Applications of Machine Learning in the Planning of Electric Vehicle Charging Stations and Charging Infrastructure: A Review

While electric vehicles (EV) and plug-in hybrid electric vehicles (PHEV) have the potential solution from an environmental perspective, they face an obstacle in accessing charging systems. Moreover, the charging system offers its own challenges compared to petrol stations due to the participation of different charging options. Researchers have been studying the optimization of PHEV/EV charging infrastructure for the past few years. Introducing electric vehicle charging infrastructure services creates new challenges and opportunities for the development of smart grid technologies. In this study, an extensive literature review has been carried out regarding the use of several optimizations and machine learning models for determining the optimal location of EV charging stations (EVCS) and infrastructure. Previous literature has also proposed different model-solving algorithms or techniques to solve the complex and dynamic nature of EVCS location problems, suggesting that the research on EVCSs has recently gained popularity. Although research seems to have advanced, findings indicate to incorporate of real-time EV user behavior for optimal geographical placement of new charging stations, satisfying the transportation demand, randomness, and variability in space as well as time. Coupled EV networks will be cost-effective from the power grid perspective. Identification of factors resulting in spatial inequities for EVCS location across different cities based on socioeconomic characteristics needs to be addressed for robust EV charging infrastructure.