Joint Location and Capacity Optimization of Electric Vehicle Charging and Battery-Swapping Stations Using Random Forest Surrogate-Assisted NSGA-III

Abstract

The increasing penetration of electric vehicles (EVs) creates new challenges for coordinated planning of charging and battery-swapping infrastructure. This study aims to develop a joint location and capacity planning framework for electric vehicle charging stations (EVCSs) and electric vehicle swapping stations (EVSSs) in an electric–traffic coupled system, considering infrastructure cost, user service requirements, and distribution-network performance. A multi-objective planning model is established based on spatial-temporal energy replenishment demand and traffic–grid coupling. To improve computational efficiency, a random forest surrogate-assisted NSGA-III (RF-SA-NSGA-III) is proposed, in which expensive user-service and voltage-related objective evaluations are selectively approximated by random forest models, combined with periodic true-model correction and final verification. Case studies show that the proposed method obtains competitive Pareto-optimal solutions and replaces 89.38% of expensive evaluations, reducing computational time from 45,706.3 s to 4689.4 s. The framework provides practical support for coordinated EVCS–EVSS siting and capacity allocation while balancing investment, user service quality, and voltage performance.