Aging diagnosis of lead-acid batteries based on electrochemical impedance spectroscopy and relaxation time analysis

ZHANG Xinyi; LIU Wei; CHEN Debo; SHOU Zebing; CHENG Hao; LU Yingying

doi:10.19799/j.cnki.2095-4239.2025.0988

您当前的位置：

首页 >

文章列表页 >

Aging diagnosis of lead-acid batteries based on electrochemical impedance spectroscopy and relaxation time analysis

Energy Storage Test\: Methods and Evaluation | 更新时间：2026-07-01

- Aging diagnosis of lead-acid batteries based on electrochemical impedance spectroscopy and relaxation time analysis
- Energy Storage Science and Technology Vol. 15, Issue 4, Pages: 1425-1437(2026)
- 作者机构：
  
  1.浙江大学化学工程与生物工程学院，浙江杭州 310027
  2.阿里云计算有限公司，浙江杭州 310024
- 作者简介：
- 基金信息：
- DOI：10.19799/j.cnki.2095-4239.2025.0988
  CLC： TM 912
- Received：03 November 2025，
  
  Revised：2025-12-25，
  
  Published：28 April 2026
- 稿件说明：
移动端阅览
张馨怡, 刘巍, 陈德波, 等. 基于电化学阻抗谱和弛豫时间分析的铅酸电池老化状态诊断研究[J]. 储能科学与技术, 2026, 15(4): 1425-1437.

ZHANG Xinyi, LIU Wei, CHEN Debo, et al. Aging diagnosis of lead-acid batteries based on electrochemical impedance spectroscopy and relaxation time analysis[J]. Energy Storage Science and Technology, 2026, 15(4): 1425-1437.
张馨怡, 刘巍, 陈德波, 等. 基于电化学阻抗谱和弛豫时间分析的铅酸电池老化状态诊断研究[J]. 储能科学与技术, 2026, 15(4): 1425-1437. DOI： 10.19799/j.cnki.2095-4239.2025.0988.

ZHANG Xinyi, LIU Wei, CHEN Debo, et al. Aging diagnosis of lead-acid batteries based on electrochemical impedance spectroscopy and relaxation time analysis[J]. Energy Storage Science and Technology, 2026, 15(4): 1425-1437. DOI： 10.19799/j.cnki.2095-4239.2025.0988.

摘要

铅酸电池作为数据中心后备电源的关键部件，在长期浮充工况下易发生硫酸盐化、板栅腐蚀等老化现象，导致容量衰减和早期失效，传统的内阻测试方法难以准确评估其健康状态。为此，本研究提出一种基于电化学阻抗谱(electrochemical impedance spectroscopy，EIS)与弛豫时间分布(distribution of relaxation times，DRT)的联合分析方法，系统研究浮充状态下铅酸电池的老化机制。实验选取GFM-360E阀控式铅酸电池，涵盖未服役、服役合格与服役不合格3类老化状态，在不同荷电状态(state of charge，SOC)下进行EIS测试，并利用DRT技术对阻抗数据进行反卷积解析，提取欧姆电阻(

ohm

)、特征峰位(P1~P4)及峰面积等关键参数。研究结果表明，EIS曲线在低频区出现扩散斜线、DRT谱图中P3与P4峰耦合形成宽大巨峰(峰面积大于150%)、

ohm

增幅超过30%等特征，可有效识别活性物质硫酸盐化、电解液干涸与板栅腐蚀等老化模式。通过构建“同SOC不同老化状态”与“同老化状态不同SOC”的对比分析框架，建立了基于EIS-DRT的老化判定指标体系，实现了对铅酸电池老化状态的多维度量化诊断。本方法克服了传统等效电路模型的主观性，提升了老化判断的精度与可靠性，为浮充场景下铅酸电池的精准健康管理提供了有效的技术手段。

Abstract

As key components of backup power systems in data centers

lead-acid batteries are prone to sulfation

grid corrosion

and other aging phenomena under long-term float charge conditions

resulting in capacity attenuation and early failure. The traditional internal resistance test method cannot accurately evaluate the health status of acid-lead batteries. Therefore

a joint analysis method based on electrochemical impedance spectroscopy (EIS) and the distribution of relaxation times (DRT) is proposed to systematically study the aging mechanism of lead-acid batteries under float charge conditions. The GFM-360E valve-regulated lead-acid battery was selected for the experiments

which covered three aging states: not in service

qualified in service

and unqualified in service. EIS measurements were conducted at different states of charge (SOC). Subsequently

DRT was used to deconvolute the impedance data and extract the ohmic resistance (

ohm

)

characteristic peak positions (P1—P4)

peak areas

and other key parameters. The results show that the EIS curve exhibits a diffusion slant in the low-frequency region

coupling the P3 and P4 peaks in the DRT spectrum to form a wide giant peak (peak area

150%)

and an increase of

ohm

of more than 30%. These can effectively identify aging modes such as the sulfation of active substances

electrolyte drying

and grid corrosion. By constructing the comparative analysis framework of "different aging states with the same SOC" and "different SOC values with the same aging state

" the aging judgment index system based on EIS-DRT was

established

and the multi-dimensional quantitative diagnosis of the aging state of lead-acid batteries was realized. This method overcomes the subjectivity of the traditional equivalent circuit model

improves the accuracy and reliability of aging predictions

and provides an effective technical means for the precise health management of lead-acid batteries under floating charge conditions.

关键词

Keywords

references

栾云东, 赵键, 张代乐. 铅酸电池技术的发展历史及展望[J]. 沈阳工程学院学报(自然科学版), 2023, 19(4): 6-9, 15. DOI:10.13888/j.cnki.jsie(ns).2023.04.002.

LUAN Y D, ZHAO J, ZHANG D L. Development history and prospect of lead-acid battery technology[J]. Journal of Shenyang Institute of Engineering (Natural Science), 2023, 19(4): 6-9, 15. DOI:10.13888/j.cnki.jsie(ns).2023.04.002.

裴增洁. 基于数据驱动的阀控式铅酸电池SOH估算与寿命预测方法研究[D]. 北京: 北京交通大学, 2024.PEI Z J. Research on data-driven method for SOH estimation and life prediction of valve-regulated lead-acid batteries[D]. Beijing: Beijing Jiaotong University, 2024.

朱杰, 张金生, 顾剑锋. 长期浮充状态下铅酸电池的SOC和SOH监测[J]. 流体测量与控制, 2025, 6(2): 36-39.

ZHU J, ZHANG J S, GU J F. SOC and SOH monitoring of lead-acid batteries under long-term floating charge[J]. Fluid Measurement & Control, 2025, 6(2): 36-39.

况成忠, 欧世锋. 阀控式密封铅酸蓄电池在线监测与健康评估技术综述[J]. 机电信息, 2024(4): 1-11. DOI:10.19514/j.cnki.cn32-1628/tm.2024.04.001.

KUANG C Z, OU S F. Summary of on-line monitoring and health assessment technology for valve-regulated sealed lead-acid batteries[J]. Mechanical and Electrical Information, 2024(4): 1-11. DOI:10.19514/j.cnki.cn32-1628/tm.2024.04.001.

PANG Z Y, YANG K, SONG Z X, et al. Effects of floating charge ageing on electrochemical impedance spectroscopy of lead-acid batteries[J]. Journal of Energy Storage, 2024, 87: 111322. DOI:10.1016/j.est.2024.111322.

刘润兴, 盖玉成, 杨品哲, 等. 基于交流阻抗谱的铅酸蓄电池健康状态检测[J]. 储能科学与技术, 2023, 12(11): 3499-3507.

LIU R X, GAI Y C, YANG P Z, et al. Health-status detection of lead-acid battery based on AC impedance spectroscopy[J]. Energy Storage Science and Technology, 2023, 12(11): 3499-3507.

KEDDAM M, STOYNOV Z, TAKENOUTI H. Impedance measurement on Pb/H 2 SO 4 batteries[J ] . Journal of Applied Electrochemistry, 1977, 7(6): 539-544. DOI:10.1007/BF00616766.

WANG W B, YAO W X, CHEN W, et al. Directional DC charge-transfer resistance on an electrode-electrolyte interface in an AC nyquist curve on lead-acid battery[J]. Applied Sciences, 2020, 10(6): 1907. DOI:10.3390/app10061907.

CAHAN B D, DAROUX M L, YEAGER E B. Effect of physical and geometric factors on the impedance of electrochemical power sources[J]. Journal of the Electrochemical Society, 1989, 136(6): 1585-1590. DOI:10.1149/1.2096973.

ZHU Y L, JIANG B, ZHU J G, et al. Adaptive state of health estimation for lithium-ion batteries using impedance-based timescale information and ensemble learning[J]. Energy, 2023, 284: 129283. DOI:10.1016/j.energy.2023.129283.

朱一昕, 吴昊, 黎莞伟, 等. 基于静态EIS模型的锂离子电池SOC估计[J]. 电池, 2025, 55(2): 267-272.

ZHU Y X, WU H, LI G W, et al. SOC estimation method of Li-ion battery based on static EIS model[J]. Battery Bimonthly, 2025, 55(2): 267-272.

BADEDA J, KWIECIEN M, SCHULTE D, et al. Battery state estimation for lead-acid batteries under float charge conditions by impedance: Benchmark of common detection methods[J]. Applied Sciences, 2018, 8(8): 1308. DOI:10.3390/app8081308.

KWIECIEN M, BADEDA J, HUCK M, et al. Determination of SoH of lead-acid batteries by electrochemical impedance spectroscopy[J]. Applied Sciences, 2018, 8(6): 873. DOI:10.3390/app8060873.

D'ALKAINE C V, MENGARDA P, IMPINNISI P R. Discharge mechanisms and electrochemical impedance spectroscopy measurements of single negative and positive lead-acid battery plates[J]. Journal of Power Sources, 2009, 191(1): 28-35. DOI:10.1016/j.jpowsour.2008.12.097.

CALBOREAN A, BRUJ O, MURARIU T, et al. Resonance frequency analysis of lead-acid cells: An EIS approach to predict the state-of-health[J]. Journal of Energy Storage, 2020, 27: 101143. DOI:10.1016/j.est.2019.101143.

YANG K, PANG Z Y, SONG Z X, et al. Investigation of distribution of relaxation times responding to valve-regulated lead acid batteries degradation process[J]. Electrochimica Acta, 2025, 514: 145682. DOI:10.1016/j.electacta.2025.145682.

MOHSIN M, PICOT A, MAUSSION P. A new lead-acid battery state-of-health evaluation method using electrochemical impedance spectroscopy for second life in rural electrification systems[J]. Journal of Energy Storage, 2022, 52: 104647. DOI:10.1016/j.est.2022.104647.

NGUYEN T T, DOAN V T, LEE G H, et al. Development of an intelligent charger with a battery diagnosis function using online impedance spectroscopy[J]. Journal of Power Electronics, 2016, 16(5): 1981-1989. DOI:10.6113/jpe.2016.16.5.1981.

BRUJ O, CALBOREAN A. Qualitative characterization of lead-acid batteries fabricated using different technological procedures: An EIS approach[J]. Batteries, 2023, 9(12): 593. DOI:10.3390/batteries9120593.

刘海峰, 万宁坤, 苏琨, 等. 智能变电站蓄电池在线评估方法研究[J]. 制造业自动化, 2024, 46(6): 191-198, 214. DOI:10.3969/j.issn.1009-0134.2024.06.030.

LIU H F, WAN N K, SU K, et al. Research on on-line evaluation method of battery in intelligent substation[J]. Manufacturing Automation, 2024, 46(6): 191-198, 214. DOI:10.3969/j.issn.1009-0134.2024.06.030.

孙国良, 李平, 叶伟, 等. 一种变电站蓄电池电化学阻抗谱的实时采集与监控装置: CN120214613A[P]. 2025-06-27.

PANG Z Y, YANG K, FENG Z Y, et al. Investigation of distribution of relaxation times (DRT) feature attribution in lead-acid battery[J]. Journal of Power Sources, 2025, 652: 237622. DOI:10.1016/j.jpowsour.2025.237622.

WU P C, HSU W C, CHEN J F. Detection on SOC of VRLA battery with EIS[C ] //2013 1st International Future Energy Electronics Conference (IFEEC). November 3-6, 2013 , Tainan, Taiwan, China. IEEE, 2013: 897-902. DOI:10.1109/IFEEC.2013. 6687629.

DENSMORE A, HANIF M. Determining battery SoC using electrochemical impedance spectroscopy and the extreme learning machine[C ] //2015 IEEE 2nd International Future Energy Electronics Conference (IFEEC). November 1-4, 2015 , Taipei, China. IEEE, 2015: 1-7. DOI:10.1109/IFEEC.2015.7361603.

YANG H, CHEN S H, GONG L Q, et al. Online electrochemical behavior analysis on the negative plate of lead-acid batteries during the high-rate partial-state-of-charge cycle[J]. Electrochimica Acta, 2020, 354: 136776. DOI:10.1016/j.electacta. 2020. 136776.

NELATURY S R, SINGH P. Extracting equivalent circuit parameters of lead-acid cells from sparse impedance measurements[J]. Journal of Power Sources, 2002, 112(2): 621-625. DOI:10.1016/S0378-7753(02)00443-3.

CIUCCI F, CHEN C. Analysis of electrochemical impedance spectroscopy data using the distribution of relaxation times: A Bayesian and hierarchical Bayesian approach[J]. Electrochimica Acta, 2015, 167: 439-454. DOI:10.1016/j.electacta.2015.03.123.

ZHUANG Z X, LI J, LUAN W L, et al. Distribution of relaxation times-based analysis of aging mechanisms and prediction of heating domain for alternating current pulse self-heating lithium-ion batteries[J]. Journal of Power Sources, 2024, 623: 235442. DOI:10.1016/j.jpowsour.2024.235442.

LU Y, ZHAO C Z, HUANG J Q, et al. The timescale identification decoupling complicated kinetic processes in lithium batteries[J]. Joule, 2022, 6(6): 1172-1198. DOI:10.1016/j.joule.2022.05.005.

SCHÖNLEBER M, IVERS-TIFFÉE E. The distribution function of differential capacity as a new tool for analyzing the capacitive properties of lithium-ion batteries[J]. Electrochemistry Communications, 2015, 61: 45-48. DOI:10.1016/j.elecom.2015.09.024.

DANZER M A. Generalized distribution of relaxation times analysis for the characterization of impedance spectra[J]. Batteries, 2019, 5(3): 53. DOI:10.3390/batteries5030053.

AKRAM A S, SOHAIB M, CHOI W. SOH estimation of lithium-ion batteries using distribution of relaxation times parameters and long short-term memory model[J]. Batteries, 2025, 11(5): 183. DOI:10.3390/batteries11050183.

SONI R, ROBINSON J B, SHEARING P R, et al. Lithium-sulfur battery diagnostics through distribution of relaxation times analysis[J]. Energy Storage Materials, 2022, 51: 97-107. DOI:10.1016/j.ensm.2022.06.016.

MACDONALD D D. Why electrochemical impedance spectroscopy is the ultimate tool in mechanistic analysis[J]. ECS Transactions, 2009, 19(20): 55-79. DOI:10.1149/1.3247566.

SCHICHLEIN H, MÜLLER A C, VOIGTS M, et al. Deconvolution of electrochemical impedance spectra for the identification of electrode reaction mechanisms in solid oxide fuel cells[J]. Journal of Applied Electrochemistry, 2002, 32(8): 875-882. DOI:10.1023/A: 1020599525160.

张子恒, 耿萌萌, 范茂松, 等. 基于弛豫时间分布法的退役动力电池健康状态评估[J]. 储能科学与技术, 2025, 14(2): 770-778.

ZHANG Z H, GENG M M, FAN M S, et al. SOH estimation based on distribution of relaxation times for the retired power lithium-ion battery[J]. Energy Storage Science and Technology, 2025, 14(2): 770-778.

王春林, 朱广焱, 张鹏博, 等. 弛豫时间分布函数应用于电化学阻抗谱分析[J]. 电源技术, 2021, 45(12): 1569-1572, 1593. DOI:10.3969/j.issn.1002-087X.2021.12.013.

WANG C L, ZHU G Y, ZHANG P B, et al. Application of distribution function of relaxation time in analyzing electrochemical impedance spectroscopy[J]. Chinese Journal of Power Sources, 2021, 45(12): 1569-1572, 1593. DOI:10.3969/j.issn.1002-087X.2021.12.013.

王佳, 黄秋安, 李伟恒, 等. 电化学阻抗谱弛豫时间分布基础[J]. 电化学, 2020, 26(5): 607-627. DOI:10.13208/j.electrochem.200641.

WANG J, HUANG Q A, LI W H, et al. Fundamentals of distribution of relaxation times for electrochemical impedance spectroscopy[J]. Journal of Electrochemistry, 2020, 26(5): 607-627. DOI:10.13208/j.electrochem.200641.

WAN T H, SACCOCCIO M, CHEN C, et al. Influence of the discretization methods on the distribution of relaxation times deconvolution: Implementing radial basis functions with DRTtools[J]. Electrochimica Acta, 2015, 184: 483-499. DOI:10.1016/j.electacta.2015.09.097.

Views

110

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Estimation of cross-characterized EIS parameters of asymmetric supercapacitors based on double-layer machine learning

Estimation of the state of charge in lithium iron phosphate batteries based on electrochemical impedance spectroscopy and HO-TFRNN model

Research progress on electrochemical impedance spectroscopy-based early warning methods for thermal runaway in lithium-ion batteries

State-of-health estimation for lithium-ion batteries using geometric analysis of electrochemical impedance spectroscopy

Investigation of influence of wall roughness on performance of high-power axial flow air turbine for compressed air energy storage

Related Author

LU Yingying

ZHAO Yibing

LI Yiduo

ZHOU Yuhuan

ZUO Zhihui

WANG Hanbo

WANG Yan

TIAN Yumei

Related Institution

College of Instrumentation and Electrical Engineering, Jilin University

College of Chemistry, Jilin University

Hunan University

Army Institute of NBC Defence

School of Automobiles, Tongji University

⁰