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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Simultaneous Reduction of NOx and Fuel Consumption for Off-Road Powertrains

Simultaneously reducing criteria pollutants and fuel consumption is important for clean air and improving vehicle total cost of ownership. The goal of this effort was focused on a 90% NOx reduction and 10% fuel savings for an off-road 407 kW diesel engine. The baseline was a production Fiat Powertrain 13L engine and aftertreatment system meeting 0.4 g/kW-hr NOx. The baseline system was quantified over the NRTC, RMC, new low load cycle and five field cycles. A next generation engine was built incorporating several fuel-efficient design features, including a higher compression ratio, increased fuel-rail pressure, low-friction piston rings, and a high-efficiency variable-geometry turbocharger. Cylinder deactivation and EGR pump technologies were added to this engine as well. The combination was optimized prior to adding advanced aftertreatment systems, showing the trade-off of engine out NOx and exhaust temperature. Two next-generation catalyst technologies were employed into a LO-SCR plus main SCR system, both with and without an electric heater upstream of the LO-SCR. These catalysts were hydrothermally aged to simulate significant field use. Dual SCR dosing with newly developed controls played a critical role in achieving the proper split between the upstream LO-SCR and the downstream main SCR. Adding a next generation mixer for the downstream SCR proved essential in obtaining the final results. The optimal configuration required adding an electric heater to elevate the exhaust temperature at the LO-SCR for early cycle NOx reduction. The final results showed a 94.8% NOx reduction and 15.7% fuel savings on the composite NRTC.

McCarthy, James [Eaton Corporation]↗
NASA NTRS2013

Real-Time and High-Fidelity Simulation Environment for Autonomous Ground Vehicle Dynamics

This paper reports on a collaborative project between U.S. Army TARDEC and Jet Propulsion Laboratory (JPL) to develop a unmanned ground vehicle (UGV) simulation model using the ROAMS vehicle modeling framework. Besides modeling the physical suspension of the vehicle, the sensing and navigation of the HMMWV vehicle are simulated. Using models of urban and off-road environments, the HMMWV simulation was tested in several ways, including navigation in an urban environment with obstacle avoidance and the performance of a lane change maneuver.

vehicle sensing↗
DOE OSTI2023

Decarbonization of Off-Road, Rail, Marine, and Aviation (DORMA) Program (2022 Annual Progress Report)

This document summarizes the progress of VTO's Decarbonization of Off-Road, Rail, Marine, and Aviation (DORMA) program during the fiscal year 2022. DORMA's R&D focus was on research, development, and demonstration of new propulsion and vehicle technologies that will reduce greenhouse gas emissions in difficult-to-decarbonize transportation applications.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2009

Stereo Vision Based Terrain Mapping for Off-Road Autonomous Navigation

Successful off-road autonomous navigation by an unmanned ground vehicle (UGV) requires reliable perception and representation of natural terrain. While perception algorithms are used to detect driving hazards, terrain mapping algorithms are used to represent the detected hazards in a world model a UGV can use to plan safe paths. There are two primary ways to detect driving hazards with perception sensors mounted to a UGV: binary obstacle detection and traversability cost analysis. Binary obstacle detectors label terrain as either traversable or non-traversable, whereas, traversability cost analysis assigns a cost to driving over a discrete patch of terrain. In uncluttered environments where the non-obstacle terrain is equally traversable, binary obstacle detection is sufficient. However, in cluttered environments, some form of traversability cost analysis is necessary. The Jet Propulsion Laboratory (JPL) has explored both approaches using stereo vision systems. A set of binary detectors has been implemented that detect positive obstacles, negative obstacles, tree trunks, tree lines, excessive slope, low overhangs, and water bodies. A compact terrain map is built from each frame of stereo images. The mapping algorithm labels cells that contain obstacles as no-go regions, and encodes terrain elevation, terrain classification, terrain roughness, traversability cost, and a confidence value. The single frame maps are merged into a world map where temporal filtering is applied. In previous papers, we have described our perception algorithms that perform binary obstacle detection. In this paper, we summarize the terrain mapping capabilities that JPL has implemented during several UGV programs over the last decade and discuss some challenges to building terrain maps with stereo range data.

passive perception↗
DOE OSTI2025

Fleet-Level Energy and Emissions Analysis of the US Off-Road Sector with VISION: Off-Road

In the United States (US), the off-road sector (i.e., agriculture, construction, etc.) contributes to approximately 10% of the country’s transportation greenhouse gas (GHG) emissions, similar to the aviation sector. The off-road sector is extremely diverse; as the EPA MOVES model classifies it into 11 sub-sectors, which include 85 different types of equipment. These equipment types have horsepower ranging from 1 to greater than 3000 and have very different utilization, which makes decarbonization a complex endeavor. To address this, Argonne’s on-road vehicle fleet model, VISION, has been expanded to the off-road sector. The GHG emission factors for several energy carriers (biofuels, electricity, and hydrogen) have been incorporated from Argonne’s GREET model for a sector-wide well-to-wheel (WTW) GHG emissions analysis of the present and future fleet. Several technology adoption and energy decarbonization scenarios were modeled to better understand the appropriate actions required to drive towards net-zero emissions of the off-road sector. Results show that WTW decarbonization up to 67% can be achieved from 2023 to 2050 in a business-as-usual scenario. But with aggressive sales increases of electric and hydrogen powertrains, WTW decarbonization up to 77% can be achieved, which can further increase to 85% if electricity production is aggressively decarbonized by 2035.

lifecycle↗
DOE OSTI2024

Propulsion Electrification Architecture Selection Process and Cost of Carbon Abatement Analysis for Heavy-Duty Off-Road Material Handler

The heavy-duty off-road industry continues to expand efforts to reduce fuel consumption and CO 2 e (carbon dioxide equivalent) emissions. Many manufacturers are pursuing electrification to decrease fuel consumption and emissions. Future policies will likely require electrification for CO2e savings, as seen in light-duty on-road vehicles. Electrified architectures vary widely in the heavy-duty off-road space, with parallel hybrids in some applications and series hybrids in others. The diverse applications for different types of equipment mean different electrified configurations are required. Companies must also determine the value in pursuing electrified architectures; this work analyzes a range of electrified architectures, from micro hybrids to parallel hybrids to series hybrids to a BEV, looking at the total cost, total CO 2 e, and cost per CO 2 e (cost of carbon abatement, or cost of carbon reduction) using data for the year 2021. This study is focused on a heavy-duty off-road material handler, the Pettibone Cary-Lift 204i. This machine’s specialty application, including events like unloading large oil pipes from a railcar, requires a unique electrified architecture that suits its specific needs. However, the results from this study may be extrapolated to similar machinery to inform fuel savings options across the heavy-duty off-road industry. In this study, a unique electrified architecture is determined for the Cary-Lift. This architecture is informed by multiple rounds of a Pugh matrix decision analysis to select a shortened list of desirable electrified architectures. The shortened list is modeled and simulated to determine CO 2 e, cost, and cost per CO 2 e. A final architecture is determined as a plug-in series hybrid that reduces fuel consumption by 65%, targeting the large fuel and CO 2 e savings that are likely to be required for the future of the heavy-duty off-road industry.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2009

Self-Supervised Learning of Terrain Traversability from Proprioceptive Sensors

Robust and reliable autonomous navigation in unstructured, off-road terrain is a critical element in making unmanned ground vehicles a reality. Existing approaches tend to rely on evaluating the traversability of terrain based on fixed parameters obtained via testing in specific environments. This results in a system that handles the terrain well that it trained in, but is unable to process terrain outside its test parameters. An adaptive system does not take the place of training, but supplements it. Whereas training imprints certain environments, an adaptive system would imprint terrain elements and the interactions amongst them, and allow the vehicle to build a map of local elements using proprioceptive sensors. Such sensors can include velocity, wheel slippage, bumper hits, and accelerometers. Data obtained by the sensors can be compared to observations from ranging sensors such as cameras and LADAR (laser detection and ranging) in order to adapt to any kind of terrain. In this way, it could sample its surroundings not only to create a map of clear space, but also of what kind of space it is and its composition. By having a set of building blocks consisting of terrain features, a vehicle can adapt to terrain that it has never seen before, and thus be robust to a changing environment. New observations could be added to its library, enabling it to infer terrain types that it wasn't trained on. This would be very useful in alien environments, where many of the physical features are known, but some are not. For example, a seemingly flat, hard plain could actually be soft sand, and the vehicle would sense the sand and avoid it automatically.

Bajracharya, Max↗
NASA NTRS2003

Negative obstacle detection by thermal signature

Detecting negative obstacles (ditches, potholes, and other depressions) is one of the most difficult problems in perception for autonomous, off-road navigation. Past work has largely relied on range imagery, because that is based on the geometry of the obstacle, is largely insensitive to illumination variables, and because there have not been other reliable alternatives. However, the visible aspect of negative obstacles shrinks rapidly with range, making them impossible to detect in time to avoid them at high speed. To relive this problem, we show that the interiors of negative obstacles generally remain warmer than the surrounding terrain throughout the night, making thermal signature a stable property for night-time negative obstacle detection. Experimental results to date have achieved detection distances 45% greater by using thermal signature than by using range data alone. Thermal signature is the first known observable with potential to reveal a deep negative obstacle without actually seeing far into it. Modeling solar illumination has potential to extend the usefulness of thermal signature through daylight hours.

unmanned ground vehicles (UGV's)↗
NASA NTRS2003

Obstacle detection in foliage with ladar and radar

Autonomous off-road navigation is central to several important applications of unmanned ground vehicles. This requires the ability to detect obstacles in vegetation. We examine the prospects for doing so with scanning ladar and with a linear array of 2.2 GHz micro-impulse radar transceivers. For ladar, we summarize our work to date on algorithms for detecting obstacles in tall grass with single-axis ladar, then present a simple probabilistic model of the distance into tall grass that ladar-based obstacle detection is possible.

ladar↗
NASA NTRS2005

Learning for Autonomous Navigation

Robotic ground vehicles for outdoor applications have achieved some remarkable successes, notably in autonomous highway following (Dickmanns, 1987), planetary exploration (1), and off-road navigation on Earth (1). Nevertheless, major challenges remain to enable reliable, high-speed, autonomous navigation in a wide variety of complex, off-road terrain. 3-D perception of terrain geometry with imaging range sensors is the mainstay of off-road driving systems. However, the stopping distance at high speed exceeds the effective lookahead distance of existing range sensors. Prospects for extending the range of 3-D sensors is strongly limited by sensor physics, eye safety of lasers, and related issues. Range sensor limitations also allow vehicles to enter large cul-de-sacs even at low speed, leading to long detours. Moreover, sensing only terrain geometry fails to reveal mechanical properties of terrain that are critical to assessing its traversability, such as potential for slippage, sinkage, and the degree of compliance of potential obstacles. Rovers in the Mars Exploration Rover (MER) mission have got stuck in sand dunes and experienced significant downhill slippage in the vicinity of large rock hazards. Earth-based off-road robots today have very limited ability to discriminate traversable vegetation from non-traversable vegetation or rough ground. It is impossible today to preprogram a system with knowledge of these properties for all types of terrain and weather conditions that might be encountered.

mixture of Gaussians↗
NASA NTRS1990

Evaluation of off-road terrain with static stereo and monoscopic displays

The National Aeronautics and Space Administration is currently funding research into the design of a Mars rover vehicle. This unmanned rover will be used to explore a number of scientific and geologic sites on the Martian surface. Since the rover can not be driven from Earth in real-time, due to lengthy communication time delays, a locomotion strategy that optimizes vehicle range and minimizes potential risk must be developed. In order to assess the degree of on-board artificial intelligence (AI) required for a rover to carry out its' mission, researchers conducted an experiment to define a no AI baseline. In the experiment 24 subjects, divided into stereo and monoscopic groups, were shown video snapshots of four terrain scenes. The subjects' task was to choose a suitable path for the vehicle through each of the four scenes. Paths were scored based on distance travelled and hazard avoidance. Study results are presented with respect to: (1) risk versus range; (2) stereo versus monocular video; (3) vehicle camera height; and (4) camera field-of-view.

Yorchak, John P.↗
DOE OSTI2025

Refining Control, Charging, and Battery Chemistry for CO 2 e Savings in Heavy-Duty Off-Road Plug-In Series Hybrid

With current and future regulations continuing to drive reductions in carbon dioxide equivalent (CO 2 e) emissions in the on-road industry, the off-road industry is also likely to be regulated for fuel and CO 2 e savings. This work focuses on converting a heavy-duty off-road material handler from a conventional diesel powertrain to a plug-in series hybrid, achieving a 49% fuel reduction and 29% CO 2 e reduction via simulation. Control strategies were refined for energy savings, including a regenerative braking strategy to increase regenerative braking and a load-following hydraulic strategy to decrease electrical energy consumption. The load-following hydraulic control shuts off the hydraulic electric machine when it is not needed—an approach not previously seen in a load-sensing, pressure-compensated system. Furthermore, these strategies achieved a 24.1% fuel savings, resulting in total savings of 61% in fuel and 41% in CO 2 e in the plug-in series compared to the conventional machine. Beyond control strategies, this study evaluated battery chemistry and charging strategy refinements for total cost of ownership (TCO) and lifetime CO 2 e. LFP batteries emerged as the most cost-effective and least emitting due to their longer lifespan, which reduced replacement frequency. Charging comparisons showed that Level 2 charging (L2C) typically resulted in lower TCO but higher lifetime CO 2 e than DC fast charging (DCFC). DCFC costs were heavily influenced by local demand charges, and DCFC emissions were heavily influenced by local grid emissions.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1989

Experiences in teleoperation of land vehicles

Teleoperation of land vehicles allows the removal of the operator from the vehicle to a remote location. This can greatly increase operator safety and comfort in applications such as security patrol or military combat. The cost includes system complexity and reduced system performance. All feedback on vehicle performance and on environmental conditions must pass through sensors, a communications channel, and displays. In particular, this requires vision to be transmitted by close-circuit television with a consequent degradation of information content. Vehicular teleoperation, as a result, places severe demands on the operator. Teleoperated land vehicles have been built and tested by many organizations, including Sandia National Laboratories (SNL). The SNL fleet presently includes eight vehicles of varying capability. These vehicles have been operated using different types of controls, displays, and visual systems. Experimentation studying the effects of vision system characteristics on off-road, remote driving was performed for conditions of fixed camera versus steering-coupled camera and of color versus black and white video display. Additionally, much experience was gained through system demonstrations and hardware development trials. The preliminary experimental findings and the results of the accumulated operational experience are discussed.

Mcgovern, Douglas E.↗
DOE OSTI2024

Decarbonization of Off-Road, Rail, Marine, and Aviation (DORMA) Program (2023 Annual Progress Report)

This document summarizes the progress of VTO's Decarbonization of Off-Road, Rail, Marine, and Aviation (DORMA) program during the fiscal year 2023. DORMA's R&D focus was on research, development, and demonstration of new propulsion and vehicle technologies that will reduce greenhouse gas emissions in difficult-to-decarbonize transportation applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Silicon Carbide Inverter for Off-Road Heavy-Duty Applications: The Importance of Thermal and Thermomechanical Design in Power Electronics Packaging

Electrification of drivetrain systems is now seen as a major opportunity by the transportation industry across the globe to reduce the greenhouse gas emissions and revolutionize the travel patterns of millions of people. The cumulative number of plug-in hybrid and battery electric vehicles (EVs) sold in the United States has now surpassed 2 million in 2021, according to the International Energy Agency. The introduction of EVs in different classes of passenger vehicles and the continued drop in prices spurred by government incentives have attracted the attention of consumers despite certain barriers, such as higher initial cost and range anxiety. In the United States, the EV market share is now growing at an exponential pace, with the domestic automakers allocating a lion's share of new and future car sales to EVs. Additionally, different market studies project decreasing cost and rising sales of medium- and heavy-duty electric trucks. Although electrification initiatives are strongly pursued in the on-road passenger vehicle market, off-highway sectors, such as construction, mining, and agriculture, are also gradually implementing electric drivetrain technologies in their machineries. As EVs grow in popularity on a global scale, innovative drivetrain technologies must meet the increasing energy demand by significantly increasing system efficiency.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Precise Motion Control of Hybrid Hydraulic Electric Architecture (HHEA)

Off-highway heavy-duty vehicles have been long-standing users of hydraulic systems for power transmission and control. However, traditional hydraulic systems suffer from significant energy losses which lead to increased operating costs and a larger carbon footprint due to higher CO2 emissions. Improving the efficiency of these mobile machines is crucial not only for reducing their environmental impact but also for saving billions of dollars in operating costs. Currently, the state-of-the-art Load Sensing Architecture uses throttling valves for control, which significantly reduces its efficiency and does not recuperate energy from over-running loads. Researchers have developed several architectures such as Common Pressure Rail systems, Displacement Control, STEAM, and Electrohydraulic Architecture to improve the efficiency of off-road mobile machines. However, each of these architectures has its drawbacks. To increase system efficiency and take advantage of electrification benefits, our research group has developed a novel Hybrid Hydraulic-Electric Architecture (HHEA). The HHEA can significantly improve efficiency, decrease the size of electrical components, and maintain control performance. This new architecture has the potential to revolutionize the off-highway mobile machine industry and lead to a more sustainable future. The HHEA uses a set of common pressure rails to provide the majority of power to the actuators via power-dense hydraulics and uses electric motors for precise control and power modulation. In the context of off-road mobile machines, energy savings are undoubtedly important but it is equally important to consider the machines’ ability to perform tasks with precision and accuracy according to given commands. Therefore, precise motion control is of utmost importance to maintain the utility of Hybrid Hydraulic-Electric Architecture (HHEA). The HHEA presents a unique challenge to motion control due to the discrete pressure changes that occur when the system switches between selected pressure rails. These changes are made to minimize system inefficiencies or to keep the system within the torque capability of the electric motor. Hence, it is important to solve the motion control challenges for HHEA. This thesis aims at developing an effective motion control strategy for HHEA. The dissertation presents a two-tiered control strategy for HHEA, comprising a high-level and a low-level controller. The primary responsibility of the high- level controller is to optimize energy efficiency by making informed pressure rail selections. On the other hand, the low-level controller is focused on achieving precise motion control of the HHEA, which is crucial for realizing the desired reference trajectories. To achieve this, the low-level controller utilizes a passivity-based backstepping integral controller as the nominal control, which handles the motion control between two pressure rail switches. Additionally, a separate least norm controller is utilized as a transition controller to manage motion control during pressure rail transitions. The effectiveness of the combined control strategy is demonstrated through experiments conducted on two hardware-in-the-loop testbeds. Furthermore, the HHEA is installed on the boom and stick actuators of a backhoe arm to build a Human-in-the-Loop system that a human operator can control. A real-time rail switching algorithm is developed to determine pressure rail switching based on present duty cycle information from the operator. Modifications have been made to the human-machine interface to achieve more intuitive control. Modifications include performing control in the task-oriented coordinates, incorporating pressure feedback to enhance control with physical interaction, and using velocity field control to simplify multi-degree-of-freedom tasks and to enable novice operators to perform them with reduced risk, improved efficiency, and productivity. The research in this dissertation makes significant contributions to the field of off-road mobile machine control, providing a novel and effective control strategy for the HHEA, and demonstrating the potential for simplified machine operation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2004

Granular Materials and Risks in ISRU

Working with soil, sand, powders, ores, cement and sintered bricks, excavating, grading construction sites, driving off-road, transporting granules in chutes and pipes, sifting gravel, separating solids from gases, and using hoppers are so routine that it seems straightforward to execute these operations on the Moon and Mars as we do on Earth. We discuss how little these processes are understood and point out the nature of trial-and-error practices that are used in today s massive over-design. Nevertheless, such designs have a high failure rate. Implementation and extensive incremental scaling up of industrial processes are routine because of the inadequate predictive tools for design. We present a number of pragmatic scenarios where granular materials play a role, the risks involved, what some of the basic issues are, and what understanding is needed to greatly reduce the risks. This talk will focus on a particular class of granular flow issues, those that pertain to dense materials, their physics, and the failure problems associated with them. In particular, key issues where basic predictability is lacking include stability of soils for the support of vehicles and facilities, ability to control the flow of dense materials (jamming and flooding/unjamming at the wrong time), the ability to predict stress profiles (hence create reliable designs) for containers such as bunkers or silos. In particular, stress fluctuations, which are not accounted for in standard granular design models, can be very large as granular materials flows, and one result is frequent catastrophic failure of granular devices.

Behringer, Robert P.↗