Adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for four-wheel drive hybrid electric vehicles
Restricted accessReview articleFirst published online October, 2019
Adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for four-wheel drive hybrid electric vehicles
An adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for a four-wheel drive plug-in hybrid electric vehicle is proposed in this paper. The equivalent factors of adaptive equivalent consumption minimisation strategy are optimised offline based on ISIGHT software over several typical driving cycles, which is integrated with AVL CRUISE and MATLAB/Simulink. To update the equivalent factor adaptively according to the predictive velocity, a neural network-based optimal equivalent factor prediction model is built, which can be used online. The torque distribution strategy considering axle load based on energy management strategy optimisation results and the vehicle dynamics control distribution is proposed: this includes two-wheel drive torque distribution, four-wheel drive torque distribution and brake torque distribution. The proposed energy management strategy is verified in New European Driving Cycle and Worldwide harmonised Light Vehicle Test Cycle driving patterns, and the simulation results show that the fuel economy of adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy is improved by 8.84% and 7.52% in New European Driving Cycle and Worldwide harmonised Light Vehicle Test Cycle, respectively, compared with the benchmark algorithm-based strategy.
WirasinghaSGEmadiA. Classification and review of control strategies for plug-in hybrid electric vehicles. IEEE T Veh Technol2011; 60: 111–122.
2.
GonderJMarkelT. Energy management strategies for plug-in hybrid electric vehicles. SAE paper 2007-01-0290, 2007.
3.
TribioliLOnoriS. Analysis of energy management strategies in plug-in hybrid electric vehicles: application to the GM Chevrolet Volt. In: 2013 American control conference, Washington, DC, 17–19 June 2013, pp.5966–5971. New York: IEEE.
4.
XieHTianGHuangY, et al. A four stage energy control strategy and fuel economy simulation for extended-range electric city bus. In: 2013 world electric vehicle symposium and exhibition (EVS27), Barcelona, 17–20 November 2013, pp.1–4. New York: IEEE.
5.
MarkelTSimpsonA.Plug-in hybrid electric vehicle energy storage system design. In: 6th International Advanced Automotive Battery and Ultracapacitor Conference (AABC), Baltimore, MD, United states, May 17–19, 2006, pp.1–6. Advanced Automotive Batteries.
6.
LiYLuXKarNC. Rule-based control strategy with novel parameters optimization using NSGA-II for power-split PHEV operation cost minimization. IEEE T Veh Technol2014; 63: 3051–3061.
7.
HeHPengJXiongR, et al. An acceleration slip regulation strategy for four-wheel drive electric vehicles based on sliding mode control. Energies2014; 7: 3748–3763.
8.
PaganelliGDelpratSGuerraTM, et al. Equivalent consumption minimization strategy for parallel hybrid powertrains. In: Vehicular technology conference, VTC Spring 2002, Birmingham, AL, 6–9 May 2002, vol. 4, pp. 2076–2081. New York: IEEE.
9.
SerraoLOnoriSRizzoniG. ECMS as a realization of Pontryagin’s minimum principle for HEV control. In: 2009 American control conference, St. Loui, MO, 10–12 June 2009, pp.3964–3969. New York: IEEE.
10.
SerraoLRizzoniG. Optimal control of power split for a hybrid electric refuse vehicle. In: 2008 American control conference, Seattle, WA, 11–13 June 2008, pp.4498–4503. New York: IEEE.
11.
MusardoCRizzoniGStacciaB. A-ECMS: an adaptive algorithm for hybrid electric vehicle energy management. In: Proceedings of the 44th IEEE conference on decision and control, Seville, 15 December 2005, pp.1816–1823. New York: IEEE.
12.
PisuPRizzoniG. A comparative study of supervisory control strategies for hybrid electric vehicles. IEEE T Contr Syst T2007; 15: 506–518.
13.
WangJHuangYXieH, et al. Driving pattern recognition and energy management for extended range electric bus. In: 2014 IEEE vehicle power and propulsion conference (VPPC), Coimbra, 27–30 October 2014, pp.1–6. New York: IEEE.
14.
WangWXiangCLiuH, et al. A model-predictive-control-based power management strategy for a power-split electromechanical transmission. Proc IMechE, Part D: J Automobile Engineering2016; 230: 1987–2001.
15.
XiangCDingFWangW, et al. Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control. Appl Energ2017; 189: 640–653.
16.
LiuHLiXWangW, et al. Markov velocity predictor and radial basis function neural network-based real-time energy management strategy for plug-in hybrid electric vehicles. Energy2018; 152: 427–444.
17.
LiLYouSYangC, et al. Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses. Appl Energ2016; 162: 868–879.
18.
LiuTHuXLiSE, et al. Reinforcement learning optimized look-ahead energy management of a parallel hybrid electric vehicle. IEEE/ASME T Mech2017; 22: 1497–1507.
19.
GongQLiYPengZR. Trip-based optimal power management of plug-in hybrid electric vehicles. IEEE T Veh Technol2008; 57: 3393–3401.
20.
MouraSJFathyHKCallawayDS, et al. A stochastic optimal control approach for power management in plug-in hybrid electric vehicles. IEEE T Contr Syst T2011; 19: 545–555.
21.
Montazeri-GhMPours madAGhalichiB. Application of genetic algorithm for optimization of control strategy in parallel hybrid electric vehicles. J Frankl Inst2006; 343: 420–435.
22.
LinCCKangJMGrizzleJW, et al. Energy management strategy for a parallel hybrid electric truck. In: Proceedings of the 2001 American control conference (Cat. No.01ch37148), Arlington, VA, 25–27 June 2001, vol. 4, pp.2878–2883. New York: IEEE.
23.
AmbühlDSundströmOSciarrettaA, et al. Explicit optimal control policy and its practical application for hybrid electric powertrains. Control Engineering Practice2010; 18: 1429–1439.
24.
YangYHeXZhangY, et al. Regenerative braking compensatory control strategy considering CVT power loss for hybrid electric vehicles. Energies2018; 11: 497.
25.
de CastroRAraújoREFreitasD. Wheel slip control of EVs based on sliding mode technique with conditional integrators. IEEE T Ind Electron2013; 60: 3256–3271.
26.
YangSChenLLiS. Dynamics of vehicle-road coupled system. Beijing, China: Science Press and Springer-Verlag Berlin Heidelberg, 2015.
27.
SezerVUyganIHartaviA, et al. Maximizing overall efficiency strategy (MOES) for power split control of a parallel hybrid electric vehicle. SAE paper 2008-10-2682, 2008.
28.
JeanneretBMarkelT. Adaptive energy management strategy for fuel cell hybrid vehicles. SAE paper 2004-01-1298, 2004.
29.
SunCSunFCHeHW. Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles. Appl Energ2017; 185: 1644–1653.
30.
TianhengFLinYQingG, et al. A supervisory control strategy for plug-in hybrid electric vehicles based on energy demand prediction and route preview. IEEE T Veh Technol2015; 64: 1691–1700.
31.
ZhangCVahidiA. Route preview in energy management of plug-in hybrid vehicles. IEEE T Contr Syst T2012; 20: 546–553.
32.
YanQ-ZWilliamsJMLiJ. Chassis control system development using simulation: software in the loop, rapid prototyping, and hardware in the loop. SAE paper 2002-01-1565, 2002.
33.
LiuYHLiTYangYY, et al. Estimation of tire-road friction coefficient based on combined APF-IEKF and iteration algorithm. Mech Syst Signal Pr2017; 88: 25–35.
