Fuzzy Rule-Based Coordinated EV Charging Management

Coordinated electric vehicle (EV) charging is a concept with a growing potential. The main goal of this work is to manage the overall charging power of a group of EV’s during charging according to the available system capacity. This concept is increasingly needed for charging stations that are located at commercial or residential buildings. These buildings are observed to have large load peaks during certain times of the day. In a previous study, a rule-based energy management method was developed. The later, allows coordinated charging according to predefined conditional rules and results in reduced load peaks. For this method, the EV’s are categorized according to specific clusters using unsupervised machine learning (ML). In this work, the rule-based energy management system, is further developed and designed using fuzzy inference system. This approach allows coordinated energy management based on rules that interpret human reasoning and decisions. The implementation of fuzzy logic is beneficial as it introduces a great amount of flexibility and scalability to the system. In addition to the fuzzy inference system, coordinated charging has been implemented in this work based on mathematical optimization and using genetic algorithm. Then, the performance of the two aforementioned approaches are compared with each other in simulation and utilizing the same test scenarios that were implemented in the previous work. The fuzzy inference system has the most favorable performance among all three concepts. As it was successful in providing, coordinated EV charging, flatten the load peak and provide EV demands according to their assigned cluster specifications.