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| 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 |
| 投稿时间：2025-06-25 修订日期：2025-08-20 |
| DOI： |
| 中文关键词: 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 |
| 英文关键词: lithium-ion battery state of health (SOH) estimation feature extraction correlation analysis adaptive threshold improved hampel filtering(ATHF) |
| 基金项目:国家自然科学基金项目（面上项目，重点项目，重大项目） |
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| 中文摘要: |
| 锂离子电池 健康状态估计是保障电池可靠性和寿命预测的关键环节。针对现有锂离子电池健康状态估计方法在异常数据处理和特征选择上的不足，提出了一种自适应门限改进Hampel滤波与多维特征组合优化锂离子电池健康状态特征处理方法。该方法分为三个阶段：在特征初选阶段，从充电过程、放电过程以及容量增量曲线三个维度提取十个特征；在特征修正阶段，设计自适应门限Hampel滤波，通过动态调整窗口和阈值修正特征数据异常值；在特征筛选阶段，采用Pearson-Spearman双相关分析筛选出关键特征。最后，基于NASA数据集，利用门控循环单元验证不同特征组合的SOH估计效果。仿真结果表明，所筛选出的三特征组合有效提升了SOH估计的准确性与稳定性。 |
| 英文摘要: |
| State of Health (SOH) estimation for lithium-ion batteries is a critical task to ensure battery reliability and enable accurate lifetime prediction. To address the limitations of existing SOH estimation methods in handling outliers and selecting effective features, proposes a novel health state feature processing approach that integrates an improved Hampel filter with adaptive thresholding and multidimensional feature fusion optimization. The proposed method consists of three stages. In the initial feature selection stage, eleven candidate 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 fil-tering algorithm is developed to dynamically adjust the win-dow size and threshold for correcting abnormal feature val-ues. In the feature selection stage, a dual-correlation analysis using both Pearson and Spearman coefficients 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. |
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