Aiming at alleviating the range anxiety problem in electric vehicles taking advantage of well-developed computational intelligence, this paper designs a multi-day tour-and-charging scheduler and measures its performance. Some tour spots have charging facilities for the vehicle battery to be charged during the tour. Our scheduler finds a multi-day visiting sequence, permitting different day-by-day start and end points. To exploit genetic algorithms for the extremely vast search space, a feasible schedule is encoded to an integer-valued vector having n+m-1 elements, where n is the number of places to visit and m is the number of tour days. The cost function evaluates the waiting time, namely, the time amount the tourist must wait for the battery to be charged enough to reach the next place. It also integrates the time budget constraint and quantizes the tour length. The performance measurement result obtained from a prototype implementation shows that our scheme achieves 100 % schedulability until 13 places for the 2-day trip and 17 places for the 3-day trip on given parameter setting.
['Junghoon Lee', 'Gyung-Leen Park']
Design of a Multi-day Tour-and-Charging Scheduler for Electric Vehicles