Engineering topics

Cappellucci, Jeffrey

Publications and source records attributed to Cappellucci, Jeffrey.

4 records

DOE OSTI2024

Assessment of Alternative Fueling Infrastructure in the United States

NHTSA uses the Corporate Average Fuel Economy (CAFE) Model to analyze potential CAFE standards and their impact on emissions and vehicle fleet composition. The CAFE model analyzes the application of potential technologies to the current automotive industry vehicle fleet to determine the feasibility of future CAFE standards and the associated costs and benefits of the standards. A significant portion of the engineering input development is related to the effectiveness (energy consumption reduction) of each fuel-saving technology and the combination of several fuel-saving technologies, including AFVs. The purpose of this report is to deepen NHTSA's understanding of alternative fueling infrastructure and its potential impact on the adoption of alternative fuel vehicles (AFVs) so that AFVs can be more accurately and comprehensively incorporated into the CAFE Model. This report analyzes the current state of alternative fueling infrastructure in the United States and its relationship to the light-, medium-, and heavy-duty AFV markets; explores the costs associated with alternative fueling infrastructure; investigates trends driving the deployment of alternative fueling infrastructure; explores how the adoption of various vehicle and fuel technologies may look in the future; and analyzes the evolution of alternative fueling corridors.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator: Fourth Quarter 2023

Electric vehicle (EV) charging infrastructure continues to rapidly change and grow. Using data from the U.S. Department of Energy's (DOE's) Alternative Fueling Station Locator, this report provides a snapshot of the state of EV charging infrastructure in the United States in the fourth calendar quarter of 2023 (Q4 2023) by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared to the infrastructure requirement scenario outlined in the National Renewable Energy Laboratory (NREL) report, The 2030 National Charging Network: Estimating U.S. Light-Duty Demand for Electric Vehicle Charging Infrastructure. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape of EV charging infrastructure. This is the sixteenth report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and NREL publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Electric Vehicle Charging Infrastructure Trends from the Alternative Fueling Station Locator: Second Quarter 2023

Electric vehicle (EV) charging infrastructure continues to rapidly change and grow. Using data from the U.S. Department of Energy's Alternative Fueling Station Locator, this report provides a snapshot of the state of EV charging infrastructure in the United States in the second calendar quarter of 2023 by charging level, network, and location. Additionally, this report measures the current state of charging infrastructure compared with a 2030 infrastructure requirement scenario. This information is intended to help transportation planners, policymakers, researchers, infrastructure developers, and others understand the rapidly changing landscape of EV charging infrastructure. This is the fourteenth report in a series. Reports from previous quarters can be found in the Alternative Fuels Data Center (AFDC) and National Renewable Energy Laboratory publication databases, as well as the AFDC Charging Infrastructure Trends page (https://afdc.energy.gov/fuels/electricity_infrastructure_trends.html).

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

RouteE-Powertrain [SWR-19-19]

RouteE-Powertrain is a tool for predicting energy usage over a set of road links. RouteE-Powertrain is a Python package that allows users to work with a set of pre-trained mesoscopic vehicle energy prediction models for a varity of vehicle types. Additionally, users can train their own models if "ground truth" energy consumption and driving data are available. RouteE-Powertrain models predict vehicle energy consumption over links in a road network, so the features considered for prediction often include traffic speeds, road grade, turns, etc. The typical user will utilize RouteE's catalog of pre-trained models. Currently, the catalog consists of light-duty vehicle models, including conventional gasoline, diesel, hybrid electric (HEV), and battery electric (BEV). These models can be applied to link-level driving data (in the form of pandas dataframes) to output energy consumption predictions. Users that wish to train new RouteE models can do so. The model training function of RouteE enables users to use their own drive-cycle data, powertrain modeling system, and road network data to train custom models. https://pypi.org/project/nrel.routee.powertrain/ pip install nrel.routee.powertrain

Holden, Jacob↗