Will Electric Vehicle Drivers Use Smart Charging? Considering Both Intentions and Preferences

To better understand the factors influencing electric vehicle (EV) drivers’ preferences for smart charging (an approach that enables charging to be scheduled or modulated based on grid conditions, electricity prices, or user preferences) and to provide a theoretical foundation for the design and promotion of smart charging infrastructure, this study developed a hybrid choice model that integrates the intention to use smart charging technology with a stated preference survey conducted in Jiangsu, China. Specifically, the model combined structural equation modeling with the theory of planned behavior and technology acceptance models to assess the impact of latent variables related to behavioral intention (BI) on smart charging BI, while employing a multiple indicators multiple causes model to examine their interaction with sociodemographic characteristics. Additionally, a random parameters binary logit model was used to analyze how sociodemographic characteristics, latent variables related to BI, and charging scenario attributes influence smart charging choice preference. The results indicate that perceived usefulness (PU), subjective norms, and attitude (AT) significantly affect the preference for adopting smart charging, with PU having the greatest total effect. Drivers with higher education levels and more extensive EV driving experience are more likely to have favorable AT toward smart charging and show a greater preference for adopting it. Furthermore, charging location, charging start time, and economic incentives significantly influence smart charging preferences, with facilities in residential and workplace areas being preferred, and charging during the afternoon and evening being more likely to involve the adoption of smart charging. These findings provide both theoretical insights and practical guidance for policymakers and operators aiming to promote the adoption of smart charging technologies for EVs.