Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)

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Battery Health Prediction of New Energy Vehicles Based on LightGBM

Authors
Haolong Li1, *
1Smart City College of Beijing Union University, Beijing Union University, Beijing, 100101, China
*Corresponding author. Email: 2023240388018@buu.edu.cn
Corresponding Author
Haolong Li
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-648-7_10How to use a DOI?
Keywords
Lightgbm; Prediction; Battery; health status
Abstract

In the era of rapid development of new energy vehicles, the health status of lithium-ion batteries (SOH) will have a direct impact on the endurance and safety of new energy vehicles, so it is very important to predict the health status of lithium-ion batteries. This study uses lightgbm as a data prediction model to study the prediction of battery health status of new energy vehicles. The influence factors of lithium-ion battery health status are taken as independent variables, and the performance of random forest, xgboost and lightgbm algorithms is systematically compared. The final prediction results show that the lightgbm model has the highest degree of fitting, and the R-squared value is as high as 0.9933, and the operation efficiency is also significantly higher than the other two models. The research shows that lightgbm can be used as an efficient solution for the SOH prediction of new energy vehicle batteries, which provides a strong scientific basis for the health management and maintenance of new energy vehicle batteries, and also provides an important reference for the optimization of battery management system, so that the new energy vehicle industry can develop better.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
Series
Advances in Computer Science Research
Publication Date
24 April 2026
ISBN
978-94-6239-648-7
ISSN
2352-538X
DOI
10.2991/978-94-6239-648-7_10How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Haolong Li
PY  - 2026
DA  - 2026/04/24
TI  - Battery Health Prediction of New Energy Vehicles Based on LightGBM
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 80
EP  - 89
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_10
DO  - 10.2991/978-94-6239-648-7_10
ID  - Li2026
ER  -