化学电源内阻测量及状态监测策略分析研究

doi:10.19799/j.cnki.2095-4239.2024.0282

储能科学与技术 ›› 2024, Vol. 13 ›› Issue (10): 3400-3422.doi: 10.19799/j.cnki.2095-4239.2024.0282

化学电源内阻测量及状态监测策略分析研究

蒋杭廷¹^,²(), 张倩倩¹(), 张松通², 祝夏雨², 孟闻捷², 邱景义², 明海²()

^1.北京工业大学材料科学与工程学院，北京 100124
^2.军事科学院防化研究院，北京 100191

收稿日期:2024-04-01 修回日期:2024-05-06 出版日期:2024-10-28 发布日期:2024-10-30
通讯作者: 张倩倩,明海 E-mail:jianghangting@emails.bjut.edu.cn;zhangqianqian@bjut.edu.cn;hai.mingenergy@hotmail.com
作者简介:蒋杭廷（1999—），男，硕士研究生，研究方向为电化学能源材料与器件，E-mail：jianghangting@emails.bjut.edu.cn；
基金资助:
国家自然科学基金项目(62075002)

Internal resistance measurement and condition monitoring strategy for chemical power systems

Hangting JIANG¹^,²(), Qianqian ZHANG¹(), Songtong ZHANG², Xiayu ZHU², Wenjie MENG², Jingyi QIU², Hai MING²()

^1.Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China
^2.Chemical Defense of Institute, Academy of Military Sciences, Beijing 100191, China

Received:2024-04-01 Revised:2024-05-06 Online:2024-10-28 Published:2024-10-30
Contact: Qianqian ZHANG, Hai MING E-mail:jianghangting@emails.bjut.edu.cn;zhangqianqian@bjut.edu.cn;hai.mingenergy@hotmail.com

摘要/Abstract

摘要：

内阻是表征电池寿命以及电池运行状态的重要参数之一，是衡量电子和离子在电极内迁移或扩散难易程度的主要标志，其测量时的准确度易受到测量温度和压力等环境变量的影响，准确检测化学电源内阻对提高电池管理的精度具有指导意义。面对当前内阻测量变量多、误差大和应用单一等问题，本文梳理分析了近年来混合脉冲功率特性法、直流内阻测试法、交流注入法、直流放电法和电化学阻抗谱法这五种典型锂离子电池内阻测量方法的相关研究工作，重点介绍了内阻受内外环境的具体影响，创新性地引入了内阻和电池寿命、电池状态以及电池安全预警之间的关系，为提高化学电源性能评估的准确性、预测化学电源寿命和优化化学电源使用提供了解决方案，最后对内阻的测量方法和机器学习模型的改进策略进行了研判和讨论，提出了内阻测量需要达到测试时间短、测试一致性好和精度高的量化评价指标，有望持续丰富内阻测量方法及其应用，从而进一步推动化学电源内阻的精准测量以及对电池模组的状态监控与分析，为提高各类化学电源内阻的测量精确度提供新的思路和方法借鉴。

关键词: 交流阻抗, 直流阻抗, 电池寿命预测, 健康状态, 机器学习

Abstract:

Internal resistance is a crucial parameter for assessing both the lifespan and battery operation state, serving as a key indicator of the challenges associated with electron and ion migration or diffusion within the electrodes. However, accurately measuring internal resistance can be challenging due to its sensitivity to environmental factors such as temperature and pressure. Precise detection of internal resistance is essential for enhancing the accuracy of battery management systems. Given the current challenges in internal resistance measurement, such as the influence of multiple variables, significant errors, and limited application scope, this paper reviews and analyzes recent research on five typical methods for measuring the internal resistance of lithium batteries, namely, the mixed pulse power characteristic method, DC internal resistance testing method, AC injection method, DC discharge method and electrochemical impedance spectroscopy method. The study focuses on the specific influence of internal and external environments on internal resistance and innovatively explore the relationship between internal resistance and battery life, operational status, and safety alerts. These insights offer a pathway to improve the accuracy of chemical power performance evaluations, predict chemical power life, and optimize chemical power use. Finally, the strategies for improving internal resistance measurement methods, including the integration of machine learning models, are examined and discussed. It proposes quantitative metrics, such as short test time, high test consistency, and superior accuracy, to further refine measurement methods and expand their applications. These advancements are expected to significantly enhance the accuracy of the internal resistance measurement in chemical power systems, improving the monitoring and analysis of battery modules, and provide new ideas for optimizing battery performance across various types of chemical power systems.

Key words: AC impedance, DC impedance, life prediction of battery, state of health, machine learning

中图分类号:

TM 911

蒋杭廷, 张倩倩, 张松通, 祝夏雨, 孟闻捷, 邱景义, 明海. 化学电源内阻测量及状态监测策略分析研究[J]. 储能科学与技术, 2024, 13(10): 3400-3422.

Hangting JIANG, Qianqian ZHANG, Songtong ZHANG, Xiayu ZHU, Wenjie MENG, Jingyi QIU, Hai MING. Internal resistance measurement and condition monitoring strategy for chemical power systems[J]. Energy Storage Science and Technology, 2024, 13(10): 3400-3422.

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Charging internal resistance correlation coefficient	0.1875C	0.3755C	0.5625C	0.7500C	0.9375C
R₀^c	0.961	0.978	0.999	0.977	0.975
R_p^c	0.969	0.966	0.979	0.994	0.994
R_t^c	0.969	0.990	0.989	0.995	0.997

Discharge internal resistance correlation coefficient	0.25C	0.50C	0.75C	1.00C	1.25C
R₀^d	0.963	0.985	0.987	0.999	0.997
R_p^d	0.994	0.992	0.970	0.963	0.965
R_r^d	0.984	0.991	0.994	0.994	0.990

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化学电源内阻测量及状态监测策略分析研究

Internal resistance measurement and condition monitoring strategy for chemical power systems

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