Prediction of Battery Swapping Demand in Electric Vehicle Battery Swap Station Clusters Based on Markov Chains

With the rapid development of the electric vehicle (EV) industry, battery swap stations(BSSs) have emerged as a novel solution for EV charging and range extension, playing an increasingly vital role in EV operations. However, the volatility and complexity of battery swapping demand present significant challenges for accurate forecasting. This paper proposes a demand prediction model for EV BSS clusters, integrating Markov Chains and Monte Carlo methods. By analyzing time-series data and combining a Markov state transition model with Monte Carlo simulations, the proposed model accurately forecasts future swapping demand. This provides a robust basis for resource allocation and operational decision-making within EV battery swap networks.