Electric vehicle routing optimization considering the inclines of roads in post-disaster charging relief

With the destruction of the ecological environment, disasters are increasing yearly. Disasters usually affect people's daily lives and significantly damage power systems. Meanwhile, the situations of limited energy and different inclines of roads after a disaster also need to be faced. This paper investigates the potential impact on the electric vehicle (EV) routing optimization if the inclines of roads are integrated into the post-disaster charging relief. Each shelter's power demand and the EV's battery level are described by the battery state of charge (
$SoC$
). We aim to find routes for electric vehicles (EVs) such that all the power demand of shelters are charged before the battery
$SoC$
at shelters reaches the minimum bound over time while minimizing the total costs, which contain the total travel time, total charging time and the penalty terms of power loading capacity and time constraints. We propose a hybrid genetic algorithm (HGA) that incorporates a genetic algorithm (GA) and the large neighborhood search (LNS) to solve this problem. Simulation results on a real case obtained from a disaster scenario in Ichihara City, Japan, indicate the effectiveness of the approach.