Optimal Route Choice and Charging Strategy for Connected and Autonomous Electric Vehicles Under Mixed Traffic Flow Considering Multiple Objectives

Abstract

The effective management of connected and autonomous electric vehicle (CAEV) fleets is critical for realising their potential to create safer and more efficient urban transportation systems. As CAEVs increasingly share the road with conventional vehicles, a primary challenge is to optimise their operations within this mixed-traffic reality. This study tackles the joint problem of route selection and charging scheduling for multiple CAEVs by developing a multi-objective optimisation model. The framework is designed to minimise a holistic cost function comprising energy consumption, travel time, charging service fees and penalties for violating user-specified time windows. Critically, the model incorporates real-world complexities, including time-of-use electricity pricing, partial charging strategy and the stochastic queuing delays introduced by non-connected vehicles at charging stations. We adapt the non-dominated sorting genetic algorithm III (NSGA-III) to efficiently solve this complex optimisation problem. The proposed model is demonstrated through a case study using an actual road network from Beijing, China. The results indicate that the proposed model can reduce charging service fees by up to 96% and travel time by 33% when compared to conventional scheduling methods. These findings can offer significant insights for policymakers and platform operators aiming to formulate effective CAEV travel and charging policies in heterogeneous traffic environments.