Adaptive Simulation of EV Charging Processes: Employing Bayesian Inference with Markov Chain Monte Carlo for Dynamic Input Updating

Simulating the charging process of electric vehicles (EVs) at public stations is crucial for effective decision-making in the planning and management of EV infrastructure. Traditional models face challenges in reflecting the dynamic and uncertain nature of real-life EV charging. This study introduces a hybrid simulation framework that incorporates geospatial demand and utilizes Bayesian Inference with the Markov Chain Monte Carlo (MCMC) method to generate dynamic, probabilistic inputs. The proposed approach could (1) dynamically respond to changing observation data, (2) reflect the uncertainty and randomness of charging progress, and (3) integrate users' demand and geospatial factors in the charging station selection. A case study was conducted involving three charging stations in Fairfax City. The results explained the evolving charging patterns and evaluated the impact of unforeseen events on station utilization. This method offers a robust tool for planning, developing, and optimizing public EV charging infrastructure, adapting to changing behaviors and demands.