Predicting the Evolution of Capacity Degradation Histograms of Rechargeable Batteries Under Dynamic Loads via Latent Gaussian Processes

Daocan Wang, Xinggang Li () and Jiahuan Lu ()
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Daocan Wang: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Xinggang Li: China North Vehicle Research Institute, Beijing 100072, China
Jiahuan Lu: College of Engineering, South China Agricultural University, Guangzhou 510642, China

Energies, 2025, vol. 18, issue 13, 1-15

Abstract: Accurate prediction of lithium-ion battery capacity degradation under dynamic loads is crucial yet challenging due to limited data availability and high cell-to-cell variability. This study proposes a Latent Gaussian Process (GP) model to forecast the full distribution of capacity fade in the form of high-dimensional histograms, rather than relying on point estimates. The model integrates Principal Component Analysis with GP regression to learn temporal degradation patterns from partial early-cycle data of a target cell, using a fully degraded reference cell. Experiments on the NASA dataset with randomized dynamic load profiles demonstrate that Latent GP enables full-lifecycle capacity distribution prediction using only early-cycle observations. Compared with standard GP, long short-term memory (LSTM), and Monte Carlo Dropout LSTM baselines, it achieves superior accuracy in terms of Kullback–Leibler divergence and mean squared error. Sensitivity analyses further confirm the model’s robustness to input noise and hyperparameter settings, highlighting its potential for practical deployment in real-world battery health prognostics.

Keywords: capacity histograms; prediction; rechargeable batteries; degradation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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