Energy management strategy (EMS) is a key technology for plug‐in hybrid electric vehicles (PHEVs). The energy management of certain series–parallel PHEVs involves the control of continuous variables, such as engine torque, and discrete variables, such as clutch engagement/disengagement. Herein, a control‐oriented model is established for a series–parallel plug‐in hybrid system with clutch engagement control from the perspective of mixed‐integer programming. Subsequently, an EMS based on continuous‐discrete reinforcement learning (CDRL), which enables simultaneous output of continuous and discrete variables, is designed. During training, state‐of‐charge (SOC) randomization is introduced to ensure that the hybrid system exhibits optimal energy‐saving performance in both high and low SOC. Finally, the effectiveness of the proposed CDRL strategy is verified by comparing EMS based on charge‐depleting charge‐sustaining (CD‐CS) with rule‐based clutch engagement control and dynamic programming (DP). In the simulation results, it is shown that, under a high SOC, the CDRL strategy proposed in this article can improve energy efficiency by 8.3% compared to CD‐CS, and the energy consumption is just 6.6% higher than the global optimum based on DP, while under a low SOC, the numbers are 4.1% and 3.9%, respectively.

中文翻译：

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通过连续离散强化学习进行离合器接合控制的插电式混合动力汽车能源管理

能源管理策略（EMS）是插电式混合动力汽车（PHEV）的关键技术。某些串并联 PHEV 的能量管理涉及连续变量（例如发动机扭矩）和离散变量（例如离合器接合/分离）的控制。本文从混合整数规划的角度建立了具有离合器接合控制的串并联插电式混合动力系统的面向控制的模型。随后，设计了一种基于连续离散强化学习（CDRL）的EMS，可以同时输出连续变量和离散变量。在训练过程中，引入充电状态（SOC）随机化，以确保混合动力系统在高和低 SOC 下都表现出最佳的节能性能。最后，通过比较基于电荷消耗电荷维持（CD-CS）的 EMS 与基于规则的离合器接合控制和动态规划（DP），验证了所提出的 CDRL 策略的有效性。仿真结果表明，在高SOC下，本文提出的CDRL策略比CD-CS能效提高8.3%，能耗仅比基于全局最优值的高6.6%。 DP，在低 SOC 下，数字分别为 4.1% 和 3.9%。