# Learning battery model parameter dynamics from data with recursive Gaussian process regression

arXiv: https://arxiv.org/abs/2304.13666 · HTML: https://mlsanity.com/paper/2304.13666/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 61 references that are in the map.
- First posted on arXiv 26 Apr 2023.

## Builds on (most cited first, 9 of 9)

- [GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration](https://mlsanity.com/paper/1809.11165/) (2018)
- [Automatic Differentiation Variational Inference](https://mlsanity.com/paper/1603.00788/) (2016)
- [Hilbert Space Methods for Reduced-Rank Gaussian Process Regression](https://mlsanity.com/paper/1401.5508/) (2014)
- [Gaussian process regression for forecasting battery state of health](https://mlsanity.com/paper/1703.05687/) (2017)
- [Battery health prediction under generalized conditions using a Gaussian process transition model](https://mlsanity.com/paper/1807.06350/) (2018)
- [Automated feature extraction and selection for data-driven models of rapid battery capacity fade and end of life](https://mlsanity.com/paper/2101.04440/) (2021)
- [Predicting battery end of life from solar off-grid system field data using machine learning](https://mlsanity.com/paper/2107.13856/) (2021)
- [Bayesian Parameter Estimation Applied to the Li-ion Battery Single Particle Model with Electrolyte Dynamics](https://mlsanity.com/paper/2001.09890/) (2020)
- [A State Observer Design for Simultaneous Estimation of Charge State and Crossover in Self-Discharging Disproportionation Redox Flow Batteries](https://mlsanity.com/paper/1905.11396/) (2019)

## Built on by (most cited first, 2 of 2)

- [Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field data](https://mlsanity.com/paper/2406.19015/) (2024)
- [Prognosis of Multivariate Battery State of Performance and Health via Transformers](https://mlsanity.com/paper/2309.10014/) (2023)

## Abstract (arXiv)

Estimating state of health is a critical function of a battery management system but remains challenging due to the variability of operating conditions and usage requirements of real applications. As a result, techniques based on fitting equivalent circuit models may exhibit inaccuracy at extremes of performance and over long-term ageing, or instability of parameter estimates. Pure data-driven techniques, on the other hand, suffer from lack of generality beyond their training dataset. In this paper, we propose a hybrid approach combining data- and model-driven techniques for battery health estimation. Specifically, we demonstrate a Bayesian data-driven method, Gaussian process regression, to estimate model parameters as functions of states, operating conditions, and lifetime. Computational efficiency is ensured through a recursive approach yielding a unified joint state-parameter estimator that learns parameter dynamics from data and is robust to gaps and varying operating conditions. Results show the efficacy of the method, on both simulated and measured data, including accurate estimates and forecasts of battery capacity and internal resistance. This opens up new opportunities to understand battery ageing in real applications.