparison comparison comparison between between between the the the HEHV HEHV HEHV (rule- (rule- (rule-based)based)based) and and HEHV HEHV (GA) (GA) electric electric motors. motors.

TheThe reason reason why why thethe electric-motor-usageelectric-motor-usageelectric-motor-usage rate raterate after afteafter optimizationr optimizationoptimization is is lessis lessless can cancan be be explainedbe explainedexplained with withwith the thethehelp helphelp of a of comparisonof aa comparisoncomparison of hydraulic ofof hydraulichydraulic motor motor power,motor power, aspower, shown asas in shownshown Figure in in15 Figure.Figure It is found 15.15. ItIt that isis found thefound HEHV thatthat after thethe HEHVEnergiesHEHVoptimization 2020 afterafter, 13 optimizationoptimization, usesx FOR more PEER REVIEW hydraulic usesuses moremore energy hydraulichydraulic than energy theenergy original thanthan controlthethe originaloriginal strategy. controlcontrol strategy.strategy. 13 of 17

FigureFigure 15. 15. ComparisonComparison between between HEHV HEHV (rule-based) (rule-based) and and HEHV HEHV (GA) (GA) hydraulic hydraulic motor motor power. power.

As suggested by the comparison of operating-point distribution of hydraulic motor/pump of two control strategies (Figure 16) and state of accumulator use (Figure 17), there are more operating points for the hydraulic motor/pump after optimization than for the original strategy, and they tend to be in the high-efficiency zone. The accumulator is applied more completely due to the wider range of applications for the hydraulic motor/pump.

Figure 16. Comparison of operating-point distribution of hydraulic motor/pump (GA vs. rule-based).

Figure 17. Comparison between HEHV (rule-based) and HEHV (GA) accumulator SOC.

Energies 2020, 13, x FOR PEER REVIEW 13 of 17

Energies 2020, 13, 322 13 of 17 Figure 15. Comparison between HEHV (rule-based) and HEHV (GA) hydraulic motor power.

AsAs suggested byby thethe comparisoncomparison of of operating-point operating-point distribution distribution of hydraulicof hydraulic motor motor/pump/pump of two of twocontrol control strategies strategies (Figure (Figure 16) and 16) stateand state of accumulator of accumulator use (Figure use (Figure 17), there 17), arethere more are operatingmore operating points pointsfor the for hydraulic the hydraulic motor motor/pump/pump after optimizationafter optimizatio thann forthan the for original the original strategy, strategy, and they and tendthey tend to be toin be the in high-e the high-efficiencyﬃciency zone. Thezone. accumulator The accumulator is applied is applied more completely more completely due to thedue wider to the range wider of rangeapplications of applications for the hydraulic for the hydraulic motor/pump. motor/pump.

FigureFigure 16. 16.Comparison Comparison ofof operating-pointoperating-point distribution distribution of of hydraulic hydraulic motor/pump motor/pump (GA (GA vs. vs. rule-based). rule-based).

FigureFigure 17. 17. ComparisonComparison between between HEHV HEHV (rule-ba (rule-based)sed) and and HEHV HEHV (GA) (GA) accumulator accumulator SOCSOC..

The analysis above indicates that the electricity economic performance of the HEHV after optimization is more improved. Figure 18 shows the battery SOC comparison of the EV and HEHV (Rule based) and HEHV (GA). It is clear that the HEHV after optimization is more improved than the HEHV with original strategy. The electricity economic performance of the HEHV (rule-based) and HEHV (GA) is drawn in Table4. Table5 shows the percentage improvement of electricity in this study. The HEHV with original rule-based control shows 36.5% improvement over the EV, and the HEHV with modiﬁed rule-based control has 43.7% improvement. Energies 2020, 13, x FOR PEER REVIEW 14 of 17

FigureFigure 18. 18. BatteryBattery SOCSOC comparisoncomparison between between HEHV HEHV (Rule (Rule based), based), HEHV HEHV (GA), (GA), and and EV. EV.

The electricity economicTable 4. Comparison performance between of the electricity HEHV economic(rule-based) performances. and HEHV (GA) is drawn in Table 4. Table 5 shows the percentage improvement of electricity in this study. The HEHV with original rule-based control shows 36.5%Power improvem Consumptionent over (kWh) the EV, Electricity and the Economy HEHV with (kWh modified/km) rule-basedHydraulic control Electric has Hybrid 43.7% Vehicle, improvement. 0.40 0.212 HEHV (Rule-Based) Hydraulic Electric Hybrid Vehicle, Table 4. Comparison between 0.355electricity economic performances. 0.188 HEHV (GA) Percent diﬀerence +11.3%Power Consumption Electricity+11.4% Economy

(kWh) (kWh/km) Hydraulic Electric Hybrid Vehicle,Table HEHV 5. Electricity improvement percentage. 0.40 0.212 (Rule-Based) Hydraulic Electric Hybrid Vehicle, HEHV (GA) 0.355 Percent of Improvement 0.188 Percent differenceElectric Vehicle (EV) +11.3% +11.4% − Hydraulic Electric Hybrid Vehicle, HEHV (Rule-Based) 36.5% Hydraulic ElectricTable Hybrid 5. Electricity Vehicle, HEHV improvement (GA) percentage. 43.7%

Percent of Improvement 5. Discussion Electric Vehicle (EV) − This researchHydraulic mainly Electric targeted Hybrid the Vehi mediumcle, HEHV and (Rule-Based) large trucks for energy 36.5% efficiency, and a hydraulic hybrid electric powertrainHydraulic Electric system Hybrid was Vehicle, proposed HEHV to apply (GA) on the medium 43.7% duty vehicle for energy efficiency. SimuLink simulation models of the EV and HHEV were built to evaluate the efficiency of 5.the Discussion HHEV system. Compared to the EV system, the HHEV system had better energy efficiency, but the controlThis algorithm research was mainly not optimized. targeted Tothe improve medium the an effidciency, large thetrucks genetic for algorithmenergy efficiency, was implemented and a hydraulicto achieve hybrid the optimized electric powertrain energy effi systemciency. was Since proposed GA was to a apply global on optimization the medium algorithm duty vehicle which for energyrequired efficiency. longer CPU SimuLink time for calculationsimulation andmodels was notof the suitable EV and for real-timeHHEV were control, built the to result evaluate of design the efficiencyvariables ofof GAthe wasHHEV required system. to Compared apply on the to the real-time EV system, control the strategy, HHEV rule-basedsystem had control. better energy In this efficiency,research, two but design the control variables algorithm of GA, thresholdswas not opti ofmized. vehicle accelerationTo improve andthe hydraulicefficiency, accumulatorthe genetic algorithmSOC, were was optimized. implemented These two to optimizedachieve the variables optimized were energy applied efficiency. in the HHEV Since simulation. GA was Froma global the optimizationsimulation result, algorithm Tables which4 and 5required, the rule-based longer modelCPU time with for GA calculation could further and improvewas not thesuitable energy for real-timeefficiency. control, The simulation the result resultsof design show variables that the of electricityGA was required economic to apply performance on the real-time of the designed control strategy,hydraulic rule-based hybrid vehicle control. was In improved this research, by 36.5% two whendesign compared variables toof that GA, of thresholds pure electric of vehicle.vehicle accelerationThe performance and hydraulic of energy consumptionaccumulator SOC after, geneticwere optimized. algorithm These optimization two optimized was improved variables by43.7%. were applied in the HHEV simulation. From the simulation result, Tables 4 and 5, the rule-based model 6. Conclusions

In this study, HEHV energy management strategy was applied, and Matlab/Simulink simulation program was utilized to establish a backward simulation model, to simulate the large-vehicle energy Energies 2020, 13, 322 15 of 17 consumption. Movement situation of power components, SOC of energy storage components, and overall electricity economic performance of the pure electric vehicle and HEHV were obtained with the NYCC driving cycle. The following results can be achieved after simulation in this study:

In the performance analysis of the pure electric vehicle, the electricity consumption of the set • driving cycle is 0.334 kWh/km, and this is taken as the basic model for future comparison. In HEHV analysis (rule-based control), the electricity economic performance after simulation of • the set driving cycle is 0.212 kWh/km, which is greatly improved than 0.334 kWh/km of pure electric vehicle, saving 36.5% of electricity. This is mainly because the hydraulic motor/pump in the pumping mode (energy recovery state) is more able to absorb, recover, and store the vehicle kinetic energy than electric motor, and the hydraulic motor/pump also avoids the application of electric motor at low speed. In the HEHV optimization analysis (genetic algorithm), 11.3% and 43.7% of electricity can be • saved as compared with the HEHV (rule-based control) and pure electric vehicle, respectively. Through the HHEV simulation, the genetic algorithm was able to improve the energy eﬃciency of • the HHEV by adjusting the chosen design variables of control strategy.

Author Contributions: Conceptualization, T.-S.L.; investigation, T.-S.L.; methodology, H.-Y.H.; project administration, J.-S.C.; software, J.-S.C.; validation, H.-Y.H. and J.-S.C. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Conﬂicts of Interest: The authors declare no conﬂict of interest.

Nomenclature

Af front area of the vehicle Ah battery capacity (ampere hour) CD aerodynamic resistance coefficient DP/M maximum hydraulic motor displacement Ft vehicle tractive effort Ftire tyre force GR final transmission gear ratio Ibat battery output current N hydraulic motor speed P accumulator pressure Pbat battery power QP/M hydraulic pump fluid flow rate Ra aerodynamic resistance Rc grading resistance Rint battery internal resistance Rr rolling resistance Rr,A front wheel rolling resistance Rr,B rear wheel rolling resistance Rs acceleration resistance Sp hydraulic pump plate angular position SOC state of charge SOCint initial value battery state of charge SOL Li lithium ion battery SOC Tdrive tyre torque TP/M hydraulic pump torque V volume of accumulator container area Vf accumulator inlet/outlet fluid Energies 2020, 13, 322 16 of 17

Voc battery open circuit voltage Vt battery terminal voltage W vehicle weight Wb accumulator boundary movement work a vehicle acceleration g gravity acceleration r tyre radius vw wind speed ηfd transmission efficiency ηtP/M hydraulic pump mechanical efficiency ηvP/M hydraulic pump volumetric efficiency θ road slope angle µr rolling resistance coefficient ρ air density ωdrive tyre angular speed ∆PP/M hydraulic pump pressure difference at the entry and exit

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