An Improved Differential Evolution Algorithm for Optimal Location of Battery Swapping Stations Considering Multi-Type Electric Vehicle Scale Evolution

Abstract

Scientific scale forecasting of multi-type electric vehicles (EVs) is critical to accurately analyze the planning and operation of battery-swapping stations (BSSs) and charging stations (CSs). This paper predicts the proportions of plug-in electric vehicles (PEVs), hybrid electric vehicles (HEVs), and battery-swapping electric vehicles (BSEVs) in the total EV fleet in multi-scenarios via a system dynamics (SD) method. Relying on the predicted evolution scale of the BSEVs and the service demand of BSSs calculated by the service radius (SR) method, an improved differential evolutional algorithm combing with Monte Carlo searching (IDEA-MCS) method is proposed to obtain the optimal location of BSSs in a certain region in Beijing, which achieves an economic optimum of BSSs under the battery-swapping mode (BSM) via centralized charging and unified distribution (CCAUD). The analytical results show that the proportion of the BSEVs in different scenarios is the major driver that impacts the location of BSSs. The distribution of BSSs' BS demand in the optimistic scenario is more inhomogeneous than that in the other scenarios. In addition, a cross-comparison of optimal profits in different scenarios is conducted to verify the optimality of BSS locations for a given scenario. Finally, the proposed IDEA-MCS method is compared with the DEA method and IDEA method to verify its optimality.

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