| 引用本文: | 蔡 涛,张钊诚,袁奥特,史致远,张博涵.锂离子电池储能安全管理中的机器学习方法综述[J].电力系统保护与控制,2022,50(24):178-187.[点击复制] |
| CAI Tao,ZHANG Zhaocheng,YUAN Aote,SHI Zhiyuan,ZHANG Bohan.Review of machine learning for safety management of li-ion battery energy storage[J].Power System Protection and Control,2022,50(24):178-187[点击复制] |
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| 摘要: |
| 随着当前电化学储能技术的广泛应用，电池储能电站的安全运维问题日渐突出。传统电池管理系统仅能获得各电池单体的电压、电流及温度，并且受限于硬件处理能力、数据传输带宽及延迟等条件，掌握海量电池单体储能系统的健康与安全运行状态成为关键技术难题。机器学习方法在锂离子电池运行状态预测领域的应用为储能电池系统安全管理创造了条件。针对锂离子电池安全管理需求，首先对锂离子电池滥用及热失控风险机理的相关研究进行了介绍。随后，讨论了锂离子电池管理系统架构及其应用特点，并详细论述了机器学习方法在锂离子电池健康与安全状态分析方面的应用。最后，对储能电站锂离子电池的安全管理进行了展望。 |
| 关键词: 锂离子电池储能 电池健康与安全 机器学习 |
| DOI：DOI: 10.19783/j.cnki.pspc.221772 |
| 投稿时间：2021-03-31修订日期：2021-08-25 |
| 基金项目:国家自然科学基金项目资助(U1966214)；武汉强磁场学科交叉基金项目资助(WHMFC202145) |
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| Review of machine learning for safety management of li-ion battery energy storage |
| CAI Tao,ZHANG Zhaocheng,YUAN Aote,SHI Zhiyuan,ZHANG Bohan |
| (State Key Laboratory of Advanced Electromagnetic Engineering and Technology
Huazhong University of Science and Technology), Wuhan 430074, China)) |
| Abstract: |
| Currently the widespread application of electrochemical energy storage technology raises prominent concern on the safety operations of battery energy storage station. The traditional battery management system (BMS) can only support measurement of battery cell’s voltage, current and temperature. And due to limited hardware processing power, data bandwidth and network time delay, it is a key technical problem to master the healthy and safety of battery energy storage system with a large number of battery cells. The application of machine learning for lithium battery state prediction enables better safety management of battery energy storage system. For lithium-ion battery safety management requirements, this paper first provides an overview of related research on the mechanism of lithium battery abuse and thermal runaway. Then, the architecture of lithium-ion battery management system and its application characteristics are discussed and summarized, the usage of machine learning methods in the health and safety state analysis of lithium batteries are introduced in detail. Based on the above review, this paper provides a prospect of the safety management of lithium batteries in energy storage power stations.
This work is supported by the National Natural Science Foundation of China (No. U1966214). |
| Key words: li-ion battery energy storage battery health and safety machine learning |
