Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)

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Accurate Battery Lifetime Estimation for Electric Vehicle Using Machine Learning Models

Authors
Ahmed Rayhan1, 2, *, Zuhaina Islam Bushra2, Mohammad Ali Siddique3, Md. Sabbir Hasan Sohag1, *, Ahmed Al Mansur1, M. Mahbubur Rahman1, A. B. M. Shawkat Ali1
1Department of Electrical and Electronic Engineering, Bangladesh University of Business and Technology, Dhaka, Bangladesh
2Department of Electrical and Electronic Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh
3Department of Electrical and Computer Engineering, University of Windsor, Windsor, Canada
*Corresponding author. Email: ahmedrayhan.eee@gmail.com
*Corresponding author. Email: s.hasan@bubt.edu.bd
Corresponding Authors
Ahmed Rayhan, Md. Sabbir Hasan Sohag
Available Online 8 June 2026.
DOI
10.2991/978-94-6239-664-7_93How to use a DOI?
Keywords
Battery; Machine Learning; Charging; EV; Capacity; Catboost
Abstract

Specific determination of battery capacity is fundamental to enhance the safety, dependability and durability of electric vehicles (EVs). This paper presents a machine learning based data-driven approach to predicting the capacity, as determined by the State of Health (SOH) of lithium-ion batteries. With the help of XJTU battery degradation data, the authors evaluate six machine learning algorithms and harvest pertinent features: CatBoost, Random Forest, K-Nearest Neighbors (KNN), LightGBM, XGBoost, and Decision Tree. Four regression measures are applied to measure performance, including MSE, RMSE, MAE, and R2. Of these, CatBoost proves to have better predictive performance with minimal error and highest, R 2. The strength of our method is justified by a comparative study with previous researches and the applicability of ensemble-based models in estimating battery capacity in the real world. In addition to demonstrating the effectiveness of these models, this study highlights how data-driven approaches can reduce the need for extensive physical testing, ultimately improving the efficiency of battery health assessment.

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 Conference on Intelligent Data Analysis and Applications (IDAA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
8 June 2026
ISBN
978-94-6239-664-7
ISSN
1951-6851
DOI
10.2991/978-94-6239-664-7_93How 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  - Ahmed Rayhan
AU  - Zuhaina Islam Bushra
AU  - Mohammad Ali Siddique
AU  - Md. Sabbir Hasan Sohag
AU  - Ahmed Al Mansur
AU  - M. Mahbubur Rahman
AU  - A. B. M. Shawkat Ali
PY  - 2026
DA  - 2026/06/08
TI  - Accurate Battery Lifetime Estimation for Electric Vehicle Using Machine Learning Models
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 1378
EP  - 1391
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_93
DO  - 10.2991/978-94-6239-664-7_93
ID  - Rayhan2026
ER  -