Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “vehicle analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Scenario-based analysis of electric vehicle adoption in the United States: Technology, infrastructure, and electricity pricing

This work investigates the impact of battery technology advancement, charging infrastructure development, and time-of-use (TOU) electricity pricing on vehicle adoption by 6 powertrain types in the United States through 2050. Using the Market Acceptance of Advanced Automotive Technologies (MA3T) model, we simulate 15 scenarios, examining individual cost factors and their combinations. We assess outcomes through market share, consumer surplus, and energy consumption. Results show that battery cost reductions are the strongest driver of EV adoption, increasing 2050 battery electric vehicle (BEV) share by 27 percentage points over baseline, raising annual consumer surplus by $511 per household, and reducing cumulative energy consumption by 16,610 trillion Btu. These gains are two to five times larger than those from other individual factors. Reducing home charging installation costs produces moderate impact, while TOU pricing alone yields only small gains, raising 2050 BEV market share by 1–2 percentage points. However, when cost factor improvements are combined, their effects are amplified beyond simple additivity. Pairing modest battery cost reductions with charging installation cost reductions and TOU pricing results in the largest 2050 BEV sales combined impact. The analysis demonstrates that moderate progress targeting multiple cost barriers may be more impactful than focusing on any single barrier.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Future Cost Benefits Analysis for Electrified Vehicles from Advances Due to U.S. Department of Energy Targets

The U.S. Department of Energy’s Vehicle Technologies Office (DOE-VTO) supports research and development (R&D), as well as deployment of efficient and sustainable transportation technologies, that will improve energy efficiency and fuel economy and enable America to use less petroleum. To accelerate the creation and adoption of new technologies, DOE-VTO has developed specific targets for a wide range of powertrain technologies (e.g., engine, battery, electric machine, lightweighting, etc.). This paper quantifies the impact of VTO R&D on vehicle energy consumption and cost compared to expected historical improvements across vehicle classes, powertrains, component technologies and timeframes. We have implemented a large scale simulation process to develop and simulate tens of thousands of vehicles on U.S. standard driving cycles using Autonomie, a vehicle simulation tool developed by Argonne National Laboratory. Results demonstrate significant additional reductions in both cost and energy consumption due to the existence of VTO R&D targets compared to predicted historical trends. It is observed that, over time, the fuel consumption of different electrified vehicles is expected to decrease by 40–50% and a reduction of 45–55% for vehicle manufacturing costs owing to significant improvements through various VTO R&D targets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

ASDFL: An adaptive super‐pixel discriminative feature‐selective learning for vehicle matching

Abstract There are a large number of cameras in modern transportation system that capture numerous vehicle images continuously. Therefore, automatic analysis of these vehicle images is helpful for traffic flow management, criminal investigations and vehicle inspections. Vehicle matching, which aims to determine whether two input images depict an identical vehicle, is one of the core tasks in vehicle analysis. Recent relevant studies have focused on local feature extraction instead of global extraction, since local details can provide crucial cues to distinguish between cars. However, these methods do not select local features; that is, they do not assign weights to local features. Therefore, in this research, we systematically study the vehicle matching task, and present a novel annotation‐free local‐based deep learning method called Adaptive super‐pixel discriminative feature‐selective learning (ASDFL) to address this issue. In ASDFL, vehicle images are segmented into clusters of super‐pixels of similar size by considering the location and colour similarities of pixels without using any component‐level annotation. These super‐pixels are deemed to be the virtual components of vehicles. Moreover, a convolutional neural network is used to extract the deep features of these virtual components. Thereafter, an instance‐specific mask generation module driven by the extracted global features is enhanced to produce a mask to select the most distinctive virtual components of each vehicle image pair in the feature space. Finally, the vehicle matching task is accomplished by classifying the selected virtual component features of each imaged vehicle pair. Extensive experiments on two popular vehicle identification benchmarks demonstrate that our method is 1.57% and 0.8% more accurate than the previous baselines in a vehicle matching task on the VeRi and VehicleID datasets, respectively, which demonstrates the effectiveness of our method.

Qin, Rong↗
DOE OSTI2022

Light-Duty Vehicle Choice Modeling and Transportation Decarbonization Analysis

Light-duty vehicles are the biggest contributor of carbon emissions in the transportation sector. This project uses the Automotive Deployment Options Projection Tool (ADOPT) to explore pathways to light-duty decarbonization through technology improvements being researched by the Department of Energies Vehicle Technology Office. It includes sensitivities to market conditions, such as proposed vehicle purchase incentives, and changes to the Corporate Average Fuel Economy and Greenhouse Gas regulations. The results suggest that achieving the Vehicle Technology Office's research goals will lead to one third of the annual carbon emissions by 2050, primarily through vehicle electrification.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2020

Light-Duty Vehicle Choice Modeling and Benefits Analysis

The U.S. Department of Energy's Vehicle Technologies and Hydrogen and Fuel Cell Technologies Offices (VTO and HFTO) support research and development of efficient and sustainable transportation technologies that will improve energy efficiency, minimize emissions, and enable America to use less petroleum. This presentation steps through the approach, updates to the approach, and preliminary estimates of light-duty vehicle energy and emissions benefits from the continuation and success of VTO and HFTO programs. The programs include research on batteries, electric drive technologies (EDT), combustion, materials, fuel cells, and hydrogen storage. Preliminary results for battery and EDT program success show an annual 24% reduction in petroleum use and 13% reduction in carbon emissions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Light-Duty Vehicle Choice Modeling and Benefits Analysis (van018)

The U.S. Department of Energy’s Vehicle Technologies and Hydrogen and Fuel Cell Technologies Offices (VTO and HFTO) support research and development of efficient and sustainable transportation technologies that will improve energy efficiency, minimize emissions, and enable America to use less petroleum. The analysis in this poster is based on technical progress goals established in VTO and HFTO in the years immediately prior to and including 2020, and it summarizes the estimated energy and emissions benefits corresponding with achievement of those goals. The goals span research activities on batteries, electric drive technologies (EDT), combustion, lightweight materials, fuel cells, and hydrogen storage. The Automotive Deployment Options Projection Tool (ADOPT) is used to estimate the benefits for light-duty vehicles. ADOPT is a vehicle choice and stock model that estimates vehicle technology improvement impacts on sales, energy, and emissions. It includes all the existing vehicle options for realism, estimates their sales using extensively validated consumer preferences, creates new market-driven vehicle options through time, and rolls up sales to estimate energy and emissions. ADOPT takes in technology progress assumptions and applies these to the modeled vehicles through time. The assumptions are represented by a No Program scenario that reflects the technology improvements assumed to occur without further contributions from VTO or HFTO, and a Program Success scenario under which VTO and HFTO program goals are realized. The benefits are calculated by comparing ADOPT's estimated national-level energy and emissions resulting from the Program Success relative to the No Program scenario. By 2050, the Program Success scenario results in 11% less annual petroleum consumption and 10% less annual carbon emissions than the No Program scenario.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Dual-solver hybrid computational approaches for design and analysis of vertical lift vehicles

Abstract The cost of Reynolds-Averaged Navier-Stokes simulations can be restrictive to implement in aeromechanics design and analysis of vertical lift configurations given the cost to resolve the flow on a mesh sufficient to provide accurate aerodynamic and structural loads. Dual-solver hybrid methods have been developed that resolve the configuration and the near field with the Reynolds-Averaged Navier-Stokes solvers, while the wake is resolved with vorticity-preserving methods that are more cost-effective. These dual-solver approaches can be integrated into an organisation’s workflow to bridge the gap between lower-fidelity methods and the expensive Reynolds-Averaged Navier-Stokes when there are complex physics present. This paper provides an overview of different dual-solver hybrid methods, coupling approaches, and future efforts to expand their capabilities in the areas of novel configurations and operations in constrained and turbulent environments.

Engineering↗
DOE OSTI2025

Hierarchical analysis of US electric vehicle subsidies for carbon emission mitigation

Electric vehicle (EV) adoptions are promoted with subsidies to reduce greenhouse gas emissions from ground transportation. In this paper, a hierarchical analysis is presented on the potential of greenhouse gas emission mitigation via the electric vehicle subsidy policy at state level in the US, through research of environmental and economic fundamentals of electric vehicle operations, energy consumptions, battery degradation and service life. It has been found that restructuring the federal subsidies to promote EV adoption can significantly reduce greenhouse gas emissions across the US. The reduction costs of greenhouse gas emissions vary between $\$1167.44$/ton in Vermont to $\$6880.13$/ton in Wyoming. A case study reveals that 15.24 % more greenhouse gas emissions can be reduced with a tiered federal subsidy structure. The restructuring of subsidies will also encourage the adoption of clean energies in the grid fuel mix and drive technological advancements to extend the battery lifetime in the future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Time Matters: A Survival Analysis of Public Electric Vehicle Charging Infrastructure Utilization

The rapid adoption of plug-in electric vehicles (PEVs) places significant demands on public charging infrastructure, making it critical to understand and optimize charger utilization. This study provides one of the most comprehensive analyses of charging behavior to date by applying a survival analysis to a dataset of nearly 16 million level 2 (L2) and direct current (DC) fast charger sessions across the United States from 2017 to 2022. Using Kaplan-Meier curves and log rank tests, our analysis reveals statistically significant and distinct duration patterns influenced by charger type, time of day, and day of the week. We find that L2 charging sessions exhibit high variability tied to venue type, whereas DC sessions are more uniform, typically lasting 30-45 min. This study introduces the operational efficiency score (OES), a metric for standardizing the performance evaluation of charging stations. Our findings offer actionable insights for optimizing charger deployment, developing dynamic pricing strategies to reduce vehicle dwell time, and improving load management for grid operators, ultimately enhancing the efficiency and availability of public charging infrastructure.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Long-Term Vehicle Speed Prediction via Historical Traffic Data Analysis for Improved Energy Efficiency of Connected Electric Vehicles

Connected and automated vehicles (CAVs) are expected to provide enhanced safety, mobility, and energy efficiency. While abundant evidence has been accumulated showing substantial energy saving potentials of CAVs through eco-driving, traffic condition prediction has remained to be the main challenge in capitalizing the gains. The coupled power and thermal subsystems of CAVs necessitate the use of different speed preview windows for effective and integrated power and thermal management. Real-time vehicle-to-infrastructure (V2I) communications can provide an accurate speed prediction over a short prediction horizon (e.g., 30 s to 60 s), but not for a long range (e.g., over 180 s). Therefore, advanced approaches are required to develop detailed speed prediction for robust optimization-based energy management of CAVs. This paper presents an integrated speed prediction framework based on historical traffic data classification and real-time V2I communications for efficient energy management of electrified CAVs. The proposed framework provides multi-range speed predictions with different fidelity over short and long horizons. The proposed multi-range 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). The simulation results over real-world urban driving cycles confirm the enhanced prediction performance of the proposed data classification strategy over a long prediction horizon. Despite the uncertainty in long-range CAVs’ 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 (i.e., human driver) and conventional BTM strategy.

Engineering↗
DOE OSTI2020

Total Cost of Vehicle Ownership - Development of Analysis Webtool and Visualization

This report has been created to give an in-depth walkthrough of the functionality of the Total Cost of Operation webtool. The webtool was created to give users the ability to calculate the total cost of owning a vehicle over from the year they bought the vehicle to the end of the vehicles lifetime. This tool was developed using a combination of front-end and back-end technologies. To create the front-end HTML, CSS, and JavaScript were utilized. On the backend PHP is used as a scripting language with a database powered by MySQL. Through a combination of these technologies, a fully featured well developed webtool was created allowing users to view a cost breakdown of vehicle ownership over the lifetime of that vehicle.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

My vehicle is a data mine

In this talk we explore how analysis of vehicle data provides information of individual vehicle behaviors, information of other vehicles in the flow of traffic, and insights into the behavior of drivers. Over the last two decades traditional passenger vehicles have been transformed from integrated two-port electrical nodes to cyber-physical systems of communicating computational nodes whose individual state and control variables are shared on a standard controller area network (CAN) bus. As driver assistance systems have crept into vehicles as safety features, driver behaviors can be observed through analysis of the data streams on the CAN bus as these new nodes communicate with one another. The properties of these data streams, as well as architectures and approaches to gather the data, are important to consider when drawing conclusions on the relevance of the data in making decisions at varying levels of the information hierarchy. We will demonstrate several technical challenges associated with these data collection processes, as well as preliminary results that demonstrate application relevance of the data to behavior, traffic, and systems domains.

42 ENGINEERING↗
DOE OSTI2023

Impact Analysis of Future Electric Vehicles Using Model of Real Distribution Feeders

Globally, the number of electric vehicles (EVs) continues to increase. This on-going trend poses challenges for power distribution systems. The inclusion of electric vehicle supply equipment (EVSE) should be compatible with a changing energy system, whose structure is becoming increasingly distributed. The impact of future EVs should be analyzed and considered in the system planning and upgrades. In this study, impact analysis of future EVs is conducted for a utility company of U.S. West Coast. Real data of distribution feeders is converted into the GridLAB-D model for running power flow analysis. An estimation of the additional loading of EVs in 2050 is provided by the utility company. Simple mitigation methods are tested to improve voltage profiles and complete this case study.

Xie, Jing↗
DOE OSTI2021

Grid Voltage Control Analysis for Heavy-Duty Electric Vehicle Charging Stations

This paper presents an analysis of grid voltage control strategies for heavy-duty electric vehicle charging stations. The performance of three voltage control approaches—including power factor control, standardized volt-volt ampere reactive (VAR) curve control, and customized volt-VAR curve control—are investigated and evaluated for three different sizes of charging stations, including a single-port charging station, a midsized three-port charging station, and six-port travel center. The charging stations are placed on multiple locations of four different types of distribution systems to obtain a comprehensive performance analysis of the voltage control approaches under different scenarios. To quantitively compare the performance of the three voltage control approaches, a series of metrics are designed to quantify the performance and contribute to the comprehensive analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Electric Vehicles at Scale - Phase II - Distribution Systems Analysis

The use of electric vehicles (EVs) in the United States has grown significantly during the last decade, posing benefits to the environment but also potential challenges to our grid. To better understand this scenario , the Department of Energy (DOE) asked Pacific Northwest National Laboratory (PNNL) to perform an authoritative study of the impacts of EVs at scale on the electric grid. The Phase I study focused on the bulk power electricity impacts . This EV-at-scale Phase II work addresses key questions of interest to DOE related to the impacts of EV in the distribution systems: 1. When (which year), where, and how many EVs will be adopted and how will they be charged? 2. Given some prospective answers to the above question, what would be the EV hosting capability of a distribution system circuit? 3. How could the hosting capability be expanded to accommodate more EVs and what would be the potential measures and cost? This report documents the methodologies developed as part of this project and provides EV adoption modeling examples using distribution system circuit data provided by industry partner, Southern California Edison (SCE).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Quasi-Dynamic Electromagnetic Field Safety Analysis and Mitigation for High-Power Dynamic Wireless Charging of Electric Vehicles

Dynamic wireless charging of electric vehicles (EV) is an emerging charging technology to enable non-contact wireless charging while the vehicle is moving. Compared to stationary wireless charging, in-motion wireless charging involves dynamic processes in which an EV is passing over the charging pads (transmitters). This in motion process makes the dynamic electromagnetic (EM) environment more complicated, and EM safety needs to be ensured under all circumstances. This is due to the fact that the entire vehicle body may be exposed to magnetic fields while the vehicle moves over the energized transmitter. This paper investigates several typical charging scenarios when EVs approach, pass over, and move away from the charging pads. Quasi-dynamic models, which are preliminarily verified by coils’ inductance measurements, are developed to analyze the dynamic process. Based on the quasi-dynamic analysis, shielding solutions are also studied to ensure EM safety for the dynamic wireless charging processes.

24 POWER TRANSMISSION AND DISTRIBUTION↗