Forecast Electric Vehicle Charging Patterns and Comparing with Previous Work

DOI

10.2991/978-94-6463-718-2_129How to use a DOI?

Keywords

Electric vehicle; Lithium battery; Machine learning; Ensemble learning

Abstract

Limited charging sites provide a significant barrier for EV manufacturers. Estimating the age of an electric vehicle’s battery helps drivers forecast its driving range. This study proposes battery management. The technology is made to predict how much of an electric vehicle’s battery is left. The battery aging in electric vehicles is predicted using a variety of ML (regression and classification algorithms) and Deep learning (CNN, ANN, MLP) techniques. Sensors which is to be connected to the ARM board provide voltage, current, and temperature readings, which are used as a dataset for Machine learning and deep learning algorithms (Ensemble learning, MLP) in order to create the model.

Copyright

© 2025 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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TY  - CONF
AU  - S. Sathishkumar
AU  - R. Yogesh Rajkumar
PY  - 2025
DA  - 2025/05/23
TI  - Forecast Electric Vehicle Charging Patterns and Comparing with Previous Work
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1549
EP  - 1556
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_129
DO  - 10.2991/978-94-6463-718-2_129
ID  - Sathishkumar2025
ER  -