Enhancing electric vehicle charging station design using multifidelity simulations

We study simulation-assisted service system design, where stochastic simulation is used to select the best design from a finite set of structural or parametric alternatives. Since high-fidelity simulation can be prohibitively time-consuming, we adopt a multifidelity approach that combines expensive high-fidelity runs with cheaper, coarser low-fidelity runs to estimate system performance and compare designs. This research is motivated by the design of an integrated electric vehicle (EV) fast-charging station. We formulate the design problem under the fixed-budget ranking and selection framework, in which the simulation budget is allocated across fidelity levels and design alternatives to maximize the probability of correct selection of the best design. We derive an asymptotic solution, develop a selection algorithm that satisfies the resulting optimality conditions, and establish its consistency and asymptotic optimality. We further demonstrate the algorithm’s empirical performance through an EV fast-charging station case study and a set of synthetic examples. These theoretical and empirical results provide actionable guidance on when and how multifidelity simulation can improve best-design selection in complex service system design problems.