Health monitoring, fault analysis, and detection are critical for the safe and sustainable operation of battery systems. We apply Gaussian process resistance models on lithium iron phosphate battery field data to effectively separate the time-dependent and operating point-dependent resistance. The data set contains 29 battery systems returned to the manufacturer for warranty, each with eight cells in series, totaling 232 cells and 131 million data rows. We develop probabilistic fault detection rules using recursive spatiotemporal Gaussian processes. These processes allow the quick processing of over a million data points, enabling advanced online monitoring and furthering the understanding of battery pack failure in the field. The analysis underlines that often, only a single cell shows abnormal behavior or a knee point, consistent with weakest-link failure for cells connected in series, amplified by local resistive heating. The results further the understanding of how batteries degrade and fail in the field and demonstrate the potential of efficient online monitoring based on data. We open-source the code and publish the large data set upon completion of the review of this article.

翻译：健康监测、故障分析与检测对于电池系统的安全可持续运行至关重要。本研究将高斯过程电阻模型应用于磷酸铁锂电池现场数据，有效分离了时间依赖性与工作点依赖性电阻。数据集包含29个因保修退回制造商的电池系统，每个系统由八个串联电芯组成，共计232个电芯与1.31亿行数据。我们利用递归时空高斯过程建立了概率化故障检测规则。该方法可快速处理超百万数据点，实现先进的在线监测，并深化对现场电池组失效机制的理解。分析表明，通常仅单个电芯呈现异常行为或拐点现象，这与串联电芯的最薄弱环节失效机制一致，且局部电阻热效应会放大该现象。研究结果深化了对电池现场退化与失效机制的认识，并展示了基于数据的高效在线监测潜力。本文评审完成后将开源代码并发布该大规模数据集。