A hybrid MCDM approach for site selection of electric vehicle battery swapping station

A promising strategy for decarbonising the transportation segment, both commercial and domestic, is based on transitioning from traditional to electric mobility, which is dependent on the Electric Vehicle (EV), their adoption is hindered by range anxiety, insufficient charging infrastructure, and long waiting times. This research aims to commission a battery swapping station (BSS) at a significant location selected through a data-driven hybrid Multi-criteria decision-making (MCDM) technique to satisfy the urban sustainable mobility needs while minimising charging congestion. Monte Carlo analysis was employed to determine whether the proposed model is resilient to input perturbations, confirming the rank stability. Alternative L4 retained the first position in 100% of simulations, while L3 consistently remained second. Spearman's rank correlation coefficient between the deterministic and simulated ranks was tested (r s =1.00), indicating perfect agreement. This paper establishes that the criteria related to EV adoption and cost overrule other identified criteria. The Expert-based cross-validation of ranking confirms that the proposed ranking is highly robust to reasonable variations in criteria weights and that the identified optimal alternative remains unchanged under uncertainty. The peripheral nodes of the Chennai Metropolitan Area (CMA) outperform other selected locations due to higher EV density and greater public awareness. This makes these locations more suitable for establishing BSS. The explicit inclusion of the EV adoption rate as a critical criterion distinguishes this research from previous works. This research adopts an applied research approach in which MCDM is used to find a solution to a real-world challenge and provides new insights into the spatial distribution of EV charging infrastructure. It confirms that objective weights derived by CRITIC provide a high fit with reality and reduce subjective influences often found in expert assessments. It enhances the precision and applicability of the findings for EV charging infrastructure planning and achieves Sustainable Development Goals (SDGs), specifically by creating cleaner transport, optimising traffic flow and promoting greener urban mobility.