With the aim of economy improvement, emission reduction and prolonging the battery service life, an adaptive parameter optimal energy management strategy is proposed for range extended electric vehicle and a method of multi-objective optimization (MOO) is proposed. Firstly, two strategies based on different threshold parameter types, namely velocity-switch-based multi-operation-point control strategy (MCSv–b) and power-switch-based multi-operation-point control strategy (MCSp–b) are designed. Then, the oil-electric conversion loss rate, comprehensive exhaust emission, and battery capacity loss rate are selected as the optimization objectives. The barebones multi-objective particle swarm optimization is applied in MCSv–b and MCSp–b for solving the MOO problem. The simulation results show a clear conflict that three optimization objectives cannot be optimal under the same solution. And then, the individual with optimal comprehensive objective is taken as the final optimization solution to evaluate the performance of the proposed methodology. As expected, the proposed MCSp–b has a positive effect on prolonging the battery service life while ensuring high fuel economy and low emission. Experimental test results thoroughly validate the proposed approach and this result can be used to improve comprehensive performance levels.
ChenZXiaBYouC, et al. A novel energy management method for series plug-in hybrid electric vehicles. Appl Energy2015; 145: 172–179.
2.
DimitrovaZMaréchalF. Techno-economic design of hybrid electric vehicles using multi objective optimization techniques. Energy2015; 91: 630–644.
3.
XiongRSunFGongX, et al. Adaptive state of charge estimator for lithiumion cells series battery pack in electric vehicles. J Power Sources2013; 242: 699–713.
4.
MartinezCMHuXCaoD, et al. Energy management in plug-in hybrid electric vehicles: recent progress and a connected vehicles perspective. IEEE Trans Veh Technol2017; 66(6): 4534–4549.
5.
LüXQuYWangY, et al. A comprehensive review on hybrid power system for PEMFC-HEV: issues and strategies. Energy Convers Manag2018; 171: 1273–1291.
6.
ZhangSXiongR. Adaptive energy management of a plug-in hybrid electric vehicle based on driving pattern recognition and dynamic programming. Appl Energy2015; 155: 68–78.
7.
PengJHeHXiongR. Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming. Appl Energy2017; 185(pt.2): 1633–1643.
8.
ChenZXiongRCaoJ. Particle swarm optimization-based optimal power management of plug-in hybrid electric vehicles considering uncertain driving conditions. Energy2016; 96: 197–208.
9.
ChenZXiongRWangK, et al. Optimal energy management strategy of a plug-in hybrid electric vehicle based on a particle swarm optimization algorithm. Energies2015; 8(5): 3661–3678.
10.
FernándezRÁCaraballoSCCillerueloFB, et al. Fuel optimization strategy for hydrogen fuel cell range extender vehicles applying genetic algorithms. Renew Sustain Energ Rev2018; 81: 655–668.
11.
ChenZMiCCXiongR, et al. Energy management of a power-split plug-in hybrid electric vehicle based on genetic algorithm and quadratic programming. J Power Sources2014; 248: 416–426.
12.
SongZHofmannHLiJ, et al. Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach. Appl Energy2015; 139: 151–162.
13.
ChenBCWuYYTsaiHC. Design and analysis of power management strategy for range extended electric vehicle using dynamic programming. Appl Energy2014; 113: 1764–1774.
14.
LiLYangCZhangY, et al. Correctional DP-based energy management strategy of plug-in hybrid electric bus for city-bus route. IEEE Trans Vehicular Technol2015; 64(7): 2792–2803.
15.
ZhangSXiongRSunF. Model predictive control for power management in a plugin hybrid electric vehicle with a hybrid energy storage system. Appl Energy2017; 185: 1654–1662.
16.
WangHHuangYKhajepourA, et al. Model predictive control-based energy management strategy for a series hybrid electric tracked vehicle. Appl Energy2016; 182: 105–114.
17.
XiangCDingFWangW, et al. Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control. Appl Energy2017; 189: 640–653.
18.
MusardoCRizzoniGGuezennecY, et al. A-ECMS: an adaptive algorithm for hybrid electric vehicle energy management. Eur J Control2005; 11(4-5): 509–524.
19.
XieSHuXXinZ, et al. Pontryagin’s minimum principle based model predictive control of energy management for a plug-in hybrid electric bus. Appl Energy2019; 236: 893–905.
20.
ZhangSXiongRZhangC. Pontryagin’s minimum principle-based power management of a dual-motor-driven electric bus. Appl Energy2015; 159: 370–380.
21.
HouCOuyangMXuL, et al. Approximate Pontryagin’s minimum principle applied to the energy management of plug-in hybrid electric vehicles. Appl Energy2014; 115: 174–189.
22.
WangXHeHSunF, et al. Comparative study on different energy management strategies for plug-in hybrid electric vehicles. Energies2013; 6(11): 5656–5675.
23.
SongZLiJHanX, et al. Multi-objective optimization of a semi-active battery/supercapacitor energy storage system for electric vehicles. Appl Energy2014; 135: 212–224.
24.
LiXHanLLiuH, et al. Real-time optimal energy management strategy for a dual-mode power-split hybrid electric vehicle based on an explicit model predictive control algorithm. Energy2019; 172: 1161–1178.
25.
MontazeriMFotouhiANaderpourA. Driving segment simulation for determination of the most effective driving features for HEV intelligent control. Veh Syst Dyn2012; 50(2): 229–246.
26.
WonJSLangariR. Intelligent energy management agent for a parallel hybrid vehicle—part II: torque distribution, charge sustenance strategies, and performance results. IEEE Trans Veh Technol2005; 54(3): 935–953.
27.
KheirNASalmanMASchoutenNJ. Emissions and fuel economy trade-off for hybrid vehicles using fuzzy logic. Math Comput Simul2004; 66(2–3): 155–172.
28.
ZhouYRaveyAPéraMC. Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer. Appl Energy2020; 258: 114057.
29.
ShenPZhaoZZhanX, et al. Particle swarm optimization of driving torque demand decision based on fuel economy for plug-in hybrid electric vehicle. Energy2017; 123: 89–107.
30.
RedelbachMÖzdemirEDFriedrichHE. Optimizing battery sizes of plug-in hybrid and extended range electric vehicles for different user types. Energy Policy2014; 73: 158–168.
31.
LiJWangYChenJ, et al. Study on energy management strategy and dynamic modeling for auxiliary power units in range-extended electric vehicles. Appl Energy2017; 194: 363–375.
32.
NandiAKChakrabortyDVazW. Design of a comfortable optimal driving strategy for electric vehicles using multi-objective optimization. J Power Sources2015; 283: 1–18.
33.
LiJJinXXiongR. Multi-objective optimization study of energy management strategy and economic analysis for a range-extended electric bus. Appl Energy2017; 194: 798–807.
34.
ZhangHPengJTanH, et al. Tackling SOC long-term dynamic for energy management of hybrid electric buses via adaptive policy optimization. Appl Energy2020; 269: 115031.
35.
Abdel-MonemMTradKOmarN, et al. Influence analysis of static and dynamic fast-charging current profiles on ageing performance of commercial lithium-ion batteries. Energy2017; 120: 179–191.
36.
LiuHLeiYFuY, et al. An optimal slip ratio-based revised regenerative braking control strategy of range-extended electric vehicle. Energies2020; 13: 1526.
37.
GeringKLSazhinSVJamisonDK, et al. Investigation of path dependence in commercial lithium-ion cells chosen for plug-in hybrid vehicle duty cycle protocols. J Power Sources2011; 196: 3395–3403.
YongZLi-juanYQianZ, et al. Multi-objective optimization of building energy performance using a particle swarm optimizer with less control parameters. J Build Eng2020; 32: 101505.
40.
ShahiSKWangGGAnL, et al. Using the Pareto set pursuing multi-objective optimization approach for hybridization of a plug-In hybrid electric vehicle. J Mech Des2012; 134(9): 503–509.
