# Gaussian process regression for forecasting battery state of health

arXiv: https://arxiv.org/abs/1703.05687 · HTML: https://mlsanity.com/paper/1703.05687/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2017.

## Built on by (most cited first, 28 of 28)

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- [Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial](https://mlsanity.com/paper/2305.04933/) (2023)
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- [Evaluating feasibility of batteries for second-life applications using machine learning](https://mlsanity.com/paper/2203.04249/) (2022)
- [Mixtures of Gaussian Process Experts with SMC2](https://mlsanity.com/paper/2208.12830/) (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)
- [Slip-Based Autonomous ZUPT through Gaussian Process to Improve Planetary Rover Localization](https://mlsanity.com/paper/2103.07587/) (2021)
- [Gaussian Process Sampling and Optimization with Approximate Upper and Lower Bounds](https://mlsanity.com/paper/2110.12087/) (2021)
- [Learning battery model parameter dynamics from data with recursive Gaussian process regression](https://mlsanity.com/paper/2304.13666/) (2023)
- [A Dynamic Battery State-of-Health Forecasting Model for Electric Trucks: Li-Ion Batteries Case-Study](https://mlsanity.com/paper/2103.16280/) (2021)
- [Enhanced Gaussian Process Dynamical Models with Knowledge Transfer for Long-term Battery Degradation Forecasting](https://mlsanity.com/paper/2212.01609/) (2022)
- [Adaptive Safe Reinforcement Learning-Enabled Optimization of Battery Fast-Charging Protocols](https://mlsanity.com/paper/2406.12309/) (2024)
- [Multivariable Fractional Polynomials for lithium-ion batteries degradation models under dynamic conditions](https://mlsanity.com/paper/2102.08111/) (2021)
- [Comparing seven methods for state-of-health time series prediction for the lithium-ion battery packs of forklifts](https://mlsanity.com/paper/2107.05489/) (2021)
- [Case-based reasoning for rare events prediction on strategic sites](https://mlsanity.com/paper/2202.04891/) (2022)
- [Exact and general decoupled solutions of the LMC Multitask Gaussian Process model](https://mlsanity.com/paper/2310.12032/) (2023)
- [Predicting Battery Capacity Fade Using Probabilistic Machine Learning Models With and Without Pre-Trained Priors](https://mlsanity.com/paper/2410.06422/) (2024)
- [Hybrid Gaussian Process Regression with Temporal Feature Extraction for Partially Interpretable Remaining Useful Life Interval Prediction in Aeroengine Prognostics](https://mlsanity.com/paper/2411.15185/) (2024)
- [Deep learning for state estimation of commercial sodium-ion batteries using partial charging profiles: validation with a multi-temperature ageing dataset](https://mlsanity.com/paper/2504.00393/) (2025)
- [State of Health Estimation of Batteries Using a Time-Informed Dynamic Sequence-Inverted Transformer](https://mlsanity.com/paper/2507.18320/) (2025)
- [Deep Kernel Bayesian Optimisation for Closed-Loop Electrode Microstructure Design with User-Defined Properties based on GANs](https://mlsanity.com/paper/2508.00833/) (2025)
- [Machine Learning Detection of Lithium Plating in Lithium-ion Cells: A Gaussian Process Approach](https://mlsanity.com/paper/2509.26234/) (2025)
- [SDG-L: A Semiparametric Deep Gaussian Process based Framework for Battery Capacity Prediction](https://mlsanity.com/paper/2510.10621/) (2025)
- [A Multi-Task Targeted Learning Framework for Lithium-Ion Battery State-of-Health and Remaining Useful Life](https://mlsanity.com/paper/2603.22323/) (2026)
- [Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation](https://mlsanity.com/paper/2607.23482/) (2026)
- [PiDDM: Physics-Informed Differentiable Degradation Modeling for Lithium-Ion Battery State-of-Health Prediction](https://mlsanity.com/paper/2607.29095/) (2026)
- [PhyMamba: Physics-Modulated Mamba for Robust Battery Health Prognostics](https://mlsanity.com/paper/2608.27978/) (2026)

## Abstract (arXiv)

Accurately predicting the future capacity and remaining useful life of batteries is necessary to ensure reliable system operation and to minimise maintenance costs. The complex nature of battery degradation has meant that mechanistic modelling of capacity fade has thus far remained intractable; however, with the advent of cloud-connected devices, data from cells in various applications is becoming increasingly available, and the feasibility of data-driven methods for battery prognostics is increasing. Here we propose Gaussian process (GP) regression for forecasting battery state of health, and highlight various advantages of GPs over other data-driven and mechanistic approaches. GPs are a type of Bayesian non-parametric method, and hence can model complex systems whilst handling uncertainty in a principled manner. Prior information can be exploited by GPs in a variety of ways: explicit mean functions can be used if the functional form of the underlying degradation model is available, and multiple-output GPs can effectively exploit correlations between data from different cells. We demonstrate the predictive capability of GPs for short-term and long-term (remaining useful life) forecasting on a selection of capacity vs. cycle datasets from lithium-ion cells.