用不完整放电数据实时估算电池健康度并预测老化趋势
Real-Time State-of-Health Estimation and Online Degradation Prognosis from Partial Battery Discharge Using Physics-Informed Neural Networks
- 结合物理规律与深度学习，从任意电压段放电数据推算健康度
- 平均绝对百分比误差低于4%，实现高精度实时估计
- 无需历史数据即可监测老化关键节点，适合多种电池场景
随着可再生能源的广泛接入，储能系统愈发重要，精准评估锂离子电池的健康状态（SOH）和退化行为至关重要。本文提出一种融合物理约束的深度学习方法，仅需从任意电压区间截取的不完整放电曲线，即可实现对电池SOH的准确预测，真实反映复杂多变的实际运行条件。该方法将数据驱动学习与物理驱动的退化机制相结合，确保在部分放电信息下仍能保持一致可靠的估计性能，平均绝对百分比误差（MAPE）低于4%。此外，引入实时退化趋势估计策略，可在无先验知识或历史数据的前提下，检测老化关键转变点，适用于多种电池类型。整体上，该方法实现了从任意放电片段中进行健康度估计，并支持持续在线退化预测，突破了以往依赖固定测试协议或早期静态预测的局限。
原文摘要 · Abstract (English)
With the increasing integration of renewable energy sources, energy storage systems have become essential, making the accurate estimation of their State of Health (SOH) and degradation behavior critical. In this work, we propose a physics-informed deep learning approach for lithium-ion battery SOH prediction using incomplete discharge curves extracted from arbitrary voltage ranges, thereby reflecting realistic and heterogeneous operating conditions. The proposed method combines data-driven learning with physically motivated degradation dynamics to ensure consistent and reliable SOH estimation from partial discharge information, achieving a MAPE below 4$\%$. In addition, a real-time degradation trend estimation strategy is introduced to detect key aging transitions without requiring prior knowledge or historical data, making it applicable to a wide range of batteries. Overall, our approach enables SOH estimation from arbitrary discharge segments and a real-time degradation forecast that continuously integrates all usage, overcoming previous methods that rely on fixed protocols or early, non-adaptive predictions.
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