Title: An adaptive power distribution control strategy for an electric vehicle with dual-motor coupling in consideration of road gradient
Authors: Jinyong Shangguan; Jianlu Gao; Hongqiang Guo; Qun Sun
Addresses: School of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng 252059, Shandong, China ' Technology Department, Liaocheng Zhongtong Light Passenger Car Co., Ltd., Liaocheng 252000, Shandong, China ' School of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng 252059, Shandong, China ' School of Mechanical & Automotive Engineering, Liaocheng University, Liaocheng 252059, Shandong, China
Abstract: An optimal power distribution control strategy is proposed in consideration of road gradient. Two original contributions are made to distinguish our work from current research. First, a sub-optimal State of Charge (SOC) predictive model is proposed based on Back Propagation (BP) neural network. The sampling set of the BP is obtained from the optimal results from Dynamic Programming (DP), based on a series of driving cycles in real-world and the corresponding road gradient. Second, an adaptive control method based on PID is proposed with the designed sub-optimal SOC predictive model. Specifically, the optimal shift schedule of the coupler is designed offline based on DP and is implemented into the controller in a prior fashion, to decouple the relationship between the coupler and the motors. Simulation results demonstrate that the proposed adaptive control strategy can realise optimally real-time power distribution control and is better than rule-based power distribution strategy.
Keywords: electric vehicle; dual-motor coupling system; sub-optimal SOC predictive model; adaptive power distribution control; shift schedule; back propagation neural network; dynamic programming; road gradient; PID; rule-based power distribution strategy.
International Journal of Vehicle Autonomous Systems, 2019 Vol.14 No.4, pp.367 - 388
Received: 29 Jul 2018
Accepted: 24 Jul 2019
Published online: 24 Sep 2019 *