Multi-motor electric heavy-duty vehicles face significant energy efficiency challenges due to the complex coordination of discrete gear selection and continuous energy flow allocation in the powertrains. This study aims to address this coordination dilemma by proposing a multi-agent deep reinforcement learning energy management strategy (EMS). The framework employs collaborative control across three agents to simultaneously optimize middle axle/rear axle gear shifts (DQN) and power distribution (DDPG), effectively handling the hybrid action space. A specialized rule is integrated to accelerate convergence and enhance real-cycle adaptability. Simulation results on CHTC-TT and CHTC-HT cycles show the proposed strategy achieves only 3.14% and 4.65% higher energy consumption, respectively, compared to a rule-optimized benchmark. This validates its practicality and robustness for real-world electric heavy-duty transportation applications.
Wan et al. (Sat,) studied this question.