A matrix approach to detect temporal behavioral patterns at electric vehicle charging stations
This work addresses the problem of optimizing charging infrastructure management for electric vehicle operators, but it is incremental as it applies existing methods to a new dataset.
The authors tackled the problem of identifying temporal charging patterns at electric vehicle charging stations by analyzing arrival times and connection durations, using both a rule-based approach and a hierarchical clustering method with a modified l-p norm, and found that the rule-based method performed well for predefined patterns while clustering revealed unexpected patterns.
Based on the electric vehicle (EV) arrival times and the duration of EV connection to the charging station, we identify charging patterns and derive groups of charging stations with similar charging patterns applying two approaches. The ruled based approach derives the charging patterns by specifying a set of time intervals and a threshold value. In the second approach, we combine the modified l-p norm (as a matrix dissimilarity measure) with hierarchical clustering and apply them to automatically identify charging patterns and groups of charging stations associated with such patterns. A dataset collected in a large network of public charging stations is used to test both approaches. Using both methods, we derived charging patterns. The first, rule-based approach, performed well at deriving predefined patterns and the latter, hierarchical clustering, showed the capability of delivering unexpected charging patterns.