Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field data

TL;DR

This paper presents a Gaussian process-based method for real-time health monitoring and fault detection in lithium-ion battery systems, utilizing large-scale field data to improve safety and understanding of battery degradation.

Contribution

It introduces probabilistic fault detection rules using recursive spatiotemporal Gaussian processes for efficient online monitoring of large battery datasets.

Findings

Single cell abnormalities often indicate weakest-link failure.

Local resistive heating correlates with failure points.

Method enables processing of over a million data points in real-time.

Abstract

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.