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The high emission and low energy efficiency caused by internal combustionengines (ICE) have become unacceptable under environmental regulations and theenergy crisis. As a promising alternative solution, multi-power source electricvehicles (MPS-EVs) introduce different clean energy systems to improvepowertrain efficiency. The energy management strategy (EMS) is a criticaltechnology for MPS-EVs to maximize efficiency, fuel economy, and range.Reinforcement learning (RL) has become an effective methodology for thedevelopment of EMS. RL has received continuous attention and research, butthere is still a lack of systematic analysis of the design elements of RL-basedEMS. To this end, this paper presents an in-depth analysis of the currentresearch on RL-based EMS (RL-EMS) and summarizes the design elements ofRL-based EMS. This paper first summarizes the previous applications of RL inEMS from five aspects: algorithm, perception scheme, decision scheme, rewardfunction, and innovative training method. The contribution of advancedalgorithms to the training effect is shown, the perception and control schemesin the literature are analyzed in detail, different reward function settingsare classified, and innovative training methods with their roles areelaborated. Finally, by comparing the development routes of RL and RL-EMS, thispaper identifies the gap between advanced RL solutions and existing RL-EMS.Finally, this paper suggests potential development directions for implementingadvanced artificial intelligence (AI) solutions in EMS.

Progress and summary of reinforcement learning on energy management of MPS-EV