The project will last three years.

Using the new controller algorithm, different DR events were simulated for a collection of over 3,000 vehicles to explore the magnitude of EV charging loads that could be reduced without adversely affecting the mobility needs of drivers. DR events were simulated to occur at a variety of different times during the day, and with duration spanning 1 hour, 2 hours, or 4 hours. It was found that EVs can reduce over 75% of their charging loads during a DR event without adversely affecting the mobility needs of any driver. Further, the study explored how the results may change if the travel itineraries for individual drivers were highly uncertainty. Even with substantial levels of uncertainty in travel needs, it was found that over 65% of EV charging loads could be removed during a DR event.

A press release is available here.

A new V2G-Sim case study demonstrates the ability of V2G-Sim to predict EV charging loads in a stochastic manner. The case study demonstrates how users can provide statistical inputs to V2G-Sim to describe how drivers use their cars to predict EV charging loads for any number of vehicles. In the case study, two scenarios are demonstrated for a collection of 1000 EVs. In the first  case, grid loads are predicted for a case where vehicles charge predominantly in the evenings. In the second case, grid loads are predicted when charging is allowed during the day and evening. All results are resolved on a second-by-second basis enabling users to see the up and down spikes in grid loads that arise from individual vehicles plugging in and unplugging. With this statistical inputs approach,

A new V2G-Sim case study quantitatively examines the assumption of having to retire EV batteries from vehicles once they reach 70-80% remaining capacity. Driving patterns from drivers across the United States are simulated in V2G-Sim within vehicles that have experienced different levels of battery degradation. Battery SOC profiles from these simulated vehicles are examined to identify if drivers run out of charge during their trips, and to determine how much range drivers would have if they needed to make an unexpected trip.

The results from this case study show that EV batteries continue to meet the needs of a vast majority of drivers well beyond degradation levels of 70-80% remaining capacity. These results suggest that battery degradation may be an over-emphasized concern in terms of meeting the mobility needs of drivers. EV batteries may well continue to serve the needs of drivers for at least as long as the lifetime of the entire vehicle. These results also challenge the assumptions of a majority of battery second life economic analysis literature because it is shown that batteries entering their second life for grid storage may have much less than 70-80% remaining capacity.