Optimising route choices for the travelling and charging of battery electric vehicles by considering multiple objectives

A battery electric vehicle (BEV) reduces greenhouse gas emissions and energy consumption to a greater extent than a conventional internal combustion engine vehicle. However, limited driving range, insufficient charging infrastructure, potentially long charging time and high monetary cost affect the route choices of BEV drivers. To provide BEV drivers with decision-making support for travelling and charging, a multi-objective optimisation model is built to optimise route choices for multiple BEVs. Three objective functions are proposed to minimise total travelling cost components, including travel times, energy consumption and charging costs. The fuzzy programming approach and fuzzy preference relations are introduced to transform the three objective functions into a single objective function that comprehensively considers the three optimisation objectives. A genetic algorithm is then designed to solve the model. In addition, a numerical example is presented to demonstrate the proposed model and solution algorithm. Four weighting conditions for BEV drivers based on different trade-offs among the objectives are considered in the numerical example. Results of the numerical example verify the feasibility and effectiveness of the proposed model and algorithm.