Multi-Type Electric Vehicle Scheduling Optimization Considering Load Capacity, Battery-Allowed Mileage, and Recharging Duration

Abstract

Pure electric public transport management optimization can promote the electrification evolution from conventional diesel emission to low/zero carbon transport revolution. However, emerging electric vehicle scheduling (EVS) takes into account battery capacity, battery-allowed mileage, and charging duration, which are a few concerns present at the conventional motor bus planning level. Concentrating on this new challenge, this paper builds a multi-type electric vehicle scheduling model, featuring rigorous load capacity, battery-allowed mileage, and recharging duration constraints. The binary decision variables involving the connection between departure and arrival times, as well as the recharging necessity, are judged simultaneously. The objective is to minimize the fleet size, idle mileage, and charging cost. A preprocessing-based genetic algorithm is used to handle this mixed-integer nonlinear programing model. Numerical examples are tested to validate the effectiveness of the proposed models and the solution algorithm. Compared with a single large-type vehicle scheme, the total cost of multi-type vehicle scheduling in one-trip, two-trip, and three-trip frequency scenarios are reduced by 20.8%, 6.3%, and 9.1%, respectively.