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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 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Spatial and unobserved heterogeneity in consumer preferences for adoption of electric and hybrid vehicles: A Bayesian hierarchical modeling approach

The transition to low carbon vehicles known as alternative fuel vehicles (AFVs) is well underway. This transition has been motivated partly by consumer demand and partly by legislation such as the Zero Emission Vehicle mandate, which requires manufacturers to sell a certain percentage of their vehicles as AFVs. While the long-term adoption of AFVs (specifically, electric and hybrids) may take several years, there is a need to understand consumer preferences for AFV adoption and the pathways of AFV adoption from a national perspective. Therefore, this study sought to provide information about consumer preferences regarding AFV ownership while considering spatial and unobserved heterogeneity in consumer preferences, which can potentially impact societal transition to low carbon fueled vehicles. The 2017 National Household Travel Survey was used to calibrate Bayesian logit and hierarchical models. Here, the findings of these models reveal that higher gasoline prices contribute toward the adoption of battery electric vehicles. The results also reveal that the perceived disadvantages of AFVs for long commutes are the key barrier in wider adoption of AFVs. Interestingly, frequent use of the internet by consumers revealed a higher likelihood for purchase of hybrid vehicles. Furthermore, West Coast residents are observed to be a large portion of the early adopters and are more likely to purchase hybrids as compared to battery electric vehicles. The knowledge generated by this study has implications for making better informed decisions about AFV adoption and developing incentives to promote wider adoption of AFVs by overcoming their perceived disadvantages.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Improving commercial truck fleet composition in emission modeling using 2021 US VIUS data

Commercial trucks are essential elements of the nation's supply chain system. Meanwhile, intensive truck movements contribute significantly to system externalities, such as energy use and air pollution. However, collecting detailed fleet composition and distribution of operational patterns remains a barrier to accurately accounting for these impacts. The recently released 2021 US Vehicle Inventory and Use Survey (US VIUS) fills a critical gap in understanding commercial truck fleet distributions, their operations, and business constraints at the national scale. This study aims to understand the latest US commercial vehicle fleet composition and operational characteristics using 2021 US VIUS data and calibrate the fleet inputs in regulatory emission models to assess the potential emission implications of the VIUS-derived fleet composition. The emission rates for commercial trucks and default fleet composition are collected from the U.S. EPA's MOtor Vehicle Emission Simulator (MOVES4). The 2021 US VIUS data is applied to improve fleet characteristics such as the long-haul fraction and the vehicle mileage accumulation rate. The study also investigates potential emission reduction benefits under various forecasted fleet electrification scenarios. The energy consumption and critical air pollutant rates by vehicle types are compared between MOVES4 and US VIUS fleets for both current and future scenarios to provide insights into the latest U.S. commercial vehicle fleet characteristics and their implications on energy and emissions. This study helps policymakers and practitioners advance the commercial fleet generation for emission models. It also deepens the understanding of the emission reduction potential of the commercial fleet under various fleet projections.

2021 US VIUS↗
DOE OSTI2025

String instability mitigation of adaptive cruise control without modifying control laws: trajectory shaper and parameter estimation

Vehicle automation technologies equip vehicles with adaptive cruise control (ACC) systems, which relieve driving fatigue. However, recent studies have shown that the current ACC systems are string-unstable (i.e., exacerbate traffic congestion). To achieve string stability, most existing studies directly modify the control algorithms of ACC systems. Alternatively, this study proposes a trajectory shaper (TS)-based method, which only modifies the trajectory information of the predecessor vehicle, so that the ego vehicle driven by a string-unstable ACC system leverages the modified trajectory information to achieve string stability. To devise the TS-based method, an offline-online parameter estimation method integrating batch optimization and an extended Kalman filter is applied to estimate the parameters of an ACC system. The proposed TS-based method is cost-effective during implementation, as it avoids modifying existing ACC control algorithms (which entails a complex analysis of control systems and parameter tuning). In conclusion, the effectiveness of the proposed TS-based method is validated through extensive numerical experiments.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

A novel method for correction of temporally- and spatially-variant optical distortion in planar particle image velocimetry

In-cylinder flow measurements are necessary to gain a fundamental understanding of swirl-supported, light-duty Diesel engine processes for high thermal efficiency and low emissions. Planar particle image velocimetry (PIV) can be used for non-intrusive, in situ measurement of swirl-plane velocity fields through a transparent piston. In order to keep the flow unchanged from all-metal engine operation, the geometry of the transparent piston must adapt the production-intent metal piston geometry. As a result, a temporally- and spatially-variant optical distortion is introduced to the particle images. Here, to ensure reliable measurement of particle displacements, this work documents a systematic exploration of optical distortion quantification and a hybrid back-projection procedure that combines ray-tracing-based geometric and in situ manual back-projection approaches.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

High pressure phase transition and strength estimate in polycrystalline alumina during laser-driven shock compression

Alumina (Al 2 O 3 ) is an important ceramic material notable for its compressive strength and hardness. It represents one of the major oxide components of the Earth's mantle. Static compression experiments have reported evidence for phase transformations from the trigonal α-corundum phase to the orthorhombic Rh 2 O 3 (II)-type structure at ~90 GPa, and then to the post-perovskite structure at ~130 GPa, but these phases have yet to be directly observed under shock compression. In this work, we describe laser-driven shock compression experiments on polycrystalline alumina conducted at the Matter in Extreme Conditions endstation of the Linac Coherent Light Source. Ultrafast x-ray pulses (50 fs, 10 12 photons/pulse) were used to probe the atomic-level response at different times during shock propagation and subsequent pressure release. At 107 ± 8 GPa on the Hugoniot, we observe diffraction peaks that match the orthorhombic Rh 2 O 3 (II) phase with a density of 5.16 ± 0.03 g cm –3 . Upon unloading, the material transforms back to the α-corundum structure. Upon release to ambient pressure, densities are lower than predicted assuming isentropic release, indicating additional lattice expansion due to plastic work heating. Furthermore, using temperature values calculated from density measurements, we provide an estimate of alumina's strength on release from shock compression.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Ion-damage-free planarization or shallow angle sectioning of solar cells for mapping grain orientation and nanoscale photovoltaic properties

Ion beam milling is the most common modern method for preparing specific features for microscopic analysis, even though concomitant ion implantation and amorphization remain persistent challenges, particularly as they often modify materials properties of interest. Atomic force microscopy (AFM), on the other hand, can mechanically mill specific nanoscale regions in plan-view without chemical or high energy ion damage, due to its resolution, directionality, and fine load control. As an example, AFM-nanomilling (AFM-NM) is implemented for top-down planarization of polycrystalline CdTe thin film solar cells, with a resulting decrease in the root mean square (RMS) roughness by an order of magnitude, even better than for a low incidence FIB polished surface. Subsequently AFM-based property maps reveal a substantially stronger contrast, in this case of the short-circuit current or open circuit voltage during light exposure. Furthermore, electron back scattering diffraction (EBSD) imaging also becomes possible upon AFM-NM, enabling direct correlations between the local materials properties and the polycrystalline microstructure. Smooth shallow-angle cross-sections are demonstrated as well, based on targeted oblique milling. As expected, this reveals a gradual decrease in the average short-circuit current and maximum power as the underlying CdS and electrode layers are approached, but a relatively consistent open-circuit voltage through the diminishing thickness of the CdTe absorber. AFM-based nanomilling is therefore a powerful tool for material characterization, uniquely providing ion-damage free, selective area, planar smoothing or low-angle sectioning of specimens while preserving their functionality. This then enables novel, co-located advanced AFM measurements, EBSD analysis, and investigations by related techniques that are otherwise hindered by surface morphology or surface damage.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Optimal hybrid power plants for electric vehicle charging demand

Transmission constraints, increasing motivations to decarbonize, and concerns over peak electric vehicle (EV) load impacts on local grids have driven electric customers to consider behind-the-meter, hybrid power plant generation and storage at the distributed-grid level for EV charging. In this study, we develop capabilities to optimize hybrid power plant component capacities for EV charging. We then demonstrate these capabilities in a case study for Boulder, Colorado, using public EV charging data as well as wind and solar resource data. Our results show system designs that balance the cost of energy with load-meeting and peak shaving performance. Within the case study, systems designed for wind, solar photovoltaic (PV), and storage resulted in lower cost of energy than those optimized for PV and storage only. This indicates that in areas where wind resource exists, hybrid power plants that include wind, PV, and battery assets can better meet EV charging loads (including peak loads that are prone to overloading local grids) than PV and battery assets alone. Future work to address limitations in this paper include extending cost modeling to include performance losses (e.g., based on operations or weather) and charging station costs to estimate levelized cost of charging, and quantifying uncertainty and error in our aggregation methods for estimating EV charging loads at the hourly timescale.

14 SOLAR ENERGY↗
DOE OSTI2020

The shape of electrified transportation

Transportation is currently the least-diversified energy demand sector, with over 90% of global transportation energy use coming from petroleum products [1]. For more than a century, petroleum fuels have been relied upon to move people and goods within and between towns and cities, and on roads, railways, farms, waterways, and in the air. These energy-dense fuels have unquestionably provided reliable and convenient mobility options to power the modern global economy. However, these benefits have also created challenges associated with geopolitics, energy security, price volatility, and environmental impacts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

The rise of electric vehicles—2020 status and future expectations

Electric vehicles (EVs) are experiencing a rise in popularity over the past few years as the technology has matured and costs have declined, and support for clean transportation has promoted awareness, increased charging opportunities, and facilitated EV adoption. Suitably, a vast body of literature has been produced exploring various facets of EVs and their role in transportation and energy systems. This paper provides a timely and comprehensive review of scientific studies looking at various aspects of EVs, including: (a) an overview of the status of the light-duty-EV market and current projections for future adoption; (b) insights on market opportunities beyond light-duty EVs; (c) a review of cost and performance evolution for batteries, power electronics, and electric machines that are key components of EV success; (d) charging-infrastructure status with a focus on modeling and studies that are used to project charging-infrastructure requirements and the economics of public charging; (e) an overview of the impact of EV charging on power systems at multiple scales, ranging from bulk power systems to distribution networks; (f) insights into life-cycle cost and emissions studies focusing on EVs; and (g) future expectations and synergies between EVs and other emerging trends and technologies. The goal of this paper is to provide readers with a snapshot of the current state of the art and help navigate this vast literature by comparing studies critically and comprehensively and synthesizing general insights. This detailed review paints a positive picture for the future of EVs for on-road transportation, and the authors remain hopeful that remaining technology, regulatory, societal, behavioral, and business-model barriers can be addressed over time to support a transition toward cleaner, more efficient, and affordable transportation solutions for all.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Coherent High-Frequency Axial Oscillations in a Partially Magnetized Direct Current Magnetron Discharge

High-frequency oscillations are observed in a neon plasma of a direct current magnetron discharge. At low discharge currents, we see highly coherent 60 MHz fluctuations. Above a distinct current threshold, secondary 5–10 MHz fluctuations emerge in addition to turbulent fluctuations in the 60–100 MHz range. The oscillations in the total discharge current suggest axial wave propagation. A lower-hybrid wave theory is invoked to model the high-frequency oscillations. Here, we attribute the low-frequency modes to a turbulence-driven inverse cascade process, as suggested by recent simulations.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

Thermal Management and Reliability of Power Electronics and Electric Machines

Increasing the number of electric-drive vehicles (EDVs) on America's roads has been identified as a strategy with near-term potential for dramatically decreasing the nation's dependence on oil - by the U.S. Department of Energy, the federal cross-agency EV-Everywhere Challenge, and the automotive industry. Mass-market deployment will rely on meeting aggressive technical targets, including improved efficiency and reduced size, weight, and cost. Many of these advances will depend on optimization of thermal management. Effective thermal management is critical to improving the performance and ensuring the reliability of EDVs. Efficient heat removal makes higher power densities and lower operating temperatures possible, and in turn enables cost and size reductions. The National Renewable Energy Laboratory (NREL), along with DOE and industry partners is working to develop cost-effective thermal management solutions to increase device and component power densities. In this presentation, the activities in recent years related to thermal management and reliability of automotive power electronics and electric machines are presented.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2018

Impact of Accelerated Stress-Tests on SiC MOSFET Precursor Parameters

Integrating SiC power MOSFETs is very attractive for advancing power electronic system performance, yet the system reliability with new devices remains in question. This work presents an overview of accelerated lifetime tests and the packaging and semiconductor failure mechanisms they excite. The experiments explained here includes High Temperature Gate Bias (HTGB), Switching Cycling, Power Cycling, and Thermal Cycling. These experiments stress different failure mechanisms, that show degradation in different device parameters including, but not limited to, threshold voltage and on-resistance. These four experiments help illustrate the spectrum between device and package degradation that can be used to design more reliable power electronic circuits.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

Optimal Battery Utilization Over Lifetime for Parallel Hybrid Electric Vehicle to Maximize Fuel Economy

This paper presents a control strategy to maximize fuel economy of a parallel hybrid electric vehicle over a target life of the battery. Many approaches to maximizing fuel economy of parallel hybrid electric vehicle do not consider the effect of control strategy on the life of the battery. This leads to an oversized and underutilized battery. There is a trade-off between how aggressively to use and 'consume' the battery versus to use the engine and consume fuel. The proposed approach addresses this trade-off by exploiting the differences in the fast dynamics of vehicle power management and slow dynamics of battery aging. The control strategy is separated into two parts, (1) Predictive Battery Management (PBM), and (2) Predictive Power Management (PPM). PBM is the higher level control with slow update rate, e.g. once per month, responsible for generating optimal set points for PPM. The considered set points in this paper are the battery power limits and State Of Charge (SOC). The problem of finding the optimal set points over the target battery life that minimize engine fuel consumption is solved using dynamic programming. PPM is the lower level control with high update rate, e.g. a second, responsible for generating the optimal HEV energy management controls and is implemented using model predictive control approach. The PPM objective is to find the engine and battery power commands to achieve the best fuel economy given the battery power and SOC constraints imposed by PBM. Simulation results with a medium duty commercial hybrid electric vehicle and the proposed two-level hierarchical control strategy show that the HEV fuel economy is maximized while meeting a specified target battery life. On the other hand, the optimal unconstrained control strategy achieves marginally higher fuel economy, but fails to meet the target battery life.

33 ADVANCED PROPULSION SYSTEMS↗