Agent-Based Model of Electric Vehicle Charging Demand for Long-Distance Driving in the State of Indiana

Abstract

Historically the U.S. transportation system has been continuously improved to adopt new policies and technologies. The transportation vehicle energy transition from fossil fuels to electricity is promoted by policymakers and major automakers with an expectation of enhancing sustainability aspects such as fossil fuel consumption reduction, carbon emissions reduction, and lower operations and maintenance costs. However, the electrification of the existing transportation infrastructure system requires substantial upgrades to overcome two major concerns from ordinary drivers and the public. One is the driver’s range anxiety based on the current capability of the electric vehicle (EV) technologies. The other is the availability of EV charging stations near the planned route. To address these two issues, we introduce an agent-based simulation model to project the consequences of electrification in the Indiana state highway system. Specifically, the model is developed to monitor the status of long-distance EV trips between different regions. The multi-agent engine method guarantees the model can adapt to diverse scenarios and complex environments. The simulation experiment verifies that the proposed model can provide the expected outcomes, including the numerical data of electric energy demand and the geospatial information (as location coordinates) of failed trips. By performing a GIS-based analysis of the results, the derived geospatial data can help state transportation agencies determine where to deploy the charging facilities to satisfy the overall charging demand. The proposed simulation framework offers a novel and strategic way to resolve the challenges for EV charging-related research and projects.