Open Access
| Issue |
E3S Web Conf.
Volume 705, 2026
Advances in Renewable Energy & Electric Vehicles (AREEV-2026) (under the aegis of ICETE 2026 Multi-Conference Platform)
|
|
|---|---|---|
| Article Number | 03001 | |
| Number of page(s) | 10 | |
| Section | Power Converters & Drives for EV | |
| DOI | https://doi.org/10.1051/e3sconf/202670503001 | |
| Published online | 15 April 2026 | |
- L. Zhang, J. Liu, and S. Wang, “State of health prediction in electric vehicle batteries using a deep learning model,” World Electric Vehicle Journal, vol. 15, no. 385, pp. 1–23, 2024. https://doi.org/10.3390/wevj15090385 [Google Scholar]
- M. Chen, Y. Li, and T. Zhao, “A general framework for lithium-ion battery state of health estimation: From laboratory tests to machine learning with transferability across domains,” Applied Energy, vol. 381, art. no. 125086, 2025. [Google Scholar]
- A. Kumar, S. Singh, and H. Lee, “Generative adversarial network for state of health estimation of lithium-ion batteries,” in Proc. IEEE Int. Conf. Prognostics and Health Management (ICPHM), 2023, pp. 1–6. [Google Scholar]
- N. Gupta and P. Sinha, “Adaptive battery management systems for the new generation of electric vehicles,” in Artificial Intelligence for Smart Battery Management Systems, Springer, 2023, ch. 4, pp. 67–84. [Google Scholar]
- Y. Wang, M. Fang, and X. Zhou, “Generating comprehensive lithium battery charging data with generative AI,” Applied Energy, vol. 377, art. no. 124604, 2025. https://doi.org/10.1016/j.apenergy.2024.124604 [Google Scholar]
- Universal 100 Ah battery data (Lead-Acid). Available: http://www.mrsolar.com/content/pdf/Universal/UB121000.pdf [Google Scholar]
- CALCE CS2_8 lithium-ion battery dataset. Available: https://calce.umd.edu/batteries/data/ [Google Scholar]
- LiPo Battery LP-503562-IS-3 dataset (Mendeley Data). Available: https://data.mendeley.com/datasets/stcppt2r68/1 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.