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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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Electric-drive vehicles use electricity as their primary fuel or to improve the efficiency of conventional vehicle designs. With the range of styles and options available, there is likely one to meet your needs. Electric vehicles (EVs) include all-electric vehicles and plug-in hybrid electric vehicles (PHEVs).
The Vehicle Technologies Office (VTO) of the Department of Energy (DOE) conducts research and development (R&D) on advanced transportation technologies that would reduce the nation’s use of imported oil and would also lead to reductions in harmful emissions. Technologies supported by VTO include electric drive components such as advanced energy storage devices (primarily batteries), power electronics and electric drive motors, advanced structural materials, energy efficient mobility systems, advanced combustion engines, and fuels. VTO is focused on funding early-stage high-reward/high-risk research to improve critical components needed for more fuel efficient (and cleaner-operating) vehicles. One of the major VTO objectives is to enable U.S. innovators to rapidly develop the next generation of technologies that achieve the cost, range, and charging infrastructure necessary for the widespread adoption of plug-in electric vehicles (PEVs). An important prerequisite for the electrification of the nation’s light duty transportation sector is development of more cost-effective, longer lasting, and more abuse-tolerant PEV batteries. One of the ultimate goals of this research, consistent with the current vehicle electrification trend, is an EV which can provide the full driving performance, convenience, and price of an internal combustion engine (ICE) vehicle. To achieve this, VTO has established the following overarching goal (Source: FY2021 Congressional Budget Justification1): …identify new battery chemistry and cell technologies with the potential to reduce the cost of electric vehicle battery packs by more than half, to less than $100/kWh (ultimate goal is $60/kWh battery cell cost), increase range to 300 miles, and decrease charge time to 15 minutes or less by 2028. VTO works with key U.S. automakers through the United States Council for Automotive Research (USCAR) – an umbrella organization for collaborative research consisting of Fiat Chrysler Automobiles (FCA), the Ford Motor Company, and General Motors. Collaboration with automakers through the partnership known as U.S. Driving Research and Innovation for Vehicle Efficiency and Energy Sustainability (U.S. DRIVE) attempts to enhance the relevance and the success potential of its research portfolio. VTO competitively selects projects for funding through funding opportunity announcements (FOAs). Directly-funded work at the national laboratories are awarded competitively through a lab-call process. During the past year, VTO continued R&D in support of PEVs. Stakeholders for VTO R&D include universities, national laboratories, other government agencies and industry (including automakers, battery manufacturers, material suppliers, component developers, private research firms, and small businesses). This document summarizes the progress of VTO battery R&D projects supported during the fiscal year 2019 (FY 2019).
The applicability of NASA's advanced power technologies to electric and hybrid vehicles was assessed using a time-stepping computer simulation to model electric and hybrid vehicles operating over the Federal Urban Driving Schedule (FUDS). Both the energy and power demands of the FUDS were taken into account and vehicle economy, range, and performance were addressed simultaneously. Results indicate that a hybrid electric vehicle (HEV) configured with a flywheel buffer energy storage device and a free-piston Stirling convertor fulfills the emissions, fuel economy, range, and performance requirements that would make it acceptable to the consumer. It is noted that an assessment to determine which of the candidate technologies are suited for the HEV application has yet to be made. A proper assessment should take into account the fuel economy and range, along with the driveability and total emissions produced.
In this paper, we report on the first direct voltage and current measurements from a laser-generated magnetic field coil target. The magnetic field was observed to scale with the laser intensity as $B ∝ \mathcal{I}^{0.66±0.13}_\text{laser}$. This scaling relation can be derived from the measured voltage approximated by the laser-heated plasma electron temperature $T_e$. The experiments used a 1053 nm laser with pulse lengths ranging from 0.5 to 20 ns and intensities ranging from 10 9 to 10 14 W/cm 2 to generate an electric potential that drives current through the coil. We show that the behavior of the coil can be described with a lumped-element circuit model.
The Electra Van 600, an electric vehicle, was tested. Tests were performed to characterize parameters of the Electra Van 600 and to provide baseline data to be used for comparison of improved batteries and to which will be incorporated into the vehicle. The vehicle tests concentrated on the electrical drive subsystem, the batteries, controller, and motor; coastdowns to characterize the road load and range evaluation for cyclic and constant speed conditions; and qualitative performance was evaluated. It is found that the Electra Van 600 range performance is approximately equal to the majority of the vehicles tested previously.
The change of pace, an electric vehicle was tested. These tests were performed to characterize certain parameters of the electric vehicle pacer and to provide baseline data that can be used for the comparison of improved batteries that may be incorporated into the vehicle at a later time. The vehicle tests were concentrated on the electrical drive subsystem, the batteries, controller and motor. Coastdowns to characterize the road load, and range evaluations for both cyclic and constant speed conditions were performed. The vehicle's performance was evaluated by comparing its constant speed range performance with described vehicles. It is found that the pacer performance is approximately equal to the majority of the vehicles tested in the 1977 assessment.
Silicon carbide (SiC)-based semiconductor electronic devices and circuits are presently being developed for use in high-temperature, high-power, and/or high-radiation conditions under which conventional semiconductors cannot adequately perform. Silicon carbide's ability to function under such extreme conditions is expected to enable significant improvements to a far-ranging variety of applications and systems. These range from greatly improved high-voltage switching [1- 4] for energy savings in public electric power distribution and electric motor drives to more powerful microwave electronics for radar and communications [5-7] to sensors and controls for cleaner-burning more fuel-efficient jet aircraft and automobile engines. In the particular area of power devices, theoretical appraisals have indicated that SiC power MOSFET's and diode rectifiers would operate over higher voltage and temperature ranges, have superior switching characteristics, and yet have die sizes nearly 20 times smaller than correspondingly rated silicon-based devices [8]. However, these tremendous theoretical advantages have yet to be realized in experimental SiC devices, primarily due to the fact that SiC's relatively immature crystal growth and device fabrication technologies are not yet sufficiently developed to the degree required for reliable incorporation into most electronic systems [9]. This chapter briefly surveys the SiC semiconductor electronics technology. In particular, the differences (both good and bad) between SiC electronics technology and well-known silicon VLSI technology are highlighted. Projected performance benefits of SiC electronics are highlighted for several large-scale applications. Key crystal growth and device-fabrication issues that presently limit the performance and capability of high temperature and/or high power SiC electronics are identified.
The widespread adoption of electric vehicles (EVs) and transportation electrification is encumbered by two chief barriers: i) the limited driving range of EVs in the market today and ii) inadequate fast-charging infrastructure for long-distance trips. Extreme fast charging (XFC) technology can recharge EVs in less than 10 minutes for 200 miles range. Firstly, a novel robust optimization-based mixed integer linear programming model is proposed to size a battery energy storage system (BESS) and PV system in an XFCS. In this part, it is assumed that the sizing and location of the XFCS are known. Secondly, the aforesaid assumption is relaxed, and a strategic multi-period coordinated planning model is proposed to optimally site and size BESS-assisted charging stations in a highway transportation network and PV systems in a power distribution network by considering the coupling between both networks. Optimal operation and control of BESS-assisted EV charging stations are vital to alleviate the adverse impact of extreme fast charging of EVs on the host power network. A joint solution is proposed to mitigate the steady state and transient impact of extremefast charging of EVs and ensure grid-friendly integration of XFCSs with the host grid. Lastly, to make the operation of the XFCS cost-effective, a multi-layered energy management framework is proposed for the XFCS by considering forecast uncertainties, monthly demand charges reduction, and BESS degradation.
As the use of combustion engine vehicles plays a deciding role in global warming, we can observe a trend to replace them with electric vehicles (EV) driven by new environmentally conscious policies and increasing technological capabilities. With improvements in driving range and reduction in prices come new challenges that may hamper the progress towards complete battery driven transportation. A major challenge for the increasing EV adoption is the planning of extensions to existing infrastructure or the inclusion of new infrastructure components in the planning process. This demands increasingly complex planning tools that can simulate the interplay between different stakeholders in modern transportation scenarios such as EVs, charging stations, energy providers, and general transportation participants. Simulation platforms for agent-based modeling in transportation have been developed as effective interactive tools that allow planners to explore different trade-offs across different scenarios with the ability to simulate the impact of policy or infrastructure decisions on the different stakeholders in the simulation. This article surveys several of the major simulation platforms that include modern EV-based forms of transportation and allow the simulation of relevant infrastructure components alongside the well established transportation simulations. These tools allow researchers to analyze expected traffic flow, identify possible charging station locations based on area demand, predict electrical grid demand, and more. Here this survey intends to make it easier for researchers to identify and apply a simulation platform in the context of supporting the increasing electrification of the transportation sector, enabling more efficient simulation and planning capabilities in this domain.
Dynamic wireless charging for electric vehicles is an emerging technology to reduce on-board battery size and extend driving range. Due to its unique characteristic of vehicle-speed-related pulse-like load profile, the high-power dynamic wireless charging system (DWCS) introduces high stress to the utility grid. In this paper, an optimization model for renewable energy integration in the DWCS is proposed to mitigate the grid impact and minimize the operation costs of the whole system. As the load profile of DWCS is related to the traffic volume and various approaching vehicle speeds, the annual average daily traffic data and a stochastic model are used to develop 24-hour load profile of DWCS. To find a tradeoff between grid impact mitigation and operation costs minimization, relationships among power demand from power grid, photovoltaic (PV) capacity, wind energy (WE) capacity and energy storage (ES) capacity are analyzed, and the optimization objective and constraints are developed. Numerical simulation results demonstrate that energy storage integration can greatly mitigate the grid impact of DWCS, and optimal ratio of PV and WE can significantly reduce the operation cost of DWCS.
Energy buffer systems, capable of storing the vehicle energy during braking and reusing this stored energy during acceleration, were examined. Some of these buffer systems when incorporated in an electric vehicle would result in an improvement in the performance and range under stop and go driving conditions. Buffer systems considered included flywheels, hydropneumatic, pneumatic, spring, and regenerative braking. Buffer ranking and rating criteria were established. Buffer systems were rated based on predicted range improvements, consumer acceptance, driveability, safety, reliability and durability, and initial and life cycle costs. A hydropneumatic buffer system was selected.
The NASA Glenn Research Center's Structural Mechanics and Dynamics Branch is developing a compact, nonpolluting, bearingless electric machine with electric power supplied by fuel cells for future "more-electric" aircraft with specific power in the projected range of 50 hp/lb, whereas conventional electric machines generate usually 0.2 hp/lb. The use of such electric drives for propulsive fans or propellers depends on the successful development of ultra-high-power-density machines. One possible candidate for such ultra-high-power-density machines, a round-rotor synchronous machine with an engineering current density as high as 20,000 A/sq cm, was selected to investigate how much torque and power can be produced.
The South Coast Technology Volkswagen Rabbit, was tested at the Jet Propulsion Laboratory's (JPL) dynamometer facility and at JPL's Edwards Test Station (ETS). The tests were performed to characterize certain parameters of the South Coast Rabbit and to provide baseline data that will be used for the comparison of near term batteries that are to be incorporated into the vehicle. The vehicle tests were concentrated on the electrical drive system; i.e., the batteries, controller, and motor. The tests included coastdowns to characterize the road load, maximum effort acceleration, and range evaluation for both cyclic and constant speed conditions. A qualitative evaluation of the vehicle was made by comparing its constant speed range performance with those vehicles described in the document 'state of the Art assessment of Electric and Hybrid Vehicles'. The Rabbit performance was near to the best of the 1977 vehicles.
Carbon fiber composites are used in a wide range of structural applications due to their excellent specific strength and stiffness. However, the anisotropic mechanical and electrical properties associated with the fibers within each composite layer present challenges, and opportunities, for Nondestructive Testing (NDT) methods used to characterize and assess the structure condition. This includes composite condition after manufacture (such as fiber orientation and density, porosity, delamination, and bond strength) and during usage (such as damage from impact, fiber breakage, thermal exposure or applied stress). Ultrasonic and thermographic methods can address some of these challenges, but eddy current methods provide an alternative method for composite structures that contain a conducting material, such as carbon fibers or a metallic liner. This presentation reviews recent advances in the development of eddy current sensors and arrays for carbon fiber composite NDT and Structural Health Monitoring (SHM) applications. The focus is on eddy current sensor constructs with linear drive windings, such as MWM -Arrays, that induce currents primarily within the linear fibers of the composite. By combining this type of sensor construct with micromechanical models that relate composite constituent properties to measurable sensor responses, insight is obtained into the volumetric distribution of electrical properties within the composite and the associated manufacturing, damage, or strain conditions. With knowledge of the fiber layup, this MWM-Array technology is able to detect damage and strain/stress as a function of depth and fiber orientation. This work has been funded by NASA, NA V AIR and the Army for applications ranging from composite overwrapped pressure vessels (COPVs) to aircraft structures and rotorcraft blades. This presentation will specifically present background on the MWM-Array technology, results from the micromechanical modeling effort, and results from ongoing efforts for high resolution imaging and volumetric strain sensing.
Control over transition rates between spin states of emitters is crucial in a wide variety of fields ranging from quantum information science to the nanochemistry of free radicals. We present an approach to drive both electric and magnetic dipole-forbidden transitions of a spin emitter by placing it in a nanomagnonic cavity, requiring a description of both the spin emitter beyond the point dipole approximation and the vacuum magnetic fields of the nanomagnonic cavity with a large spatial gradient over the volume of the spin emitter. We specifically study the silicon vacancy (SiV – ) defect in diamond, whose Zeemansplit ground states comprise a logical qubit for solid-state quantum information processing, coupled to a magnetic nanoparticle serving as a model nanomagnonic cavity capable of concentrating microwave magnetic fields into deeply subwavelength volumes. Through first-principles modeling of the SiV – spin orbitals, we calculate the spin transition densities of magnetic dipole-allowed and -forbidden transitions and calculate their coupling rates to various multipolar modes of the nanomagnonic cavity. We envision using such a framework for manipulation of quantum spin states.
A dynamometer (road load simulator) is used to test and evaluate electric vehicle propulsion systems. To improve correlation between system tests on the road load simulator and on the road, similar performance tests are conducted using the same vehicle. The results of track tests on the electric propulsion system test vehicle are described. The tests include range at constant speeds and over SAE J227a driving cycles, maximum accelerations, maximum gradability, and tire rolling resistance determination. Road power requirements and energy consumption were also determined from coast down tests.
Electric vehicle (EV) performance can vary substantially under real-world operating conditions, particularly due to ambient temperature effects on energy consumption, battery behavior, and thermal management requirements. This study quantifies how weather conditions, daily driving patterns, and State-of-Charge (SOC) usage strategies jointly influence EV driving range, charging frequency, and overall energy efficiency. A detailed and experimentally validated Autonomie vehicle model is developed, integrating a powertrain, a mono-zonal cabin model, and a battery electro-thermal model. Three battery sizes (200-, 300-, and 400-mile homologated ranges) are assessed across five commute profiles (20–200 miles) and six ambient temperatures (−18 °C to 50 °C), including scenarios with and without preconditioning. Results show that extreme temperatures could significantly decrease the maximum achievable range by up to 55% in cold conditions (−18 °C) and 40% in hot conditions (50 °C), relative to moderate conditions. Larger battery packs retain a greater fraction of their nominal range under thermal stress, while smaller packs experience sharper relative penalties due to the higher contribution of thermal loads to total energy demand. The analysis further demonstrates that limiting operation to partial SOC windows (e.g., 80–20%), a common real-world practice, significantly reduces achievable range and increases charging frequency, particularly in cold weather. Thermal preconditioning while plugged in is shown to mitigate these effects for short trips, reducing energy consumption by up to 31% in hot conditions and 7% in cold conditions. The findings demonstrate how climate, SOC usage behavior, and thermal management jointly shape the practical driving capability of EVs, highlighting the importance of efficient thermal management and realistic user charging strategies for ensuring reliable EV operation across diverse climatic scenarios.
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.