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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Integration Methods for High-Density Integrated Electric Drives (Final Technical Report)

The project goal was to research, design and fabricate high-power-density, high-performance SiC power electronic modules for motor drive inverters in electric vehicles (EVs) to enable the subsequent motor drive to achieve a power density of 100 kW/l. EVs use electric motors instead of internal combustion engines as the prime mover, and their core components are semiconductor-based power electronic modules. Efficient power electronic modules play a vital role in power conversion systems. The performance of power modules directly affects the energy efficiency and overall performance of electric vehicles. Therefore, it is particularly important to develop power modules with high power density and high electrical performance that will perform reliably under EV environmental conditions. To achieve these goals, advanced electronic packaging technologies are required. This report describes a 5-year effort to integrate key aspects surrounding the basic power devices into the module itself to increase power density. These include sensing and sensing readout circuits, gate driver circuits, and two-sided cooling solutions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Detailed Simulation Datasets Quantifying U.S. DOE VTO/HFTO R&D Benefits Across Light- to Heavy-Duty Vehicles

For more than 20 years, Argonne National Laboratory’s Vehicle & Mobility Systems Department has assessed how R&D investments by the U.S. Department of Energy’s Transportation Technologies Office and Alternative Fuels and Feedstocks Office affect vehicle energy use and cost. The analyses are performed using Autonomie, Argonne’s full-vehicle simulation tool for energy consumption, performance, and cost. The study covers five time frames ranging from present day through 2050, with more than 30 vehicle classes and applications (10 light duty and >20 medium and heavy duty), as well as six powertrain configurations (conventional, start-stop, hybrid electric vehicle, plug-in hybrid electric vehicle, battery-electric vehicle, and fuel cell electric vehicle) and five fuels (gasoline, diesel, natural gas, hydrogen, and electricity). Low and high technology uncertainty scenarios have been considered to capture a realistic range of outcomes. The resulting datasets include the assumptions used (i.e., efficiency, $/kWh), vehicle-level data (power, energy, weight, and cost), and outputs such as energy consumption, manufacturer’s suggested retail price, and total cost of ownership. These data are critical to stakeholders working in transportation, technology assessment, and long-term R&D planning.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Driving the grid forward: How electric vehicle adoption shapes power system infrastructure and emissions

We model the effect of plug-in electric vehicle (EV) adoption on U.S. power system generator capacity investment, operations, and emissions through 2050 by estimating power systems outcomes under a range of EV adoption trajectory scenarios. Our EV adoption scenarios are informed by 1) an Energy Information Administration scenario with no policy intervention, 2) EV growth expected under the Inflation Reduction Act (IRA), 3) a Biden Administration 50% EV sales target by 2030, 4) the Environmental Protection Agency’s projections under vehicle emissions standards, and 5) the International Energy Agency’s roadmap to Net Zero by 2050. We find across these scenarios that increasing EV adoption induces investment in new wind, solar, storage, and natural gas capacity, affecting power generation mix and emissions. The net effect of increasing EV adoption beyond our IRA base case is to increase power sector emissions by about 5 mtCO 2 eq per EV-year in 2026 (comparable to displaced gasoline vehicle combustion emissions), but this effect rapidly drops to annual levels below 1 mtCO 2 eq per EV-year by 2032 and continues below this level through 2050. Consequential effects of EV adoption vary regionally, with most regions primarily increasing wind or solar capacity and some regions primarily increasing natural gas capacity, even in 2050. Our national emissions estimates per EV-year are relatively robust to the level of EV adoption beyond our baseline and to variation in assumptions about power systems, EV behavior, and policy.

Science & Technology - Other Topics↗
DOE OSTI2022

Harmonic Modeling, Data Generation and Analysis of Power Electronics-Interfaced Residential Loads

The share of electronics-based residential load is expected to rise as devices such as variable frequency drives (VFDs), electric vehicle chargers, and inverter-based distributed energy resources (DERs), e.g., photovoltaic (PV) systems become more common. These loads may introduce significant harmonics into power networks that need to be closely studied in order to perform accurate load modeling and forecasting. However, it can be difficult to obtain harmonic-rich voltage and current data - necessary for identifying accurate load models - for residential electrical loads. Recognizing this need, we identify and model a set of electronics-based end-use loads and DERs in an electromagnetic transients program (EMTP) tool for a residence with a single- phase split-phase supply. Further, a procedure is developed to model harmonic interactions between end-use loads connected to the same non-ideal supply voltage in a residential setting. Finally, a harmonic-rich dataset produced via the proposed procedure is utilized to identify frequency coupling matrix (FCM) based load model. Numerical results demonstrate the accuracy of the model, and explore model identifiability with limited data points.

harmonics, power quality, load modeling↗
DOE OSTI2022

Power Electronics Thermal Management

This project conducts research to develop new power electronics thermal management technologies to increase power density, enable high WBG temperature operation, and decrease cost. One of the main challenges to achieving high power densities is associated with packaging of high-temperature (up to 250 degrees C) WBG devices near lower-temperature-rated components (e.g., electrical boards and capacitors). Additionally, the high junction temperatures of the WBG devices will result in large temperature gradients through the power module layers, which will present reliability challenges and require higher-temperature substrates and bonding materials.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

A method for determining optimal electric range by considering electric vehicle lightweighting on perceived ownership cost

The limited driving range due to high costs and low energy densities of batteries constraints the battery electric vehicle (BEV) market growth. Lightweighting in theory can reduce energy consumption rate and extend the driving range. The knowledge gap is to quantitatively link the cost-effectiveness of light-weight technologies with range extension and consumer acceptance of BEVs. In this study, a physics-based energy consumption model of BEVs is constructed and associated with a statistics-based model on the basis of travel surveys. A perceived cost of ownership (PCO) is then developed by adding intangible costs to traditional total cost of ownership models. We estimate, at the disaggregate vehicle model and driver level and the aggregate market level, 1) the extended range due to lightweighting for a given battery size; and 2) the optimal electric range based on lightweighting decisions. The cost-effectiveness of lightweighting for BEV range extension is found to vary with income-dependent daily range limitation value, driving patterns and lightweighting technology costs. In general, adopting lightweighting in BEVs is more cost-effective for consumers with higher daily limitation value, as well as for those with higher driving intensity or suitable daily driving patterns. When the lightweighting involves a higher vehicle production cost, less lightweighting could reduce the overall PCO for BEV owners. 4 of the selected top ten BEV models are found to benefit from additional 2.09%–4.45% lightweighting. Finally, the method built in this study can guide automakers in planning R&D investments in battery and lightweighting technologies.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1980

ac propulsion system for an electric vehicle

It is pointed out that dc drives will be the logical choice for current production electric vehicles (EV). However, by the mid-80's, there is a good chance that the price and reliability of suitable high-power semiconductors will allow for a competitive ac system. The driving force behind the ac approach is the induction motor, which has specific advantages relative to a dc shunt or series traction motor. These advantages would be an important factor in the case of a vehicle for which low maintenance characteristics are of primary importance. A description of an EV ac propulsion system is provided, taking into account the logic controller, the inverter, the motor, and a two-speed transmission-differential-axle assembly. The main barrier to the employment of the considered propulsion system in EV is not any technical problem, but inverter transistor cost.

Geppert, S.↗
NASA NTRS1979

Near-term hybrid vehicle program, phase 1. Appendix B: Design trade-off studies

The relative attractiveness of various hybrid/electric power train configurations and electrical and mechanical drive-line components was studied. The initial screening was concerned primarily with total vehicle weight and economic factors and identified the hybrid power train combinations which warranted detailed evaluation over various driving cycles. This was done using a second-by-second vehicle simulation program which permitted the calculations of fuel economy, electricity usage, and emissions as a function of distance traveled in urban and highway driving. Power train arrangement possibilities were examined in terms of their effect on vehicle handling, safety, serviceability, and passenger comfort. A dc electric drive system utilizing a separately excited motor with field control and battery switching was selected for the near term hybrid vehicle. Hybrid vehicle simulations showed that for the first 30 mi (the electric range of the vehicle) in urban driving, the fuel economy was 80 mpg using a gasoline engine and 100 mpg using a diesel engine. In urban driving the hybrid would save about 75% of the fuel used by the conventional vehicle and in combined urban/highway driving the fuel saving is about 50%.

Source record↗
DOE OSTI2026

Cooperative Automated Cohort Driving on Connected Infrastructure, Arterial Roadways, and Highways: Final Project Demonstration and System-of-Systems Model Correlation

This project seeks to synergize vehicle automated driving and connectivity data to improve mobility and energy efficiency of groups of mixed vehicles operating in close proximity (vehicle cohort) on various infrastructure. A custom cellular communication network links vehicles operating as a cohort with infrastructure to a centralized system-of-systems digital twin with an AI-based optimal behavior planner. The data contained in this set are from final testing and technology demonstrations to U.S. Department of Energy staff at the American Center for Mobility. The data contain single-lane, single-light scenarios; multi-lane, multi-light arterial scenarios; and limited-access highway scenarios. All test cases were derived from simulations and replicated on the test track. The project employed two and four light-duty vehicles with connectivity and drive automation for the testing. The baseline scenario without connectivity was run under the control of the system-of-systems centralized planner but operating each vehicle with an intelligent driver model controlling the velocity, lane utilization, and vehicle gap. This was to ensure the highest compatibility with the simulation in terms of dynamic behavior. The connected cohort case utilized AI optimization to perform coordinated and cooperative control for energy, as well as safe, comfortable behavior for the cohort. The dataset is appropriately named with unconnected and connected designations, with comparisons sharing the same run index number. The included PowerPoint and PDF files describe the test setup and provide an overview of results from the project. ![image](de-EE0009209_March_2023_Data_Arterial_Scenario_Results.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A Multirange Vehicle Speed Prediction With Application to Model Predictive Control-Based Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles

Abstract Connectivity and automated driving technologies have opened up new research directions in the energy management of vehicles which exploit look-ahead preview and enhance the situational awareness. Despite this advancement, the vehicle speed preview that can be obtained from vehicle-to-vehicle/infrastructure (V2V/I) communications is often limited to a relatively short time-horizon. The vehicular energy systems, specifically those of the electrified vehicles, consist of multiple interacting power and thermal subsystems that respond over different time-scales. Consequently, their optimal energy management can greatly benefit from long-term speed prediction beyond that available through V2V/I communications. Accurately extending the look-ahead preview, on the other hand, is fundamentally challenging due to the dynamic nature of the traffic environment. To address this challenge, we propose a data-driven multirange vehicle speed prediction strategy for arterial corridors with signalized intersections, providing the vehicle speed preview for three different ranges, i.e., short-, medium-, and long-range. The short-range preview is obtained by V2V/I communications. The medium-range preview is realized using a neural network (NN), while the long-range preview is predicted based on a Bayesian network (BN). The predictions are updated in real-time based on the current state of traffic and incorporated into a multihorizon model predictive control (MH-MPC) for integrated power and thermal management (iPTM) of connected vehicles. The results of design and evaluation of the performance of the proposed data-informed MH-MPC for iPTM of connected hybrid electric vehicles (HEVs) using traffic data for real-world city driving are reported.

Automation & Control Systems↗
DOE OSTI2024

Eco-driving Profile Optimization by Dynamic Programming for Battery Electric Vehicles

Although full automation has not yet been achieved, automated vehicles are a valid research area. Not only would automated vehicles provide ultimate driver convenience, but they would maximize energy efficiency by eliminating undesired human driving behaviors and optimally controlling the powertrain. From the perspective of control related to energy saving, speed profile optimization is important for improving system efficiency and satisfying passenger demands. This study employs Dynamic Programming (DP) to solve the constrained optimal problem for travel time, distance, and speed limit by exploring all possible control options. The solutions obtained by DP demonstrate consistent control patterns combining four control modes-acceleration, cruising, coasting, and braking, with cruising or coasting being selective depending on the boundary conditions. Further, this study introduces DP-based simulation results and attempts to provide comprehensive interpretations of the optimal policy by analyzing the essential factors that affect the control problem, including boundary conditions, road load, and powertrain characteristics. Based on these interpretations, the control concepts can be explained as the optimal policy selecting the best control option based on system efficiency and boundary conditions. The results of DP are compared with a human-like driver model to show that the optimal speed profiles can effectively reduce energy consumption.

Autonomous vehicles↗
DOE OSTI2025

Forecasting EV Charging Demand on the Distribution System

The U.S. transportation and electricity sectors have historically operated independently, but the growth of electric vehicles (EVs) is driving their convergence. After decades of stagnant demand, utilities must prepare for rising load growth, driven in part by transportation electrification. Utilities must anticipate when and where these new loads will materialize to effectively manage EV growth and maintain grid reliability. This presentation outlines NREL's approach to developing high-resolution EV load datasets for distribution planning, with insights from the Multi-State Transportation Electrification Impact Study on EV and load forecasting, infrastructure requirements, and managed charging strategies.

25 ENERGY STORAGE↗
DOE OSTI2023

Reversible Iron Oxyfluoride (FeOF)–Graphene Composites as Sustainable Cathodes for High Energy Density Lithium Batteries

Two large barriers are impeding the wide implementation of electric vehicles, namely driving-range and cost, primarily due to the low specific energy and high cost of mono-valence cathodes used in lithium-ion batteries. Iron is the ideal element for cathode materials considering its abundance, low cost and toxicity. However, the poor reversibility of (de)lithiation and low electronic conductivity prevent iron-based high specific energy multi-valence conversion cathodes from practical applications. In this work, a sustainable FeOF nanocomposite is developed with extraordinary performance. The specific capacity and energy reach 621 mAh g –1 and 1124 Wh kg –1 with more than 100 cycles, which triples the specific capacity, and doubles the specific energy of current mono-valence intercalation LiCoO 2 . This is the result of an effective approach, combing the nanostructured FeOF with graphene, realized by making the (de)lithiation reversible by immobilizing FeOF nanoparticles and the discharge products over the graphene surface and providing the interparticle electric conduction. Importantly, it demonstrates that introducing small amount of graphene can create new materials with desired properties, opening a new avenue for altering the (de)lithiation process. Finally, such extraordinary performance represents a significant breakthrough in developing sustainable conversion materials, eventually overcoming the driving range and cost barriers.

25 ENERGY STORAGE↗
DOE OSTI2022

Insights into the Crossover Effects in Cells with High‐Nickel Layered Oxide Cathodes and Silicon/Graphite Composite Anodes

Abstract Silicon anodes are regarded as one of the most promising alternatives to graphite (Gr) anodes due to their ultrahigh capacity, abundance, and low cost. Coupling Si‐based anodes with high‐nickel layered oxide cathodes LiNi x Mn y Co 1− x − y O 2 (NMC, x ≥ 0.8) can enhance the driving range for electric vehicles. Transition‐metal (TM) ion dissolution and deposition has been a long‐known failure mode for Gr‐based lithium‐ion batteries. However, the mechanistic insight associated with TM ion deposition on Si‐based anodes has rarely been reported. Herein, the impact of in situ deposited TM ions on SiO x /Gr composite anodes and the individual influences of Ni, Mn, and Co on the structural and electrochemical stability along with the underlying degradation mechanisms are presented. TM ion dissolution causes a greater deleterious impact on Si than on Gr, with different TM ions exhibiting different influences on anode‐electrolyte interphase formation. Specifically, Ni deposit induces more aggressive salt decomposition; Co deposit has negligible effect on salt decomposition, but significantly accelerates solvent decomposition; and Mn deposit aggravates both salt and solvent decompositions, resulting in worse full cell performance. The extent of degradation decreases in the order Mn 2+ > Ni 2+ > Co 2+ . The systematic comparisons presented can guide further development of high energy systems.

Zhang, Xianhui↗
DOE OSTI2025

A Mössbauer Spectroscopy Investigation of Nickel‐Zinc Ferrites Synthesized by a Self‐Combustion Method for Soft Magnetic Core Applications

Soft ferrites are materials of interest for magnetic cores, as used for wireless charging transformers. Their low permeabilities, high resistivity, and magnetic polarization make them interesting for high-power electric vehicle charging and drive systems. The nickel-zinc-doped ferrites are of particular interest; however, the compositional space is quite large with respect to dopant concentrations, stoichiometric ratios and synthesis technique. Nickel-zinc spinel ferrites with varying nickel-zinc ratios prepared by a self-combustion reaction followed by heat treatment exhibit good crystallinity, and their low-temperature Mössbauer spectra show local magnetism and site occupation in agreement with materials prepared by solid-state reaction. Thus, the combustion synthesis method offers a facile tunability of compositions, which, combined with the possibility of rapid characterization of atomic-scale magnetism by Mössbauer spectroscopy, enables advances in the compositional and processing space at a fast pace. Low-temperature Mössbauer spectroscopy data for samples with increasing nickel content reveals a systematic increase in average hyperfine field (2.8 T/Ni) and decrease in average isomer shift (−0.036 mm/s/Ni) that can determine the nickel/zinc content, even in the absence of applied magnetic field data. Furthermore, a gradual evolution of color is also observed with increasing nickel content, albeit trends in color depend on sintering conditions.

Mössbauer spectroscopy↗
DOE OSTI2020

Optimization of Oxygen Electrode Combined with Soluble Catalyst to Enhance the Performance of Lithium-Oxygen Battery

Lithium-air batteries possess a high theoretical energy density among the current battery technologies, and are expected to become the driving power for electric vehicles. However, the practical application of lithium air batteries is handicapped by their poor cycle life, high over-charge potential and low energy efficiency. Numerous studies have performed to improve the kinetics of the electrochemical reaction. Making a good oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) catalyst system is under intense scrutiny. In this work, we report a rational design of air electrode, which derives from biomass and presents a three-dimensional porous carbon architecture with high specific surface and large volume expansion coefficient. Combining with a soluble catalyst, this oxygen electrode presents an ultrahigh areal specific capacity of 7.95 mAh/cm 2 and a long cycling life up to 1000 cycles with suppressed polarization (about 0.46 V). Finally, the greatly improved overall performance is due to the synergetic effect of the novel designed oxygen electrode capable of mediating the redox mediators to trigger the ORR and OER process.

25 ENERGY STORAGE↗