基于真实驾驶数据的五种健康指标，实现高精度电池健康度实时估计。
Domain knowledge-guided machine learning framework for state of health estimation in Lithium-ion batteries
- 从实际驾驶数据中提取五类可在线计算的健康指标。
- 部分数据缺失时仍能实现1.5%~2.5%的容量估计误差。
- 适合电动车电池管理系统的实时健康监测应用。
准确估算电池健康状态对电动汽车电池管理至关重要。本文提出五个可从真实世界电动汽车运行数据中在线提取的健康指标，并构建基于机器学习的电池健康状态估计方法。这些指标能揭示电池能量与功率衰减的物理机制，在部分数据缺失情况下仍可实现高精度容量估计。它们可在充电过程及实际驾驶放电条件下计算，支持实时电池退化评估。指标基于五组在电动汽车工况下老化的真实电池实验数据提取，采用线性回归模型进行健康状态估计。结果显示，使用功率自相关与能量特征训练的模型，最大绝对百分比误差控制在1.5%至2.5%之间。
原文摘要 · Abstract (English)
Accurate estimation of battery state of health is crucial for effective electric vehicle battery management. Here, we propose five health indicators that can be extracted online from real-world electric vehicle operation and develop a machine learning-based method to estimate the battery state of health. The proposed indicators provide physical insights into the energy and power fade of the battery and enable accurate capacity estimation even with partially missing data. Moreover, they can be computed for portions of the charging profile and real-world driving discharging conditions, facilitating real-time battery degradation estimation. The indicators are computed using experimental data from five cells aged under electric vehicle conditions, and a linear regression model is used to estimate the state of health. The results show that models trained with power autocorrelation and energy-based features achieve capacity estimation with maximum absolute percentage error within 1.5% to 2.5% .
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