Charging patterns analysis and multiscale infrastructure deployment: based on the real trajectories and battery data of the plug-in electric vehicles in Shanghai

Abstract

The rapid development of transportation electrification aims to achieve a fuel-free and zero-carbon emission world. Electric vehicles (EVs) are booming worldwide, especially in China. Even if EVs' cost decreases and performances make significant progress, the convenience and deployment problem of charging infrastructure is still the key restriction for large-scale adoption. Therefore, this study proposes a real-EV-data-based model with the aim of optimal charging infrastructure deployment oriented to charging convenience and charging demand. Traveling and charging behaviors based on the real EV data from Shanghai, where the EV penetration rate is highest in China, are revealed for the first time to reflect the interactions between drivers and infrastructures. Especially, the charging behaviors in Shanghai are beneficial for the battery life of EVs. The deployment and related optimization models are then established to meet the charging demand and promote quantified convenience. Moreover, infrastructure deployment considering both the charging type and the scale can be programmed. The proposed capacity-removal heuristic algorithm have advantages in robustness for deployment solving. The deployment results promote the charging convenience by 119.4%, and the expectation of the average distance to the nearest charger is decreased by 32.0%. For the slow charger, fast charger and mixed charger deployment schemes, the charging experience of drivers can all be improved by over 10% and the return analysis based on the investment cost and charging service fee shows a good rate of return with 8.38% and 18.5%. Besides, scenario analysis shows that charging station deployment with higher-power and larger-capacity chargers is the future trend.