融合PER和TL的燃料电池客车能量管理策略更新方法研究

For the problems of low training efficiency and delayed updating in deep reinforcement learning based energy management strategies （EMSs）， taking the fuel cell bas as the research object， an intelligent EMS up dating method integrating prioritized experience replay （PER） and transfer learning （TL） for fuel cell buses is pro posed in this paper. A sampling mechanism-enhanced soft actor-critic （ESAC） algorithm is designed to improve EMS training efficiency by incorporating PER into the SAC framework. Furthermore， a TL-based EMS updating method is proposed to enhance the updating efficiency and long-term optimization performance by leveraging the knowledge-sharing mechanism for cross-cycle knowledge transfer and policy reuse of the ESAC-based EMS. Final ly， the updated EMS is deployed to the energy management controller for online power distribution optimization. The experimental simulation results show that， compared with SAC， the proposed ESAC algorithm improves training effi ciency by 58.33%. Additionally， the proposed updating method enhances EMS updating efficiency by 63.01% and fuel economy by 5.24% over baseline methods， while demonstrating real-time application potential.