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 effi-ciency

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.