Beyond Charging Anxiety: An Explainable Approach to Understanding User Preferences of EV Charging Stations Using Review Data
Zifei Wang, Emmanuel Abolarin, Kai Wu, Venkatarao Rebba, Jian Hu, Zhen Hu, Shan Bao, Feng Zhou

TL;DR
This paper uses review data and explainable AI techniques to identify key factors influencing EV charging station user satisfaction, providing actionable insights for improving infrastructure and promoting EV adoption.
Contribution
It introduces a novel approach combining review analysis, sentiment modeling, and explainability to understand EV user preferences at both individual and collective levels.
Findings
Amenities and location positively influence satisfaction
Reliability and maintenance negatively impact user ratings
Model achieves high prediction accuracy with LightGBM
Abstract
Electric vehicles (EVs) charging infrastructure is directly related to the overall EV user experience and thus impacts the widespread adoption of EVs. Understanding key factors that affect EV users' charging experience is essential for building a robust and user-friendly EV charging infrastructure. This study leverages about charging station (CS) reviews on Google Maps to explore EV user preferences for charging stations, employing ChatGPT 4.0 for aspect-based sentiment analysis. We identify twelve key aspects influencing user satisfaction, ranging from accessibility and reliability to amenities and pricing. Two distinct preference models are developed: a micro-level model focused on individual user satisfaction and a macro-level model capturing collective sentiment towards specific charging stations. Both models utilize the LightGBM algorithm for user preference prediction,…
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