In order to fully exploit the energy-saving advantages of front-and-rear-axle-independent-drive-type electric vehicle (FRID EV) configuration, this paper designs a drive mode control strategy for it. Based on the characteristics of the drive system with FRID EV, this paper categorizes the drive modes into three, and creates a mathematical model to determine the operating range. Then, an improved particle swarm optimization algorithm is used to optimize the torque distribution based on the principle of system efficiency optimality, so as to develop the optimal drive mode switching control strategy after torque optimization. Finally, the whole vehicle model is built in Cruise for joint simulation with MATLAB /Simulink. Results of the simulation indicate that the drive control strategy with improved particle swarm optimization can effectively improve the energy efficiency of the drive system and boost the vehicle economy, when compared to both the simple two-motor torque equalization strategy and the standard particle swarm optimization strategy.
ChuSMajumdarA.Opportunities and challenges for a sustainable energy future. Nature2012; 488(7411): 294–303.
2.
DavisonJBernardYBorken-KleefeldJ, et al. Distance-based emission factors from vehicle emission remote sensing measurements. Sci Total Environ2020; 739: 139688.
3.
Rahbari-AsrNChowMY.Cooperative distributed demand management for community charging of PHEV/PEVs based on KKT conditions and consensus networks. IEEE Trans Ind Inform2014; 10(3): 1907–1916.
4.
ZhouMLiuRDingN, et al. Economy analysis of extended range electric vehicle in different control strategies. In: Proceedings of 2013 2nd international conference on measurement, information and control, Harbin, 16–18 August 2013, pp.1055–1059. New York: IEEE.
5.
MillerMAHolmesAGConlonBM, et al. The GM “Voltec” 4ET50 multi-mode electric transaxle. SAE Int J Engines2011; 4(1): 1102–1114.
6.
WuJLiangJRuanJ, et al. Efficiency comparison of electric vehicles powertrains with dual motor and single motor input. Mech Mach Theory2018; 128: 569–585.
7.
GuJOuyangMLuD, et al. Energy efficiency optimization of electric vehicle driven by in-wheel motors. Int J Automot Technol2013; 14: 763–772.
8.
LuDOuyangMGuJ, et al. Torque distribution algorithm for a permanent brushless DC hub motor for four-wheel drive electric vehicles. J Tsinghua Univ (Sci Technol)2012; 52(4): 451–456.
9.
YuanXWangJ.Torque distribution strategy for a front-and rear-wheel-driven electric vehicle. IEEE Trans Veh Technol2012; 61(8): 3365–3374.
10.
KimMJungDMinK.Fuel economy research on series-type HEV intracity buses with different traction motor capacity combinations. SAE Int J Commer Veh2012; 5(1): 371–385.
11.
GaoYWangWLiY. Optimization of control strategy for dual-motor coupling propulsion system based on dynamic programming method. In: 2019 3rd conference on vehicle control and intelligence (CVCI), Hefei, China, 21–22 September 2019, pp.1–6. New York: IEEE.
12.
QiXWangQXieF, et al. Researching of efficiency optimized torque distribution based on front and rear wheel independently drive electrical vehicle. In: 2016 19th international conference on electrical machines and systems (ICEMS), Chiba, Japan, 13–16 November 2016, pp.1–6. New York: IEEE.
13.
HuMZengJXuS, et al. Efficiency study of a dual-motor coupling EV powertrain. IEEE Trans Veh Technol2014; 64(6): 2252–2260.
14.
YangYPShihYCChenJM.Real-time torque-distribution strategy for a pure electric vehicle with multiple traction motors by particle swarm optimisation. IET Electr Syst Transp2016; 6(2): 76–87.
15.
ZhengQTianSZhangQ.Optimal torque split strategy of dual-motor electric vehicle using adaptive nonlinear particle swarm optimization. Math Probl Eng2020; 2020: 1–21.
16.
HuJZhengLJiaM, et al. Optimization and model validation of operation control strategies for a novel dual-motor coupling-propulsion pure electric vehicle. Energies2018; 11(4): 754.
17.
LinXZhangGWeiS, et al. Energy consumption estimation model for dual-motor electric vehicles based on multiple linear regression. Int J Green Energy2020; 17(8): 488–500.
18.
LuQSorniottiAGruberP, et al. H∞ loop shaping for the torque-vectoring control of electric vehicles: theoretical design and experimental assessment. Mechatronics2016; 35: 32–43.
19.
MutohNKazamaTTakitaK.Driving characteristics of an electric vehicle system with independently driven front and rear wheels. IEEE Trans Ind Electron2006; 53(3): 803–813.
20.
CaoYKroezeRCKreinPT.Multi-timescale parametric electrical battery model for use in dynamic electric vehicle simulations. IEEE Trans Transp Electrif2016; 2(4): 432–442.
21.
LiuZHWeiHLZhongQC, et al. Parameter estimation for VSI-fed PMSM based on a dynamic PSO with learning strategies. IEEE Trans Power Electron2016; 32(4): 3154–3165.
22.
BanksAVincentJAnyakohaC.A review of particle swarm optimization. Part I: background and development. Nat Comput2007; 6: 467–484.
23.
TreleaIC.The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf Process Lett2003; 85(6): 317–325.
24.
YangDLiGChengG.On the efficiency of chaos optimization algorithms for global optimization. Chaos Solit Fractals2007; 34(4): 1366–1375.
25.
ChengZGZhangL QLiX L, et al. Chaotic hybrid particle swarm optimization algorithm based on tent map. Syst Eng Electron2007; 29(1): 103–106.
26.
MingjunJHuanwenT.Application of chaos in simulated annealing. Chaos Solit Fractals2004; 21(4): 933–941.
