A novel multi-objective optimization model of electric vehicles’ path planning and charging scheduling based on users’ comprehensive satisfaction

The popularization of electric vehicles (EVs), has sparked a flurry of interest in the problem of path planning and charging scheduling for EVs, distinct from those issues in conventional petrol-powered vehicles. In this regard, this study proposes a novel mixed integer optimization model based on users’ comprehensive satisfaction to describe the EV path planning and charging scheduling problem. In particular, the travel distance and charging waiting time, charging and discharging duration, and economic costs are, respectively, introduced to characterize path planning and charging scheduling of EVs. Considering the mutual influence of EV users’ charging station selection decisions, we derive the waiting time for different EV users at charging stations. Further, the weights of various parameters of the objective function for the EV path planning and charging scheduling model are determined through the fuzzy analytic hierarchy process (FAHP) based on users’ comprehensive satisfaction. In view of the characteristics of the model, an improved ant colony optimization (ACO) and adaptive large neighborhood search (ALNS)-based bilevel algorithm is designed to solve the multi-objective optimization problem. Finally, simulations for path planning and charging scheduling of EVs are conducted using the Suzhou-Wuxi Highway Network to exhibit the effectiveness of the proposed model and algorithm. It is shown that the superiority of heuristic path solutions is validated through the comparisons with two other heuristic algorithms, indicating the excellence of the proposed multi-objective optimization model.