Optimal Torque Split Strategy of Dual-Motor Electric Vehicle Using Adaptive Nonlinear Particle Swarm Optimization
Qingxing Zheng, Shaopeng Tian, Qian Zhang
Abstract
In order to exploit the potential of energy saving of dual-motor powertrain over single-motor powertrain, this paper proposes a time-efficient optimal torque split strategy for a front-and-rear-axle dual-motor electric powertrain. Firstly, a physical model of electric vehicle powertrain is established in Matlab/Simulink platform and further validated by real-vehicle experiments. Subsequently, a three-layer energy management strategy composed of demanded torque calculation layer, mode decision layer, and torque split layer is devised to enhance the total operating efficiency of two motors. Specifically, the optimal torque split strategy using adaptive nonlinear particle swarm optimization (ANLPSO) is embedded in the torque split layer. Finally, two conventional strategies (even distributed strategy and rule-based strategy) for dual-motor powertrain are considered for comparison to verify the efficacy of the proposed strategy. Tremendous results demonstrate that the dual-motor powertrain with this proposed optimal torque split strategy develops energy saving by 11.88% and 12.18% against single-motor powertrain in the NEDC and WLTP. Compared to two conventional torque split strategies, it is able to reduce the total motor loss by 12.17% and 8.1% in NEDC and 11.91% and 8.07% in WLTP, respectively, which indicates the prominent optimization performance and a great potential in realistic applications.