  We propose and evaluate controllers for the coordination of the charging of
electric vehicles. The controllers are based on neural networks and are
completely de-centralized, in the sense that the charging current is completely
decided by the controller itself. One of the versions of the controllers does
not require any outside communication at all.
  We test controllers based on two different architectures of neural networks -
the feed-forward networks and the echo state networks. The networks are
optimized by either an evolutionary algorithm (CMA-ES) or by a gradient-based
method. The results of the different architectures and the different
optimization algorithms are compared in a realistic scenario. We show that the
controllers are able to charge the cars while keeping the peak consumptions
almost the same as when no charging is performed. Moreover, the controllers
fill the valleys of the consumption thus reducing the difference between the
maximum and minimum consumption in the grid.
