基于深度强化学习的锂离子电池安全快速充电控制策略

IF 2.6 4区 化学 Q3 CHEMISTRY, PHYSICAL
Ionics Pub Date : 2025-05-29 DOI:10.1007/s11581-025-06411-0
Hengwei Xie, Shengzhe Liu, Ruifeng An, Xiaojun Tan
{"title":"基于深度强化学习的锂离子电池安全快速充电控制策略","authors":"Hengwei Xie,&nbsp;Shengzhe Liu,&nbsp;Ruifeng An,&nbsp;Xiaojun Tan","doi":"10.1007/s11581-025-06411-0","DOIUrl":null,"url":null,"abstract":"<div><p>With increasing concerns about charging and range anxiety in electric vehicles (EVs), developing safe and fast charging control strategies is particularly important for ensuring the safety of EVs and improving user’s charging experience. This paper proposes a novel safe and fast charging control strategy for lithium-ion batteries based on deep reinforcement learning, capable of adapting to dynamic changes in the charging environment. Firstly, a unilateral sampling soft actor-critic (USSAC) algorithm is proposed and integrated with an electrochemical-thermal coupling model to train an agent capable of optimizing charging speeds while adhering to multiphysical constraints. The trained agent can then provide a safe and fast charging control strategy and be updated online. Secondly, an adaptive negative pulse regulation method that autonomously adds negative pulses base on multiphysical constraints is proposed to further enhance the safety of the charging process. Finally, the proposed charging strategy is simulated and experimentally verified under different environment conditions. The experimental results show that, compared to the commonly used fast charging strategies, the proposed USSAC-based charging strategy can dynamically provide the optimal charging current in real-time according to the battery environment and its own status, effectively mitigating the risks of overcharge, lithium plating, and thermal hazards.</p></div>","PeriodicalId":599,"journal":{"name":"Ionics","volume":"31 7","pages":"6865 - 6888"},"PeriodicalIF":2.6000,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Safe and fast charging control strategy for lithium-ion batteries based on deep reinforcement learning\",\"authors\":\"Hengwei Xie,&nbsp;Shengzhe Liu,&nbsp;Ruifeng An,&nbsp;Xiaojun Tan\",\"doi\":\"10.1007/s11581-025-06411-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>With increasing concerns about charging and range anxiety in electric vehicles (EVs), developing safe and fast charging control strategies is particularly important for ensuring the safety of EVs and improving user’s charging experience. This paper proposes a novel safe and fast charging control strategy for lithium-ion batteries based on deep reinforcement learning, capable of adapting to dynamic changes in the charging environment. Firstly, a unilateral sampling soft actor-critic (USSAC) algorithm is proposed and integrated with an electrochemical-thermal coupling model to train an agent capable of optimizing charging speeds while adhering to multiphysical constraints. The trained agent can then provide a safe and fast charging control strategy and be updated online. Secondly, an adaptive negative pulse regulation method that autonomously adds negative pulses base on multiphysical constraints is proposed to further enhance the safety of the charging process. Finally, the proposed charging strategy is simulated and experimentally verified under different environment conditions. The experimental results show that, compared to the commonly used fast charging strategies, the proposed USSAC-based charging strategy can dynamically provide the optimal charging current in real-time according to the battery environment and its own status, effectively mitigating the risks of overcharge, lithium plating, and thermal hazards.</p></div>\",\"PeriodicalId\":599,\"journal\":{\"name\":\"Ionics\",\"volume\":\"31 7\",\"pages\":\"6865 - 6888\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2025-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ionics\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11581-025-06411-0\",\"RegionNum\":4,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ionics","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s11581-025-06411-0","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0

摘要

随着人们对电动汽车充电和里程焦虑的关注日益增加,开发安全快速充电控制策略对于确保电动汽车的安全性和改善用户的充电体验尤为重要。提出了一种基于深度强化学习的锂离子电池安全快速充电控制策略,能够适应充电环境的动态变化。首先,提出了一种单边采样软行为者评价(USSAC)算法,并将其与电化学-热耦合模型相结合,训练出能够在遵守多物理约束条件下优化充电速度的智能体。经过训练的智能体可以提供安全快速的充电控制策略,并在线更新。其次,提出了一种基于多物理约束自主添加负脉冲的自适应负脉冲调节方法,进一步提高了充电过程的安全性;最后,在不同的环境条件下对所提出的充电策略进行了仿真和实验验证。实验结果表明,与常用的快速充电策略相比,所提出的基于ussac的充电策略可以根据电池环境和自身状态实时动态提供最优充电电流,有效降低过充、镀锂和热危害风险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Safe and fast charging control strategy for lithium-ion batteries based on deep reinforcement learning

With increasing concerns about charging and range anxiety in electric vehicles (EVs), developing safe and fast charging control strategies is particularly important for ensuring the safety of EVs and improving user’s charging experience. This paper proposes a novel safe and fast charging control strategy for lithium-ion batteries based on deep reinforcement learning, capable of adapting to dynamic changes in the charging environment. Firstly, a unilateral sampling soft actor-critic (USSAC) algorithm is proposed and integrated with an electrochemical-thermal coupling model to train an agent capable of optimizing charging speeds while adhering to multiphysical constraints. The trained agent can then provide a safe and fast charging control strategy and be updated online. Secondly, an adaptive negative pulse regulation method that autonomously adds negative pulses base on multiphysical constraints is proposed to further enhance the safety of the charging process. Finally, the proposed charging strategy is simulated and experimentally verified under different environment conditions. The experimental results show that, compared to the commonly used fast charging strategies, the proposed USSAC-based charging strategy can dynamically provide the optimal charging current in real-time according to the battery environment and its own status, effectively mitigating the risks of overcharge, lithium plating, and thermal hazards.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Ionics
Ionics 化学-电化学
CiteScore
5.30
自引率
7.10%
发文量
427
审稿时长
2.2 months
期刊介绍: Ionics is publishing original results in the fields of science and technology of ionic motion. This includes theoretical, experimental and practical work on electrolytes, electrode, ionic/electronic interfaces, ionic transport aspects of corrosion, galvanic cells, e.g. for thermodynamic and kinetic studies, batteries, fuel cells, sensors and electrochromics. Fast solid ionic conductors are presently providing new opportunities in view of several advantages, in addition to conventional liquid electrolytes.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信