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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Electric Vehicle Efficiency Ratios for Light-Duty Vehicles Registered in the United States

Electric vehicles (EVs) use energy more efficiently than gasoline vehicles. This is one of their primary attributes, enabling other benefits such as improved torque and reduced operating costs and greenhouse gas emissions. An electric vehicle efficiency ratio (EVER) is therefore important when calculating the financial and environmental benefits of EVs, calculating the impact that EVs have on a manufacturer's Corporate Average Fuel Economy rating, calculating credits in trading schemes such as California's Low Carbon Fuel Standard, fuel price leveling, designing electricity tariffs from utility-owned public charging infrastructure, creating EV alternatives to gasoline excise taxes, and more.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

A real-time energy and cost efficient vehicle route assignment neural recommender system

Here, this paper presents a neural network recommender system algorithm for assigning vehicles to routes based on energy and cost criteria. In this work, we applied this new approach to efficiently identify the most cost-effective medium and heavy duty truck (MDHDT) powertrain technology, from a total cost of ownership (TCO) perspective, for given trips. We employ a machine learning based approach to efficiently estimate the energy consumption of various candidate vehicles over given routes, defined as sequences of links (road segments), with little information known about internal dynamics, i.e. using high level macroscopic route information. A complete recommendation logic is then developed to allow for real-time optimum assignment for each route, subject to the operational constraints of the fleet. We show how this framework can be used to (1) efficiently provide a single trip recommendation with a top-k vehicles star ranking system, and (2) engage in more general assignment problems where n vehicles need to be deployed over m (m ≤ n) trips. This new assignment system has been deployed and integrated into the POLARIS. Transportation System Simulation Tool for use in research conducted by the Department of Energy's Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium (SMART, 2024).

Energy consumption↗
DOE OSTI2021

Household Transportation Energy Affordability by Region and Socioeconomic Factors

Transportation fuel is an important component of household budgets, as 3.3% of total household expenditures are for vehicle fuel nationwide and over 50% of annual household expenditures on energy are for transportation. These average values vary geographically, and higher energy cost burdens are faced by households with lower incomes. Defining transportation energy affordability burden as the percentage of annual household income spent on vehicle fuel, this study aims to quantify affordability as a function of household characteristics and geography. Here, through analysis at the census tract level, this study (i) projects annual household vehicle miles traveled (VMT) based on demographic factors using machine-learning techniques, (ii) estimates local differences in vehicle fuel economy and fuel price, and (iii) quantifies resulting transportation energy affordability by census tract. This study found that the average burden by tract varies from 0.15% to 8%. The variation in affordability can be largely explained by income level and vehicle fuel efficiency. Suburban and rural households spend more on transportation energy compared with urban households because of the usage of less fuel-efficient vehicle technologies and higher annual VMT. Lower-income groups have a wide distribution of the percentage of income spent on transportation energy, 1.2% to 8%, whereas the range for the highest income group ($125,000+) is from 0.15% to 3.9%. This detailed transportation energy affordability analysis provides a better understanding of regional variations in household travel behavior, helps to determine where fuel-efficient vehicle technologies are more likely to be used, and improves estimates of vehicle ownership costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Multi-range vehicle speed prediction using vehicle connectivity for enhanced energy efficiency of vehicles

An integrated speed prediction framework based on historical traffic data mining and real-time V2I communications for CAVs. The present framework provides multi-horizon speed predictions with different fidelity over short and long horizons. The present multi-horizon speed prediction is integrated with an economic model predictive control (MPC) strategy for the battery thermal management (BTM) of connected and automated electric vehicles (EVs) as a case study. The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the present data mining strategy over long prediction horizons. Despite the uncertainty in long-range CAV speed predictions, the vehicle level simulation results show that 14% and 19% energy savings can be accumulated sequentially through eco-driving and BTM optimization (eco-cooling), respectively, when compared with normal-driving and conventional BTM strategy.

Amini, Mohammad Reza↗
DOE OSTI2020

Efficient proactive vehicle relocation for on-demand mobility service with recurrent neural networks

One major challenge for on-demand mobility service (OMS) providers is to seamlessly match empty vehicles with trip requests so that the total vacant mileage is minimized. In this work, we develop an innovative data-driven approach for devising efficient vehicle relocation policy for OMS that (1) proactively relocates vehicles before the demand is observed and (2) reduces the inequality among drivers' income so that the proactive relocation policy is fair and is likely to be followed by drivers. Our approach represents the fusion of optimization and machine learning methods, which comprises three steps: First, we formulate the optimal proactive relocation as an optimal/stable matching problems and solve for global optimal solutions based on historical data. Second, the optimal solutions are then grouped and fed to train the deep learning models which consist of fully connected layers and long short-term memory networks. Low rank approximation is introduced to reduce the model complexity and improve the training performances. Finally, we use the trained model to predict the relocation policy which can be implemented in real time. We conduct comprehensive numerical experiments and sensitivity analyses to demonstrate the performances of the proposed method using New York City taxi data. Here, the results suggest that our method will reduce empty mileage per trip by 54-70% under optimal matching strategy, and a 25-32% reduction can also be achieved by following stable matching strategy. We also validate that the predicted relocation policies are robust in the presence of uncertain passenger demand level and passenger trip-requesting behavior.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

ResStock Measure Documentation: Efficient Electric Vehicle Adoption With Level 2 Charging

This report is part of a series describing different ResStock (TM) measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the "Efficient Electric Vehicle Adoption With Level 2 Charging" measure upgrade methodology and briefly discusses key results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

ResStock Measure Documentation: Efficient Electric Vehicle Adoption With Level 2 Charging and Demand Flexibility

This report is part of a series describing different ResStock measures. "Measures" refers to energy efficiency retrofits that can be applied to buildings during modeling. This documentation covers the "Efficient Electric Vehicle Adoption With Level 2 Charging and Demand Flexibility" measure upgrade methodology and briefly discusses key results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

IMPROVING TRANSPORTATION EFFICIENCY THROUGH INTEGRATED VEHICLE, ENGINE, AND POWERTRAIN RESEARCH -SUPERTRUCK II

Daimler Truck North America (DTNA) completed a five year, $40.1 M SuperTruck 2 project to demonstrate technologies to achieve both vehicle and engine efficiency improvements. The vehicle objective was to develop and demonstrate a concept vehicle with at least 115% vehicle freight efficiency improvement over a weighted average of four cycles relative to a 2009 best-in-class baseline vehicle. The engine technology development goal was to achieve 55% brake thermal efficiency (BTE) as tested on a dynamometer at an equivalent of 65 MPH. Both project objectives were to develop technologies that are cost effective. SuperTruck 2 started in 2017 and built on the knowledge gained in SuperTruck 1. SuperTruck 2 enabled the ability to evaluate high risk high reward research centered on four areas that show the most potential for commercialization: aerodynamics, powertrain, rolling resistance and energy management. In addition, several industry, University and National laboratories were able to collaborate in developing technologies found to be successful in meeting the program objectives. Several technologies show promise towards production while some technologies do not show a quick path towards production.

Villeneuve, Darek↗
DOE OSTI2026

MAT 329: Cost Efficient, Bio-Derivable, and Lightweight Materials for Vehicle's Thermal Energy Management

Objective: Enable lightweight and multifunctional composites by combining CF composites with encapsulated phase-change materials for temperature regulation. Impacts: The multifunctionality of the composites provides benefits over today's aluminum enclosures which could help drive CF composites adoption; and By implementing CF composites, battery enclosures can be lightweighted leading to a greater vehicle efficiency.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

FASTSim™ (Future Automotive Systems Technology Simulator) [SWR-20-101 and SWR-12-07]

The Future Automotive Systems Technology Simulator (FASTSim™) provides a simple way to compare powertrains and estimate the impact of technology improvements on light-, medium-, and heavy-duty vehicle efficiency, performance, cost, and battery life. This extremely fast simulation tool features a streamlined user interface and can rapidly perform a variety of tasks using basic computing resources: • less than 0.1 second to simulate second-by-second standard duty cycles • less than 10 seconds to estimate vehicle efficiency, fuel economy, acceleration, battery life, and cost • less than 5 minutes to perform powertrain comparisons of efficiency and cost. FASTSim models a variety of vehicle powertrains and fuel converter types: • Conventional vehicles - spark ignition, Atkinson, diesel, and hybrid diesel • Electric-drive vehicles - hybrid, plug-in hybrid, and all-electric • Hydrogen fuel cell vehicles. The default model includes a variety of vehicles and duty cycles and an option for adding additional vehicles and custom cycles: • Packaged with more than 20 vehicles • User interface enables addition of more vehicles • Includes standard U.S. drive cycles as well as European and Japanese cycles. FASTSim is available for download in Microsoft Excel and Python formats here: https://www.nrel.gov/transportation/fastsim.html

Baker, Chad↗
DOE OSTI2023

Energy Cost Analysis and Operational Range Prediction Based on Medium- and Heavy-Duty Electric Vehicle Real-World Deployments across the United States

While the market for medium- and heavy-duty battery-electric vehicles (MHD EVs) is still nascent, a growing number of these vehicles are being deployed across the U.S. This study used over 2.3 million miles of operational data from multiple types of MHD EVs across various regions and operating conditions to address knowledge gaps in total cost of ownership and operational range. First, real-world energy cost savings were determined: MHD fleets should experience energy cost savings each year from 2021 to 2035, regardless of vehicle platform, with the greatest savings seen in transit buses (up to USD 4459 annually) and HD trucks (up to USD 3284 annually). Second, to help fleets across various geographies throughout the U.S. assess the suitability of EVs for their year-round operating needs, operational range was modeled using the XGBoost algorithm (R2: 70%) given 22 input features relevant to vehicle efficiency. Finally, this paper recommends (1) that MHD fleets apply energy-saving practices to minimize the impacts of cold temperatures and high congestion levels on vehicle efficiency and range, and (2) that local hauling fleets select trucks with a nominal range nearly double the expected maximum daily range to account for range losses under local, urban driving conditions.

Qiu, Yin (ORCID:0009000900948794)↗
DOE OSTI2022

The Automated Mobility District Implementation Catalog, 2nd Edition: Safe and Efficient Automated Vehicle Fleet Operations for Public Mobility

This second volume of the Automated Mobility District (AMD) Implementation Catalog series has been prepared in three parts to assist readers in their review and understanding of the technical information herein. The material may be more easily ingested if each of the three parts is read and then contemplated for its ramifications to the specific interest of the reader before continuing to the next part. Further, each part has been prepared with the intent that it could be read independently from the others. Part 1 is Progress of Automated Vehicle R&D for Deployments in Passenger Service; Part 2 is 10 Early Deployment Sites as Prototypes of AMD Implementation; Part 3 is Five Cardinal Principles for AMD Implementation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Greenhouse gas consequences of the China dual credit policy

For over ten years, China has been the largest vehicle market in the world. In order to address energy security and air quality concerns, China issued the Dual Credit policy to improve vehicle efficiency and accelerate New Energy Vehicle adoption. In this paper, a market-penetration model is combined with a vehicle fleet model to assess implications on greenhouse gas (GHG) emissions and energy demand. Here we use this integrated modeling framework to study several scenarios, including hypothetical policy tweaks, oil price, battery cost and charging infrastructure for the Chinese passenger vehicle fleet. The model shows that the total GHGs of the Chinese passenger vehicle fleet are expected to peak in 2032 under the Dual Credit policy. A significant reduction in GHG emissions is possible if more efficient internal combustion engines continue to be part of the technology mix in the short term with more New Energy Vehicle penetration in the long term.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

TSDC: Transportation Secure Data Center: Real-World Data for Planning, Modeling, and Analysis

The Transportation Secure Data Center is a centralized repository for high-resolution transportation data from hundreds of travel and transit surveys and studies. It makes vital transportation data broadly available to users while preserving the privacy of survey participants. It houses surveys and studies conducted by state departments of transportation, metropolitan planning organizations, transit agencies, cities, and other public agencies. Meanwhile, the Livewire Data Platform empowers research, industry, and academic partners to easily and securely preserve, maintain, share, discover, and gain access to transportation and mobility data. Livewire accommodates a range of datasets, including behavioral, experimental, model, analytical, and raw data at the vehicle, traveler, and system levels. Datasets support mobility research and planning spanning urban science, connected and automated vehicles, fueling and charging infrastructure, mobility decision science, multimodal transportation, vehicle efficiency, and more.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Two trillion gallons: Fuel savings from fuel economy improvements to US light-duty vehicles, 1975–2018

Since 1975, the fuel economy of new light-duty vehicles sold in the U.S. has almost doubled. Fuel economy improvements on laboratory tests gradually became real improvements on the road as newer, more efficient vehicles replaced older less efficient ones. Fleet-wide fuel economy gains produced large fuel savings. In this paper, we show that fuel economy gains measured on laboratory test cycles, adjusted for on-road conditions and weighted by the distribution of vehicles by age and their relative use, closely match estimates by the Federal Highway Administration based primarily on traffic counts and motor fuel tax records. Furthermore, adjusting for the rebound effect of fuel economy on vehicle miles traveled, we estimate the fuel savings, CO 2 emissions reductions and dollars saved on fuel due to fuel economy improvements over the past 43 years. Through the end of 2018, estimated cumulative fuel savings amount to approximately 2 trillion gallons of gasoline. We estimate that roughly one-fifth of the savings can be attributed to gasoline price increases over the period and four-fifths to fuel economy and greenhouse gas (ghg) standards.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Cradle-to-Grave Lifecycle Analysis of U.S. Medium- and Heavy-Duty Vehicle-Fuel Pathways: A Greenhouse Gas Emissions Assessment of Current (2021) and Future (2035) Technologies

This study presents a cradle-to-grave lifecycle analysis of energy use and greenhouse gas (GHG) emissions for U.S. medium- and heavy-duty vehicles across current (2021) and future (2035) technologies using the Greenhouse gas, Regulated Emissions, and Energy use in Technologies (GREET) model with industry-vetted assumptions. Results vary across vehicle classes but point to common trends: today, battery electric vehicles (BEVs) offer significant (10–60%) GHG emissions reduction compared to diesel internal combustion engine vehicles and are the lowest emissions option per ton-mile of cargo movement, followed by hydrogen fuel cell electric vehicles (FCEVs) (5–50% emissions reduction). Emissions savings depend largely on the duty cycle and fuel economy of the vehicle type. Future vehicle technology advancements result in comparable emission reductions associated with BEVs and hydrogen FCEVs. Weight-limited BEV trucks see less per-ton-mile emissions reduction due to the impact of battery weight on increased vehicle weight and reduced payload capacity. By 2035, improvements in vehicle efficiency can reduce emissions across all powertrains. However, very low levels of emissions require switching vehicles’ use-phase fuel/energy to low-carbon fuels and electricity. Renewable diesel, e-fuels, hydrogen produced from natural gas with carbon capture and storage or renewables, and use of low-carbon electricity can all achieve over 70% reduction in GHG emissions from the current day diesel-based internal combustion engine vehicle.

alternative fuels↗
DOE OSTI2026

Analysis of Truck Platooning on Rural Highways

An analysis by the National Laboratory of the Rockies on truck platooning technology used on Ohio highways found that truck platooning operated for 40% of driving distances, showcasing its potential to enhance freight efficiency and safety. While energy savings were evident, further optimization of gap distances and operational consistency is needed to fully evaluate fuel savings benefits across diverse driving conditions. While more study is needed, advanced connected and automated vehicle technologies such as platooning demonstrate significant promise for transforming commercial vehicle efficiency and operations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗