Online Capacity Estimation Method for Lithium-Ion Batteries Based on Low-Frequency Features of Relaxation Voltage

Peng Pai; Yang Yi; Cao Xuebin; Zhou Qi; Li Kai; Yu Jianyu; Wang Hongjun; Xu Chengmei

doi:10.19799/j.cnki.2095-4239.2026.0226

您当前的位置：

首页 >

文章列表页 >

Online Capacity Estimation Method for Lithium-Ion Batteries Based on Low-Frequency Features of Relaxation Voltage

更新时间：2026-05-13

- Online Capacity Estimation Method for Lithium-Ion Batteries Based on Low-Frequency Features of Relaxation Voltage
- Energy Storage Science and Technology Pages: 1-11(2026)
- 作者机构：
  
  1.国网江苏省电力有限公司电力科学研究院，江苏南京，211103
  2.国网江苏省电力有限公司，江苏南京 210024
  3.云储新能源科技有限公司，山东烟台 264004
- 作者简介：
- 基金信息：
- DOI：10.19799/j.cnki.2095-4239.2026.0226
  CLC： TM 911
- Received：20 March 2026，
  
  Revised：2026-05-06，
  
  Online First：13 May 2026，
- 稿件说明：
移动端阅览
彭湃, 杨毅, 曹学彬, 等. 基于弛豫电压低频特征的锂电池容量在线评估方法[J]. 储能科学与技术, XXXX, XX(XX): 1-11.

Peng Pai, Yang Yi, Cao Xuebin, et al. Online Capacity Estimation Method for Lithium-Ion Batteries Based on Low-Frequency Features of Relaxation Voltage[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-11.
彭湃, 杨毅, 曹学彬, 等. 基于弛豫电压低频特征的锂电池容量在线评估方法[J]. 储能科学与技术, XXXX, XX(XX): 1-11. DOI： 10.19799/j.cnki.2095-4239.2026.0226.

Peng Pai, Yang Yi, Cao Xuebin, et al. Online Capacity Estimation Method for Lithium-Ion Batteries Based on Low-Frequency Features of Relaxation Voltage[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-11. DOI： 10.19799/j.cnki.2095-4239.2026.0226.

摘要

锂离子电池容量的准确估计是电池健康管理的核心环节，对保障电池系统安全运行、识别潜在失效风险具有重要意义。然而，现有容量估计方法依赖特定充放电条件、仅使用简单统计特征，且缺乏对新样本的快速适应能力，导致预测精度与泛化能力受限。针对这些问题，本文提出了一种基于弛豫电压低频特征的锂离子电池容量在线评估方法。该方法通过对电池弛豫电压信号进行离散小波变换，提取能量、熵和标准差等低频特征。在此基础上，采用增量支持向量回归实现新样本的在线更新，并结合粒子群优化对超参数进行自适应调整，显著提升了模型的预测精度与稳定性。实验结果表明，与传统统计特征方法及多种主流机器学习模型相比，本方法在估计精度与鲁棒性方面均具备显著优势，平均绝对百分比误差为0.58%，在多样化工况新样本接入时误差仍能保持在2%以下，表现出良好的泛化能力与快速适应性能。本研究为电池容量高精度在线估计提供了有效技术方案，也为复杂应用场景下的电池健康管理和寿命预测奠定了基础。

Abstract

Accurate estimation of lithium-ion battery capacity is central to battery health management and is essential for ensuring the safe operation of battery systems and identifying potential failure risks. However

existing capacity estimation methods rely on specific charge-discharge conditions

utilize only simple statistical features

and lack the ability to quickly adapt to new samples

resulting in limited prediction accuracy and generalization capability. To address these issues

this study proposes an online capacity estimation method for lithium-ion batteries based on low-frequency features of relaxation voltage. This method extracts low-frequency features such as wavelet energy

wavelet entropy

and standard deviation by performing a discrete wavelet transform (DWT) on the battery relaxation voltage signal. Furthermore

Incremental Support Vector Regression (ISVR) is employed to enable online model updates with new samples

and Particle Swarm Optimization (PSO) is used to adaptively adjust hyperparameters

significantly improving prediction accuracy and stability. Experimental results demonstrate that

compared with traditional statistical feature-based methods and various mainstream machine learning models

the proposed approach exhibits significant advantages in both estimation accuracy and robustness. It achieves a mean absolute percentage error (MAPE) of 0.58%

and maintains an error below 2% even when new samples are introduced under diverse operating conditions

showcasing strong generalization capability and rapid adaptability. This study provides an effective technical solution for high-accuracy online estimation of battery capacity and lays a foundation for battery health management and lifetime prediction in complex application scenarios.

关键词

Keywords

references

KENNEDY B, PATTERSON D, CAMILLERI S. Use of lithium-ion batteries in electric vehicles[J], Journal of Power Sources, 2000, 90(2): 156-162.

田成文, 孙丙香, 赵鑫泽, 等. 基于数据驱动的锂离子电池快速寿命预测[J] 储能科学与技术, 2024, 13(09): 3103-3111.

TIAN C, SUN B, ZHAO X, et al. Accelerated life prediction of lithium-ion batteries using data-driven approaches[J]. Energy Storage Science and Technology, 2024, 13(09):3103-3111.

FAN E, LI L, Wang Z, et al. Sustainable recycling technology for Li-Ion batteries and beyond: challenges and future prospects[J], Chemical Reviews, 2020, 120(14): 7020-7063.

LV X, QU Y, WANG Y, et al, A comprehensive review on hybrid power system for PEMFC-HEV: issues and strategies[J], Energy Conversion and Management, 2018, 171: 1273-1291.

LI, X, JIANG J, Wang L, Chen D, et al. A capacity model based on charging process for state of health estimation of lithium-ion batteries[J], Applied Energy, 2016, 177: 537-543.

HU X, ZOU C, ZHANG C et al. Technological Developments in Batteries: A Survey of Principal Roles, Types, and Management Needs[J], IEEE Power and Energy Magazine, 2017, 15(5), 20-31.

VILSEN S B, STROE D I. Battery state-of-health modelling by multiple linear regression[J]. Journal of Cleaner Production, 2021, 290: 125700.

SUI X, HE S, MENG J H, et al. Fuzzy entropy-based state of health estimation for Li-ion batteriess[J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2021, 9(4): 5125-5137.

LIN M Q, WU D G, MENG J H, et al. A multi-feature-based multimodel fusion method for state of health estimation of lithium-ion model fusion method for state of health estimation of lithium-ion batteries[J]. Journal of Power Sources, 2022, 518: 230774.

TAO J, WANG S, CAO W, et al. A comprehensive review of multiple physical and data-driven model fusion methods for accurate lithium-ion battery inner state factor estimation[J]. Batteries, 2024, 10(12): 442.

PRASADN G. K, RAHN G. K, Model based identification of aging parameters in lithium-ion batteries, Journal of Power Sources, 2013, 232: 79-85.

LU J, WU T, AMINE K. State-of-the-art characterization techniques for advanced lithium-ion batteries[J]. Nature Energy,2017,2: 17011.

EDDAHECH A, BRIAT O, et al. Remaining useful life prediction of lithium batteries in calendar ageing for automotive applications[J]. Microelectronics Reliability, 2012, 52(9-10): 2438-2442.

LI D, WANG W, ISMAIL F. A Mutated Particle Filter Technique for System State Estimation and Battery Life Prediction[J]. IEEE Transactions on Instrumentation and Measurement, 2014, 63(8): 2034-2043.

YU Q,XIONG R,YANG R,et al. Online capacity estimation for lithium-ion batteries through joint estimation method[J]. Applied Energy, 2019, 255: 113817.

徐志成,杨达,张闯,等.基于极化衰减特征与通道关注混合神经网络的锂离子电池容量在线估计[J]. 电工技术学报, 2025, 40(17): 5683-5702.

XU Z, YANG D, ZHANG C, et al. Online estimation of lithium-ion battery capacity Based on Polarization Decay Feature and Hybrid Neural Network with Channel Attention[J]. Transactions of China Electrotechnical Society, 2025, 40(17): 5683-5702.

赵金诺, 王岩, 李阳. 基于随机森林模型的锂离子电池放电容量预测[J]. 电源技术, 2025, 49(09): 1958-1961.

ZHAO J, WANG Y, LI Y. Prediction of discharge capacity of lithium ion batteries based on random forest model[J].Chinese Journal of Power Sources, 2025, 49(09): 1958-1961.

FAN Y, XIAO F, LI C, et al. A novel deep learning framework for state of health estimation of lithium-ion battery[J]. Journal of Energy Storage, 2020, 32: 101741.

TANG A, HUANG Y, XU Y, et al. Data-physics-driven estimation of battery state of charge and capacity[J]. Energy, 2024, 294: 130776.

PAN H, LU Z, Wang H, et al. Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine[J]. Energy, 2018, 160: 466-477.

WIDODO A, SHIM M C, CAESARENDRA W,et al. Intelligent prognostics for battery health monitoring based on sample entropy[J]. Expert Systems with Applications,2011,38(9):11763-11769.

莫易敏, 陈佳浩, 叶鹏, 等. 基于多健康因子LSTM的电池容量估计方法[J ] . 电源学报, 2025, 13 .

MO Y, CHEN J, YE P, et al. Multi-health Factor Estimation of Battery Capacity Based on LSTM[J]. Journal of Power Supply, 2025, 13.

LI X, Yuan C, Wang Z. State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression[J]. Energy, 2020, 203: 117852.

韦海燕, 陈孝杰, 吕治强,等. 灰色神经网络模型在线估算锂离子电池SOH[J]. 电网技术, 2017, 41(12): 4038-4044.

WEI H, CHEN X, LV Z. Online Estimation of Lithium-Ion Battery State of Health Using Grey Neural Network[J]. Power System Technology, 2017, 41(12): 4038-4044.

ZHANG S, ZHU H, WU J, et al. Voltage relaxation-based state-of-health estimation of lithium-ion batteries using convolutional neural networks and transfer learning[J]. Journal of Energy Storage, 2023, 73: 108579.

王海瑞, 徐长宇, 朱贵富, 等. 一种并行多尺度特征融合模型开展的基于弛豫电压的锂电池SOH估计研究[J]. 储能科学与技术, 2025, 14(2):

799-811. WANG H, XU C, ZHU G, et al. A parallel multi cale-featured fusion model for state-of-health estimation of lithium-ion batteries based on relaxation voltage[J]. Energy Storage Science and Technology, 2025, 14(2): 799-811.

VAPNIK V. An overview of statistical learning theory[J], IEEE Transactions on Neural Networks, 1999, 10(5): 988-999.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A parallel multi cale-featured fusion model for state-of-health estimation of lithium-ion batteries based on relaxation voltage

Adaptive hyperparameter-optimized transformer algorithm for lithium-ion battery SOH prediction

Operational status monitoring and evaluation of lithium-ion battery energy storage stations

Study on the effect of arrangement configuration on thermal runaway propagation characteristics of lithium battery packs

A control system for magnesium-based solid-state hydrogen storage devices based on fuzzy PID algorithms

Related Author

WANG Hairui

XU Changyu

ZHU Guifu

HOU Xiaojian

WANG Hairui

XU Changyu

ZHU Guifu

HOU Xiaojian

Related Institution

School of Information Engineering And Automation, Kunming University of Science and Technology

Information Technology Construction Management Center, Kunming University of Science and Technology

School of Information Engineering And Automation(Kunming University of Science And Technology)

Information Technology Construction Management Center(Kunming University of Science And Technology)

Three Gorges Electric Energy Co., Ltd.

⁰