通过充电末期放电脉冲分析，实现锂电池健康状态实时监测。
Real-time monitoring of the SoH of lithium-ion batteries
- 利用充电结束时的放电脉冲，建模电压变化来估算电池健康度。
- 在两组电池上训练后，对另两组电池预测误差低于1%，解释性达0.9。
- 适合需连续运行的微电网场景，可直接集成到电池管理系统中。
锂电池健康状态（SoH）的实时监测仍是重大挑战，尤其在微电网中，运行限制使得传统方法难以应用。作为4BLife项目的一部分，我们提出一种新方法：在充电末期施加放电脉冲，分析其电压响应，通过等效电路模型参数估计电池健康度。基于现有实验数据，初步结果显示该方法有效。使用两组容量衰减约85%的电池参数进行训练后，成功预测了另外两组循环至约90% SoH电池的衰减情况，最坏情况下平均绝对误差约为1%，且估计器解释性得分接近0.9。若性能得到验证，该方法可轻松集成至电池管理系统（BMS），为持续运行条件下的优化电池管理开辟道路。
原文摘要 · Abstract (English)
Real-time monitoring of the state of health (SoH) of batteries remains a major challenge, particularly in microgrids where operational constraints limit the use of traditional methods. As part of the 4BLife project, we propose an innovative method based on the analysis of a discharge pulse at the end of the charge phase. The parameters of the equivalent electrical model describing the voltage evolution across the battery terminals during this current pulse are then used to estimate the SoH. Based on the experimental data acquired so far, the initial results demonstrate the relevance of the proposed approach. After training using the parameters of two batteries with a capacity degradation of around 85%, we successfully predicted the degradation of two other batteries, cycled down to approximately 90% SoH, with a mean absolute error of around 1% in the worst case, and an explainability score of the estimator close to 0.9. If these performances are confirmed, this method can be easily integrated into battery management systems (BMS) and paves the way for optimized battery management under continuous operation.
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