Electrons and Algorithms: ML Interpretations of Battery Innovation in EV Adoption

DOI

10.2991/978-94-6239-654-8_21How to use a DOI?

Keywords

Global EV Sales; EV Adoption; ML Algorithm; Random Forest; Random Committee; Kstar; Plug-in Hybrid EV’s; Fuel Cell Electric Vehicles (FCEVs)

Abstract

The research article gives a world-wide Electric Vehicles sales from 2010 to 2024 that clearly States how the car industry is transforming to the trending EV to support sustainable transportation. This research article shows very clearly about EV adoption that change over time focusing on the alternate types of power usages like battery electric vehicle, plug in hybrid and fuel cell electric vehicles. The battery electric vehicles are the most commonly used EV sold throughout the world where China is the best place for both EV adoption and battery electric vehicle penetration followed by the North America and Europe. This transformation of larger trend in the development of electric vehicles is dynamic. In early inventions in 19th century and then in 21st century the technological advancements and government incentive, the environmental concern brought this EV back. Proposed research article implies how environment is influenced on the economic reasons, the government rules and customer demand all over make put up together to shape the EV markets. Implementation of Machine learning models were used to predict, classify and adopt the trends to better transforming transportations. The most selected classifiers used in building a model using machine learning algorithms are random forest, random committee with variable attributes. Random committee is strong Contender with least correlation but greatly reduced errors which make the built model useful for ensemble-based validation. The best results are obtained by Random Forest and Random Committee, with correlations above 0.87, indicating good predictive ML model. Even though KStar’s 10-fold cross validation clearly outperforms its 5-fold version, ensemble approaches still outperform it. Finally, the results claim the EV cars are not only a momentary trend but a key part of changing transportation all over the world.

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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TY  - CONF
AU  - S. ArunaMary
AU  - Sudhagar Sudhagar
AU  - G. Kalaiselvi
PY  - 2026
DA  - 2026/04/24
TI  - Electrons and Algorithms: ML Interpretations of Battery Innovation in EV Adoption
BT  - Proceedings of the Global Conference on Sustainable Energy Systems, Smart Electronics and Intelligent Computing (GCSESEIC 2025)
PB  - Atlantis Press
SP  - 243
EP  - 254
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-654-8_21
DO  - 10.2991/978-94-6239-654-8_21
ID  - ArunaMary2026
ER  -