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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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Research and development at the National Renewable Energy Laboratory supports the U.S. Department of Energy's Vehicle Technologies Office in advancing efficient and sustainable transportation technologies that will improve energy efficiency, fuel economy, and mobility energy productivity, and will enable America to achieve a resilient, secure, clean, and affordable transportation future.
Research and development at the National Renewable Energy Laboratory supports the U.S. Department of Energy's Vehicle Technologies Office in advancing efficient and sustainable transportation technologies that will improve energy efficiency, fuel economy, and mobility energy productivity, and will enable America to achieve a resilient, secure, clean, and affordable transportation future.
Research and development at the National Renewable Energy Laboratory supports the U.S. Department of Energy's Vehicle Technologies Office in advancing efficient and sustainable transportation technologies that will improve energy efficiency, fuel economy, and mobility energy productivity, and will enable America to achieve a resilient, secure, clean, and affordable transportation future.
Federal regulations are driving the adoption of electrification technologies to reduce carbon dioxide equivalent (CO2e) emissions, a metric that quantifies the global warming potential of various greenhouse gases in terms of carbon dioxide (CO2). Although no specific CO2 regulations exist for heavy-duty off-road machines, future reductions are likely, given stricter emissions standards for on-road vehicles. The heavy-duty off-road sector offers significant fuel-saving potential, as its focus has traditionally been on reliability and performance rather than fuel efficiency. This dissertation examines fuel and CO2e savings opportunities on a heavy-duty off-road material handler, the Pettibone Cary-Lift 204i, from stock configuration to simple modifications to a complete teardown and reconfiguration of the machine with a plug-in series hybrid architecture using electrified hydraulics. The study begins by modeling the baseline machine’s fuel and energy consumption, calibrating with experimental data from custom operating cycles. An energy analysis identifies key areas for fuel savings. Two simple powertrain modifications result in a combined 16.2% fuel savings. Next, a Pugh-style analysis narrows a list of electrified architectures, leading to high-fidelity models that evaluate total lifetime CO2e and costs. Higher electrification levels reduce CO2e emissions but increase costs, and electricity grid emissions significantly impact CO2e for plug-in architectures. A plug-in series hybrid is chosen for the project. In its base control form, 49% fuel and 29% CO2e savings are expected from the plug-in series hybrid compared to the baseline machine. Further savings are pursued through regenerative braking (6.3%) and load-following hydraulic control (17.8%), totaling 24.1% fuel savings, and leading to a total of 61% fuel and 41% CO2e savings compared to the baseline. Battery chemistries and charging strategies are also analyzed for cost and CO2e impacts, finding LFP batteries as superior due to longevity, and overnight level 2 charging usually at a lower cost but resulting in higher emissions than opportunity DC fast-charging (DCFC). DCFC emissions are highly dependent on grid emissions, and DCFC cost is highly dependent on grid demand charges. Finally, artificial intelligence is applied to operating cycle recognition. Neural network accuracy ranges from 81% to 99%, with applications to worksite efficiency and safety improvements.
Woody aboveground biomass (AGB) including short-rotation Populus is used as a feedstock for renewable and carbon-neutral bioenergy. While woody AGB can be estimated with allometric equations requiring labor-intensive field data, remote sensing technologies like mobile terrestrial light detection and ranging (LiDAR) can estimate woody AGB quickly and accurately. Therefore, the goals of this study were to develop a model to predict woody AGB of 2-year-old Populus spp. from three taxa (P. deltoides, P. deltoides × P. maximowiczii and P. deltoides × P. trichocarpa) using allometric (height and diameter at breast height (DBH)) or LiDAR-derived metrics from a mobile terrestrial (backpack) system. Likewise, we sought to compare LiDAR-estimated tree height and DBH with field-measured values. We found that a taxa-specific model containing LiDAR-measured tree height, crown volume, and taxa interactions with the height of the 10 th percentile, and the density of the lowest interval (density metric 0) explained 84 % of the variation in woody AGB with a root mean square error (RMSE) of 28.7 % and performed slightly better than the allometric model. The best model excluding taxa had a slightly higher RMSE but lower bias than the allometric model. LiDAR-derived tree heights were highly correlated with field-measured heights, but DBH could not be estimated accurately. Therefore, terrestrial mobile LiDAR systems can accurately estimate woody AGB and tree height of Populus in short rotation systems to aid in the fast and efficient quantification of woody bioenergy production and renewable energy resources.
In the face of climate change, developing countries are innovating to rapidly lower emissions and enable resilient, secure, and just energy transitions. Transportation accounts for roughly one- fifth of global carbon dioxide emissions, making mobility a key focus area for the international community, and is also critical in supporting social equity and human well-being. At the intersection of economic development, mobility, and clean energy, sustainable aviation systems can help simultaneously deliver key climate and clean energy goals to support advanced and decarbonized energy systems.
This project explores the application of AI-driven methods to optimize public transit operations for the Chattanooga Area Regional Transportation Authority (CARTA). By leveraging data analytics, machine learning, and predictive modeling, the initiative seeks to enhance system efficiency, improve rider experience, and support sustainability goals. This research, supported by the National Science Foundation and the U.S. Department of Energy, integrates real-time transit data with advanced computational tools to inform decision-making, optimize routes, and balance operational demands. The work exemplifies a forward-looking model for mid-sized cities aiming to modernize mobility systems through intelligent technology integration.
Cities, public transit agencies, and new private ride hailing services seek to understand emerging traveler dynamics, the shifting demographics of urban travelers,and new energy-efficient mobility opportunities. This includes exploring how new infrastructure investments, public and private mobility services, and smart-phone mobility apps are reshaping behaviors, demands (e.g. mobility-on-demand services), travel experiences and energy-efficient urban travel preferences. Currently, cities and metropolitan regions are providing and experimenting with many new mobility options, technologies, and personalized information services at the intersection of urban mobility, energy, and infrastructure systems (e.g., new commuter rail). To date, technology alone has not been able to crack the nut of 'creating faster trip times, less congestion, safer streets, and cleaner air for its citizens through fewer cars on the road'. This paper focuses on this gap by offering new concepts and potential for integrated approaches. Accommodating more vehicles miles traveled in cities, without increases in person miles traveled (PMT), could be costly, generating: 1) tremendous demands for new infrastructure, land, road space, materials, and energy; 2) higher traffic fatality risks; and 3) worsening air quality. Therefore, this study focuses on reducing single occupancy vehicle use by enhancing integrated mobility, helping transit and ridehailing increase occupancy in ways that also reduce energy use, and improve quality of life for urban travelers and communities. This study focuses on a survey of urban travelers in Denver, as a representative case study for metropolitan regions experiencing rapid growth, ageing populations, increased urban sprawl, traffic-related delays, and inefficient energy use per PMT.
When access to transit solutions is restricted to parts of a community, a division occurs what leads to disadvantaged socioeconomic conditions that only grows over time. This restriction causes difficulties in accessing needed resources such as medical facilities, employment, healthy food, education, healthy food, and more. To solve this restricted access, Labyrinth Smart Mobility and partners are developing a virtual community train that encourages scalability, energy efficiency, and equitability to all regardless of individual circumstances and limitation. The implementation of virtual community trains allows access to resources previously out of reach. The virtual community train enables one operator to drive a lead vehicle. This lead vehicle would then provide direction to a follow vehicle through vehicle-to-vehicle communication. Labyrinth Smart Mobility in conjunction with partners used various methods and resources to prove market feasibility, technical feasibility, economic need and viability, and more as well as design system requirements and architecture emphasis efficiency and safety. In doing so, Labyrinth Smart Mobility and partners will continue these advancements to further develop the virtual community train.
This project investigated and demonstrated the use of Wi-Fi Location-Based Services (LBS) to perform occupancy sensing in commercial buildings. Wi-Fi LBS can be used to detect the presence of Wi-Fi enabled mobile devices and laptops that accompany occupants as they move through the building. These signals can be used to determine occupant presence, head count, and location. When integrated with the building automation system, this emerging technology approach can be used to manage other connected systems such as lighting and HVAC to reduce energy usage in the building and improve occupant comfort. An open source location detection algorithm was developed, which uses data collected from three or more Wi-Fi access points to determine the presence and estimate the location of mobile devices and laptops. Access points can detect Wi-Fi enabled devices even if they are not connected to the existing Wi-Fi network. Building occupancy is determined based on the presence, location, and movement of these devices through the space. From lab and small-scale in-situ testing, the Location Detection Algorithm (LDA) was found to be accurate to within 10 feet and could be further refined by tuning the algorithm for the specific space characteristics such as layout and obstructions (walls, furniture, etc.). An open source method to integrate the occupancy data with existing building automations systems was investigated. The Wi-Fi occupancy sensing approach was then demonstrated and validated at commercial buildings located in Saint Paul, MN; Madison, WI; New York City; and Fort Worth, TX.
The Smart Cities movement is about a decade old, energized by the USDOT Smart Cities grant that sparked cities to dream big. Now, 10 years later, and multitudes of conferences and papers later, the biggest question regarding Smart Cities is whether any progress has been made? Smart Cities forums are typically led by the technology sector, either telecommunications, internet access, or most recently artificial intelligence (AI) concerns. Each of these areas have grown and found new applications in the context of Smart Cities, however, these technology applications are just tools toward ends. Do digital communications and assistance enable or foster improved access to real, tactile, non-virtual goods and services such as transportation, food, housing, trash removal, health care, education, social interaction? These and other topics are not 'big tech', but are each critical to quality of life outcomes and are day-to-day necessities, comprising many of the touch points through which citizens interact with municipal agencies. Regarding Smart Cities, relationships have not been fully refined as to how best to merge the emerging generation of technology and related trends with more mundane but essential requirements of tactile services. How should municipal managers and operators bring the range of new technology tools (e.g. on-demand transit, automated vehicles, digital connectivity) to bear to improve functions serving real- physical- non-virtual needs? How do we make cities more efficient in providing access to services and opportunities for improved quality of life? This paper examines the current Smart City movement, emerging trends, and the need to put metrics with respect to the delivery, access, and provision of real-life needs. Using a few case studies of past, present, and projected future scenarios, this paper attempts to bring Smart Cities into the real cities domain, perhaps pushing the moniker away from 'Smart' to 'Efficient' - something that can be measured, counted, and assessed. Through the consideration of the next stage of the evolution of cities from 'Smart' to 'Efficient', the combined efforts and experience of multiple researchers at the NREL and their network of collaborators highlights the need for updates metrics that can be used to start guiding efforts toward productive initiatives to improve quality of life.
We report how to improve charge transport properties and reduce energy losses are the two main challenges of organic solar cells (OSCs). Herein, a new device fabrication strategy that combines the layer-by-layer (LBL) method and a quaternary active layer is demonstrated to realize highly efficient photovoltaic performance of the OSCs based on PM6:PM7/Y6:O1-2F. The introduction of the second donor, PM7,forms a cascade energy-level alignment with PM6 that facilitates effective charge transfer. The second acceptor, O1-2F, which is poorly miscible with the donors, helps to reduce the miscibility between the donors and acceptors, and thus delivers a more desirable vertical phase separation of the active layer. Furthermore, the alloy acceptor of O1-2F and Y6 can optimize the horizontal and vertical morphology of the active layer, forming an effective charge separation and continuous charge transport channel. As a result, the PM6:PM7/Y6:O1-2F system shows a distinct increase in charge mobility and a reduced nonradiative lossΔVnrof 0.231 V compared to the PM6/Y6 system, achieving a high-power conversion efficiency of18.23%. The results indicate that the quaternary LBL OSCs are promising for future large-scale and industrial manufacture of the OSCs.
The Automated Mobility Platforms (AMPs) concept is envisioned as an on-demand network of automated lightweight electric vehicles serving mobility needs in a large facility or district-scale setting. Supplanting the function of legacy automated people mover systems, AMPs is scalable to meet the needs of moving people in large interior spaces with destinations that are too distant for walking and in which larger motor vehicles would be unfeasible or undesirable. The AMPs system is designed for automated management and operation, extending to how and where the vehicles are sent to serve mobility needs, as organized through a supervisory management system. AMPs vehicles are requested by users via smartphone app or buttons located on interactive infrastructure elements. AMPs vehicles function similarly to elevators, though in the horizontal plane, wherein users summon vehicles, and are moved point-to-point, with user requested destinations set through intuitive interfaces. The concept targets extensibility, manageability, and performance improvements over legacy technologies, with accessibility and safety features integrally designed into the system.
Intersections are known to be traffic bottlenecks where a significant amount of energy consumption could be caused due to deceleration/acceleration in the presence of red signals. With an increased level of connectivity and automation of intelligent transportation systems, connected autonomous vehicles (CAVs) are expected to be able to proactively adjust their driving strategies subject to constraints imposed by the predicted future traffic. As a result, many potential benefits can be achieved, such as improved energy efficiency, enhanced traffic safety, among many others. Notably, the way CAVs are controlled affects the following legacy vehicles (LVs) due to complex traffic dynamics. Here, we are particularly interested in studying the energy and mobility impact of CAVs with an improved traffic prediction method on mixed vehicle platoons at various market penetration rates. Leveraging traffic prediction, CAVs are controlled with co-optimization of their speed and gear position. Specifically, a traffic prediction framework in a rolling horizon fashion is employed based upon a modified Payne–Whitham (PW) model capable of handling mixed traffic consisting of CAVs and LVs. The prediction error of the modified PW model is reduced by 53.62% compared to that of the standard PW model under test scenarios. According to the predicted traffic conditions, speed and gear position of CAVs are co-optimized with the primary goal of minimizing energy consumption when driving on a signalized arterial. The energy benefits achieved by CAVs and the impact of CAVs on LVs behind are studied comprehensively for mixed vehicle platoons. The lead LV follows a real-world speed profile collected on TH-55 in Minnesota. Numerical results show that energy benefits achieved by the vehicle platoon range from 2% to 16%, and a 1% to 5% reduction in travel time for LVs behind CAVs is also observed, at different penetration rates of CAVs in various traffic scenarios. Furthermore, it is observed that CAVs using the proposed eco-driving approach appear to have a positive impact on the LVs behind in terms of energy consumption, regardless of the driving styles of the LVs ahead.
Designed for mobility and flexibility, NREL's Consolidated Utility Base Energy (CUBE) is a hybrid power generation system that converts energy from different sources - solar panels, batteries, diesel generators, and host grid power - into tactical electricity, improving the efficiency and reliability of power for the military's forward operating bases. By reducing dependence on diesel generators, the CUBE also helps reduce the number of soldiers, sailors, airmen and marines shipping fuel across dangerous territory.
With the recent electrification trend in the fluid power area, more research has been incentivized to the proposal of cost-effective and energy-efficient solutions for hydraulic systems. Hence, electro-hydraulic actuator (EHA) architectures receive increasing attention. The paper proposes a novel open-circuit EHA architecture, with the goal to obtain a cost-effective mobile solution while maximizing the overall system efficiency. The proposed EHA is capable of meeting or exceeding traditional off-road machine performance, therefore enabling further electrification of off-road machines. Four-quadrant functionality, covering the full speed range, is achieved by a combination of a variable electro-hydraulic drive and valves with different functions and is validated by numerical as well as experimental methods. Here, a simulation model based on the Amesim environment and a dedicated test setup was developed to verify the performance. The good match between simulation and experimental results confirms the potential of the formulation approach of the proposed EHA for applications with different duty cycles and power levels.
Nearly omnipresent in many cities of all sizes across the United States, micromobility vehicles-e-scooters, manual bicycles, e-bicycles, and larger seated electric scooters-are notably missing from SMART Mobility research. This project aims to expand the spectrum of modes currently being researched within SMART Mobility by exploring micromobility as an important tool toward meeting energy-efficient mobility goals. It expands on findings from SMART Mobility 1.0 that revealed preferences to reduce transportation-related expenses through use of a network of mobility-as-a-service (MaaS) and other shared mobility options, and builds on findings from a 2019 Vehicle Technology Analysis Program (VTAP) funded micromobility project conducted by our team. We will explore multiple facets of micromobility, including behavior and decision-making, the integration of micromobility within transportation infrastructure, energy estimates, and operations. Guiding research questions include: (1) What are the potential energy savings from low, medium, and high market penetration of micromobility (in passenger, multimodal, and freight domains)? (2) Which scenarios for micromobility use and related enablement of increased public transit use should be modeled/considered in the SMART 2.0 Workflow? (3) To what degree can micromobility supplement/complement transit system operations? (4) What are people's preferences towards micromobility? How do preferences vary across various sociodemographic segments? How can this knowledge inform operations? (5) What are optimal strategies to attain high user adoption and shift users toward more energy-efficient mode choices in terms of micromobility operation? How do these strategies affect energy savings, person-miles traveled, lifecycle energy use, and adoption rates? These questions will be addressed through applied research in five project emphasis areas: (1) Energy estimates of micromobility for Workflow scenarios: Expand and refine previous micromobility work to augment the Workflow approaches to modeling urban travel. (2) Multimodal connection with transit: Utilizing Mobility-Energy Productivity (MEP) tools to evaluate multimodal travel patterns enabled by micromobility, including assessing how to reduce barriers of inequity of access to mobility options and destinations. (3) Mode choice, induced demand, and infrastructure: Understanding the mode shift induced through micromobility to inform energy impact analysis. (4) Energy optimization of micromobility operations: Identification of micromobility operations parameters and development of operations scenarios to better understand present-day micromobility operations for integration into the Workflow, in partnership with BEAM and POLARIS modeling teams. (5) Micro-freight: Characterize the current state of micro-freight activities, including energy effects and geospatial analyses, to inform Workflow.
The ultrafast dynamics of photon-to-charge conversion in an organic light-harvesting system is studied by femtosecond time-resolved X-ray photoemission spectroscopy (TR-XPS) at the free-electron laser FLASH. This novel experimental technique provides site-specific information about charge separation and enables the monitoring of free charge carrier generation dynamics on their natural timescale, here applied to the model donor-acceptor system CuPc:C 60 . A previously unobserved channel for exciton dissociation into mobile charge carriers is identified, providing the first direct, real-time characterization of the timescale and efficiency of charge generation from low-energy charge-transfer states in an organic heterojunction. The findings give strong support to the emerging realization that charge separation even from energetically disfavored excitonic states is contributing significantly, indicating new options for light harvesting in organic heterojunctions.