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 allowable 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, it is shown how it is possible to employ history data of the electric vehicle supply equipment in order to provide a modified charging energy management. The method allows coordinated charging according to predefined conditional rules and results in reduced load peaks. For this novel approach the EV’s are categorized according to specific clusters using unsupervised machine learning (ML). The ML model was able to find among these large data sets common inferences and hidden patterns thus successfully categorized the EV’s. In this work, the rule-based energy management system, is further developed and designed using fuzzy logic. 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 especially when additional EV’s or clusters are introduced. Fuzzy logic provides nuanced decision-making that qualifies it as more suitable for this implementation than any other AI method. In addition to that, it provides robust decision-making and distribution of electric power according to different clusters and categories due to its aggregated rules. This combined action of ML and fuzzy logic for coordinated EV charging is an efficient and powerful approach to reduce electric load peaks and provide energy economy charging.
Session:
Charging Infrastructure I
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| 08:30 - 09:00