Learning-based intelligent energy management systems for plug-in hybrid electric vehicles (PHEVs) are crucial for achieving efficient energy utilization. However, their application faces system reliability challenges in the real world, which prevents widespread acceptance by original equipment manufacturers (OEMs). This paper begins by establishing a PHEV model based on physical and data-driven models, focusing on the high-fidelity training environment. It then proposes a real-vehicle application-oriented control framework, combining horizon-extended reinforcement learning (RL)-based energy management with the equivalent consumption minimization strategy (ECMS) to enhance practical applicability, and improves the flawed method of equivalent factor evaluation based on instantaneous driving cycle and powertrain states found in existing research. Finally, comprehensive simulation and hardware-in-the-loop validation are carried out which demonstrates the advantages of the proposed control framework in fuel economy over adaptive-ECMS and rule-based strategies. Compared to conventional RL architectures that directly control powertrain components, the proposed control method not only achieves similar optimality but also significantly enhances the disturbance resistance of the energy management system, providing an effective control framework for RL-based energy management strategies aimed at real-vehicle applications by OEMs.

翻译：基于学习的插电式混合动力汽车智能能量管理系统对于实现高效能量利用至关重要。然而，其实际应用面临系统可靠性挑战，阻碍了原始设备制造商的广泛接受。本文首先基于物理和数据驱动模型建立PHEV模型，重点关注高保真训练环境。随后提出一种面向实车应用的控制框架，将基于时域扩展强化学习的能量管理与等效燃油消耗最小化策略相结合以提升实际适用性，并改进了现有研究中基于瞬时驾驶循环与动力总成状态的等效因子评估缺陷方法。最后通过综合仿真与硬件在环验证表明，所提控制框架在燃油经济性方面优于自适应ECMS及基于规则的策略。相较于直接控制动力总成组件的传统强化学习架构，所提控制方法不仅实现了相近的最优性，还显著提升了能量管理系统的抗干扰能力，为原始设备制造商面向实车应用的强化学习能量管理策略提供了有效控制框架。