Thermal Management Methodology Based on a Hybrid Deep Deterministic Policy Gradient With Memory Function for Battery Electric Vehicles in Hot Weather Conditions

Abstract

Thermal management systems (TMSs) are crucial for driving safety, mileage, and comfort in battery electric vehicles (BEVs). To maximize the potential of the integrated TMSs in terms of temperature control and energy saving, the deep deterministic policy gradient (DDPG) is utilized to design a learning thermal management methodology (TMM) for it. Considering the key challenges faced, the linear mapping trick and the gated recurrent unit (GRU) are employed to improve the original DDPG. The former empowers the DDPG agents with the ability to make decisions in the discrete-continuous hybrid action space while helping it avoid the “curse of dimensionality.” The latter provides the DDPG agents with beneficial historical information, which enhances its decision-making quality. Simulation results show that the proposed TMM decreases the convergence episode to 77 while increasing the convergence reward to 553.4. Further adaptive tests demonstrate that the suggested TMM promptly stabilizes the motor and battery temperatures at the desired values. Simultaneously, energy consumption decreases by 14.71% and 11.45% for two cases compared to the conventional rule-based TMM. In conclusion, the proposed method provides a theoretical foundation for addressing the hybrid action space optimization problem in integrated TMSs.