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

arXiv: https://arxiv.org/abs/2406.19015 · HTML: https://mlsanity.com/paper/2406.19015/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 69 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [Gaussian process regression for forecasting battery state of health](https://mlsanity.com/paper/1703.05687/) (2017)
- [Predicting battery end of life from solar off-grid system field data using machine learning](https://mlsanity.com/paper/2107.13856/) (2021)
- [Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries](https://mlsanity.com/paper/2112.12979/) (2021)
- [Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra](https://mlsanity.com/paper/2302.03362/) (2023)
- [Interpretation of High-Dimensional Linear Regression: Effects of Nullspace and Regularization Demonstrated on Battery Data](https://mlsanity.com/paper/2309.00564/) (2023)
- [Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More](https://mlsanity.com/paper/2404.04049/) (2024)
- [Learning battery model parameter dynamics from data with recursive Gaussian process regression](https://mlsanity.com/paper/2304.13666/) (2023)

## Built on by (most cited first, 3 of 3)

- [Systematic Feature Design for Cycle Life Prediction of Lithium-Ion Batteries During Formation](https://mlsanity.com/paper/2410.07458/) (2024)
- [Diagnostic-free onboard battery health assessment](https://mlsanity.com/paper/2503.07383/) (2025)
- [Traceable Fault Diagnosis for Battery Energy Storage Systems via Retrieval-Augmented Multi-Agent O&M Assistant](https://mlsanity.com/paper/2607.01992/) (2026)

## Abstract (arXiv)

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.