# Predicting battery end of life from solar off-grid system field data using machine learning

arXiv: https://arxiv.org/abs/2107.13856 · HTML: https://mlsanity.com/paper/2107.13856/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 54 references that are in the map.
- First posted on arXiv 29 Jul 2021.

## Built on by (most cited first, 8 of 8)

- [Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination](https://mlsanity.com/paper/2206.04734/) (2022)
- [MINN: Learning the dynamics of differential-algebraic equations and application to battery modeling](https://mlsanity.com/paper/2304.14422/) (2023)
- [Machine Learning Benchmarks for the Classification of Equivalent Circuit Models from Electrochemical Impedance Spectra](https://mlsanity.com/paper/2302.03362/) (2023)
- [Evaluating feasibility of batteries for second-life applications using machine learning](https://mlsanity.com/paper/2203.04249/) (2022)
- [Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field data](https://mlsanity.com/paper/2406.19015/) (2024)
- [Learning battery model parameter dynamics from data with recursive Gaussian process regression](https://mlsanity.com/paper/2304.13666/) (2023)
- [Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modeling](https://mlsanity.com/paper/2310.03750/) (2023)
- [Discovery Learning accelerates battery design evaluation](https://mlsanity.com/paper/2508.06985/) (2025)

## Abstract (arXiv)

Hundreds of millions of people lack access to electricity. Decentralised solar-battery systems are key for addressing this whilst avoiding carbon emissions and air pollution, but are hindered by relatively high costs and rural locations that inhibit timely preventative maintenance. Accurate diagnosis of battery health and prediction of end of life from operational data improves user experience and reduces costs. But lack of controlled validation tests and variable data quality mean existing lab-based techniques fail to work. We apply a scaleable probabilistic machine learning approach to diagnose health in 1027 solar-connected lead-acid batteries, each running for 400-760 days, totalling 620 million data rows. We demonstrate 73% accurate prediction of end of life, eight weeks in advance, rising to 82% at the point of failure. This work highlights the opportunity to estimate health from existing measurements using `big data' techniques, without additional equipment, extending lifetime and improving performance in real-world applications.