Abstract
This paper presents a design and evaluates the performance of a charging task scheduler for electric vehicles, aiming at reducing the peak load and improving the service ratio in charging stations. Based on a consumption profile and the real-time task model consisting of actuation time, operation length, and deadline, the proposed scheduler fills the time table, by which the power controller turns on or off the electric connection switch to the vehicle on each time slot boundary. Genetic evolutions yield better results by making the initial population include both heuristic-generated schedules for fast convergence and randomly generated schedules for diversity loss compensation. Our heuristic scheme sequentially fills the time slots having lowest load for different orders such as deadline and operation length. The performance measurement result obtained from a prototype implementation reveals that our scheme can reduce the peak load for the given charging task sets by up to 4.9%, compared with conventional schemes.
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