大数据驱动的动力电池健康状态估计方法综述

doi:10.3901/JME.2023.02.151

机械工程学报 ›› 2023, Vol. 59 ›› Issue (2): 151-168.doi: 10.3901/JME.2023.02.151

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大数据驱动的动力电池健康状态估计方法综述

王震坡^1,2,3,4, 王秋诗^1,2, 刘鹏^1,2,3,4, 张照生^1,2,3,4

1. 北京理工大学电动车辆国家工程实验室北京 100081;
2. 北京电动车辆协同创新中心北京 100081;
3. 新能源汽车北京实验室北京 100081;
4. 北京理工大学重庆创新中心重庆 401120

收稿日期:2022-01-28 修回日期:2022-09-25 出版日期:2023-01-20 发布日期:2023-03-30
通讯作者: 刘鹏(通信作者),男,1983年出生,博士,副教授,硕士研究生导师。主要研究方向为新能源汽车大数据分析。E-mail:bitliupeng@bit.edu.cn
作者简介:王震坡,男,1976年出生,博士,教授,博士研究生导师。主要研究方向为动力电池成组理论与新能源汽车大数据分析。E-mail:wangzhenpo@bit.edu.cn;王秋诗,男,1994年出生,博士研究生。主要研究方向为新能源汽车动力电池健康管理与新能源汽车大数据分析。E-mail:wangqs_bit@163.com;张照生,男,1984年出生,博士,副教授,硕士研究生导师。主要研究方向为新能源汽车大数据分析。E-mail:zhangzhaosheng@bit.edu.cn
基金资助:
国家重点研发计划（2019YFB1600800）和国家自然科学基金（5207120585）资助项目。

Review on Techniques for Power Battery State of Health Estimation Driven by Big Data Methods

WANG Zhenpo^1,2,3,4, WANG Qiushi^1,2, LIU Peng^1,2,3,4, ZHANG Zhaosheng^1,2,3,4

1. National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081;
2. Collaborative Innovation Center for Electric Vehicles in Beijing, Beijing 100081;
3. Beijing Laboratory of New Energy Vehicles, Beijing 100081;
4. Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120

Received:2022-01-28 Revised:2022-09-25 Online:2023-01-20 Published:2023-03-30

摘要/Abstract

摘要： 动力电池健康状态估计是电池管理系统关键算法之一，对提高动力电池能量利用效率、降低电池热失控风险，以及动力电池的维保和残值评估具有重要意义。对比分析试验法、模型法、数据驱动法的优势和不足，并以数据驱动方法为核心，分别从动力电池健康状态数据集构建、健康状态特征参数提取、健康状态估计模型三个方面对现阶段健康状态估计方法的理论基础和技术方案进行综述。总结常用的大数据采集方法以及数据预处理方法，明确大数据在健康状态评估中的意义。比较现有健康状态特征提取方法，对其优劣以及适用场景做了分析。阐述不同健康状态估计模型的基本原理，提出模型融合是未来技术发展方向。最后，面向未来大数据实车应用场景，对动力电池健康状态估计方面存在的问题和发展前景进行了总结和展望。

关键词: 大数据, 新能源汽车, 动力电池, 健康状态

Abstract: State of health estimation of power batteries is one of the key algorithms of the battery management systems, which is of great significance for improving power battery energy utilization efficiency, reducing thermal runaway risk, as well as power battery maintenance and residual value evaluation. Comparative analysis has been done on experimental-based, model-based and data-driven methods, and data-driven methods are elaborated from three aspects:dataset construction, health indicators extraction, model establishment. The big data collection methods and data preprocessing methods are summarized. The health indicators extraction methods are compared by their pros and cons and applicable scenarios. The basic principles of different health state estimation models are discussed. The conclusion that model fusion is the direction of future technology development is proposed. Finally, facing the future application scenarios of big data in electric vehicles, the current issue and prospective are depicted.

Key words: bid data, new energy vehicle, power battery, state of health

中图分类号:

TM912

王震坡, 王秋诗, 刘鹏, 张照生. 大数据驱动的动力电池健康状态估计方法综述[J]. 机械工程学报, 2023, 59(2): 151-168.

WANG Zhenpo, WANG Qiushi, LIU Peng, ZHANG Zhaosheng. Review on Techniques for Power Battery State of Health Estimation Driven by Big Data Methods[J]. Journal of Mechanical Engineering, 2023, 59(2): 151-168.

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大数据驱动的动力电池健康状态估计方法综述

Review on Techniques for Power Battery State of Health Estimation Driven by Big Data Methods

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