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Energy Management Optimization for Connected Hybrid Electric Vehicle with Offline Reinforcement Learning

  • Niu Zegong
  • , He Hongwen*
  • , Wang Yong
  • , Huang Ruchen
  • *此作品的通讯作者
  • Beijing Institute of Technology

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Recently, offline deep reinforcement learning (DRL) which is a branch of DRL has been widely concerned and sets off an upsurge of researches related to it. In this paper, we apply a new algorithm called batch-constrained deep Q-learning (BCQ) to search the optimal control strategy for hybrid electric vehicles (HEVs). Then, thanks to the superiority of offline DRL, we put forward a scheduled training framework of energy management strategy (EMS) based on connected vehicles. The results show the BCQ based strategy performs well in both the stabilization of battery SoC and the fuel economy, which reaches 95.05% of DP. Offline DRL possesses a significant applied perspective and provides a new approach for power allocation of HEVs.

源语言英语
主期刊名ICEIEC 2022 - Proceedings of 2022 IEEE 12th International Conference on Electronics Information and Emergency Communication
编辑Li Wenzheng
出版商Institute of Electrical and Electronics Engineers Inc.
103-106
页数4
ISBN(电子版)9781665407540
DOI
出版状态已出版 - 2022
活动12th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2022 - Beijing, 中国
期限: 15 7月 202217 7月 2022

出版系列

姓名ICEIEC 2022 - Proceedings of 2022 IEEE 12th International Conference on Electronics Information and Emergency Communication

会议

会议12th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2022
国家/地区中国
Beijing
时期15/07/2217/07/22

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