Adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for four-wheel drive hybrid electric vehicles. (October 2019)

• Record Type: Journal Article
• Authors: Liu, Hui; Li, Xunming; Wang, Weida; Han, Lijin; Xin, Huibin; Xiang, Changle
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 233:Number 12(2019:Dec.)
• Page Start: 3125
• Page End: 3146
• Publication Date: 2019-10
• Subjects: Plug-in hybrid electric vehicle -- four-wheel drive -- energy management strategy -- equivalent consumption minimisation strategy -- ISIGHT -- CRUISE; Mechanical engineering -- Congresses; Transportation engineering -- Congresses; 629.2
• DOI: 10.1177/0954407018816564
• Languages: English
• ISSNs: 0954-4070

Abstract

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.