Document Type

Article

Publication Date

2026

DOI

10.3390/en19051203

Publication Title

Energies

Volume

19

Issue

5

Pages

1203

Abstract

This paper evaluates and compares four data-driven methods (Gaussian Process Regression (GPR), echo state network (ESN), gated recurrent unit (GRU), and long short-term memory (LSTM)) for lithium-ion capacity prognostics adapted to electric vehicle conditions. This comparison aims to find the most efficient prognosis method considering two constraints: the limitation of computational power and the unavailability of on-board capacity measurement that requires full charge and discharge conditions. The machine learning models are trained using capacity values estimated under vehicle conditions. The ageing data is collected from cycling tests of two battery chemistries, Lithium Fer Phosphate (LFP) and Nickel Manganese Cobalt (NMC), with different ageing trends. The prognosis algorithms are tuned with three different percentages of the data, allowing for the evaluation of the methods at different ageing stages. The comparison and analysis of the results show that ESN outperforms other methods; it has the lowest prediction error (mean absolute percentage error less than 1.4% at initial ageing of the cells) and the shortest training time, making it the most appropriate method for automotive applications.

Rights

© 2026 by the authors.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Data Availability

Article states: "The data will be available on request."

Original Publication Citation

Hammou, A., Petrone, R., Diallo, D., Tala-Ighil, B., Boussiengue, P. M., Chaoui, H., & Gualous, H. (2026). Machine learning-based lifetime prediction of lithium batteries: A comparative assessment for electric vehicle applications. Energies, 19(5), Article 1203. https://doi.org/10.3390/en19051203

ORCID

0000-0001-8728-3653 (Chaoui)

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