用可解释特征选择+DLinear模型，提升电动车电池健康度预测精度
State-of-Health Prediction for EV Lithium-Ion Batteries via DLinear and Robust Explainable Feature Selection
- 结合皮尔逊相关与SHAP值选出关键特征，捕捉电池个体差异
- DLinear比LSTM和Transformer更准且更省算力，仅需少量训练轮次
- 适合需要可解释性与实时性的电动车电池管理系统应用
精确预测锂离子电池的健康状态（SOH）对保障电动汽车（EV）的安全、可靠与高效运行至关重要。由于电池组内各单体间存在非均匀退化（细胞间变异性，CtCV），给实时电池管理带来挑战。本文提出一种面向电动车主机系统的可解释数据驱动型SOH预测框架，融合鲁棒特征工程与DLinear模型。基于NASA电池老化数据集，从电压、电流、温度和时间曲线中提取20个有意义特征，并采用皮尔逊相关与SHAP值进行特征筛选。基于SHAP的特征选择在多个电池上呈现一致重要性，有效反映CtCV特性。DLinear在预测精度上优于LSTM与Transformer，且训练轮次更少、计算成本更低。本研究提供了一种可扩展、可解释的SOH预测框架，适用于实际电动车电池管理系统，推动更安全高效的电动出行。
原文摘要 · Abstract (English)
Accurate prediction of the state-of-health (SOH) of lithium-ion batteries is essential for ensuring the safety, reliability, and efficient operation of electric vehicles (EVs). Battery packs in EVs experience nonuniform degradation due to cell-to-cell variability (CtCV), posing a major challenge for real-time battery management. In this work, we propose an explainable, data-driven SOH prediction framework tailored for EV battery management systems (BMS). The approach combines robust feature engineering with a DLinear. Using NASA's battery aging dataset, we extract twenty meaningful features from voltage, current, temperature, and time profiles, and select key features using Pearson correlation and Shapley additive explanations (SHAP). The SHAP-based selection yields consistent feature importance across multiple cells, effectively capturing CtCV. The DLinear algorithm outperforms long short-term memory (LSTM) and Transformer architectures in prediction accuracy, while requiring fewer training cycles and lower computational cost. This work offers a scalable and interpretable framework for SOH forecasting, enabling practical implementation in EV BMS and promoting safer, more efficient electric mobility.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。