Characterizing Electric Vehicle Plug-in Behaviors Using Customer Classification Approach

This paper proposes a customer classification approach to analyze the plug-in behavior of Electric Vehicle (EV) users for developing sustainable and efficient charging infrastructure. The features influencing EV adoption are selected from available EV models in Australia using a feature importance technique. The selected influential features are employed to classify EV customers using a k-means clustering algorithm into different clusters. Each cluster’s energy demand and plug-in behavior are assessed considering EV uncertainties. The results exhibit the efficacy of the customer-segmentation approach for managing and deploying charging infrastructures for large-scale EV penetration. This study underscores the significance of EV users’ plug-in behaviors and characteristics toward transport electrification for achieving net-zero emissions by 2050.