Location and capacity optimization of EV charging stations using genetic algorithms and fuzzy analytic hierarchy process

The pressing challenge of persistent air pollution and greenhouse gas emissions, which contribute to global boiling beyond global warming, requires urgent solutions across all sectors. In the transportation sector, zero-emission electric vehicles (EVs) are increasingly recognized as a key strategy for achieving carbon neutrality. However, the competitiveness of EVs is constrained by limitations in charging infrastructure and charging time. To address these challenges, this study focuses on optimizing the location of EV charging stations in Seoul for the year 2030, considering the existing fast charging stations and gas stations as of 2023. We use a genetic algorithm (GA) combined with a fuzzy analytic hierarchy process (Fuzzy AHP) to identify optimal locations for charging stations, while reorganizing the ratio of fast to slow chargers within these stations to alleviate road congestion and reduce unnecessary trips. Our methodology integrates various urban and transportation metrics, including parking index, public transit connectivity, and land use plans, to refine this optimization process. Our findings suggest that retaining 63% of existing fast charging stations, with some relocation to gas stations, will result in reduced vehicle miles traveled, shorter travel times, and significant reductions in carbon emissions. By quantifying the environmental benefits of this optimized placement, this study underscores the potential of electric vehicles to contribute to environmental sustainability and supports the paradigm shift toward electric mobility. Graphical abstract: (Figure presented.)