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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 55 records · Page 3

Fine Gradient Electrode and Micro Porous Layer Structures for Improved Heavy Duty Fuel Cells (Final Report)

The commercial deployment of Heavy Duty Fuel Cells(HDFC) for applications such as large trucks (for example, Class 8 capable of carrying 50,000 lb. loads) depends vitally on achieving high efficiency and durability at reasonable costs. Furthermore, the HDFC must operate under practical conditions such as with fuel and air impurities, multiple stop-start cycles, and under the extremes of climate our planet offers – from hot and cold to dry and wet. These conditions place a premium on the stability and utilization of the materials comprising the membrane electrode assemblies (MEAs) powering the fuel cell. and the current catalysts, MicroPorous Layers, and Electrode structures and additives are insufficient for these needs. In Phase I Pajarito and Advent will develop durable electrocatalysts, MicroPorous Layer and electrode additives, and electrode structures for heavy duty fuel cells designed for zero-emission long-haul trucking. The electrocatalyst products are designed to solve the challenging durability and performance needs of fuel cells designed for long life and high efficiency through a combination of uniquely structured designed catalysts as well as new MicroPorous Layers (MPLs) and electrode structure additives. These improved materials will provide Pajarito both an expanded commercial opportunity in electrocatalysts, as well as new markets for MPL and electrode additives. The resulting public benefits include improved economics of fuel cells, a leading zero-emission technology for mobility, as well as reduced reliance on the critical minerals and metals used in the heavy-duty trucks industry. Possible follow-up Phase II and III projects would add full MEA products based on the Phase I efforts catalysts and additives, with full system validation by leading Fuel Cell Truck developers.

08 HYDROGEN↗

Hydrogen Storage System Cost Analysis (2017-2021) (Final Report)

This final technical report summarizes hydrogen storage system cost analysis results from 2017-2021. Results include onboard hydrogen storage system costs for light-duty vehicles, medium-duty vehicles, heavy-duty vehicles, class 8 long haul trucks, and passenger buses. Multiple storage systems are included, primarily focusing on compressed and cryo-compressed hydrogen in Type 3 and Type 4 storage systems. Additional analysis includes large-scale gaseous and liquid hydrogen storage at refueling stations in tube trailers and Dewars; advanced materials-based storage systems such as metal organic frameworks and metal hydrides; and a baseline setting analysis of compressed natural gas storage systems in support of the Institute for Advanced Composites Manufacturing Initiative (IACMI). Analyses were primarily conducted using a Design for Manufacture and Assembly ® methodology, which is a bottom-up process-based approach to estimating factory costs. Multiple annual production rates are reported to project high-volume costs relevant to mature markets.

08 HYDROGEN↗

New roads and challenges for fuel cells in heavy-duty transportation

The recent release of hydrogen economy roadmaps for several major countries emphasizes the need for accelerated worldwide investment in research and development activities for hydrogen production, storage, infrastructure and utilization in transportation, industry and the electrical grid. Due to the high gravimetric energy density of hydrogen, the focus of technologies that utilize this fuel has recently shifted from light-duty automotive to heavy-duty vehicle applications. Decades of development of cost-effective and durable polymer electrolyte membrane fuel cells must now be leveraged to meet the increased efficiency and durability requirements of the heavy-duty vehicle market. This Review summarizes the latest market outlooks and targets for truck, bus, locomotive and marine applications. Required changes to the fuel-cell system and operating conditions for meeting Class 8 long-haul truck targets are presented. The necessary improvements in fuel-cell materials and integration are also discussed against the benchmark of current passenger fuel-cell electric vehicles.

08 HYDROGEN↗

The Port of Los Angeles Zero- and Near-Zero-Emission Freight Facilities "Shore to Store" Project (Final Project Report)

The City of Los Angeles Harbor Department (Harbor Department, POLA) partnered with Equilon Enterprises LLC (d/b/a Shell Oil Products US) (Shell), Toyota Motor North America (Toyota) and Kenworth Truck Company (Kenworth) partnered with the Port of Hueneme (POH), United Parcel Service (UPS), Total Transportation Services Inc. (TTSI), Southern Counties Express (SCE), Toyota Logistics Services (TLS), Air Liquide, National Renewable Energy Laboratory (NREL), Coalition For A Safe Environment, and the South Coast Air Quality Management District (South Coast AQMD) to introduce hydrogen (H 2 ) fuel into the Southern California drayage truck market by demonstrating near-commercial heavy-duty H 2 fuel cell electric trucks at and between freight facilities throughout the region, while continuing to lay the groundwork for battery-electric operations. The "Shore to Store" (S2S) project built on project team experience to help realize our vision of zero-emission freight operations in the future. Ten Kenworth zero-emission Class 8 fuel cell electric trucks, integrated with Toyota's fuel cell drive technology, were operated by UPS, TTSI, SCE, and TLS in revenue service. The demonstration fleet fueled at the S2S hydrogen fueling stations that were built in Ontario, California and Wilmington, California. An additional station at the Port of Long Beach (Portal Station) was available for fueling the fleet. Portal Station was supported by grants from the California Energy Commission (CEC) and South Coast AQMD and used as match funding for the S2S project. POH demonstrated two battery-electric yard tractors, and TLS demonstrated two zero-emission forklifts at their warehouse facility, showcasing elements of the entire supply chain operating on zero-emissions. This project showcased a snapshot of the zero-emission supply chain of the future, providing a model by which freight facilities can support zero-emission operations.

33 ADVANCED PROPULSION SYSTEMS↗

Performance and Total Cost of Ownership of a Fuel Cell Hybrid Mining Truck

The main objective of this work was to investigate the potential of hydrogen and fuel cells replacing diesel and internal combustion engines in the ultraclass haul trucks deployed in the mining sector. Performance, range, durability, and cost are the main criteria considered for comparing the two fuels and engine options. Fuel cell system (FCS) performance is characterized in terms of heat rejection, efficiency, and fuel consumption for a hybrid platform equivalent to a 3500 hp diesel engine operating on a representative open pit mining duty cycle. A hybrid platform was chosen because the heat rejection, with a constrained radiator frontal area, limits the maximum fuel cell-rated power by about 50% compared to that of the diesel truck. The hybrid powertrain was 81–88% more efficient than the diesel powertrain on the truck duty cycle. A liquid hydrogen storage system is required for an equal range or time between refilling, but the packaging remains a challenge. Fuel cell and battery durability were evaluated for their performance degradation and lifetime. Achieving a fuel cell lifetime comparable to the time between major overhauls for diesel trucks necessitates the oversizing of the membrane-active area, catalyst overloading, and voltage clipping. For an equal lifetime, the battery must be oversized to control its depth of discharge and charge/discharge rates. A total cost of ownership (TCO) analysis considering the initial capital expenditures, as well as the lifetime cost of fuel, operation, and maintenance, indicates that fuel cells and hydrogen can compete with diesel. A breakeven fuel cost for TCO parity is obtained if H2 is available at USD 5.79–6.85/kg vs. diesel at USD 3.25/gal and the FCS-specific cost is USD 323/kW e relative to USD 250/kW for a diesel genset. Volume manufacturing is required for FCS cost reduction. High volume is possible through the standardization, modularity, and proliferation of class 8 long-haul truck systems across different heavy-duty applications.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Mass Detection for Heavy-Duty Vehicles using Gaussian Belief Propagation

Predicting vehicle mass is critical to accurately estimate energy use and emissions of commercial trucks. However, data from vehicle telematics is often not at sufficient temporal resolution or accuracy for use in model-based detection methods. In this work, a new statistical mass prediction technique is described for heavy-duty vehicles that incorporates the use Gaussian Belief Propagation (GBP) for probabilistic inference. Similar to Bayesian inference models, the GBP model typically requires less labeled training data than other contemporary machine learning techniques. First, a factor graph is constructed, and a set of Gaussian belief nodes with associated means and variances are fitted to the training data. To better handle noisy input data, the GBP mass prediction model utilizes a k-nearest factors (kNF) algorithm for probabilistic inference on unseen testing data. The proposed method is compared with a classical weighted k-nearest neighbors (kNN) regressor. This statistical kNF-GBP model works even with low-quantity, low-quality initial training data, while being capable of realtime mass estimation. Unlike the kNN regressor, the GBP model produces a measure of uncertainty with its predictions. The proposed method is validated using curve-sampled driving data collected from multiple cloud-connected Class 8 regional haul diesel trucks. Both the kNN regressor and the kNF-GBP mass prediction model were able to predict payload mass with coefficients of determination above 0.97 with minimal data preprocessing.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Model-Based Framework to Optimize Charger Station Deployment for Battery Electric Vehicles

The development of battery electric vehicles (BEVs) is accelerating due to their environmental advantages over gasoline and diesel-powered vehicles, including a decrease in air pollution and an increase in energy efficiency. The deployment of charging infrastructure will need to increase to keep pace with demand, especially for large commercial vehicles for which few public chargers currently exist. In this paper, a new flexible framework is proposed for optimizing the placement of charging stations for BEVs, within which different physical models and optimization techniques may be used. Furthermore, a set of metrics is suggested to help enforce complex constraints and facilitate direct comparison between different optimization techniques. Unlike many existing charger placement techniques, the proposed method directly considers the historical driving patterns on a vehicle-by-vehicle basis, using transparent models to assess impacts of candidate charger placements, thus improving the explainability of the results. In the developed framework, modeled BEVs are first generated along the road network to mimic historical traffic data and are simulated traveling along a given route according to a simplified vehicle model. During the simulation, the charger placement problem is initially relaxed to allow vehicles to charge at any node along the road network, and vehicle states are tracked to assess areas of high charging demand. Charging stations are then placed based on the results of the relaxed simulation, and suggested placements are evaluated via road network simulation with fixed charger locations. This proposed framework is applied to a sample problem of placing charging stations along five major highway corridors for Class 8 over-the-road electric trucks. A novel mixed integer programming (MIP) formulation is proposed to optimize charger placements based upon the expected charging demand. Constraints were imposed on the final placement results to limit expected wait times at each station and ensure a minimum threshold of trucking routes are viable for BEVs. The results demonstrate the flexibility and potential effectiveness of the developed model-based framework for scalable charger station deployment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Evaluating Connected and Automated Vehicles in Co-Simulation Environment of Traffic Microsimulation and Vehicle Dynamics

Connected and automated vehicles (CAVs) have the potential to improve many aspects of the current transportation systems such as safety, mobility, and energy efficiency. In order to evaluate the benefits and impacts of a CAV, the CAV control algorithm is typically implemented on vehicles simulated in a traffic microsimulation environment. However, traffic microsimulation usually lacks detailed vehicle and powertrain dynamics, making it challenging to fully understand how a CAV control algorithm will perform and respond on an actual vehicle. Whether the same benefits measured in the simulation will also be observed in real-world remains an open question. One potential approach to fill in this gap is to conduct a co-simulation of traffic microsimulation with detailed vehicle and powertrain dynamics models, often developed in MATLAB Simulink. However, current microsimulation tools such as VISSIM and SUMO do not have a ready-to-use interface for co-simulation with vehicle dynamics and Simulink. Also, even if such an interface exists, it will be tool-specific, making it challenging to shift from one tool to another or test CAV controls in different tools. There are needs for tool-agnostic co-simulation as different microsimulation tools have their pros and cons, and researchers often need to use different tools based on the purposes of the simulation, project needs, and applications. In this work, Flexible Interface for X-in-the-loop Simulation (FIXS) is developed that can support the co-simulation of microsimulation, CAV control algorithm, and vehicle dynamics model in Simulink. Enabled by the FIXS, the benefit and performance of a CAV control algorithm can be better understood with the consideration of vehicle responses and dynamics. The connection to VISSIM and SUMO is handled internally by the interface, and users can easily switch tools by changing a configuration file. The co-simulation capability is demonstrated for a VISSIM eco-approach and departure CAV scenario and a SUMO cooperative merging scenario for both a passenger CAV and a class 8 heavy-duty connected and automated trucks.

Shao, Yunli↗

Volvo Pathway to Cost-Effective Commercialized Freight Efficiency (SuperTruck 2)

Volvo’s SuperTruck 2 (ST2) built on the success of the SuperTruck 1 (ST1) project, with the objective to research, develop, and demonstrate a Class 8 long-haul tractor-trailer concept truck designed with an integrated approach to maximize freight efficiency and achieve the following goals: - Greater than 100% improvement in vehicle freight efficiency (FE) on a ton-mile-per-gallon basis, with a stretch goal of 120% improvement relative to a 2009 baseline. - Greater than or equal to 55% engine brake thermal efficiency (BTE) demonstrated in an operational engine at a 65-mph cruise point on a dynamometer. - Develop technologies that are commercially cost effective in terms of a simple payback. This cooperative agreement with the US Department of Energy (DOE) had a total project cost of $40,000,000 with a 50% cost share, or $20,000,000, paid by the DOE. This project was awarded to Volvo Technology of America, LLC, who was the prime recipient and project lead. Metalsa, Michelin, Wabash, Bergstrom, University of Michigan, Oak Ridge National Laboratory, Johnson Matthey, Knight Transportation, Wegmans, and Motivo were partners / sub-recipients in this project.

33 ADVANCED PROPULSION SYSTEMS↗

Techno-Economic Evaluation of Electrified Vehicle Options in Drayage Fleets

The electrification of drayage fleets offers potential economic and operational benefits, but the financial viability of electrified vehicles remains sensitive to battery cost, energy price, and fleet usage patterns. While total cost of ownership (TCO) is a useful benchmark, fleet operators and investors are equally concerned with investment performance metrics such as payback period (PB) and Internal Rate of Return (IRR), which better reflect financial risks and investment return timelines. This study develops a unified techno-economic framework that jointly evaluates TCO, PB, and IRR to determine when electrified trucks become cost-effective alternatives to diesel trucks. Building on a previously developed cost modeling tool and using real-world telematics data from a Class 8 drayage fleet at the Port of Savannah, the analysis incorporates projected battery cost trajectories, electricity and diesel price trends, vehicle efficiency improvements, and multiple battery capacities. Parameter ranges reflect widely cited projections and observed drayage-duty-cycle variability. A surrogate-modeling method approximates economic performance across thousands of battery cost–electricity price combinations, enabling high-resolution identification of conditions that achieve TCO parity, acceptable PB thresholds, and target IRR levels. Additionally, the study estimates the evolving share of the fleet that can feasibly electrify over time under multiple economic metrics. This integrated framework offers a novel, data-driven approach to inform risk-aware decision-making for fleet electrification and supports investment planning under evolving cost and operational conditions.

Sun, Ruixiao [ORNL] (ORCID:0000000341768676)↗

A Method of Optimal Control for Class 8 Vehicle Platoons Over Hilly Terrain

This work develops and implements an NMPC control system to facilitate fuel-optimal platooning of Class 8 vehicles over challenging terrain. Prior research has shown that Cooperative Adaptive Cruise Control (CACC), which allows multiple Class 8 vehicles to follow in close succession, can save between 3 and 8% in overall fuel consumption on flat terrain. However, on more challenging terrain, e.g. rolling hills, platooning vehicles can experience diminished fuel savings, and, in some cases, an increase in fuel consumption relative to individual vehicle operation. This research explores the use of Nonlinear Model Predictive Control (NMPC) with predefined route grade profiles to allow platooning vehicles to generate an optimal velocity trajectory with respect to fuel consumption. In order to successfully implement the NMPC system, a model relating vehicle velocity to fuel consumption was generated and validated using experimental data. Additionally, the predefined route grade profiles were created by using the vehicle's GPS velocity over the desired terrain. The real-time NMPC system was then implemented on a two-truck platoon operating over challenging terrain, with a reference vehicle running individually. The results from NMPC platooning are compared against fuel results from a classical proportional-integral-derivative (PID) headway control method. Furthermore, this comparison yields the comparative fuel savings and energy efficiency benefit of NMPC system. In the final analysis, significant fuel savings of greater than 14 and 20% were seen for the lead and following vehicles relative to their respective traditional cruise control and platooning architectures.

33 ADVANCED PROPULSION SYSTEMS↗

Truck Platooning Performance with ADAS and Onboard Camera Data Describing Traffic Interactions

This project was part of the Characterizing Behaviors and Capabilities for Emerging Connected and Automated Vehicle Technologies, Sensors, and Connectivity project. The National Laboratory of the Rockies partnered with Cummins Inc. to collect data from Class 8 tractor trailer combinations in platoon (cooperative adaptive cruise control) operations on public roads in southern Indiana. Data collected include J1939 CAN bus, radar, intervehicle position, and video data. The video data could not be shared in the raw form, so they were processed to extract information on the other vehicles on the road, their relative positions, and intrusion events. This information was then columnized for modeling use and further enhanced by appending road information including road type, speed limit, altitude, and grade. The test route included free-flowing traffic, highway interchanges, and construction zones, as well as low-, medium-, and high-grade sections. Individual test conditions varied by day, with advanced driver-assistance system (ADAS) features engaged or disengaged and different combined vehicle masses tested in addition to uncontrolled variables such as weather and traffic interactions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Identifying rolling resistance and air resistance simultaneously for an electric truck

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.

47 OTHER INSTRUMENTATION↗

Cargo Logistics Airlift Systems Study (CLASS). Volume 3: Cross impact between the 1990 market and the air physical distribution systems, book 2

Book 2 of this volume is divided into the following sections: (1) commodities and system networks; (2) future mode choice decisions and commodity air eligibility; (3) comparative cargo transportation costs - air, truck, rail and water; (4) elasticities of demand; (5) operating cost; (6) operating profit, rate making, and returns; (7) importance of rate and service on future aircraft; (8) potential market demand for new aircraft; (9) scenario of events affecting system/market growth; and (10) future study and technology requirements.

Burby, R. J.↗

Zero Emission Drayage Trucks Demonstration (ZECT 1) (Final Report)

The objective of this project was to develop and demonstrate zero emission capable heavy-duty electric truck technologies in real world drayage operations and assess its technical feasibility to support demanding drayage duty cycles. Further objectives of the project included the promotion and acceleration of market adoption of electric truck technologies in cargo transport operations, by engaging trucking fleets and end users in demonstrating ad experiencing these technologies first-hand. The project realized the development and demonstration of two battery-electric and two battery-electric hybrid platforms that advanced the technologies and demonstrated proof of concept.

33 ADVANCED PROPULSION SYSTEMS↗

Comprehensive Total Cost of Ownership Quantification for Vehicles with Different Size Classes and Powertrains

In order to accurately compare the costs of two vehicles, the total cost of ownership (TCO) should consist of all costs related to both purchasing and operating the vehicle. This TCO analysis builds on previous work to provide a comprehensive perspective of all relevant vehicle costs of ownership. In this report, we present what we believe to be the most comprehensive explicit financial analysis of the costs that will be incurred by a vehicle owner. This study considers vehicle cost and depreciation, financing, fuel costs, insurance costs, maintenance and repair costs, taxes and fees, and other operational costs to formulate a holistic total cost of ownership and operation of multiple different vehicles. For each of these cost parameters that together constitute a comprehensive TCO, extensive literature review and data analysis were performed to find representative values in order to build a holistic TCO for vehicles of all size classes. The light- and heavy-duty vehicles selected for analysis in this report are representative of those that are on the road today and expected to be available in the future. Important additive analyses in this study include systematic analysis of vehicle depreciation, in-depth examination of insurance premium costs, comprehensive maintenance and repair estimates, analysis of all relevant taxes and fees, and considerations of specific costs applicable to commercial vehicles. We find that cars depreciate faster than light trucks and that older plug-in electric vehicles have a greater depreciation rate than newer electric vehicles. Light-duty vehicle (LDV) insurance costs show comparable costs for different powertrains, and lower costs for larger size classes. Medium- and heavy-duty vehicle (MHDV) insurance costs vary significantly by vocation. Electric and electrified powertrains have lower maintenance and repair costs than internal combustion engine (ICE) powertrains for all vehicle sizes, relative to vehicle price. MHDV maintenance and repair costs depend heavily on vocation and duty cycle. LDV taxes and fees are comparable across powertrain types and size classes, though marginally higher registration fees exist for alternative fuel vehicles. MHDV fees depend on the vocation, weight rating, and state. Many electric tractor trailers would be affected by additional battery weight, reducing the available payload capacity, and this cost can be substantial. Electric vehicle charging for commercial vehicles can be time-consuming; labor rates can cause this cost to dominate TCO. With improved knowledge of each of the cost components, we calculate a lifetime TCO for comparison across vehicles of different types and attributes. For a simulated small sport utility vehicle in 2025, modeled using Autonomie, the hybrid electric vehicle (HEV) has the lowest cost, followed by the conventional ICE vehicle. For MHDV, TCO can be drastically different depending on the vocation. Long-haul vehicles typically have the lowest per-mile costs. Excluding labor costs, the class 4 delivery has a comparable TCO to the day cab. Vocational trucks, refuse trucks, and transit buses have a high per-mile cost of ownership due to maintenance and insurance. For all of these vehicles, the cost of operating the vehicle is heavily weighted by the labor of the driver, followed by the fuel costs. While the HEV begins as the lowest cost powertrain for passenger vehicles, fuel cells are forecast to reach cost parity by 2030 when hydrogen prices reach $\$ 5$/kg while battery electric vehicles (BEV) reach cost parity by 2035 at a battery cost of $\$ 98$ per usable kWh of capacity, with these two technologies being the lowest cost in 2050. For the class 8 day cab tractor, the HEV and ICE vehicle begin as the lowest cost powertrains, and the 250-mile BEV reduces in cost from the most expensive to the least expensive by 2030.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Experimental Fuel Consumption Results from a Heterogeneous Four-Truck Platoon

Platooning has the potential to reduce greenhouse gas missions of heavy-duty vehicles. Prior platooning studies have chiefly focused on the fuel economy characteristics and three-truck platoons, and most have investigated aerodynamically homogeneous platoons with trucks of the same trim. For real world application and accurate return on investment for potential adopters, non-uniform platoons and the impacts of grade and disturbances on a platoon’s fuel economy must also be characterized. This study investigates the fuel economy of a heterogeneous four-truck platoon on a closed test track. Tests were run for one hour at a speed of 45 mph. The trucks used for this study are two 2015 Peterbilt 579’s with a Cummins ISX15 and a Paccar MX-13, and two 2009 Freightliner M915A5’s, one armored and the other unarmored. Many analysis methodologies were leveraged to describe and compare the fuel data, including lap-wise and track-segment analysis. The methodology for dividing the data into laps is described in detail. The influence of other factors beyond the aerodynamics of platooning is discussed. CAN fuel rate analysis showed excellent agreement with previous experimental trends for two and three-truck platoons. In general, the indicated fuel economy benefits in this study were 5-11% for following vehicles and 0-4% for the lead vehicle in platoon relative to their baseline fuel consumption. On a cumulative basis, all platoons saved fuel, ranging from 6% to 8% versus the sum of the standalone trucks’ fuel consumption. The practical implications of the fuel economy results are discussed, as well as avenues for future research. Introduction and Motivation platooning is controlled coordination of two or more vehicles in a convoy, sometimes called CACC (Coordinated or Cooperative Adaptive Cruise Control). The distance between vehicles in a platoon can be controlled tightly at no additional fatigue to the driver. Platooning vehicles can also respond quickly to braking events of the leader, much more quickly than the typical human driver’s reaction time of 1-1.5 s. Therefore, vehicles in a platoon can follow each other much more closely than would usually be considered a safe following distance under human operation. It is implied that under very close following conditions, platooning technology must be extremely robust before it is safe for wide-scale implementation. Platooning is under investigation as a fuel-saving technology. Close-following significantly reduces aerodynamic drag for both leading and trailing vehicles. According to the NRC in 2010, aerodynamic drag represents roughly half of a Class-8 truck’s on-highway fuel usage, meaning a 20% reduction in drag roughly equals a 10% reduction in fuel usage, if all other sources of energy loss remain equal (i.e. accessory, rolling resistance, drivetrain, braking). It is by aerodynamics that platooning saves fuel. At risk of oversimplifying the aerodynamics, following (or trailing) vehicles experience reduced wind velocity due to shielding, and the leading vehicles experience an increased aft pressure, especially at distances closer than 75’ (23 m).

greenhouse gas emissions, truck platoon, heavy-dut↗

Dynamic Radioisotope Power System (DRPS) Design Reference Mission (DRM) Lunar Rover

The Radioisotope Power Systems (RPS) Program tasked the Compass Team to evaluate use of Dynamic Radioisotope Power Systems (DRPS) for lunar science rovers. The object was to identify their advantages and challenges as well as to influence the technology developments with flight-type requirements. This was easily done by using the promising Volatiles Investigating Polar Exploration Rover (VIPER) solar- powered rover mission as a platform to ‘swap in’ a DRPS. The ‘pickup truck bed’ approach allowed both simplified installation and operation of the DRPS while keeping the forward lunar surface ‘blocked’ from the DRPS waste heat which could sublimate the icy surface. It was found that with the Stirling DRPS option the mass is within the planned VIPER lander capability and is very close to VIPER mass and size (the DRPS replaces large battery pack/solar arrays). The Stirling DRPS option produced ~300 Watts electrical (We) using six general purpose heat source (GPHS) bricks and eight Stirling convertors. Replacing the solar/battery power with radioisotope power allows a continuous presence (instead of 6 hours) in a permanently shadowed region (PSR) and over 18 months of operations with minimal science impact (rearward surface heating). It was also found that use of a dynamic system (instead of a thermoelectric system) reduces the heat impact on the science environment two-to-three times. The DRPS, along with a relay link (like Gateway), can provide continuous access to PSRs. The system was also found to be capable of roving for 8 hours per day with a range of over 500 km in 18 months. Preliminary cost estimates fit into a Class D mission but only assuming VIPER heritage and launch, lander, operations, nuclear specific costs [National Environmental Policy Act (NEPA), fueling, transport, Launch Services Program (LSP), etc.] and DRPS are not included.

DRPS↗