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

Abstract

Health monitoring, fault analysis, and detection methods are important to operate battery systems safely. We apply Gaussian process resistance models on lithium-iron-phosphate (LFP) battery field data to separate the time-dependent and operating-point-dependent resistances. The dataset contains 28 battery systems returned to the manufacturer for warranty, each with eight cells in series, totaling 224 cells and 133 million data rows. We develop probabilistic fault detection rules using recursive spatiotemporal Gaussian processes. These processes scale linearly with the number of data points, allowing online monitoring. The fault 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 battery packs degrade and fail in the field and demonstrate the potential of online monitoring. We open source the code and publish the dataset with this article.