Optimization of Charging Stops for Fleet of Electric Vehicles: A Genetic Approach

Electrification of transport is one of the approach to improve transport efficiency and sustainability. The current cost of transport associated with electrical vehicles is mainly related to the cost of acquisition and maintenance of batteries. Finding an efficient way of managing the available energy allows reducing the size of the batteries and thus the cost associated with transport. Recently taxi services and urban delivery companies are introducing electric vehicles in their fleet. Available route planners do not consider properly the characteristics and charging stop requirements of EV fleets in decision making which results in non-optimal routing solution. The proposed work addresses the problem of finding the routes for a fleet of electric vehicles which will not only consider the battery limit of the vehicle, but also the concurrent use of charging stations along the route. The proposed solution computes routes for the fleet of vehicles that minimizes the associated cost which is a combination of travel time, charging time and the energy consumption along the route and is based on an evolutionary genetic algorithm with learning strategy. The results demonstrate that, the proposed algorithm finds a feasible solution in a reasonable amount of time and distributes the vehicles amongst the charging station to minimize the concurrency. The stated problem is non-polynomial and while genetic algorithm allows to efficiently explore large solution spaces, the work also presents some approximations and some strategies that allow to reduce the computational requirements and to find a solution in reasonable time.