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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Control architecture for predictive and optimal vehicle operations in a single vehicle environment

There is disclosed a cloud computing control system for vehicle speed control and also for control of a vehicle in a platoon. The cloud computing control system determines a speed trajectory and neutral coasting command for a first vehicle of the platoon and a vehicle controller determines a reference speed for the first vehicle in response to the speed trajectory and the neutral coasting command, and is response to one or more vehicle specific factors associated with the first vehicle.

Borhan, Hoseinali↗
DOE OSTI2019

Understanding the linkage between electric vehicle charging network coverage and charging opportunity using GPS travel data

Using GPS travel survey data from three metropolitan areas, this paper estimates the relationship between plug-in electric vehicle (PEV) public charging infrastructure coverage and charging opportunity. The former is defined as the percentage of public areas with charging stations installed and the latter represents the drivers probability of accessing a public charger at their trip stops (stop-based) or during at least one stop of their travel day (trip chain-based). Understanding this relationship is important to PEV manufacturers and electric vehicle supply equipment (EVSE) providers, as well as government agencies, for evaluating different levels of EVSE deployment and estimating the impact of its availability on PEV adoption. Heuristic procedures are developed to estimate stop- and trip chain-based charging opportunities. Charging opportunity reflects the visibility of EVSE as the incremental security that charging stations enable, while evaluating the potential of a driver to access EVSE at travel stops or during the day, for different levels of EVSE deployment on the transportation network. The metrics proposed do not assess the probability of completing trips with electric vehicles of a given range. The analysis results indicate that drivers’ trip destinations concentrate in a few popular places and EVSE deployment in these areas could achieve relatively high levels of charging opportunity. This finding is also robust to regional variations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Trends and 2025 Insights on the Rise of Electric Vehicles in the USA

Plug-in electric vehicles (EVs) are reshaping the transportation energy landscape, providing a practical alternative to petroleum fuels for a growing number of applications. EV sales grew 55x in the past decade (2014-2024) and 6x since 2020, driven by technological progress enabled by policies to reduce transportation emissions as well as industrial plans motivated by strategic value of EVs for global competitiveness, jobs and geopolitics. In 2024, 22% of passenger cars sold globally were EVs and opportunities for EVs beyond on-road applications are growing, including solutions to electrify off-road vehicles, maritime and aviation. This Review updates and expands our 2020 assessment of the scientific literature and describes the current status and future projections of EV markets, charging infrastructures, vehicle-grid integration and supply chains in the USA. EV is the lowest-emission motorized on-road transportation option, with life-cycle emissions decreasing as electricity emissions continue to decrease. Charging infrastructure grew in line with EV adoption but providing ubiquitous reliable and convenient charging remains a challenge. EVs are reducing electricity costs in several US markets and coordinated EV charging can improve grid resilience and reduce electricity costs for all consumers. The current trajectory of technology improvement and industrial investments points to continued acceleration of EVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Evaluating China’s Passenger Vehicle Market under the Vehicle Policies of 2021–2023

China is well known for its determination on large-scale vehicle electrification, which currently is mainly driven by fuel economy and electric vehicle policies mixed with the extensive charging infrastructure support and monetary incentives from the government. This study adopted the New Energy and Oil Consumption Credits (NEOCC) model 2020 version, a vehicle policy analysis tool developed by the Oak Ridge National Laboratory, in order to systematically quantify the potential impacts of the “Passenger Cars Corporate Average Fuel Consumption and New Energy Vehicle Credit Regulation”, which is a revised version released in June 2020 for the timeframe 2021–2023, the so-called dual credit policy (2021–2023). It was found that, under the dual credit policy (2021–2023), the sales of hybrid electric vehicles could reach 0.91 million by the end of 2023, which would increase much faster than they did in 2018–2020. The annual sales share of plug-in electric vehicles (PEVs) could reach 11.7%, and the PEV stocks could achieve 11.70 million by the end of 2023 if it keeps the expansion to the level of how it was in 2017. In addition, the BEVs with long electric range (such as 400 km) and the plug-in hybrid electric SUVs could be the most popular PEV types.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Projecting Recent Advancements in Battery Technology to Next–Generation Electric Vehicles

Electric vehicles (EVs) have seen rapid growth in adoption over the last several years. Advancements to increase battery life and performance, policy shifts, and high charging rate are expected to further accelerate the development of next generation of EVs. Battery improvements continue to emerge, enabling increased driving range, total distance driven over the life of vehicles, and ability to charge at high rates. Herein, an analysis framework to provide insights into inclusive design metrics, such as specific energy of batteries, energy consumption of vehicles, and charging power infrastructure development, is developed. Various cell-level fast charge protocols to realistic battery designs to understand the infrastructure needs associated with achieving range replacement of 32.25 km min -1 (20 mi min -1 ) are also scaled. Additionally, by calculating scaled power and peak to average power ratio, it is found that there needs to be more distinct alignment between the research efforts focused at the cell level and what is being developed for EV charging infrastructure needs. Finally, impact of high direct current voltage architecture in next-generation EVs is discussed. The findings in this work provide an insight into recent advancements in battery technology to next-generation EVs.

20 mi/min↗
DOE OSTI2024

Predicting U.S. federal fleet electric vehicle charging patterns using internal combustion engine vehicle fueling transaction statistics

Utilizing fueling transactions from internal combustion engine vehicles (ICEVs), the authors estimated how frequently midday public charging would be required for U.S. federal fleet battery electric vehicles (BEVs). Fueling transaction summary statistics are more widely available than trip-level telematics data, making this methodology more accessible and transferable to other researchers and fleet managers considering BEV replacements. For example, readers can easily apply a linear model using only the count of back-to-back fueling events at gas stations over 57 straight-line miles apart to predict days exceeding range. This linear regression predicted binned days exceeding 250 miles at 80% accuracy on a hold-out test set from the same fleet as the training data and 66 % accuracy on a new fleet displaying different driving behaviors. The authors additionally provide linear equations for days exceeding 200 and 300 miles as alternative range estimates to account for differences in BEV range and temperature impacts. Beyond the single-feature linear models which readers can apply, the authors tuned and trained other machine learning models on a variety of fueling transaction statistics including consecutive transaction distances, transaction distance from garage, estimated miles traveled from fuel economy and fuel quantity, and transaction periodicity. Utilizing a subset of 1678 light-duty federal fleet vehicles which contained daily vehicle miles traveled (VMT) in addition to fueling statistics, the authors determined which fueling transaction statistics were most relevant in predicting driving days exceeding 250 miles (an approximation of BEV rated driving range). In support of the U.S. federal fleet transition to zero-emission vehicles (ZEVs), the authors used these statistics and machine learning models to predict the frequency of BEV midday charging. After training models on the subset with VMT, the authors predicted days exceeding rated range for 112,902 light-duty vehicles operating in similar circumstances in the federal fleet using a Support Vector Regressor (SVR). In conclusion, they then used the projections as part of the ZEV Planning and Charging (ZPAC) tool to identify optimal candidates for BEVs for the federal fleet. An anonymized version of ZPAC is included in the supplementary materials.

25 ENERGY STORAGE↗
DOE OSTI2012

Sensitivity of Battery Electric Vehicle Economics to Drive Patterns, Vehicle Range, and Charge Strategies

Battery electric vehicles (BEVs) offer the potential to reduce both oil imports and greenhouse gas emissions, but high upfront costs discourage many potential purchasers. Making an economic comparison with conventional alternatives is complicated in part by strong sensitivity to drive patterns, vehicle range, and charge strategies that affect vehicle utilization and battery wear. Identifying justifiable battery replacement schedules and sufficiently accounting for the limited range of a BEV add further complexity to the issue. The National Renewable Energy Laboratory developed the Battery Ownership Model to address these and related questions. The Battery Ownership Model is applied here to examine the sensitivity of BEV economics to drive patterns, vehicle range, and charge strategies when a high-fidelity battery degradation model, financially justified battery replacement schedules, and two different means of accounting for a BEV's unachievable vehicle miles traveled (VMT) are employed. We find that the value of unachievable VMT with a BEV has a strong impact on the cost-optimal range, charge strategy, and battery replacement schedule; that the overall cost competitiveness of a BEV is highly sensitive to vehicle-specific drive patterns; and that common cross-sectional drive patterns do not provide consistent representation of the relative cost of a BEV.

25 ENERGY STORAGE↗
DOE OSTI2015

Vehicle Lightweighting: 40% and 45% Weight Savings Analysis: Technical Cost Modeling for Vehicle Lightweighting

The U.S. Department of Energy’s Vehicle Technologies Office, Materials area commissioned a study to model and assess manufacturing economics of alternative design and production strategies for a series of lightweight vehicle concepts. The strategic targets were a 40% and a 45% mass reduction relative to a standard North American midsize passenger sedan at an effective cost of $3.42 per pound (lb) saved. The baseline vehicle was an average of several available vehicles in this class. Mass and cost breakdowns from several sources were used, including original equipment manufacturers’ (OEMs’) input through U.S. Department of Energy’s Vehicle Technologies Office programs and public presentations, A2Mac1 LLC’s teardown information, Lotus Engineering Limited and FEV, Inc. breakdowns in their respective lightweighting studies, and IBIS Associates, Inc.’s decades of experience in automotive lightweighting and materials substitution analyses. Information on lightweighting strategies in this analysis came from these same sources and the ongoing U.S. Department of Energy-funded Vehma International of America, Inc. /Ford Motor Company Multi-Material Lightweight Prototype Vehicle Demonstration Project, the Aluminum Association Transportation Group, and many United States Council for Automotive Research’s/United States Automotive Materials Partnership LLC lightweight materials programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

R&D Insights for Extreme Fast Charging of Medium- and Heavy-Duty Vehicles: Insights from the NREL Commercial Vehicles and Extreme Fast Charging Research Needs Workshop, August 27-28, 2019

As battery costs have declined and battery performance has improved, the applicability of vehicle electrification has expanded beyond passenger cars to the commercial vehicle sector. However, due to the larger batteries that would be needed for the medium- and heavy-duty (MDHD) sector, the electric charging capabilities to serve these larger commercial vehicles will need to be substantially more powerful than light-duty chargers. More specifically, such 'extreme fast charging' (XFC) will likely need to reach the megawatt scale to provide a full charge in less than 30 minutes in some applications. In addition, the combined cost of electrified vehicles and charging must be competitive with the costs of petroleum-based technologies and other alternatives to encourage widespread adoption of battery electric vehicles (BEVs) among MDHD fleets. Most of these fleets have a commercial mission and demand low total cost of ownership (TCO) (which motivates minimal refueling times) and high performance from their vehicles.

25 ENERGY STORAGE↗
DOE OSTI2021

A Framework for Characterizing the Initial Thermal Conditions of Light-Duty Vehicles in Response to Representative Utilization Patterns, Ambient Conditions, and Vehicle Technologies

It is widely understood that the thermal state of a light-duty vehicle at the beginning of a trip influences the vehicle performance throughout the drive cycle. Cold starts, or initial states with component temperatures near ambient conditions, are strongly correlated with reduced vehicle performance and energy efficiency and with increased emissions. Despite this understanding, there is little literature available that characterizes initial thermal states beyond empirical studies and simplified analyses of dwell times. We introduce a framework that considers vehicle activity patterns, including the previous drive event, duration of the previous dwell event, and relevant ambient conditions occurring during these events. Moreover, the framework allows for technologies to influence the prominence of cold starts and warm starts. An example application of the methodology using a powertrain model approach revealed the significance of all constituent factors: controlling for ambient conditions, the shortest dwell lengths corresponded to an average initial oil temperature of 70°C versus 23°C for the long dwell length simulations, and aggressive conditioning cycles corresponded to an average initial oil temperature of 51°C versus only 35°C for a short conditioning cycle. The coldest ambient conditions evaluated (-30°C to -10°C) produced a weighted initial oil temperature of 19°C versus 57°C for the warmest ambient conditions evaluated (30°C to 50°C). Finally, we introduce weighting factors that account for the real-world frequency of conditioning cycle and dwell length combinations. The procedure outlined may be useful for discovering real-world performance improvements associated with vehicle technologies, a topic of interest to both vehicle manufacturers and regulators.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Total Thermal Management of Battery Electric Vehicles (BEVs)

The key hurdles to achieving wide consumer acceptance of battery electric vehicles (BEVs) are weather-dependent drive range, higher cost, and limited battery life. These translate into a strong need to reduce a significant energy drain and resulting drive range loss due to auxiliary electrical loads the predominant of which is the cabin thermal management load. Studies have shown that thermal subsystem loads can reduce the drive range by as much as 45% under ambient temperatures below -10°C. Often, cabin heating relies purely on positive temperature coefficient (PTC) resistive heating, contributing to a significant range loss. Reducing this range loss may improve consumer acceptance of BEVs. The authors present a unified thermal management system (UTEMPRA) that satisfies diverse thermal and design needs of the auxiliary loads in BEVs. Demonstrated on a 2015 Fiat 500e BEV, this system integrates a semi-hermetic refrigeration loop with a coolant network and serves three functions: (1) heating and/or cooling vehicle traction components (battery, power electronics, and motor) (2) heating and cooling of the cabin, and (3) waste energy harvesting and re-use. The modes of operation allow a heat pump and air conditioning system to function without reversing the refrigeration cycle to improve thermal efficiency. The refrigeration loop consists of an electric compressor, a thermal expansion valve, a coolant-cooled condenser, and a chiller, the latter two exchanging heat with hot and cold coolant streams that may be directed to various components of the thermal system. The coolant-based heat distribution is adaptable and saves significant amounts of refrigerant per vehicle. Also, a coolant-based system reduces refrigerant emissions by requiring fewer refrigerant pipe joints. The authors present bench-level test data and simulation analysis and describe a preliminary control scheme for this system.

Chowdhury, Sourav↗
NASA NTRSDate not supplied

Preliminary Development of Multi-Vehicle (m:N) Operations with NASA Langley’s Remote Vehicle Operations Center

To achieve the vision of Advanced Air Mobility (AAM), a transition from localized operations of aircraft to remote operations is being pursued across many use cases. This transition will allow fewer human operators to manage more increasingly autonomous aircraft (i.e., m operators managing N vehicles, or m:N). To study this operational concept, the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) has developed a prototype remote vehicle operations center and ground control station (GCS) software to conduct research with simulated and real flight operations. To date, flight operations at LaRC have been limited to one vehicle per operator. However, the current paper describes initial development and considerations for enabling m:N flight operations at LaRC. Further, the research described in this paper provides the foundation for a concept of operations (ConOps) that will be developed to support remote operators managing multiple increasingly autonomous vehicles, with the goal of exploring human-autonomy teaming (HAT) concepts that enable more advanced m:N operations. Two key enablers have been identified to facilitate successful m:N operations: GCS software updates for multi-vehicle management and procedural updates for vehicle handoffs during off-nominal events. Additionally, specific modifications were identified across five key areas: technology and software, team structure, inter-team communication, contingency plans, and operator decision flows. Next steps in forming the LaRC m:N ConOps will include working with subject-matter experts to identify off-nominal scenarios, implementing the recommended GCS functionality for m:N operations, and performing integration testing of facility capabilities and new operational procedures. Although the future m:N ConOps will be tailored to the NASA LaRC remote operations facility and flight range, it is intended to be a transparent, accessible, and reality-based exemplar for external organizations seeking to create or evaluate their own m:N operational concepts.

m:N↗
DOE OSTI2024

Depot-Based Vehicle Data for National Analysis of Medium- and Heavy-Duty Electric Vehicle Charging

Medium- and heavy-duty vehicles (MHDVs) are a major source of greenhouse gases and local criteria air pollutants. Electrifying MHDVs may reduce these harmful emissions, which disproportionately impact disadvantaged communities. Due to their relatively high per-vehicle energy needs, consistent fleet operations, and frequent colocation of multiple vehicles at depots, MHDVs may have more spatially and temporally concentrated charging demands than light-duty passenger electric vehicles. That charging concentration means their electrification may require careful advance planning and coordination to manage potential impacts to the electrical grid via charge management or infrastructure upgrades. However, MHDV duty cycles and parking schedules are highly variable across vocations of operation, and there is a shortage of nationally representative, vocationally diverse public data describing typical MHDV operations. This report summarizes the methodology - designed with national representativeness in mind - used to create a new set of data describing typical daily driving distances, dwell durations, and normalized electric vehicle depot charging load curves for MHDVs. The dataset reflects the subset of MHDV operating patterns that may originate from a consistent depot each day and rely on the same depot for charging. In addition to trucks with depot-centric vocational patterns, the data describes operations of transit buses and school buses, each with a depot-centric focus. The dataset is available to the public and suitable for national analysis. It can inform research, infrastructure planning, and policymaking regarding the electrification of MHDVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Parametric Optimization Problem Formulation for Connected Hybrid Electric Vehicles using Neural Network based Equivalent Model

The dynamics of powertrain control systems are complicated and involve both nonlinear plant model and control functionalities, albeit they are well defined and formulated using first principle approaches. This constitutes difficulties in exploring implementable optimal tuning rules for some selected control parameters using vehicle-to-vehicle (V2V) communications. This paper presents a way to use neural networks (NN) to represent the problem of parameter tuning for optimizing fuel consumption. For this purpose, physical modelling and validation have been firstly performed for the closed loop powertrain system of the concerned vehicle for some given driving cycles. This is then followed by the sensitivity analysis that selects most influential control parameters to optimize. Using the data generated from the obtained physical models, an equivalent NN formulation has finally been obtained that gives simple yet unified objectives and constraints ready to be used to solve the optimization problem that produces optimal tuning rules for the selected control parameters to minimize fuel consumption.

Parameter optimization, V2X, Powertrain modeling↗
DOE OSTI2025

A New Vehicle-to-Vehicle Communication System: Visual-Enhanced Cooperative Traffic Operations

The advent of Connected and Autonomous Vehicles (CAVs) has highlighted the necessity for robust communication systems between vehicles and their environment. This study introduces a novel vehicle-to-vehicle (V2V) communication system, termed the Visual-Enhanced Cooperative Traffic Operations (VECTOR) system. The VECTOR system addresses the need for robust communication by converting dynamic data (including velocity and yaw angle data) into binary code, which is displayed on an LED panel mounted on the top of the vehicle. Following vehicles detect this panel and decode the information using a camera, implementing a visual-based communication method. VECTOR system employs a comprehensive five-module process. Initially, polynomial fitting techniques are applied to velocity data over fixed time intervals using third-degree polynomials, with validation via R² and MSE metrics. The second module converts velocity and yaw angle data into binary form, thereby enhancing detection and processing efficiency. The third module focuses on improving detection stability across various environmental conditions to enhance traffic safety. The fourth module decodes the binary data back into trajectory information, ensuring the fidelity of velocity and yaw angles. The final module integrates eco-control through the VECTOR system, employing advanced control algorithms to minimize energy consumption in CAVs. Experimental evaluations conducted using a modified CAV test platform based on the Lincoln MKZ demonstrate the feasibility and efficiency of the VECTOR system, achieving a 75% R-squared accuracy rate in replicating original velocity data. This methodology not only highlights potential applications but also underscores significant implications for advancing CAV technology.

Ma, Ke↗
NASA NTRS1963

Launch Vehicle Flight Report - Nasa Project Apollo Little Joe 2 Qualification Test Vehicle 12-50-1

The Little Joe II Qualification Test Vehicle, Model 12-50-1, was launched from Army Launch Area 3 {ALA-3) at White Sands Missile Range, New Mexico, on 28 August 1963. This was the first launch of this class of boosters. The Little Joe II Launch Vehicle was designed as a test vehicle for boosting payloads into flight. For the Apollo Program, its mission is to serve as a launch vehicle for flight testing of the Apollo spacecraft. Accomplishment of this mission requires that the vehicle be capable of boosting the Apollo payload to parameters ranging from high dynamic pressures at low altitude to very high altitude flight. The fixed-fin 12-50 version was designed to accomplish the low-altitude parameter. The 12-51 version incorporates an attitude control system to accomplish the high altitude mission. This launch was designed to demonstrate the Little Joe II capability of meeting the high dynamic pressure parameter for the Apollo Program. For this test, a boiler-plate version of the Apollo capsule, service module and escape tower were attached to the launch vehicle to simulate weight, center of gravity and aerodynamic shape of the Apollo configuration. No attempt was made to separate the payload in flight. The test was conducted in compliance with Project Apollo Flight Mission Directive for QTV-1, NASA-MSC, dated 3 June 1963, under authority of NASA Contract NAS 9-492,

Source record↗
DOE OSTI2017

Electric vehicle station equipment for grid-integrated vehicles

Methods, systems, and apparatus transferring power between the grid and an electric vehicle are disclosed. The apparatus may include at least one vehicle communication port for interfacing with electric vehicle equipment (EVE) and a processor coupled to the at least one vehicle communication port to establish communication with the EVE, receive EVE attributes from the EVE, and transmit electric vehicle station equipment (EVSE) attributes to the EVE. Power may be transferred between the grid and the electric vehicle by maintaining EVSE attributes, establishing communication with the EVE, and transmitting the EVSE maintained attributes to the EVE.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Energy and mobility impacts of connected autonomous vehicles with co-optimization of speed and powertrain on mixed vehicle platoons

Intersections are known to be traffic bottlenecks where a significant amount of energy consumption could be caused due to deceleration/acceleration in the presence of red signals. With an increased level of connectivity and automation of intelligent transportation systems, connected autonomous vehicles (CAVs) are expected to be able to proactively adjust their driving strategies subject to constraints imposed by the predicted future traffic. As a result, many potential benefits can be achieved, such as improved energy efficiency, enhanced traffic safety, among many others. Notably, the way CAVs are controlled affects the following legacy vehicles (LVs) due to complex traffic dynamics. Here, we are particularly interested in studying the energy and mobility impact of CAVs with an improved traffic prediction method on mixed vehicle platoons at various market penetration rates. Leveraging traffic prediction, CAVs are controlled with co-optimization of their speed and gear position. Specifically, a traffic prediction framework in a rolling horizon fashion is employed based upon a modified Payne–Whitham (PW) model capable of handling mixed traffic consisting of CAVs and LVs. The prediction error of the modified PW model is reduced by 53.62% compared to that of the standard PW model under test scenarios. According to the predicted traffic conditions, speed and gear position of CAVs are co-optimized with the primary goal of minimizing energy consumption when driving on a signalized arterial. The energy benefits achieved by CAVs and the impact of CAVs on LVs behind are studied comprehensively for mixed vehicle platoons. The lead LV follows a real-world speed profile collected on TH-55 in Minnesota. Numerical results show that energy benefits achieved by the vehicle platoon range from 2% to 16%, and a 1% to 5% reduction in travel time for LVs behind CAVs is also observed, at different penetration rates of CAVs in various traffic scenarios. Furthermore, it is observed that CAVs using the proposed eco-driving approach appear to have a positive impact on the LVs behind in terms of energy consumption, regardless of the driving styles of the LVs ahead.

33 ADVANCED PROPULSION SYSTEMS↗