The U.S. Department of Energy’s Vehicle Technologies Office (DOE-VTO) is driving advancements in highway transportation by targeting energy efficiency, environmental sustainability, and cost reductions. This study investigates the fuel economy potential and cost implications of advanced powertrain technologies using comprehensive system simulations. Leveraging tools such as Autonomie and TechScape, developed by Argonne National Laboratory, this study evaluates multiple timeframes (2023–2050) and powertrain types, including conventional internal combustion engines, hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs). Simulations conducted across standard regulatory driving cycles provide detailed insights into fuel economy improvements, cost trajectories, and total cost of ownership. The findings highlight key innovations in battery energy density, lightweighting, and powertrain optimization, demonstrating the growing viability of BEVs and their projected economic competitiveness with conventional vehicles by 2050. This work delivers actionable insights for policymakers and industry stakeholders, underscoring the transformative potential of vehicle electrification in achieving sustainable transportation goals.

This paper presents a neural network recommender system algorithm for assigning vehicles to routes based on energy and cost criteria. In this work, we applied this new approach to efficiently identify the most cost-effective medium and heavy duty truck (MDHDT) powertrain technology, from a total cost of ownership (TCO) perspective, for given trips. We employ a machine learning based approach to efficiently estimate the energy consumption of various candidate vehicles over given routes, defined as sequences of links (road segments), with little information known about internal dynamics, i.e using high level macroscopic route information. A complete recommendation logic is then developed to allow for real-time optimum assignment for each route, subject to the operational constraints of the fleet. We show how this framework can be used to (1) efficiently provide a single trip recommendation with a top-$k$ vehicles star ranking system, and (2) engage in more general assignment problems where $n$ vehicles need to be deployed over $m \leq n$ trips. This new assignment system has been deployed and integrated into the POLARIS Transportation System Simulation Tool for use in research conducted by the Department of Energy's Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium

This paper presents AutonomieAI, a novel toolkit designed for efficient energy estimation of vehicles across diverse trip scenarios, routes, and drive cycles, applicable to a broad range of vehicle powertrain technologies. It leverages state-of-the-art Machine Learning techniques to deliver real-time energy prediction of vehicles, enabling co-simulation with transportation level system tools and opening doors for large-scale optimization at city, network or national level. Benchmark results show that AutonomieAI achieves high accuracy, with an average percentage error below 2% for most powertrain types, and computational efficiency capable of processing over 10,000 trips per second. Applications of AutonomieAI have potential to offer the flexibility to assist in solving eco-routing problems, optimize for vehicle and powertrain selection, study charging decision behavior, and optimize for charging station placement. AutonomieAI is the result of large neural network based model architectures, trained on very large and unique high fidelity vehicle simulation data. It is lightweight, deployable, efficient and has accuracy comparable to specialized and complex physics based simulation softwares.

This study evaluates the performance of alternative powertrains for Class 8 heavy-duty trucks under various real-world driving conditions, cargo loads, and operating ranges. Energy consumption, greenhouse gas emissions, and the Levelized Cost of Driving (LCOD) were assessed for different powertrain technologies in 2024, 2035, and 2050, considering anticipated technological advancements. The analysis employed simulation models that accurately reflect vehicle dynamics, powertrain components, and energy storage systems, leveraging real-world driving data. An integrated simulation workflow was implemented using Argonne National Laboratory's POLARIS, SVTrip, Autonomie, and TechScape software. Additionally, a sensitivity analysis was performed to assess how fluctuations in energy and fuel costs impact the cost-effectiveness of various powertrain options. By 2035, battery electric trucks (BEVs) demonstrate strong cost competitiveness in the 0-250 mile and 250-500 mile ranges, especially when primarily charged at depots. Fuel cell electric vehicles (FCEVs) remain competitive in the 250-500-mile range, particularly under higher diesel prices. For distances over 500 miles, FCEVs become the preferred solution, providing greater range and operational flexibility. By 2050, technological advancements and reduced truck costs further enhance the feasibility of both BEVs and FCEVs. The BEV500 shows improved efficiency and resilience to energy price fluctuations, making it viable for medium-range operations and cost-effective even with high en-route electricity rates. FCEVs are expected to remain competitive in both medium and long-range operations, especially when diesel prices are elevated, positioning them as strong alternatives to conventional powertrains for long-haul routes.

The National Highway Traffic Safety Administration (NHTSA) plays a crucial role in guiding the formulation of Corporate Average Fuel Economy (CAFE) standards, and at the forefront of this regulatory process stands Argonne National Laboratory (Argonne). Argonne, a U.S. Department of Energy (DOE) research institution, has developed Autonomie—an advanced and comprehensive full-vehicle simulation tool that has solidified its status as an industry standard for evaluating vehicle performance, energy consumption, and the effectiveness of various technologies. Under the purview of an Inter-Agency Agreement (IAA), the DOE Argonne Site Office (ASO) and Argonne have assumed the responsibility of conducting full-vehicle simulations to support NHTSA's CAFE rulemaking initiatives. This paper introduces an innovative approach that hinges on a large-scale simulation process, encompassing standard regulatory driving cycles tailored to various vehicle classes and spanning diverse timeframes. What sets this methodology apart is its integration of lightweighting advancements specifically tailored to different vehicle powertrains. Moreover, beyond describing the novel methodology, this paper offers a comprehensive analysis of energy consumption reduction. It delves into the intricate dynamics across a spectrum of vehicle powertrains, providing a nuanced understanding of how advancements in lightweighting contribute to energy efficiency gains. This work not only highlights an innovative approach to vehicle simulation, but also contributes valuable insights to the ongoing discourse surrounding CAFE standards and sustainable automotive technologies.

The Battery Performance and Cost Model (BatPaC), developed by Argonne National Laboratory, is a versatile tool designed for lithium-ion battery (LIB) pack engineering. It accommodates user-defined specifications, generating detailed bill-of-materials calculations and insights into cell dimensions and pack characteristics. Pre-loaded with default data sets, BatPaC aids in estimating production costs for battery packs produced at scale (5 to 50 GWh annually). Acknowledging inherent uncertainties in parameters, the tool remains accessible and valuable for designers and engineers. BatPaC plays a crucial role in National Highway Transportation Traffic Safety Administration (NHTSA) regulatory assessments, providing estimated battery pack manufacturing costs and weight metrics for electric vehicles. Integrated with Argonne's Autonomie simulations, BatPaC streamlines large-scale processes, replacing traditional models with lookup tables. This integration highlights BatPaC's adaptability to emerging technologies, ensuring efficiency and accuracy in Corporate Average Fuel Economy (CAFE) rulemaking evaluations. In short, BatPaC is a robust LIB pack design and cost estimation tool, contributing to the evolving landscape of lithium-ion battery technology. Its integration with Autonomie positions it as a key player in large-scale simulations and regulatory assessments, emphasizing the tool's relevance and effectiveness in the dynamic electric vehicle and battery technology landscape. Continuous refinement will be essential to address market dynamics and ensure BatPaC's ongoing impact.

The establishment of Corporate Average Fuel Economy (CAFE) standards by the Energy Policy and Conservation Act (EPCA) of 1975 marked a pivotal moment in the automotive industry's pursuit of greater fuel efficiency. The responsibility for the development and enforcement of these standards was assigned to the U.S. Department of Transportation (DOT), with the National Highway Traffic Safety Administration (NHTSA) assuming a critical role in their oversight and implementation. In collaboration with Argonne National Laboratory (Argonne), supported by the U.S. Department of Energy (DOE), significant strides have been made in advancing fuel efficiency through the development of Autonomie, a leading full-vehicle simulation tool. Through an Inter-Agency Agreement between the DOE Argonne Site Office and Argonne, comprehensive full-vehicle simulations are conducted to support NHTSA's CAFE rulemaking processes. This paper introduces an innovative approach to CAFE standards development, emphasizing large-scale simulation processes that encompass standard regulatory driving cycles, diverse vehicle classes, and various time frames. At the core of this approach lies Autonomie's capability to integrate advanced engine technologies tailored to specific vehicle classes and powertrains, facilitating a comprehensive understanding of their impact on fuel economy. By customizing simulations to mirror real-world conditions across different vehicle types, the research provides nuanced insights crucial for effective CAFE standards development and implementation. Furthermore, the paper delves into the reductions in energy consumption across diverse vehicle powertrains, offering a comprehensive analysis of the potential for fuel efficiency improvements. Insights gained from this in-depth analysis contribute valuable knowledge to the ongoing pursuit of sustainable transportation solutions. This collaborative effort exemplifies a commitment to advancing understanding in vehicle dynamics, energy usage, and technology performance through large-scale simulations, ultimately fostering informed decision-making for a more sustainable automotive future.

This paper presents a comprehensive analysis of emerging powertrain technologies for a wide spectrum of vehicles, ranging from light-duty passenger vehicles to medium and heavy-duty trucks. The study focuses on the anticipated evolution of these technologies over the coming decades, assessing their potential benefits and impact on sustainability. The analysis encompasses simulations across a wide range of vehicle classes, including compact, midsize, small SUVs, midsize SUVs, and pickups, as well as various truck types, such as class 4 step vans, class 6 box trucks, and class 8 regional and long-haul trucks. It evaluates key performance metrics, including fuel consumption, estimated purchase price, and total cost of ownership, for these vehicles equipped with advanced powertrain technologies such as mild hybrid, full hybrid, plug-in hybrid, battery electric, and fuel cell powertrains. Comparative assessments are conducted against conventional gasoline, diesel, and natural gas internal combustion engine vehicles, as applicable. A large-scale simulation process is employed, utilizing Autonomie for vehicle sizing and consumption evaluations and TechScape for techno-economic analysis, both of which were developed at Argonne National Laboratory. The research extends technology projections from 2023 to 2050, incorporating two technology progress scenarios: a business-as-usual (BAU) case representing currently projected improvements in vehicle efficiency, and a 'program success' case reflecting potential advancements resulting from the Department of Energy Vehicle Technologies Office and Hydrogen and Fuel Cell Technologies Office research and development investments. The results reveal the significant potential of these emerging technologies to enhance cost-effectiveness and fuel efficiency. Battery-electric and fuel-cell-powered electric vehicles are identified as key contributors to the transition towards economically competitive and environmentally friendly transportation alternatives. The findings underscore the urgency of accelerating the development of component technologies to improve the range and cost-effectiveness of electrified powertrains, particularly in the trucking sector.

In response to the stipulations of the Energy Policy and Conservation Act and the global momentum toward carbon mitigation, there has been a pronounced tightening of fuel economy standards for manufacturers. This stricter regulation is coupled with an accelerated transition to electric vehicles, catalyzed by advances in electrification technology and a decline in battery cost. Improvements in the fuel economy of medium- and heavy-duty vehicles through electrification are particularly noteworthy. Estimating the magnitude of fuel economy improvements that result from technological advances in these vehicles is key to effective policymaking. In this research, we generated vehicle models based on assumptions regarding advanced transportation component technologies and powertrains to estimate potential vehicle-level fuel savings. We also developed a systematic approach to evaluating a vehicle’s fuel economy by calibrating the size of the components to satisfy performance requirements. We used Autonomie, a high-fidelity vehicle modeling and simulation tool developed by Argonne National Laboratory, integrating Pattern Search solvers to optimize component sizing based on our assumptions. Pattern Search, a direct-method numerical optimization algorithm, is widely used in a variety of applications. The method requires extensive evaluation and iteration but provides good optimization performance for the computational cost. This paper presents the potential energy savings for a medium-duty electric vehicle determined using both rule-based and optimized component sizing.

The Transportation Annual Technology Baseline (ATB) provides detailed cost and performance data, estimates, and assumptions for vehicle and fuel technologies in the United States. It includes current and projected estimates for vehicle technologies as well as conventional and alternative fuels, and it details the assumptions used to calculate those costs, such as gas and electricity prices, discount rates, and vehicle miles traveled. The 2022 update added more classes of light-duty vehicles, medium and heavy-duty vehicles, an aviation page, and it aligned diesel pathways with aviation pathways.

The National Highway Traffic Safety Administration (NHTSA) has been leading U.S. efforts related to the rulemaking process for Corporate Average Fuel Economy (CAFE) standards. Argonne National Laboratory, a U.S. Department of Energy (DOE) national laboratory, has developed a full-vehicle simulation tool called Autonomie that has become one of the industry standard tools for analyzing vehicle performance, energy consumption, and technology effectiveness. Through an Interagency Agreement, the DOE Argonne Site Office and Argonne National Laboratory have been tasked with conducting full vehicle simulation to support NHTSA CAFE rulemaking. This paper presents an innovative approach focused on large-scale simulation processes spanning standard regulatory driving cycles, diverse vehicle classes, and various timeframes. A key element of this approach is Autonomie’s capacity to integrate advanced engine technologies tailored to specific vehicle classes and powertrains. By customizing simulations to replicate real-world conditions for different vehicle types, this research provides nuanced insights essential for the development and implementation of effective CAFE standards. Our CAFE analysis encompassed simulation of more than 10,000 vehicle combinations across different heavy-duty pickup and van classes. The wide range of vehicle combinations we analyzed included electrified powertrains combined with various component performance scenarios. This paper details Argonne’s investigation into the performance and energy consumption of electrified powertrains simulated for the NHTSA’s Notice of Proposed Rulemaking published in 2023.

(FCEVs). In evaluating the vehicle-fuel combinations, this study considers both low-volume and high-volume “CURRENT TECHNOLOGY” cases (nominally 2015) and a high-volume “FUTURE TECHNOLOGY” lower-carbon case (nominally 2025–2030). For the CURRENT TECHNOLOGY case, low-volume vehicle and fuel production pathways are examined to determine costs in the near term.

This paper aims to quantify the potential benefits of advanced transportation technologies for medium- and heavy-duty vehicles, based on technologies expected to be integrated over the next few decades. The analysis involved simulations of over 20 truck types, ranging from Class 2 to Class 8, and provides fuel consumption, estimated purchase price, and total cost of ownership for trucks equipped with advanced technologies, namely mild hybrid, full hybrid, plug-in hybrid, battery electric, and fuel cell powertrains. The energy consumption and technoeconomic analysis were done using the Autonomie and TechScape tools developed at Argonne National Laboratory. This study extends new technology projections from 2025 to 2050, and the results demonstrate that while they can improve the cost-effectiveness and fuel economy of medium- and heavy-duty vehicles, they will be necessary to make battery-electric and fuelcell powered electric trucks economically attractive compared with conventional diesel powertrains. This study provides vehicle-level perspective and estimated projections about the future of advanced medium- and heavy-duty truck technologies that can inform other advanced transportation studies, as well as support decision-makers with setting decarbonization targets and strategies.

Over the past couple of years, Argonne National Laboratory has tested, analyzed, and validated automobile models for the light duty vehicle class, including several types of powertrains including conventional, hybrid electric, plug-in hybrid electric and battery electric vehicles. Argonne’s previous works focused on the light duty vehicle models, but no work has been done on medium and heavy-duty vehicles. This study focuses on the validation of shifting control in advanced automatic transmission technologies for medium duty vehicles by using Argonne’s model-based high-fidelity, forward-looking, vehicle simulation tool, Autonomie. Different medium duty vehicles, from Argonne’s own fleet, including the Ram 2500, Ford F-250 and Ford F-350, were tested with the equipment for OBD (on-board diagnostics) signal data record. For the medium duty vehicles, a workflow process was used to import test data. In addition to importing measured test signals into the Autonomie environment, the process also calculated some of the critical missing signals, such as each component effort or flow signal. Numerous analysis functions have been developed to quickly analyze the shifting map, using the integrated test data in Autonomie to generate model parameters. In addition, a set of calibrations for the generic shifting algorithm was developed to match the test data. Finally, we demonstrated the validation of Autonomie transmission component models and shifting control strategy by using medium duty vehicle test data over different driving records.

midsize SUV, and pickup trucks); and Fuels (i.e., gasoline, diesel, hydrogen, and battery electricity). These various technologies are assessed for six different timeframes: laboratory years 2015, 2020, 2025, 2030, and 2045. A delay of 5 years is assumed between laboratory year and model year (year technology is introduced into production). Finally, uncertainties are included for both technology performance and cost aspects by considering two cases: Low case, aligned with DOE technology manager estimates of expected original equipment manufacturer (OEM) improvements based on regulations, business as usual; and High case, aligned with aggressive technology advancements based on R&D targets developed through support by VTO & HFTO. These scenarios are not intended as predictions of future performances. The energy and cost impact of different technologies were estimated using Autonomie (www.autonomie.net), Argonne vehicle system simulation tool. Autonomie is a state-of-the-art vehicle system simulation tool used to assess the energy consumption, performance and cost of multiple advanced vehicle technologies across classes (from light to heavy duty), powertrains (from conventional to HEVs, FCEVs, PHEVs and BEVs), components and control strategies. Autonomie is packaged with a complete set of vehicle models for a wide range of vehicle classes, powertrain configurations and component technologies, including vehicle level and component level controls. These controls were developed and calibrated using dynamometer test data. Autonomie has been used to support a wide range of studies including analyzing various component technologies, sizing powertrains components for different vehicle requirements, comparing the benefits of powertrain configurations, optimizing both heuristic and route based vehicle energy control and predicting transportation energy use when paired with a traffic modeling tool such as POLARIS. This report documents the assumptions and estimates the vehicle-level energy consumption benefits and associated technology costs for the various types of light duty vehicles. All details of vehicle assumptions and simulation results are available in the spreadsheets accompanying this report.

In 1975, Congress passed the Energy Policy and Conservation Act (EPCA), requiring standards for corporate average fuel economy (CAFE), and charging the U.S. Department of Transportation (DOT) with the establishment and enforcement of these standards. The Secretary of Transportation has delegated these responsibilities to the National Highway Traffic Safety Administration (NHTSA). NHTSA has contracted the DOT Volpe National Transportation Systems Center (Volpe Center) to provide analytical support for NHTSA’s regulatory and analytical activities related to fuel economy standards. Unlike long-standing safety and criteria pollutant emissions standards, fuel economy standards apply to manufacturers’ overall fleets rather than to individual vehicle models. In developing the standards, NHTSA made use of the CAFE Compliance and Effects Modeling System (the CAFE model), which was developed by DOT’s Volpe Center for the 2005–2007 CAFE rulemaking and has been continually updated since. The model is the primary tool used by the agency to evaluate potential CAFÉ stringency levels by applying technologies incrementally to each manufacturer’s fleet until the requirements under consideration are met. The CAFE model relies on numerous technology-related and economic inputs, such as market forecasts and technology cost and effectiveness estimates. These inputs are categorized by vehicle classification, technology synergies, phase-in rates, cost learning curve adjustments, and technology decision trees. The Volpe Center assists NHTSA in the development of the engineering and economic inputs to the CAFE model by analyzing the application of potential technologies to the current automotive industry vehicle fleet to determine the feasibility of future CAFÉ standards, the associated costs, and the benefits of the standards. Part of the CAFE model’s function is to estimate CAFE improvements that a given manufacturer could achieve by applying additional technologies to specific vehicles in its product line. Because CAFÉ standards apply to the average fuel economy across manufacturers’ entire fleets of new passenger cars and light trucks, the model, when simulating manufacturers’ potential application of technology, considers the entire range of each manufacturer’s product line. This typically involves accounting for more than 1,000 distinct vehicle models and variants, many more than can be practically examined using full vehicle simulation (or the other techniques mentioned above). Instead, the model uses estimates of the effectiveness of specific technologies for a representative vehicle in each vehicle class, and arranges technologies in decision trees defining logical progressions from lower to higher levels of cost, complexity, development requirements, and/or implementation challenges. All inputs to CAFE’s decision tree model are related to the effectiveness (fuel consumption reduction) of each fuel-saving technology. Although vehicle testing could be used to estimate these factors, vehicle testing that spans many vehicle types and technology combinations could be prohibitively resource-intensive. Another alternative, either as a substitute for or as a complement to vehicle testing, is to make greater use of vehicle simulation. Full vehicle simulation tools use physics-based mathematical equations, engineering characteristics (e.g., engine maps, transmission shift points, hybrid vehicle control strategies), and explicit drive cycles to predict the effectiveness of individual fuel-saving technologies as well as their combinations. Argonne National Laboratory, a U.S. DOE national laboratory, has developed a full vehicle simulation tool, Autonomie, which has become one of the industry’s standard tools for analyzing vehicle performance, energy consumption, and technology effectiveness. Through an Inter Agency Agreement, the DOE Argonne Site Office and Argonne National Laboratory have been tasked with conducting full vehicle simulation t

The technological advance of electrochemical energy storage and the electric powertrain has led to rapid growth in the deployment of electric vehicles. The high cost and the added weight of the batteries have limited the size (energy storage capacity) and, therefore, the driving range of these vehicles. However, consumers are steadily purchasing these vehicles because of the fast acceleration, quiet ride, and high energy efficiency. The higher pack-to-wheel efficiency and the lower energy cost per mile, as well as the lower expense for maintenance and repair, translate to operating savings over conventional vehicles. This paper compares battery electric vehicles with internal combustion engine vehicles based on the total cost of ownership. It is seen that the higher initial cost of electric vehicles can be recovered in as little as 5 years. This is especially true for electric vehicles with shorter driving ranges. Specifically, a vehicle with an electric driving range under 200 miles may achieve cost parity with an equivalent internal combustion engine vehicle in 8 years or less.