强化学习
能源管理
工程类
系统工程
计算机科学
风险分析(工程)
能量(信号处理)
人工智能
业务
数学
统计
作者
Hongwen He,Xiangfei Meng,Yong Wang,Amir Khajepour,Xiaowen An,Renguang Wang,Fengchun Sun
标识
DOI:10.1016/j.rser.2023.114248
摘要
Electrified vehicles provide an effective solution to address the unfavorable impacts of fossil fuel use in the transportation sector. Energy management strategy (EMS) is the core technology supporting the outstanding performance of electrified vehicles. However, technical bottlenecks in conventional control methods, such as poor real-time performance and limited generalization capability, significantly hinder the development of energy management technology. The recent advances in deep reinforcement learning (DRL) hold enormous potential in addressing relevant problems. To this end, this paper systematically surveys DRL-based EMSs. First, DRL algorithms and useful DRL extensions are briefly reviewed. Next, a comprehensive literature survey of pioneering and representative working is presented. The effectiveness and configuration methods of DRL in energy management issues, as well as a guideline for DRL-based EMSs in different vehicular structures, are appropriately analyzed and summarized. Finally, the main challenges and potential solutions are extracted to enhance further real-world implementation.
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