新能源汽车动力电池衰退机制与健康状态估计研究概述

doi:10.3901/JME.2024.22.241

机械工程学报 ›› 2024, Vol. 60 ›› Issue (22): 241-256.doi: 10.3901/JME.2024.22.241

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新能源汽车动力电池衰退机制与健康状态估计研究概述

张大禹^1,2, 王震坡^1,2,3,4, 刘鹏^1,2,3,4, 林倪^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研究中心北京 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:2024-01-05 修回日期:2024-05-10 出版日期:2024-11-20 发布日期:2025-01-02
作者简介:张大禹,男,1993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:dayu_zhang@bit.edu.cn;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;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;林倪,男,1990年出生,博士,副研究员,硕士研究生导师。主要研究方向为动力电池健康、安全、成组与管理系统设计。E-mail:6120200190@qq.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:
国家自然科学基金资助项目(52072040)。

Overview of Research on Degradation Mechanism and State of Health Estimation for Traction Battery in New Energy Vehicles

ZHANG Dayu^1,2, WANG Zhenpo^1,2,3,4, LIU Peng^1,2,3,4, LIN Ni^1,2,3,4, ZHANG Zhaosheng^1,2,3,4

1. National Engineering Research Center of 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:2024-01-05 Revised:2024-05-10 Online:2024-11-20 Published:2025-01-02
About author:10.3901/JME.2024.22.241

摘要/Abstract

摘要： 锂离子电池作为新能源汽车的核心部件，准确、高效的衰退机制辨识与健康状态估计对于提升动力电池系统的运行可靠性、降低安全风险以及残值评估具有重要意义。随着新能源汽车智能网联化程度的不断提高及大数据分析手段的快速发展，基于数据驱动的动力电池健康状态估计得到了广泛关注。为系统梳理锂离子电池的衰退机制及健康状态估计研究最新进展，从以下两方面进行总结：在衰退机制方面，分别从电池的负极、正极等结构出发，阐述了不同内部副反应对电池老化的影响，并结合新能源汽车实际运行场景分析了强关联外部使用因素对电池衰退的主导作用；在健康状态估计方面，根据不同数据驱动算法的特点及侧重点对现有研究进行了分类概述，分析比较其优点、局限性与应用场景，并进一步讨论各类典型方法在现阶段实车应用的可行性。最后，面向新能源汽车的实际运行需求，对动力电池健康状态估计领域存在的挑战与发展方向进行了总结与展望。

关键词: 新能源汽车, 动力电池, 衰退机制, 健康状态, 数据驱动

Abstract: Lithium-ion batteries as the core component of new energy vehicles(NEVs), accurate and efficient degradation mechanism identification and state of health(SOH) estimation are of great significance for improving the operational reliability of traction battery systems, reducing safety risks and evaluating residual values. With the increasing degree of intelligent network connections for NEVs and the rapid development of big data analysis technology, data-driven based SOH estimation has gained widespread attention. In order to systematically sort out the latest progress in research on the decline mechanism and health state estimation of lithium-ion batteries, the following two aspects are summarized. Regarding the ageing mechanism, the effects of different internal side reactions on lithium-ion battery degradation are discussed based on the anode, cathode and other battery structures, and combined with the actual operation scenario of NEVs to analyze the dominant role of strongly associated external factors on battery degradation. As for the SOH diagnosis, an overview of existing research is categorized according to the characteristics and focus of different data-driven algorithms, their advantages, limitations and application scenarios are analyzed and compared, and further discussed the feasibility of typical methods in the current stage of real vehicle application. Finally, the challenges and development directions in the field of SOH estimation research are summarized and prospected for the actual operation requirements of NEVs.

Key words: new energy vehicle, traction battery, degradation mechanism, state of health, data-driven

中图分类号:

TM912

张大禹, 王震坡, 刘鹏, 林倪, 张照生. 新能源汽车动力电池衰退机制与健康状态估计研究概述[J]. 机械工程学报, 2024, 60(22): 241-256.

ZHANG Dayu, WANG Zhenpo, LIU Peng, LIN Ni, ZHANG Zhaosheng. Overview of Research on Degradation Mechanism and State of Health Estimation for Traction Battery in New Energy Vehicles[J]. Journal of Mechanical Engineering, 2024, 60(22): 241-256.

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新能源汽车动力电池衰退机制与健康状态估计研究概述

Overview of Research on Degradation Mechanism and State of Health Estimation for Traction Battery in New Energy Vehicles

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