# SDG-L: A Semiparametric Deep Gaussian Process based Framework for Battery Capacity Prediction

arXiv: https://arxiv.org/abs/2510.10621 · HTML: https://mlsanity.com/paper/2510.10621/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 12 Oct 2025.

## Builds on (most cited first, 3 of 3)

- [Deep Gaussian Processes for Regression using Approximate Expectation Propagation](https://mlsanity.com/paper/1602.04133/) (2016)
- [Gaussian process regression for forecasting battery state of health](https://mlsanity.com/paper/1703.05687/) (2017)
- [Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-ion Batteries](https://mlsanity.com/paper/2101.00035/) (2020)

## Abstract (arXiv)

Lithium-ion batteries are becoming increasingly omnipresent in energy supply. However, the durability of energy storage using lithium-ion batteries is threatened by their dropping capacity with the growing number of charging/discharging cycles. An accurate capacity prediction is the key to ensure system efficiency and reliability, where the exploitation of battery state information in each cycle has been largely undervalued. In this paper, we propose a semiparametric deep Gaussian process regression framework named SDG-L to give predictions based on the modeling of time series battery state data. By introducing an LSTM feature extractor, the SDG-L is specially designed to better utilize the auxiliary profiling information during charging/discharging process. In experimental studies based on NASA dataset, our proposed method obtains an average test MSE error of 1.2%. We also show that SDG-L achieves better performance compared to existing works and validate the framework using ablation studies.