Driving Assistance for Optimal Trip Planning of Electric Vehicle Using Multi-objective Evolutionary Algorithms

Route planning for electric vehicles

“…The final objective deals with minimizing the total charging cost of P. If the EV stops for charging at node a or b, the charging cost of the corresponding edge e i will be estimated and summed to the total charging cost of path P. Thus, the accumulative charging cost of P is defined by Equation (14).…”

“…The results proved that the shortest route increases the energy consumption and the time cost by 0.7% and 1% compared to the minimum energy route, and by 0.6% and 5% compared to the fastest route, respectively. Khanra et al [14] proposed a Driving Assistance system for Optimal Trip Planning (DAOTP) that aims at minimizing, simultaneously, the energy consumption, travel time, and discomfort. They used the multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) to deliver a set of optimal solutions, from which the driver can select an adequate path based on his/her preferences.…”