电力需求侧管理2026，Vol.28Issue(1)：41-47,7.DOI:10.3969/j.issn.1009-1831.2026.01.006
基于自适应门限改进Hampel滤波与多维特征组合优化的锂离子电池健康状态估计方法
A method for estimating the state of health of lithium-ion batteries based on adaptive threshold improved Hampel filtering and multi-dimensional feature combination optimization
摘要
Abstract
The estimation of state of health(SOH)for lithium-ion batteries is considered a critical task to ensure battery reliability and en-able accurate lifetime prediction.To address the limitations of existing SOH estimation methods in handling outliers and selecting effective features,a novel health state feature processing approach is proposed.An improved Hampel filter with adaptive thresholding and multidi-mensional feature fusion optimization is integrated.The method is composed of three stages.In the initial feature selection stage,ten candi-date features are extracted from three perspectives:the charging process,the discharging process,and the capacity increment curve.In the feature correction stage,an adaptive-threshold Hampel filtering algorithm is developed,which dynamically adjusts the window size and threshold to correct abnormal feature values.In the feature selection stage,dual-correlation analysis using both Pearson and Spearman co-efficients is employed to identify key features.Finally,using the NASA battery dataset,a gated recurrent unit(GRU)network is adopted to evaluate the performance of different feature combinations for SOH estimation.Simulation results demonstrate that the selected three-feature combination significantly improves the accuracy and stability of SOH estimation.关键词
锂离子电池/健康状态估计/特征提取/相关性分析/自适应门限Hampel滤波Key words
lithium-ion battery/state of health estimation/feature extraction/correlation analysis/adaptive threshold improved hampel fil-tering分类
信息技术与安全科学引用本文复制引用
傅质馨,叶雯文,金振强,王健,王昊,邓超..基于自适应门限改进Hampel滤波与多维特征组合优化的锂离子电池健康状态估计方法[J].电力需求侧管理,2026,28(1):41-47,7.基金项目
国家自然科学基金青年项目(52207091) （52207091）
