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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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Spatially Resolved Domicile Charging Demands for Light-, Medium-, and Heavy-Duty Electric Vehicles in Virginia

The use of plug-in electric vehicles (PEVs) and resulting grid impacts are likely to grow rapidly, and evaluation of optimal smart charge management and grid integration strategies is warranted now. Evaluating distribution grid impacts requires fine-grained models of PEV operations to estimate charging loads across diverse vehicles at high spatial resolution. We propose such a model and consider a high-electrification scenario in Richmond and Newport News, Virginia. Our framework considers four categories of vehicle that are amenable to early aggressive electrification: light-duty passenger vehicles (LDV), trucks and vans with a focus on delivery or other local operations, school buses, and transit buses. These vehicles have a relatively consistent domicile, reducing the need for public charging infrastructure rollout to electrify. We apply a recent LDV model and propose new models for each vocation of medium- and heavy-duty vehicle, leveraging telematics data. We demonstrate our framework in Virginia and find energy demands in the region may total 15 GWh day, with most consumed by LDV. However, considering power demand at high spatial resolution reveals a different trend: LDVs have relatively small peak loads at specific sites (peak site demand around 800 kW) compared to average and high demand medium- and heavy-duty vehicle charging sites (peak site demand around 6,000 kW at a transit bus depot, 1,500 kW at a local freight hub, and 1,000 kW at a school). Our framework yields insights on the relative impacts of each vocation and enables future work to tailor grid integration strategies to each vehicle category.

33 ADVANCED PROPULSION SYSTEMS↗

A Forward-Looking Dataset of EV Managed Charging Resource and Costs

This presentation summarizes a high-resolution, forward-looking dataset of EV adoption, EV charging, and managed charging resource. Vehicle-level data are grounded in current adoption and charging patterns, and ~200,000 real-world vehicle-weeks of travel data covering all on-road segments (i.e., light-duty, transit and school buses, local, regional and long-haul medium- and heavy-duty). The data, which include multiple charging profiles per vehicle to bound flexibility, are then processed and aggregated to describe baseline charging and charge management resource by county, hour, year, scenario, and vehicle type. Coupled with one of four scenarios of how EV managed charging costs might evolve over time, the dataset enables a power sector capacity expansion model to select cost-optimal quantities of EV managed charging and supply-side resources to reliably satisfy demand. Five integration strategies: Baseline, Daytime and Flat (passive), Flex (active), and Stress (anti-strategy), illustrate how baseline charging and flexibility potential changes with EVSE build-out and charging preferences.

33 ADVANCED PROPULSION SYSTEMS↗