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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 145 records · Page 8
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DOE OSTI2026

Measuring Flexibility through Reduction Potential

As electrical loads, electric vehicles (EVs) are distinguished in many cases by their substantial flexibility. Harnessing this flexibility, however, requires a precise characterization of its timing and degree. Current approaches to flexibility characterization tend to be limited either by the complexity of their representation or their inability to capture aggregate flexibility across multiple vehicles. In this paper, we present the reduction potential matrix, a novel metric capturing fleet-level EV load flexibility while being both straightforward to calculate and intuitive to interpret. We use the metric to quantify flexibility for two operationally distinct commercial groups: freight vehicles and transit buses. While we find that both vehicle groups have substantial flexibility, its timing, magnitude, and duration varies across the groups. We describe how this variability translates into variation in the each group's role as a grid resource, and conclude with a discussion of how system planners, fleet operators, and other stakeholders can use the novel metric to cultivate symbiosis between vehicles and the grid.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

EVs-at-RISC: A Secure and Resilient Interoperable SCM Control System Architecture for Electric Vehicle’s-at-Scale (Final Technical Report)

The EVs-at-RISC project was a five-year research, development, and demonstration initiative to create foundational tools for utility-scale fleet aggregation and Smart Charge Management (SCM) of Electric Vehicles (EV), Electric Vehicle Charging Infrastructure (EVCI), and related Distributed Energy Resources (DER). Rather than seeking to develop and demonstrate highly perfected SCM algorithms and control strategies, this project instead focused on creating foundational software solutions that enable unprecedented digital interoperability across the communications technologies and vendor platforms used to manage EV , EVCI, and DER, as well as existing energy management infrastructure operated by utilities, grid operators, and aggregators. This project then extends these novel interoperability capabilities to develop and deploy powerful middleware abstractions across grid edge networks and EVCI/DER fleet aggregations incorporating modern software tools and best practices, such as CI/CD, to bring the immense capabilities of infrastructure-as-code and policy-as-code to modern grid edge network environments. This addresses the foremost systemic issues preventing realization of any net operational benefits from scaled deployment of behind-the-meter EV, EVCI, and DER assets in electric power grids and markets today. The results of this approach and project unlock massive potential for new SCM capabilities to be easily prototyped, evaluated, and deployed at-scale within the existing grid edge network infrastructure and EVCI/DER technology ecosystem. The EVs-at-RISC project achieves this by extending Open Field Message Bus (OpenFMB), a conceptual model for digital interoperability and distributed intelligence in traditional front-of-meter utility SCADA networks, validating our hypothesis that OpenFMB could be similarly used to solve systemic digital interoperability issues in behind-the-meter environments and unlock real-world utility-scale SCM capabilities without requiring any new proprietary vendor solutions or significant infrastructure reconfiguration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Advanced Fuels Campaign Time-at-Temperature Irradiation Campaign and Post-Irradiation Examination Plan

With increasing per capita energy consumption and rapidly growing energy consumption predicted for data centers, the US faces challenges in meeting projected energy demands. The current administration is attempting to accelerate the deployment of new nuclear construction by providing significant investments in advanced nuclear and by modernizing regulatory approaches for the licensing new reactors. However, enabling extended power uprates (EPUs) for the current nuclear reactor fleet would provide a shorter-term solution to better calibrate near-term energy generation capacity to the ever-growing energy demands of the modern era. One pathway to achieve power uprates is to increase the operational window by reassessing fuel safety limits around anticipated transients. Existing light-water reactors (LWRs) utilize a targeted operational window with well-defined operational efficiencies, yet this window is also often bound by conservative safety limits that result in suboptimal operational performance. For example, the operation of the existing fleet is highly constrained by safeguards to operation related to anticipated operational occurrences (AOOs), which are moderate-frequency transients expected with a frequency greater than 0.01 per reactor-year. These AOOs include temperature excursions where the critical heat flux for the system is exceeded, resulting in a departure from nucleate boiling (DNB) at the cladding surface. This DNB event is a thermohydraulic condition that, with current conservative fuel safety limits, results in the fuel rods being classified as “failed,” meaning that the cladding may not be returned to service.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Advanced Fuels Campaign Time-at-Temperature Irradiation Campaign and Post-Irradiation Examination Plan

With increasing per capita energy consumption and rapidly growing energy consumption predicted for data centers, the US faces challenges in meeting projected energy demands. The current administration is attempting to accelerate the deployment of new nuclear construction by providing significant investments in advanced nuclear and by modernizing regulatory approaches for the licensing new reactors. However, enabling extended power uprates (EPUs) for the current nuclear reactor fleet would provide a shorter-term solution to better calibrate near-term energy generation capacity to the ever-growing energy demands of the modern era. One pathway to achieve power uprates is to increase the operational window by reassessing fuel safety limits around anticipated transients. Existing light-water reactors (LWRs) utilize a targeted operational window with well-defined operational efficiencies, yet this window is also often bound by conservative safety limits that result in suboptimal operational performance. For example, the operation of the existing fleet is highly constrained by safeguards to operation related to anticipated operational occurrences (AOOs), which are moderate-frequency transients expected with a frequency greater than 0.01 per reactor-year. These AOOs include temperature excursions where the critical heat flux for the system is exceeded, resulting in a departure from nucleate boiling (DNB) at the cladding surface. This DNB event is a thermohydraulic condition that, with current conservative fuel safety limits, results in the fuel rods being classified as “failed,” meaning that the cladding may not be returned to service.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
DOE OSTI2025

CMIP6-based Multi-model Hydropower Projection over the Conterminous US, Version 1.1

This dataset presents a suite of hydropower projections for the conterminous United States (CONUS), derived from multiple downscaled and bias-corrected Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The CMIP6 GCMs are downscaled using either statistical (DBCCA) or dynamical (RegCM) approaches, based on two meteorological reference datasets (Daymet and Livneh). The resulting downscaled precipitation, temperature, and wind speed data are then used to drive two calibrated hydrologic models (VIC and PRMS), enabling simulations of projected future hydrologic responses across the CONUS. Simulated total runoff is subsequently employed to drive two hydropower models (WMP, now implemented as mosartwmpy-power, and WRES) to evaluate how climate change may affect future hydropower production for both federal and non-federal hydropower fleets. This dataset was developed to support the SECURE Water Act Section 9505 Assessment for the U.S. Department of Energy (DOE) Water Power Technologies Office (WPTO). For further details, see Broman et al. (2024), Thurber et al. (2024), Kao et al. (2022), and Zhou et al. (2023).

Voisin, Nathalie [Pacific Northwest National Labor↗
DOE OSTI2026

Design of a SMART Valve Testbed for Nuclear Thermal Dispatch

By the year 2050, the United States aims to achieve net-zero carbon emissions. To achieve this target, the licensing of the Light Water Reactor (LWR) fleet has been extended for 20 more years. To stay economically competitive with other power sources such as renewable and fossil-fuel power plants, the U.S. Department of Energy has introduced a plan to modernize the existing LWR fleet and diversify the revenue stream. One of the plans is to dispatch thermal energy to endothermic industrial processes. SMART valves will play an important role in this initiative by efficiently balancing the load by regulating valves in a coordinated manner while monitoring the thermal-hydraulic systems to enhance safety and maintain the integrity of the power plant. This research aims to develop a facility to test the coordinated control algorithm and produce various test results for training the monitoring system. The constructed facility is capable of simulating various operational and accidental scenarios by coordinating all the valves (positions) and pump (flowrate). The facility is developed with an Internet of Things (IoT)-based custom system and a python-based valve position control and coordination mechanism. It has achieved stable sensor outputs, pump control, and coordinated valve regulation in all three valves with minimum obstruction in the system.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Viability Assessment of Wind and Solar Renewable Energy Generation in Support of Nationwide Vehicle Electrification

In 2022, the U.S. transportation sector was the largest source of greenhouse gas emissions in the country, with the combination of passenger and commercial vehicles contributing 80% of these emissions. As adoption of passenger electric vehicles continues to climb, sights are being set on the electrification of heavy-duty commercial vehicle (HDCV) fleets. The sustainability of these shifts relies in part on the addition of significant renewable energy generation resources to both bolster the grid in the face of increased demand, and to prevent a shift in the source of greenhouse gas (GHG) emissions to the grid, as opposed to a true net reduction. Additionally, it is necessary to quantify the variations in economic viability across the country for these technologies as it pertains to their productive capabilities. Doing so will encourage investment and ensure that the transition to electrified HDCV fleets is commercially viable, as well as sustainable. In an effort to meet these goals, multiple computational frameworks are used to locate suitable land for renewable infrastructure development, and to quantify spatiotemporal variations in the potential energy generation and financial viability of development sites across the Unites States. First, the Oak Ridge Siting Analysis for power Generation Expansion tool (OR-SAGE) is used to assess the suitability of land for potential wind and solar energy development across the contiguous U.S. From there, resource data from the National Solar Radiation Database (NSRDB) and the Wind Integration National Dataset (WIND) are used in concert with the National Renewable Energy Laboratory (NREL) Renewable Energy Potential (ReV) model to calculate the variation in potential generation capacity for each resource. Additionally, the capital and operational expenditures are calculated for an example configuration of each renewable technology. These measures are then used to calculate the levelized cost of energy (LCOE) of potential sites. All of these results are then processed and analyzed to determine where in the U.S. solar and wind energy are most viable. This viability is based on available generation potential, consistency and stability of energy generation over time, and economic viability with respect to LCOE.

Miller, Brandon [ORNL] (ORCID:0009000300169201)↗
DOE OSTI2024

Sustainable Public Transport: Providing Responsive, On-Demand Service with Clean Energy

The National Renewable Energy Laboratory (NREL) uses the Mobility Energy Productivity (MEP) as a metric and a lens to guide applied research into high performance public mobility. In the current initiative to abate global warming, the US needs not only zero-emission vehicles in the transit fleet (such as buses and shuttles) but also time- and cost-effective services to connect people with goods, services and employment toward a high-quality of life. Our current transportation system is overly dependent on personally-owned automobiles for high quality mobility, with public modes being less viable in many areas. Simply electrifying the drivetrains of existing public transit modes will fail to improve the quality of mobility for those that do not have access to private automobiles. The slow rebound by transit from the pandemic reveals the need to reinvent public transit service. Using the MEP lens, NREL researchers have tracked various novel developments in the public mobility space, with the confluence of shared, on-demand transit (ODT) services using light duty vehicles emerging as a key enabler of high-efficiency public mobility. Deployments such as those in Arlington, TX, Dallas, TX, Fort Erie, ON, and Innisfil, ON showcase the use of fleets of light-duty vehicles as the basis for community circulation and first/last mile to intra-regional transit. ODT services have demonstrated improvements in being more time efficient for riders, more energy efficient in operation (even before the introduction of fully electric vehicles), as well as being cost effective. It appears that aspects of the long-awaited promise of Personal Rapid Transit from the 1970s are beginning to be realized through ODT deployments, leveraging transportation network company (TNC) logistics, popularized by Uber and Lyft, but applied to public mobility. Currently, manually driven ODT operations are already cost competitive with traditional transit systems on a cost per ride basis, and full automation promises to reduce costs by 50% while providing additional safety and verified customer service. Connecting these ODT systems with efficient and effective intra-regional backbone transit service is the next step, with transit agencies like DART providing early results. This discussion will walk through the evidence for this postulated outcome and show results from a series of case-studies.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2024

Quantifying Error in Photovoltaic Installation Metadata: Preprint

In this research, we quantify the level of metadata error for a fleet of 2860 photovoltaic (PV) systems, using metadata values provided by fleet owners. Using satellite imagery and time series analysis techniques available in open-source Python packages Panel-Segmentation and PVAnalytics, respectively, we evaluate the accuracy of PV system metadata such as location, azimuth, tilt, and mounting configuration (fixed tilt vs. tracking). We find that approximately 75% of provided latitude-longitude coordinates are within 190 meters of the actual solar installation. We were unable to link 7.8% of latitude-longitude coordinates to any solar installation via satellite imagery analysis. We evaluate the level of error in owner-provided mounting configuration (fixed tilt vs. single-axis tracking), finding only 8 systems with an incorrect mounting configuration. When evaluating azimuth and tilt parameters, we find that approximately 64% of the data is correct, with data for 860 systems (approximately 30%) not provided by system owners. To illustrate the importance of having correct solar metadata, we evaluate how incorrect metadata affects solar performance estimates by modeling system AC energy output at ground-truth vs. incorrect latitude-longitude coordinates, mounting configurations, and azimuth-tilt configurations. Energy output estimates can vary significantly if incorrect metadata parameters are used, with incorrect mounting configuration leading to the largest discrepancy with over 20% variation in expected energy output.

azimuth↗
DOE OSTI2024

Safe Operations at Roadway Junctions: Intelligent Roadway Infrastructure as Functional Interlocking

Automated vehicle (AV) technology is quickly maturing, and the corresponding infrastructure systems that evaluate traffic and communicate to vehicles requires sophisticated sensing and perception technologies, referred to as intelligent roadway infrastructure (IRI), to complement emerging AV capabilities. IRI provides signals to vehicles, indicating right-of-way for vehicles and communicating to approaching AVs that no other vehicle is failing to yield. This capability, denoted as safety-affirmative signaling, provides a green light or a green arrow as appropriate and affirms through communication links to connected vehicles when it is safe to proceed. About 36% of collisions occur at intersections, with most occurring upon left turns (22.2%) or crossing over (12.6%), and only a small percentage (1.2%) while turning right at an intersection. Of all intersection crashes about half (52.5%) of those vehicles were traveling through a signalized intersection 2. Safety-affirmative signaling would guarantee safety of AV fleet vehicles, by providing the interlocking principle, a term from automated train control that only allows progression through a railway intersection after affirming no opportunity for a crash exists. IRI through safety-affirmative signaling would bring performance and safety to complex roadway intersections where AV transit fleet service is most needed, as well as safety benefits to traditional, non-automated vehicles and vulnerable road users. The implementation of IRI has functional, programmatic, and technical challenges. Research work performed at the National Renewable Energy Laboratory (NREL) in an integrative approach encapsulating these themes, and termed infrastructure perception and control (IPC) is motivated by improved performance (travel time), improved safety (reduced collisions), and improved energy efficiency (less fuel burned and minimized production of greenhouse gases). IPC is intended not only for roadway and intersection applications but also in extension to inform complementary buildings and grid systems to enable better co-management, as vehicles and their charging needs become increasingly integrated into the built environment. The NREL IPC project presents an open-source framework, architecture, and supporting technology to implement IRI, addressing critical issues such as fusion of data, reliability, standardization of data interfaces, and confidence of detection. The framework is informed by previous experience in U.S. Department of Defense research technology, specifically in the use of radar to detect, identify, and track aerial threats. These principles combined with multi-sensor fusion provides for a complete digital twin with known and measurable confidence and accuracy from which safety-affirmative signaling can be developed and deployed.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2024

Assessment of Cloud-based Applications for Enabling a Scalable Riskinformed Predictive Maintenance Strategy

The current light-water reactor fleet uses time-based maintenance strategies to achieve high-capacity factors. But to make nuclear more competitive in the energy market, these reactors could utilize emerging artificial intelligence (AI) and cloud computing technologies to achieve a cost-effective, predictive-maintenance strategy. This paper presents discussion and results on the application of cloud computing in the nuclear industry. The technical viability of cloud computing was analyzed using data from a boiling-water reactor’s safety relief valve. The models were hosted on three different systems: a local personal computer, Idaho National Laboratory’s high-performance computer system, and Microsoft Azure. The data were loaded and processed, and two types of models were trained in an A/B fashion. Based on the speed at which these actions were completed, it was determined that cloud computing affords adequate computing resources. Additionally, the computing power can scale with the demanded load. To enable cloud computing in the existing fleet, additional sensors, networks, and other requirements must be implemented to ensure a smooth transition from current maintenance strategies. However, the benefit is that the plants no longer need to manage their own servers, software, cybersecurity, and information technology support staff for in-house data analytics purpose. Many of these features can be offloaded to the cloud provider for a potential cost savings. Demonstrating how AI can improve the maintenance and operation of non-safety-related systems seems the likely path forward for implementing AI and cloud computing resources inside nuclear power plants.

azure↗
DOE OSTI2025

EVI-Rental: A Scalable Model to Quantify the Impact of Rental Car Electrification

This fact sheet describes the Electric Vehicle Infrastructure - Rental Car (EVI-Rental) tool, a flexible and comprehensive simulation tool that addresses key questions about the charging demand, infrastructure needs, and business impacts of adding growing numbers of electric vehicles (EVs) to rental fleets. To validate the EVI-Rental model, the Athena research team conducted a case study at Dallas-Fort Worth International Airport (DFW) to understand the impact of state-of-charge requirements, different charger types and charging schedules, solar power generation, and behind-the-meter storage on a fully electrified rental car fleet.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Improving Experience Beyond the Ride: Joint Assignment and Repositioning for Rideshare

In this study, a novel joint assignment and repositioning strategy is designed, implemented, and tested in a simulated traffic environment. The strategy addresses limitations of previously constructed rideshare repositioning algorithms by integrating the consideration of rider pooling into the optimization. By considering pooling, the strategy can minimize excess vehicle movement and increase riders' use of pooled services over a zonal-flow-based repositioning strategy and a fleet operating with no repositioning at all. The new strategy can increase pooling by up to 2.3% compared to no repositioning in a large region, and up to 0.1% in a small region, without compromising overall fleet revenue. Additionally, compared to a baseline, flow-based repositioning strategy, the new strategy can reduce empty vehicle travel by up to 30% in the larger region, and 2.1% in the smaller simulated area.

Paul, Joseph↗
DOE OSTI2024

Identifying Nuclear Cost Targets for Competitive Maritime Decarbonization

The shipping industry contributes to a substantial quantity of global carbon emissions. Reaching a “Net-Zero World” will ultimately require complete decarbonization of the existing fleet. Nuclear energy may provide a viable solution toward this end goal. To better understand the economic dynamics of relying on nuclear energy to power maritime fleets, cost targets were identified in this study. Two use cases were considered: (1) nuclear energy is used to generate synthetic fuel (termed synfuel) to power maritime propulsion, and (2) nuclear reactors for vessel propulsion. The study found that in both cases realistic cost targets for nuclear reactor costs could be reached. However, option 2 was deemed to be much more competitive overall but with higher technical risks.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗
DOE OSTI2026

Economic Benefits of Alternative Fuels in Rural Alabama

This fact sheet provides an overview of potential alternative fuel vehicle operational cost savings, opportunities to generate revenue for the state and income for individuals, and several examples of alternative fuels supporting jobs in Alabama. Alternative transportation fuels could potentially provide economic benefits to rural Alabama by lowering operating costs for vehicle owners and creating new revenue streams and job opportunities for fuel providers, technicians, and dealerships. The use of electricity, compressed natural gas (CNG), liquefied natural gas (LNG), liquefied petroleum gas (LPG or propane) could result in reduced fuel costs and maintenance expenses for both public and private fleets, as well as individual consumers. Fleet operators often invest in private fueling infrastructure, while publicly accessible stations could attract traffic and commerce to local businesses and communities. Technicians capable of retrofitting alternative fuels and maintaining the vehicles and fueling infrastructure could develop business opportunities as well, but they require special training and often certifications. These cost savings and increased revenues may be recirculated through the economy and impact more than just the beneficiaries listed above.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Fuel Cell Electric Bus Status Report 2025

This report highlights recent changes in the FCEB market, status updates for deployments, and a detailed performance evaluation of a large fleet of FCEBs operated by Foothill Transit in California during the period from June 2023 to December 2024.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Estimating Electrification Potential for Class 8 Regional-Haul Trucks

This one-page highlight details the key takeaways from a project that utilized NREL's Fleet Research, Energy Data, and Insights (FleetREDI) data analysis pipeline. As part of the North American Council for Freight Efficiency's (NACFE's) Run on Less Depot data workshop, NREL sought to understand how Tesla semi-trucks would perform in real-world regional haul applications. Analysis reveals that the modeled Tesla trucks, with an average efficiency of 1.78 kWh/mi, struggle to achieve full operational coverage using current battery and charging configurations assuming operations remain unchanged. However, in an extreme case where ubiquitous charging exists, 100% EV coverage is possible for the given drive cycles. These findings highlight the trade-off between battery size and charge rate in electrification potential and emphasize the necessity for advancements in charging infrastructure to enable electric trucks for regional haul operations.

ADVANCED PROPULSION SYSTEMS↗