Human-centered electric vehicle adoption framework for smart mobility: Modeling perceived range and charging anxiety as a psychological barrier

Electric vehicles (EVs) offer a transformative pathway toward reducing the environmental, economic, and health-related externalities of internal combustion engine vehicles in urban settings. Despite substantial advances in battery technology, charging infrastructure expansion, and supportive policy incentives, EV penetration remains limited which poses challenges for smart and sustainable mobility planning. A critical yet insufficiently modeled barrier to adoption lies in the psychological perceptions surrounding electric driving range and charging reliability, which is commonly framed as “range anxiety,” but more broadly reflecting perceived range and charging anxiety. To address this gap, this study introduces a latent psychological construct capturing individuals’ perceived range and charging anxiety and integrates it into a two-level nested logit (NL) model of vehicle transaction and fuel type choice. The model jointly represents (a) vehicle transaction decisions (keep, sell, trade, and add) and (b) fuel technology choices (conventional vehicle, hybrid EV, plug-in hybrid EV (PHEV), and battery EV (BEV)), thereby providing a behaviorally realistic representation of household vehicle dynamics. Bootstrap-based inference is employed to ensure robust estimation of both measurement and structural parameters. Using the sample dataset from the State of California, the results show that perceived range and charging anxiety significantly reduces the likelihood of adopting BEVs, particularly in fleet-expansion (add) decisions, while exerting no significant effect on PHEV adoption. This distinction suggests that PHEVs may function as transitional technologies for consumers concerned about electric mobility reliability. The findings highlight the importance of smart mobility strategies that address not only technological performance and infrastructure deployment, but also psychological perceptions shaping consumer behavior. By embedding latent behavioral factors into the vehicle choice process, the proposed framework advances citizen-centered mobility modeling and provides a robust decision-support tool for urban planners and transportation policymakers seeking to accelerate sustainable vehicle transitions.