The adoption of electric vehicles (EVs) has grown substantially in recent years, offering a cleaner and highly promising pathway toward the decarbonization of urban environments. However, this trend introduces new challenges in charging infrastructure and management. This paper proposes a synergistic integration of Business Intelligence (BI) and Artificial Intelligence (AI) techniques—including machine learning and data analytics—for solving the EV charging problem. We begin with an in-depth analysis of charging behaviors, leveraging extensive datasets from EVs, charging stations (CSs), and auxiliary sources. Based on this analysis, we introduce a BI framework utilizing advanced data mining methods to utilize large-scale data effectively. We then present a BI-based decision-making model that enables comprehensive analysis and optimized solutions for EV charge scheduling and the cooperation among different CS owners. The model is validated across multiple real-world scenarios and case studies, demonstrating significant improvements in charging efficiency, utilization, and reliability. By showcasing the practical applications of BI-driven analytics, our findings underscore the transformative impact of data-informed methodologies on EV charging operations. This paper concludes with a discussion of open research opportunities in AI- and BI-driven intelligent transportation—specifically in EV charging optimization, grid integration, and predictive analytics.
Alexandra Bousia (Thu,) studied this question.
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