A multi-scenario charging pile reservation mechanism considering consumers’ personalized preferences

Electric vehicle (EV) users experience “charging anxiety” due to the uncertainty and negative emotions around accessing or using charging piles (CPs). How to improve EV users’ charging experience with limited resources of charging infrastructure is a valuable research topic. This paper provides a multi-scenario CP advance reservation system (CPARS) considering user’s personalized preferences. Firstly, an online reviews-driven personalized preference matching method for pairing EVs with CPs is proposed to obtain the vehicle-pile matching degree (MD). Then, a charging scheduling mechanism is designed to coordinate the charging time of EV users with the potential available time of CPs. Finally, the recommendation problems are formulated as three binary linear programming models to maximize the user’s perceived utility for different scenarios considering total time, total fees, SOC levels, real-time traffic information, and individual preferences. To solve it, a discrete Flying Fox Optimization (FFO) is introduced. A case study using a real map and CP network is conducted to evaluate the performance of the proposed method. The simulation results demonstrate that the proposed strategies significantly improve the perceived utility of EV users in specific scenarios and offer advantages in increasing the serviceable quantity of CPs. The robustness of the proposed charging strategies is tested by varying the number of users. Furthermore, it reveals the significant effects of consumer’s risk aversion, regret aversion, and time value on users’ perceived utility.