Scholarlink

<p>In order to prevent safety hazards and economic losses caused by sudden battery failures, accurately assessing the state of health (SOH) of lead-acid batteries is essential. Electrochemical impedance spectroscopy (EIS), a noninvasive and information-rich characterization technology, has proven effective for this purpose. This review focuses on the application of EIS in evaluating lead-acid battery SOH. We first provide a comprehensive overview of primary aging mechanisms and their corresponding impedance signatures. We then systematically analyze conventional methods for extracting SOH information from EIS data, including equivalent circuit models, characteristic frequency selection, distribution of relaxation times (DRT), and machine learning. A comparative approach is employed to evaluate the advantages, limitations, and applicable scenarios of each method. Finally, we discuss key challenges in the online application of EIS and offer perspectives on future development trends. This review synthesizes recent research findings and methodological advances to promote intelligent health management technologies for lead-acid batteries.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Application and Research Progress of Electrochemical Impedance Spectroscopy in State-of-Health Estimation for Lead-Acid Batteries

  • 期刊论文

  • 发表日期：

    2026年5月23日

查看全文
  • Le Kang,
  • Jiayuan Xiang,
  • Wei Chen,
  • Yuhong Zhang,
  • Xin Shi,
  • Yuhan Wang,
  • Yang Cao,
  • Jun Xie

摘要

In order to prevent safety hazards and economic losses caused by sudden battery failures, accurately assessing the state of health (SOH) of lead-acid batteries is essential. Electrochemical impedance spectroscopy (EIS), a noninvasive and information-rich characterization technology, has proven effective for this purpose. This review focuses on the application of EIS in evaluating lead-acid battery SOH. We first provide a comprehensive overview of primary aging mechanisms and their corresponding impedance signatures. We then systematically analyze conventional methods for extracting SOH information from EIS data, including equivalent circuit models, characteristic frequency selection, distribution of relaxation times (DRT), and machine learning. A comparative approach is employed to evaluate the advantages, limitations, and applicable scenarios of each method. Finally, we discuss key challenges in the online application of EIS and offer perspectives on future development trends. This review synthesizes recent research findings and methodological advances to promote intelligent health management technologies for lead-acid batteries.