  Electric vehicles (EVs) are finally making their way onto the roads, but the
challenges concerning long charging times and impact on congestion of the power
distribution grid are still not resolved. Proposed solutions depend on heavy
communication and rigorous computation and mostly need real-time connectivity
for optimal operation; thereby, they are not scalable. With the availability of
historical measurement data, EV chargers can take better-informed actions while
staying mostly off-line. This study develops a distributed and data-driven
congestion detection methodology together with the Additive Increase
Multiplicative Decrease (AIMD) algorithm to control mass EV charging in a
distribution grid. The proposed distributed AIMD algorithm performs very
closely to the ideal AIMD in terms of fairness and congestion handling, and its
communication need is significantly low. The results can provide crucial
insights on how data can be used to reveal the inner dynamics and structure of
the power grid and help develop more advanced data-driven algorithms for grid
integrated power electronics control.
