Robust location and sizing of electric vehicle battery swapping stations considering users’ choice behaviors

Abstract

Battery swapping electric vehicle (EV) is an effective way to achieve vehicle electrification due to its advantage in reducing the waiting time and the upfront purchase cost, and alleviating users’ range anxiety. This paper proposes a robust battery swapping stations location and sizing model for the EV battery swapping service network design problem considering the users’ choice behaviors. Specifically, this paper develops a multinomial logit model to characterize the interaction between the service provider’s decisions and the EV users’ choice, and derives closed-form results for the sizing decisions to satisfy the pre-specified service level criteria by utilizing the distributionally robust chance constraint method and GI∖G∖m queueing model. This paper develops an equivalent mixed integer second-order cone programming (SOCP) reformulation and a generalized Benders decomposition algorithm with speedup strategies to solve the non-convex robust model efficiently. We extend the proposed model to consider the power network constraints, and propose a data-driven algorithm to estimate EV users’ swapping demands from traffic network data. Experimental results show that the proposed algorithm outperforms the state-of-the-art Gurobi solver in terms of run time and solution quality. This paper conducts a case study using real-world data to examine the influence of EV users’ choice behavior, service levels, charging rate, and battery swapping service rate on the optimal decisions and the expected profit, and presents useful managerial implications for practitioners.