Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)

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A Multi-Dimensional Analytical Framework for Electric Vehicle Performance: Integrating Machine Learning, Econometric Modeling, and Sustainability Assessment

Authors
Yashwant A. Waykar1, *, Sucheta S. Yambal2, Vijay Rambhau Bhosale3
1Assistant Professor, Department of Management Science, Babasaheb Ambedkar Marathwada University, Chh. Sambhaji Nagar, India
2Assistant Professor, Department of Management Science, Babasaheb Ambedkar Marathwada University, Chh. Sambhaji Nagar, India
3U.D. Pathrikar School of Business Management, Dongargaon (Kawad), Dist – Chhatrapati Sambhajinagar, Tal – Phulambri, India
*Corresponding author. Email: yawaykar.mgmtsci@bamu.ac.in
Corresponding Author
Yashwant A. Waykar
Available Online 6 January 2026.
DOI
10.2991/978-94-6463-948-3_57How to use a DOI?
Keywords
Electric Vehicles; Battery Degradation; Total Cost of Ownership; Vehicle Clustering; Sustainable Transportation; Predictive Modeling
Abstract

The transition to electric mobility necessitates a holistic understanding of the complex interplay between EV performance, economic viability, and environmental impact, which remains inadequately addressed in a unified framework. This study employs a multi-method analytical approach—integrating machine learning (Gradient Boosting, SHAP), statistical modeling, and econometric analysis—on a comprehensive dataset of 3,000 vehicles to dissect these interdependencies. Our analysis identifies a market bifurcation into a high-capacity segment (97.58 kWh, 491 km, $26,994 resale) and a standard segment (53.62 kWh, 266 km, $17,849 resale). We reveal that battery health degradation is a complex, non-linear process where charging behavior and energy consumption are more critical than usage volume. While technological advancements (2016–2024) boosted battery capacity by 73% and range by 36%, energy efficiency saw minimal gains. Economically, environmental benefits offset 4.7% of the total cost of ownership, with SUVs emerging as the most economical segment. The findings provide critical insights for consumers, manufacturers, and policymakers, underscoring the need for smart charging infrastructure, advanced battery management systems, and segment-specific strategies to optimize the sustainability and adoption of electric vehicles.

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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Volume Title
Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
Series
Advances in Intelligent Systems Research
Publication Date
6 January 2026
ISBN
978-94-6463-948-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-948-3_57How to use a DOI?
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.

Cite this article

risenwbib
TY  - CONF
AU  - Yashwant A. Waykar
AU  - Sucheta S. Yambal
AU  - Vijay Rambhau Bhosale
PY  - 2026
DA  - 2026/01/06
TI  - A Multi-Dimensional Analytical Framework for Electric Vehicle Performance: Integrating Machine Learning, Econometric Modeling, and Sustainability Assessment
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 807
EP  - 829
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_57
DO  - 10.2991/978-94-6463-948-3_57
ID  - Waykar2026
ER  -