| Issue |
E3S Web Conf.
Volume 616, 2025
2nd International Conference on Renewable Energy, Green Computing and Sustainable Development (ICREGCSD 2025)
|
|
|---|---|---|
| Article Number | 03030 | |
| Number of page(s) | 9 | |
| Section | Sustainable Development | |
| DOI | https://doi.org/10.1051/e3sconf/202561603030 | |
| Published online | 24 February 2025 | |
Improving Electric Vehicle Battery Health Management with Explainable AI
1 Assistant Professor, Department of Electrical and Electronics Engineering, CVR College of Engineering, Ibrahimpatnam, Hyderabad, India
2 Professor, Department of Electronics and Instrumentation Engineering, CVR College of Engineering, Ibrahimpatnam, Hyderabad, India
3 Professor, Department of Electrical and Electronics Engineering, CVR College of Engineering, Ibrahimpatnam, Hyderabad, India
4 Senior Assistant Professor, Department of Electrical and Electronics Engineering, CVR College of Engineering, Ibrahimpatnam, Hyderabad, India
* Corresponding author: divya.gongidi@cvr.ac.in
Effective battery health management is crucial for the performance, safety, and longevity of electric vehicles (EVs). Traditional battery monitoring systems face challenges in accurately predicting battery health due to the complex electrochemical processes involved. This paper explores the use of Explainable Artificial Intelligence (XAI) to enhance battery health management in electric vehicles. XAI provides insights into complex machine learning models, allowing for transparent decision-making and improved understanding of the factors affecting battery health. This approach not only enhances the accuracy of battery diagnostics and prognostics but also fosters greater trust in AI-driven battery management systems. The study highlights the potential of XAI to offer actionable insights, facilitate predictive maintenance, and contribute to the overall efficiency and reliability of EV batteries, paving the way for safer and more sustainable electric mobility.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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