  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.
