Infrastructure Optimization of In-Motion Charging Networks for Electric Vehicles Using Agent-Based Modeling

As the market share of electric vehicles increases, the associated charging infrastructure must be further developed to meet the growing demand for charging. While stationary plug-in methods have been the traditional approach to satisfying this demand, in-motion charging technologies have the potential to eliminate the inconvenience of long charging wait times and the high cost of large batteries. In this research, an agent-based model is developed to simulate vehicle charging demand and then validated against real traffic data. Driver behavior is estimated from travel survey data, and a method is introduced to estimate route-planning decisions in the presence of multiple charging options. The model is technology agnostic, allowing for its application to any kind of in-motion charging technology (i.e., inductive, conductive, and capacitive). A genetic algorithm is used to optimize the location of roadways with dynamic charging capabilities in the presence of the existing charging infrastructure. Both major highways and arterial roads were considered as potential candidates for dynamic charger installation. Results are presented for a case study in Salt Lake County, Utah.