基于弛豫电压低频特征的锂电池容量在线评估方法

彭湃; 杨毅; 曹学彬; 周琦; 李凯; 喻建瑜; 王红军; 徐成梅

doi:10.19799/j.cnki.2095-4239.2026.0226

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基于弛豫电压低频特征的锂电池容量在线评估方法

储能测试与评价 | 更新时间：2026-05-13

- 基于弛豫电压低频特征的锂电池容量在线评估方法
- Online Capacity Estimation Method for Lithium-Ion Batteries Based on Low-Frequency Features of Relaxation Voltage
- 储能科学与技术 2026年页码：1-11
- 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：
  
  彭湃（1993—），男，博士研究生学历，中级工程师，研究方向为新能源、储能技术研究与试验，E-mail：vampirerichie@163.com；
- 基金信息：
  
  国家重点研发计划资助项目(2023YFB2407904);国网江苏省电力有限公司科技项目(J2024210)
- DOI：10.19799/j.cnki.2095-4239.2026.0226
  中图分类号： TM 911
- 收稿：2026-03-20，
  
  修回：2026-05-06，
  
  网络首发：2026-05-13，
- 稿件说明：
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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

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references

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