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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Modeling charging infrastructure impact on the electric vehicle market in China

The plug-in electric vehicle (PEV) is deemed as a critical technological revolution, and the governments are imposing various vehicle policies to promote its development. Meanwhile, the market success of PEVs depends on many aspects. The study reported herein integrates one’s use of charging infrastructure at home, public place and workplace into the market dynamics analysis tool, New Energy and Oil Consumption Credits (NEOCC) model, to systematically assess the charging infrastructure (home parking ratio, public charging opportunity, and charging costs) impact on PEV ownership costs and analyze how the PEV market shares may be affected by the attributes of the charging infrastructure. Compared to the charging infrastructure, the impact of battery costs is incontrovertibly decisive on PEV market shares, the charging infrastructure is still non-negligible in the PEV market dynamics. The simulation results find that the public charging infrastructure has more effectiveness on promoting the PEV sales in the PEV emerging market than it does in the PEV mature market. However, the improvement of charging infrastructure does not necessarily lead to a larger PEV market if the charging infrastructure incentives do not coordinate well with other PEV policies. Besides, the increase of public charging opportunities has limited motivations on the growth of public PEV fleets, which are highly correlated to the number of public fast charging stations or outlets. It also finds that more home parking spaces can stimulate more sales of personal plug-in hybrid electric vehicles instead of personal battery electric vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Agent-based simulations of shared automated vehicle operations: reflecting travel-party size, season and day-of-week demand variations

Here, this paper explores the effects of day of week and season of year demand variations for shared rides, along with realistic travel party sizes, on shared autonomous vehicle (SAV) services across the Austin, Texas region. Using the agent-based POLARIS program, synthetic person-trips that reflect travel-party size (from one to four persons) and demand variations over days and months, as evident in the National Household Travel Survey data were simulated in each scenario over a 24 h travel day. Results show that realistic party sizes can bring considerable changes to SAV fleet performance, including up to 8.5% higher service rates (number of requests accepted within 15 min), 5 min shorter journey times (wait time + travel time), 28% higher vehicle occupancies on weekends, and roughly 4% lower empty fleet VMT. Weekend travel is most impacted by season of year, with weekday travel patterns looking more uniform (thanks to work and school trips). Various performance metrics for the Austin network, like total and empty VMT, change by up to 30% when considering realistic variations in party size and time of year. This paper underscores the value of recognizing day-to-day and month-to-month variations in travel demand, and the importance of agent-based model equations to reflect travel-party size. Such realism can help quantify SAV seat occupancies more accurately, highlighting the importance of shared mobility. However, it also creates demand and supply issues for operators that now need more information on party size to manage dynamic ride-sharing, or those that may wish to shift their fleet vehicles to other regions for special events to protect profits while offering reasonable wait times to customers throughout the year.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

CalderaCast Web Interface

CalderaCast may be accessed as a web-based tool at the first link in the references section of this dataset. All of the necessary datasets to run the tool are built into the simulation software running behind the web interface. These input datasets are referenced by the additional links in the references section below. Many of those datasets are taken into machine-learning algorithms by the Caldera team and heavily processed to create internal datasets, which are then relied upon by the simulation to produce individual results. These internal datasets are not accessible and are not necessary for use of the CalderaCast tool.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Closed-form solutions towards real-time energy-optimal and collision-free speed planning with limited information

Under real-world driving conditions, available information is limited due to communication range, and connected and automated vehicles (CAVs) must plan and follow energy-optimal and collision-free speed trajectory with a high updating rate. To this end, this paper presents a holistic design of a speed planning algorithm using analytical closed-form optimal solutions. Using the simplest vehicle model, we derive closed-form solutions as a function of boundary conditions, and summarize them without and with state constraints imposed by speed limits and vehicle in front. Then, we introduce multiple modes (e.g., eco-approach to traffic signal) to properly respond dynamically changing situations, and present how to set boundary conditions for each mode, while guaranteeing nature of globally optimal solutions. Finally, we perform a large-scale simulation study to identify and quantify the energy impacts of CAVs for real-world driving routes. A simple but effective algorithm based on closed-form solutions shows a significant energy saving potential in comparison with human-driven vehicle and adaptive cruise controlled vehicle with connectivity.

Han, Jihun↗
DOE OSTI2022

Control Oriented Model of Cabin-HVAC System in a Long-Haul Trucks for Energy Management Applications

Super Truck II is a 48V mild hybrid class 8 truck with an all auxiliary loads powered purely by the battery pack. Electric Heating Ventilation and Air Conditioning (HVAC) load is the most prominent battery load during the hotel period, when the truck driver is resting inside the sleeper. For the PACCAR Super Truck II (ST-II) project a 48 V battery system provides the required power during the hotel period. A cabin-HVAC model estimates the electric load on the 48V battery system, allowing the control system to implement an efficient energy management strategy that avoids engine idling during the hotel period. The thermal model accounts for the sun load due to the time of day and the geographic location of the truck during the hotel period. The cabin-HVAC model has two parts. First, a grey box model with two heat exchangers (Condenser and Evaporator) working in unison with refrigerant mass flow rate as an input and HVAC load as an output. Second, a two-node cabin model formulated to estimate the cabin temperature as a function of the Global Horizontal Irradiance (GHI), HVAC load and ambient temperature. The models are calibrated using experimental cabin-HVAC system data as for long-haul class 8 truck (e.g. ST-II). Here, the model simulations show that the overall Root Mean Square Error (RMSE) value of 0.4°C between the experimental and simulated cabin temperature.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Modeling the Air Quality Impacts of Future Energy Scenarios

Many greenhouse gas (GHG) emission reduction measures achieve simultaneous reductions in air pollutants. Human-Earth system models can estimate such emission changes in the energy system but using them in chemistry-transport models (CTMs) to study their air quality impacts involves resource-intensive emissions processing. This is greatly simplified by an emissions scaling approach linking state-level emissions estimated by a human-Earth system model to a CTM. A scenario continuing pre-2022 energy policy in the U.S. to 2050 shows widespread air quality improvements over the 2015 baseline from SO 2 and NO x emission reductions of 50–80% from electricity generation and light-duty vehicles. Scenarios of GHG mitigation and vehicle electrification at the state and national level add further benefits. However, PM 2.5 increases from increased use of wood heating and bioenergy suggest that additional PM 2.5 management may be needed when using biofuels. In conclusion, this approach helps assess multiple future energy scenarios efficiently without sacrificing chemical detail in the air quality simulations.

air quality↗
DOE OSTI2025

Heliostat optical error inspection with polarimetric imaging drone

On a Concentrated Solar Power (CSP) field, optical errors have significant impacts on the collection efficiency of heliostats. Fast, cost-effective, labor-efficient, and non-intrusive autonomous field inspection remains a challenge. Approaches using imaging drone, i.e., Unmanned Aerial Vehicle (UAV) system integrated with high resolution visible imaging sensors, have been developed to address these challenges; however, these approaches are often limited by insufficient imaging contrast. Here, in this study, we report a polarimetry-based method with a polarization imaging system integrated on UAV to enhance imaging contrast for in-situ detection of heliostat mirrors without interrupting field operation. We developed an optical model for skylight polarization pattern to simulate the polarization images of heliostat mirrors and obtained optimized waypoints for polarimetric imaging drone flight path to capture images with enhanced contrast. The polarimetric imaging-based method improved the success rate of edge detections in scenarios which were challenging for mirror edge detection with conventional imaging sensors. We have performed field tests to achieve significantly enhanced heliostat edge detection success rate and investigate the feasibility of integrating polarimetric imaging method with existing imaging-based heliostat inspection methods, i.e., Polarimetric Imaging Heliostat Inspection Method (PIHIM). Our preliminary field test results suggest that the PIHIM hold the promise to enable sufficient imaging contrast for real-time autonomous imaging and detection of heliostat field, thus suitable for non-interruptive fast CSP field inspection during its operation.

CSP Field↗
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 OSTI2026

Control and Real-Time Simulation of Microgrids with EV Fast-Charging and Grid-Forming Resources

Fast charging stations (FCSs) for electric vehicles (EVs) can behave as constant power loads, which is problematic for the operation of grid-forming (GFM) inverter-based resources (IBRs). To tackle this problem, this paper proposes a control suite that makes FCSs responsive to ac voltage and frequency disturbances. To test the performance of the FCS controls, the paper also sets forth theory to compensate for time-delays appearing in the real-time simulation of microgrid models divided into several central processing units (CPUs). Furthermore, these contributions are showcased via multi-CPU real-time simulations of a faulted microgrid having EV FCSs and GFM IBRs powered by photovoltaic solar arrays as well as power hardware-in-the-loop experiments. These contributions are significant to address NERC recommendations and IEEE standards.

14 SOLAR ENERGY↗
DOE OSTI2021

Electrification of Pakistan's Transport System: Modeling Electric Vehicle Penetration and Energy Supply Chain Impacts

Initial analysis of the National Electric Vehicle Policy (NEVP) of Pakistan was undertaken by an integrated energy planning team from November 2019 through August 2020. This technical reference document seeks to present the background, inputs and assumptions, methodology, and results of the policy analysis in detail. The intended audience is the technical modeler, analyst, or reviewer who seeks to understand specifically the modeling effort within this initial analytical phase, with the end-goal of interpreting, re-producing, or modifying the simulations or extending the models to conduct follow-on studies subsequently. These studies may examine in greater detail the energy sector, impacts to emissions, charging station infrastructure needs, and overall benefits-cost trade of the policy, among other areas of interest.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Spatial Impacts of Electric Vehicle Charging on Power Grid Stability: A Downtown Atlanta Case Study

The rapid increase in electric vehicle (EV) charging demand poses a potential risk to power grid stability, particularly as the spatial distribution of this demand remains underexplored. Existing research often focuses on technical optimization models while overlooking the geographic and human dynamics that affect energy consumption. This study addresses this gap by incorporating mobility data to estimate both building energy use and EV charging demand while also considering geographic factors for a better understanding of grid load. Using agent-based simulations and the Open-Source Distribution System Simulator, the study evaluates the effect of various EV penetration scenarios on grid voltage and unbalance. The results show that, although voltage remains within acceptable limits at lower EV penetration rates, significant voltage drop and unbalance occur as EV penetration exceeds 40%, particularly in residential areas with high charging demand. This study offers a framework for integrating spatial analysis and mobility data in power network simulations, providing insights for future EV infrastructure planning.

Pan, Melrose [ORNL] (ORCID:000000031627448X)↗
DOE OSTI2022

Evaluating Class 6 Delivery Truck Fuel Economy and Emissions Using Vehicle System Simulations for Conventional and Hybrid Powertrains and Co-Optima Fuel Blends

The US Department of Energy’s Co-Optimization of Engine and Fuels Initiative (Co-Optima) investigated how unique properties of bio-blendstocks considered within Co-Optima help address emissions challenges with mixing controlled compression ignition (i.e., conventional diesel combustion) and enable advanced compression ignition modes suitable for implementation in a diesel engine. Additionally, the potential synergies of these Co-Optima technologies in hybrid vehicle applications in the medium- and heavy-duty sector was also investigated. In this work, vehicles system were simulated using the Autonomie software tool for quantifying the benefits of Co-Optima engine technologies for medium-duty trucks. A Class 6 delivery truck with a 6.7 L diesel engine was used for simulations over representative real-world and certification drive cycles with four different powertrains to investigate fuel economy, criteria emissions, and performance. Comparisons were made between ultralow-sulfur diesel and a blend of 25% hexyl hexanoate with diesel. Model validation data were informed by 2019 model year Cummins ISB 6.7 L diesel engine maps and transient validation data in a pre-production hybrid configuration and a direct dyno coupled configuration with diesel fuel and a blend of 25% hexyl hexanoate with diesel.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Route Optimization for Energy Efficient Airport Shuttle Operations - A Case Study from Dallas Fort Worth International Airport

Air travel and requisite surface traffic supporting passenger arrival/departure constitutes a significant portion of travel and emissions in cities with large airports. An airport trip can segment into three parts namely: i) travel from a location in the city to the airport; ii) travel from a parking lot or rental car center to the terminal (i.e., within the airport premises), and iii) travel inside the terminal. Depending on the airport access mode all or a part of these legs comprise a traveler’s journey to the airport. The priority of airport ground transport management teams is to provide passengers with a seamless travel experience within the airport, so it is understandable that within airport shuttle routes might not be optimized for minimizing energy consumption. Solutions that meet the dual objective of reducing energy consumption from airport shuttle operations without compromising on passenger travel experience are key to improving system efficiency. There is currently a dearth of research and tools that can inform airports in making such decisions. Addressing this need, this research effort puts forth an optimization model that generates optimal shuttle routes for a given set of constraints, and a discrete-event simulator that evaluates the optimal solutions in a stochastic environment to understand the tradeoffs between passenger wait times, and within airport shuttle energy consumption. The proposed set of tools are tested in the context of optimizing airport shuttles routes within the Dallas Fort Worth International Airport (DFW). In addition to shuttle spatial positioning, and passenger demand information, high-fidelity vehicle data was collected using data loggers installed on DFW shuttles. Results show that 20% energy reduction in shuttle operations is possible with a modest two-minute increase in average passenger wait times. The tools developed in this research effort are designed to be generalizable and can help optimize shuttle operations planning at any major airport.

air travel↗
DOE OSTI2025

GPR_calculator: An on-the-fly surrogate model to accelerate massive nudged elastic band calculations

We present GPR_calculator, a package based on Python and C++ programming languages to build an on-the-fly surrogate model using Gaussian Process Regression (GPR) to approximate computationally expensive electronic structure calculations. The key idea is to dynamically train a GPR model during the simulation that can accurately predict energies and forces with uncertainty quantification. When the uncertainty is high, the costly electronic structure calculation is performed to obtain the ground truth data, which is then used to update the GPR model. To illustrate the effectiveness of GPR_calculator, we demonstrate its application in Nudged Elastic Band (NEB) simulations of surface diffusion and reactions, achieving 3-10 times acceleration compared to pure ab initio calculations. The source code is available at https://github.com/MaterSim/GPR_calculator.

Gaussian process regression↗
DOE OSTI2024

Thermal Analysis of a Polypropylene Capacitor for Resonant Tuning Networks in WPT Applications

This study presents the thermal analysis of polypropylene capacitors operating at high frequencies and high currents used in the resonant tuning networks for electric vehicle (EV) wireless charging systems. The thermal analysis and performance results of the CELEM polypropylene capacitors are presented at different currents for a polyphase wireless power transfer (WPT) system while the study can be expanded to other capacitors by different manufacturers. Thermal equivalent circuit of the capacitor is derived and required cold plate size and design calculations are conducted by modeling the polypropene capacitor in MATLAB. The polypropene capacitor's finite element analysis (FEA) model is developed using COMSOL Multiphysics simulation software, and the thermal characteristics and analysis of the capacitor are presented in this paper.

Aktas, Ahmet↗
DOE OSTI2023

Design and Optimization of a Fast-Switching Electromagnetic Valve Actuator for Off-highway Vehicle Hydraulic Systems

Electrification of off-highway vehicles offers improved efficiency and reduction in greenhouse gas emissions. However, the extremely low power densities of electric machines relative to the hydraulics used in conventional off-highway vehicles makes electrification challenging. Although the recently proposed Hybrid Hydraulic Electric Architecture (HHEA) can overcome this limitation, it requires frequent switching between the pressure rails using switching valves resulting in losses. Fast switching minimizes the switching losses in these valves, which otherwise significantly reduce the overall efficiency of the system. The primary contribution of this paper is the design and optimization of a linear electromagnetic actuator for a fast-switching valve. This paper identifies the actuator requirements to enable fast switching and presents a design and optimization technique to realize actuators that meet the design objectives. In addition, a multi-physics approach is proposed to model the performance of the complete switching valve by considering the electromagnetic, fluidic, and mechanical physics. An optimal actuator design is obtained using the developed optimization framework, and the multi-physics model demonstrates that the selected design achieves a 30% reduction in throttling-related switching losses by switching 70 % faster than comparable commercial valve designs.

Linear actuators, off-highway vehicle electrificat↗
DOE OSTI2021

Validation of the HyRAM+ physics models for use with propane

The Hydrogen Risk Assessment Model Plus (HyRAM+) toolkit combines quantitative risk assessment with simulations of unignited dispersion, ignited turbulent diffusion flames, and indoor accumulation with delayed ignition of fuels. HyRAM+ is differentiated from HyRAM in that it includes models and leak data for other alternate fuels. The models of the physical phenomena need to be validated for each of the fuels in the toolkit. This report shows the validation for propane which is being used as a surrogate for autogas, which is a mixture of propane and butane and used in internal combustion engines in vehicles. For flame length comparisons, five previously published experiments from peer reviewed journals were used to validate our models. The validation looked at flame lengths and flame widths with respect to different leak diameters, mass flow rates, and source pressures. Most of the sources included more than one set of experimental data, which were collected using different methods (CCD cameras, IR visualization etc.). In general, HyRAM+ overpredicts the flame lengths by around 65%. For heat and radiation models, we compared the heat flux and radiation data reported from two different sources to the values calculated by HyRAM+. For higher mass flow rates, the HyRAM+ calculated flame length results gave a better estimate of what is found in the experiments (65% error), but a higher error (85%) is observed between the HyRAM+ calculated lengths and the experimental flame lengthsfor lower mass flows. Some differences can be attributed to outdoor environmental effects (i.e. wind speed) and uncertainties in jet flame shapes. The propane flame trajectory is predicted for a high Reynolds number case with Re = 12,500 and a low Reynolds number case where Re = 2,000. The Re=12,500 case which is momentum dominated matches well with the experimental flame trajectory, but the agreement for the bouancy driven low Reynolds number case is not as good. Dispersion modeling for unignited propane was also analyzed. We compared the mole fraction, mixture fraction, mean velocity, concentration half width, and inverse mass concentration over an axial distance from different credible journals to the values calculated by HyRAM+. The results display good agreement but generally, HyRAM+ predicts a wider profile for mole fraction and mixture fraction experiments. Overall, HyRAM+’s results are reasonable for predicting the flame length, heat flux, flame trajectory, and dispersion for propane and can be used in risk analyses

08 HYDROGEN↗