Evolutionary integrated thermal and energy management strategy based on reinforcement learning for distributed four-wheel drive electric vehicles

Under the impetus of transportation electrification and intelligentization, distributed four-wheel drive electric vehicles (4WD-EVs) have emerged as a pivotal new energy vehicle architecture. However, high-frequency dynamic coordination of multiple motors leads to challenges in energy and thermal management. Traditional energy management strategies and thermal management strategies have been independently optimized, neglecting the intricate coupling relationship between them. This study elucidates the nonlinear coupling between energy management strategies and thermal management strategies in 4WD-EVs and proposes an improved soft actor-critic (SAC) integrated with an evolutionary strategy (ES) to develop an integrated thermal and energy management strategy (ITEMS). The collaborative learning framework enables joint optimization of power distribution and thermodynamic regulation. Compared to model predictive control-based ITEMS, simulation results show the proposed improved SAC based ITEMS reduces energy consumption by 5.52 % for energy management tasks and 15.94 % for thermal management tasks. Meanwhile, it achieves more precise temperature control for motors, battery, and the cabin among these comparison ITEMSs. Moreover, the embedded ES effectively lowers the power consumption of thermal management accessories while improving motor efficiency. This study provides a novel learning framework for breaking through the performance ceiling of 4WD-EVs.