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.

应用高斯过程电阻模型对锂铁磷酸盐电池的实测数据进行分析，实现了对时间依赖性和工作点依赖性电阻的有效分离，进而通过递归时空高斯过程开发了概率性故障检测规则，能够进行高效的在线监测，进一步深入了解电池组在实际应用中的失效过程。