Robust Multistage Location of Battery Swapping Stations

Abstract

Battery swapping is undergoing a revival with the thriving electric vehicle industry and advancements in battery technology. Limited budgets and growing demands make the battery swapping industry can only develop over time. However, there are little literature that concerns the multistage location of battery swapping stations. To fill the gap, this paper aims to address the multistage location of battery swapping stations, considering decision-dependent and time-dependent swapping demands. To the best of our knowledge, this work is the first attempt to address this specific challenge. We propose a dynamic distributionally robust optimization framework that incorporates a specific ambiguity set to encapsulate the time-and-decision-dependent characteristics of the problem. Furthermore, we model the swapping station operation as a continuous Markov process and integrate it into the location model. To efficiently solve the problem, we propose a revised stochastic dual dynamic integer programming algorithm, utilizing logic-based Benders cuts and an upper-bound algorithm. Through a comprehensive numerical analysis, we validate the efficacy of our proposed model and give management insights to guide the battery swapping station planning.

Keywords: battery swapping station; multistage location; distributionally robust optimization; time-and-decision-dependent uncertainty; stochastic dual dynamic integer programming