Cooperative Reinforcement Learning for Car-Following and Energy Management Optimization of Dual-Motor Electric Vehicles

Abstract

For distributed drive electric vehicles, energy consumption is affected by the power demand and energy management strategy. In this paper, an adaptive cruise control and energy management strategy cooperative framework for dual-motor electric vehicles is proposed based on the deep deterministic policy gradient algorithm. Firstly, the energy management problem in the car-following scenario is decomposed into two subproblems: the adaptive cruise control governs vehicle acceleration, while the energy management strategy allocates driving torque. Then, based on the cooperative architecture, the speed trajectory and torque allocation strategy are co-optimized to realize cooperation between the adaptive cruise control and the energy management strategy. Finally, the proposed cooperative strategy is compared with the traditional hierarchical strategy under the worldwide harmonized light vehicles test cycle. Results show that the proposed cooperative strategy can reduce the maximum acceleration and maximum deceleration by 10.8% and 10.4%, respectively, and improve energy consumption by 4.3% compared with the traditional hierarchical strategy.