Mesoscopic Approach to Modeling Electric Vehicle Fleets based upon Driving Activity Data to Investigate Recharge Strategies' Impact on Grid Loads

This paper presents a mesoscopic model to populate a set of sub-fleets of battery electric vehicles (BEVs) that simulates how grid power demand changes with varied mixes of different charging strategies. The proposed model considers real driving activities of vehicles based on the National Household Travel Survey (NHTS) and various charging strategies while parked (at home and in other locations). To be easily scaled to any fleet size, a mesoscopic model is implemented, where BEVs with the same physical properties and driving schedules and clustered together into sub-fleets. The degree of granularity is explored for stable results. Monte Carlo simulations were run to demonstrate the potential of this model's use in grid loads under various charging strategies. Furthermore, an example cost function was optimized to demonstrate finding optimal allocations of charging strategies.