An accurate estimation of the state of health (SOH) of batteries is critical to ensuring the safe and reliable operation of electric vehicles (EVs). Feature-based machine learning methods have exhibited enormous potential for rapidly and precisely monitoring battery health status. However, simultaneously using various health indicators (HIs) may weaken estimation performance due to feature redundancy. Furthermore, ignoring real-world driving behaviors can lead to inaccurate estimation results as some features are rarely accessible in practical scenarios. To address these issues, we proposed a feature-based machine learning pipeline for reliable battery health monitoring, enabled by evaluating the acquisition probability of features under real-world driving conditions. We first summarized and analyzed various individual HIs with mechanism-related interpretations, which provide insightful guidance on how these features relate to battery degradation modes. Moreover, all features were carefully evaluated and screened based on estimation accuracy and correlation analysis on three public battery degradation datasets. Finally, the scenario-based feature fusion and acquisition probability-based practicality evaluation method construct a useful tool for feature extraction with consideration of driving behaviors. This work highlights the importance of balancing the performance and practicality of HIs during the development of feature-based battery health monitoring algorithms.

翻译：电池健康状态（SOH）的精确估计对于确保电动汽车（EV）安全可靠运行至关重要。基于特征的机器学习方法在快速准确监测电池健康状态方面展现出巨大潜力。然而，同时使用多种健康指标（HI）可能因特征冗余而削弱估计性能。此外，忽略实际驾驶行为会导致估计结果不准确，因为部分特征在实际场景中难以获取。为解决这些问题，我们提出了一种基于特征的机器学习流程，通过评估实际驾驶条件下特征的可获取概率，实现可靠的电池健康监测。首先，我们总结并分析了多种具有机理相关解释的个体健康指标，为理解这些特征与电池退化模式的关系提供了深刻指导。进一步地，基于三个公开电池退化数据集，通过估计精度分析与相关性分析对所有特征进行了仔细评估与筛选。最后，基于场景的特征融合与基于获取概率的实用性评估方法，构建了考虑驾驶行为的特征提取实用工具。本研究强调了在开发基于特征的电池健康监测算法时，平衡健康指标性能与实用性的重要性。