Author Identifier (ORCID)
Mohammad Nur-E-Alam: https://orcid.org/0000-0003-1969-3348
Abstract
The increasing global adoption of electric vehicles (EVs) has led to a growing demand for a cost-effective and reliable charging infrastructure. This study presents a novel data-driven approach to assessing EV station performance by analyzing power consumption efficiency, station utilization rates, no-power session occurrences, and CO2 reduction metrics. A dataset of 17,500 charging sessions from 305 stations across a regional network was analyzed to identify operational inefficiencies and opportunities for infrastructure optimization. Results indicate a strong correlation between station utilization and energy efficiency, highlighting the importance of strategic station placement. The findings also emphasize the impact of no-power sessions on network inefficiency and the need for real-time station monitoring. CO2 reduction analysis demonstrates that optimizing EV charging performance can significantly contribute to sustainability goals. Based on these insights, this study recommends the implementation of predictive maintenance strategies, real-time user notifications, and diversified provider networks to improve station availability and efficiency. The proposed data-driven framework offers actionable solutions for policymakers, charging network operators, and urban planners to enhance EV infrastructure reliability and sustainability.
Document Type
Journal Article
Date of Publication
1-1-2025
Publication Title
Global Energy Interconnection
Publisher
Elsevier
School
School of Science
Funders
Universiti Malaya / Clarkson University
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Comments
Mostofa, K. Z., Islam, M. F., Islam, M. A., Basher, M. K., Abedin, T., Yap, B. K., & Nur-E-Alam, M. (2025). Data-driven insights for optimizing EV charging infrastructure: A case study on efficiency and utilization. Global Energy Interconnection. Advance online publication. https://doi.org/10.1016/j.gloei.2025.05.005