【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3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3个维度提取10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健康状态估计效果。仿真结果表明，所筛选出的三特征组合有效提升了健康状态估计的准确性与稳定性。更多还原

【Abstract】 The estimation of state of health(SOH)for lithium-ion batteries is considered 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, a novel health state feature processing approach is proposed. An improved Hampel filter with adaptive thresholding and multidimensional feature fusion optimization is integrated. The method is composed of three stages. In the initial feature selection stage, ten 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 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 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 threefeature combination significantly improves the accuracy and stability of SOH estimation.更多还原