Real-time control for EV charging and discharging using bi-layer fuzzy inference mechanism

Due to the randomness of electric vehicle (EV) travel patterns, charging schedules rely on real-time information transmission and updates, which are difficult to achieve in the absence of communication conditions. While the autonomous optimization algorithm can overcome these limitations, requiring only the most basic information of EVs, while not aggregating or exchanging any private information of each EV. This study proposes an autonomous EV charging and discharging control method based on a real-time bi-layer fuzzy inference mechanism. Considering the jitter phenomenon in the output of normal fuzzy inference, a willingness dead zone is added between the two layers of the fuzzy inference structure. The simulation results show that the proposed method can improve load performance and reduce charging costs, meanwhile the introduce of dead zone can mitigate the adverse effect. •An EV spatio-temporal travel chain has been constructed based on real-world data.•A bi-layer FIM control structure is proposed, utilizing both EV data and grid data as inputs to ensure outputs cater to the interests of both sides simultaneously.•The simulation results over a one-week period demonstrate the superiority of this method in optimizing load and reducing costs.