Abstract
This paper presents a design and evaluates the performance of an efficient charging scheduler for electric vehicles, aiming at reducing the peak load of a fast charging station while meeting the time constraint of all charging requests. Upon the task model consist of actuation time, operation length, deadline, and a consumption profile, the proposed scheduler fills the allocation table, by which the power controller turns on or off the electric connection switch to the vehicle on each time slot boundary. For the sake of combining the time-efficiency of heuristic-based approaches and the iterative evolution of genetic algorithms, the initial population is decided by a heuristic which selects necessary time slots having the lowest power load until the previous task allocation. Then, the regular genetic operations further improve the schedule, additionally creating a new chromosome only from the valid range. The performance measurement result obtained from a prototype implementation shows that our scheme can reduce the peak load for the given charging task sets by up to 4.9 %, compared with conventional schemes.
This research was supported by the MKE, Korea, under IT/SW Creative research program supervised by the NIPA (NIPA-2011-(C1820-1101-0002)).
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Lee, J., Kim, HJ., Park, GL., Jeon, H. (2012). Genetic Algorithm-Based Charging Task Scheduler for Electric Vehicles in Smart Transportation. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7196. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28487-8_21
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DOI: https://doi.org/10.1007/978-3-642-28487-8_21
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