Internal resistance measurement and condition monitoring strategy for chemical power systems

doi:10.19799/j.cnki.2095-4239.2024.0282

Abstract

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

CLC Number:

TM 911

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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