Plug‐In Hybrid Electric Vehicle Energy Management with Clutch Engagement Control via Continuous‐Discrete Reinforcement Learning

Abstract

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.

Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review

“…The integration of real-time sensor data into machine learning models for EMS in PHEVs faces several primary challenges. First, the complexity of the hybrid powertrain system, as highlighted in [24], presents a significant obstacle due to its highly non-linear nature. Second, the need to output both continuous and discrete variables simultaneously adds another layer of complexity to the integration process.…”