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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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This dataset contains telemetry data from the 220EV box truck as provided by the manufacturer, Dana. Data are recorded in a time series interval of 1 second. Recorded data contain odometer reading (kilometers), axle speed, state of charge of the battery, and cabin heater setting.
This dataset contains telemetry data from the 220EV box truck as provided by the manufacturer, Dana. Data are recorded in a time series interval of 1 second. Recorded data contain odometer reading (kilometers), axle speed, state of charge of the battery, and cabin heater setting.
This dataset contains telemetry data from the 220EV box truck as provided by the manufacturer, Dana. Data are recorded in a time series interval of 1 second. Recorded data contain odometer reading (kilometers), axle speed, state of charge of the battery, and cabin heater setting.
Low-temperature polymer electrolyte membrane fuel cell systems can achieve higher efficiency than diesel engines, but heat rejection remains a major challenge in class-8 heavy-duty fuel cell trucks. For the same rated power, the radiator heat load is greater than that in a diesel engine, while the allowable operating temperatures are lower. This work proposes and evaluates 400 kWe fuel cell–battery hybrid (FCH) platforms and operating strategies that manage heat rejection without enlarging the radiator frontal area. Three FCH platforms are identified, each varying in fuel cell system (FCS) rated power, battery energy storage system (ESS) capacity, and maximum stack coolant exit temperature (T h1 ). All three satisfy key system and vehicle requirements, including 175 kWe FCS power at top sustained speed, 400 kWe FCH power on a 6% grade climb, a target stack power density (PD) of 750 mW e /cm 2 , and heat rejection constraints. The first FCH has the smallest FCS, the largest ESS, and a T h1 of 90 °C. The second achieves the highest PD of 840 mW e /cm 2 at a T h1 of 95 °C. The third has the largest FCS, the smallest ESS, and a T h1 of 102 °C. At a Th1 of 115 °C, the platform can be configured as a stand-alone 400 kWe(net) FCS without hybridization, but the achievable PD drops to 460 mW e /cm 2 .
Accurately estimating rolling and air resistance is essential for predicting the energy consumption of vehicles. This study presents a field-based approach using a rolldown test to simultaneously determine rolling and air resistance coefficients. Unlike prior methods that frequently used simulations or models, we employ a goal programming methodology to improve precision and evaluate the actual vehicle and environmental conditions. Our methodology was tested using a Class 8 Freightliner eCascadia on a surveyed road section, ensuring controlled conditions for data collection. By analyzing the time–velocity relationship across multiple test runs, we derived resistance coefficients for both loaded and unloaded conditions. The study confirms that rolling resistance is largely independent of velocity at low speeds but exhibits a nonlinear dependency at higher speeds. Additionally, road surface conditions, tire condition, axle configuration, aerodynamic properties, and weather conditions significantly impact resistance values, emphasizing the need for real-world testing rather than relying solely on standardized projections. Our results align with existing literature while demonstrating the efficacy of the goal programming approach in refining resistance estimates. This work contributes to improved vehicle energy modeling, offering practical insights for fleet operators and policymakers seeking accurate energy consumption predictions for electric trucks operating under varying environmental conditions.
Electric vehicles (EVs) can provide power to the grid or buildings similar to distributed energy resources (DER) for energy balancing applications or optimizing the operation of the microgrids in harmony with the other DER assets. This article presents the operating modes of a bidirectional wireless power transfer (WPT) system designed for a medium-duty package delivery vehicle. The WPT system designed for this study can transfer 20 kW of power across 11 in of air gap using custom-designed double-D (DD) couplers with LCC–LCC tuning networks. The proposed system utilizes a 480-V three-phase grid connection, a plug-in hybrid delivery truck with bidirectional WPT, and a stationary energy storage system (SESS) that can be connected to the primary-side dc link. Due to the differences in primary and secondary dc bus voltages, and considering the voltage of the SESS, asymmetric voltage gains were used in the system. Sensitivity analyses of this system with respect to these voltage levels are presented. Five different operating modes of the grid, SESS, and the EV battery are investigated with experimental results. Control algorithms are described for grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operating modes. A bidirectional WPT system is operated with a power factor of 0.99 on the grid side in every operating mode. The EV battery was charged with 20.3 kW with an overall efficiency of 93.02% in the G2V operating mode. Finally, in the V2G operating mode, the WPT system provided 12.82 kW of power back to the grid with an overall efficiency of 89.08%.
Medium- and heavy-duty electric truck charging simulation results for the Houston and Dallas megaregion along the I-45 corridor under 96 scenarios
This dataset contains fueling data for two diesel trucks operated by SWS, provided by SWS. Data fields include date and time of fueling, total amount of fuel, odometer reading at the time of fueling, and total cost of fueling.
This dataset contains fueling data for two diesel trucks operated by SWS, provided by SWS. Data fields include date and time of fueling, total amount of fuel, odometer reading at the time of fueling, and total cost of fueling.
The motivation for this project stemmed from the challenges associated with rapidly refueling heavy-duty hydrogen fuel cell electric trucks (FCETs). Current manual refueling processes for fast refueling involve large, heavy equipment (e.g., hoses three times heavier than standard) and pose ergonomic risks and potential for equipment damage. The goal was to develop and test an autonomous fueling system to improve ergonomics, enhance safety, increase equipment durability through design improvements, and potentially speed up the fueling process. This project aimed to add to the understanding of autonomous systems in the context of heavy-duty hydrogen refueling, evaluating the technical effectiveness of potential concepts. A successfully developed system would benefit the public by facilitating the adoption of zero-emission heavy-duty transport, reducing reliance on manual labor for a physically demanding task, and potentially improving the safety and efficiency of hydrogen refueling infrastructure. The major accomplishment during the project's active period was the completion of the system-level architecture task. This involved establishing a detailed list of system requirements covering interfaces, environmental conditions, regulatory compliance, industry standards, safety, security, performance capabilities, and optional features. Five key use cases for the autonomous system were also identified. However, due to internal restructuring at Nikola, the necessary resources could not be allocated to continue the project. Consequently, Nikola opted to discontinue the project. The award was mutually terminated by Nikola and the DOE.
This collaborative multi-year advanced engine technology project summarized in the report is to develop low mass and high efficiency medium-duty truck engine. The project was proposed as a large-scale engine design and demonstration enabled by an advanced materials and manufacturing development program, with a comprehensive plan spanning a period of four years (two phases).
Charge management, which lowers charging costs for fleets and prevents straining the electrical grid, is critical to the successful deployment of medium- and heavy-duty battery-electric trucks (MHD BETs). This study introduces an energy demand and cost management framework that optimizes depot charging for MHD BETs by combining an energy consumption machine learning model and a linear program optimization model. The framework considers key factors impacting real-world MHD BET operations, including vehicle and charger configurations, duty cycles, use cases, geographic and climate conditions, operation schedules, and utilities’ time-of-use (TOU) rates and demand charges. The framework was applied to a hypothetical fleet of 100 MHD BETs in California under three different utilities for 365 days, with results compared to unmanaged charging. The optimized charging solution avoided more than 90% of on-peak charging, reduced fleet charging peak load by 64–75%, and lowered fleet energy variable costs by 54–64%. This study concluded that the proposed charge management framework significantly reduces energy costs and peak loads for MHD BET fleets while making recommendations for fleet electrification infrastructure planning and the design of utility TOU rates and demand charges.
Decarbonizing regional and long-haul freight is challenging due to the limitations of battery-electric commercial vehicles and infrastructure constraints. Hydrogen fuel cell medium- and heavy-duty vehicles (MHDVs) offer a viable alternative, aligning with the decarbonization goals of the Department of Energy and commercial entities. Historically, alternative fuels like compressed natural gas and liquefied propane gas have faced slow adoption due to barriers like infrastructure availability. To avoid similar issues, effective planning and deploying zero-emission hydrogen fueling infrastructure is crucial. This research develops deployment plans for affordable, accessible, and sustainable hydrogen refueling stations, supporting stakeholders in the decarbonized commercial vehicle freight system. It aims to benefit underserved and rural energy-stressed communities by improving air quality, reducing noise pollution, and enhancing energy resiliency. This research also provides a blueprint for replacing diesel in over-the-road Class 8 freight truck applications with hydrogen fueling solutions. The study focuses on the Texas Triangle Megaregion (I-45, I-35, and I-10), the I-10 corridor between San Antonio, TX, and Los Angeles, CA, and the I-5/CA-99 corridors between Los Angeles, CA, and San Francisco, CA. This area represents a significant portion of U.S. heavy-duty freight movement, carrying ~8.5% of the national freight volume. Using the OR-AGENT (Optimal Regional Architecture Generation for Efficient National Transport) modeling framework, the study conducts an advanced assessment of commercial vehicles, road and freight networks, and energy systems. The framework integrates data on freight mobility, traffic, weather, and energy pathways to deliver a region-specific, optimized vehicles powertrain architectures, infrastructure deployment solutions, operational logistics, and energy pathways. By considering all vehicle origin-destination pairs utilizing these corridors and all feasible fueling station location options, the framework's genetic algorithm identifies the minimum number and optimal locations of hydrogen refueling stations, ensuring no vehicle is stranded. It also determines fuel schedules and quantities at each station. A roadmap for station deployment based on multiple adoption trajectories ensures a strategic rollout of hydrogen refueling infrastructure.
The use of graphite fiber/resin matrix composite to effectively reinforce a standard steel truck frame rail is studied. A preliminary design was made and it was determined that the reinforcement weight could be reduced by a factor of 10 when compared to a steel reinforcement. A section of a 1/3 scale reinforced rail was fabricated to demonstrate low cost manufacturing techniques. The scale rail section was then tested and increased stiffness was confirmed. No evidence of composite fatigue was found after 500,000 cycles to a fiber stress of 34,000 psi. The test specimen failed in bending in a static test at a load 50 percent greater than that predicted for a non-reinforced rail.
Energy-saving concepts in truck design are emerging from developing new shapes with improved aerodynamic flow properties that can reduce air-drag coefficient of conventional tractor-trailers without requiring severe design changes or compromising load-carrying capability. Improvements are expected to decrease somewhat with increased wind velocities and would be affected by factors such as terrain, driving techniques, and mechanical condition.
A standard two-axle truck with a box-shaped cargo compartment was tested to determine whether significant reductions in aerodynamic drag could be obtained by modifying the front of the cargo compartment. The coastdown method was used to determine the total drag of the baseline vehicle, which had a square-cornered cargo box, and of several modified configurations. Test velocities ranged from 56.3 to 94.6 kilometers per hour (35 to 60 miles per hour). At 88.5 kilometers per hour (55 miles per hour), the aerodynamic drag reductions obtained with the modified configurations ranged from 8 to 30 percent.
Improvements in fuel economy for a broad spectrum of truck engines and waste heat utilization concepts are evaluated and compared. The engines considered are the diesel, spark ignition, gas turbine, and Stirling. The waste heat utilization concepts include preheating, regeneration, turbocharging, turbocompounding, and Rankine engine compounding. Predictions were based on fuel-air cycle analyses, computer simulation, and engine test data. The results reveal that diesel driving cycle performance can be increased by 20% through increased turbocharging, turbocompounding, and Rankine engine compounding. The Rankine engine compounding provides about three times as much improvement as turbocompounding but also costs about three times as much. Performance for either is approximately doubled if applied to an adiabatic diesel.
Recent results indicate that microwave radiometry has the potential for inferring the snow depth and water equivalent information from snowpacks. In order to assess this potential for determining the water equivalent of a snowpack, it is necessary to understand the microwave emission and scattering behavior of the snow at various wavelengths under carefully controlled conditions. Truck-mounted microwave instrumentation was used to study the microwave characteristics of the snowpack in the Colorado Rocky Mountain region during the winters of 1977 to 78 and 7978 to 79. The spectral signatures of C, X, K sub u, and K sub a band radiometers with dual polarization were used, together with measurements of snowpack density, temperature an ram profiles, liquid water content, and rough characterization of the crystal sizes. These data compared favorably with calculated results based on recent microscopic scattering models.