Optimizing Electric Vehicle Charging Networks: A Multidimensional User-Centric Approach

The proliferation of electric vehicles has rendered the strategic deployment of electric vehicle charging stations an imperative for industry advancement. This scholarly work enhances the configuration of electric vehicle charging networks through an in-depth examination and analysis of the multifaceted user requirements. Initially, Markov chains and spatial traffic characteristics are employed to dissect the spatiotemporal dynamics of charging demand, shedding light on the influence of seasonal fluctuations and the ease of transportation on user charging needs. Thereafter, leveraging Point of Interest (POI) data surrounding the charging stations, an enhanced Huff model evaluates the allure of these stations to users, a pivotal aspect in deciphering charging behavior patterns. Clustering algorithms are subsequently applied to the dataset to uncover the range anxiety and anticipated charging actions of users. The synthesis of these analytical outcomes is achieved through the Grey Wolf Optimizer, which refines the charging network layout. The experimental outcomes have substantiated the viability and applicability of this methodology, thereby offering robust endorsement for the strategic and scientific delineation of electric vehicle charging infrastructure.