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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.

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.

It is widely recognized that internal combustion engines (ICE) are needed for transport worldwide for years to come, however, demands on ICE fuel efficiency, emissions, cost, and performance are extremely challenging. Gasoline compression ignition (GCI) is one approach to achieve demanding efficiency and emissions targets. At Aramco Research Center-Detroit, an advanced, multi-cylinder GCI engine was designed and built using the latest combustion system, engine controls, and lean aftertreatment. The combustion system uses Aramco’s PPCI-diffusion process for ultra-low NOx and smoke. A P2 48V mild hybrid system was integrated on the engine for braking energy recovery and improved cold starts. For robust low-load operation, a 2-step valvetrain system was used for exhaust rebreathing. Test data showed that part-load fuel consumption was reduced 7 to 10 percent relative to a competitive 2.0L European diesel engine.

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.

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.

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.

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.

Hybrid electric vehicles (HEVs) have seen tremendous improvements in performance, fuel economy and cost over the last two decades. As battery and motor prices decrease, HEVs are likely to be even more attractive to consumers. This study considers how HEVs can improve and whether advancements in engines and other components will play a large role in the HEV segment. Past studies have relied on a rule-based component sizing approach for hybrids to meet certain performance criteria. By going beyond this approach, we can explore the design space by varying engine power and electric drivetrain power. This can provide more insights into the fuel-saving potential of HEVs, and the trade-offs required on performance or cost characteristics to achieve those savings. In this study, we examine the fuel-saving potential of three main hybrid powertrain architectures (parallel, series, and power-split) with varying degrees of hybridization (DOH) and using various engine technologies.

Modern vehicles use automated driving assistance systems (ADAS) products to automate certain aspects of driving, which improves operational safety. In the U.S. in 2020, 38,824 fatalities occurred due to automotive accidents, and typically about 25% of these are associated with inclement weather. ADAS features have been shown to reduce potential collisions by up to 21%, thus reducing overall accidents. But ADAS typically utilize camera sensors that rely on lane visibility and the absence of obstructions in order to function, rendering them ineffective in inclement weather. To address this research gap, we propose a new technique to estimate snow coverage so that existing and new ADAS features can be used during inclement weather. In this study, we use a single camera sensor and historical weather data to estimate snow coverage on the road. Camera data was collected over 6 miles of arterial roadways in Kalamazoo, MI.

Perception in adverse weather conditions is one of the most prominent challenges for automated driving features. The sensors used for mid-to-long range perception most impacted by weather (i.e., camera and LiDAR) are susceptible to data degradation, causing potential system failures. This research series aims to better understand sensor data degradation characteristics in real-world, dynamic environmental conditions, focusing on adverse weather. To achieve this, a dataset containing LiDAR (Velodyne VLP-16) and camera (Mako G-507) data was gathered under static scenarios using a single vehicle target to quantify the sensor detection performance. The relative position between the sensors and the target vehicle varied longitudinally and laterally. The longitudinal position was varied from 10m to 175m at 25m increments and the lateral position was adjusted by moving the sensor set angle between 0 degrees (left position), 4.5 degrees (center position), and 9 degrees (right position).

The US Department of Energy’s Co-Optimization of Engine and Fuels Initiative (Co-Optima) investigated how unique properties of bio-blendstocks considered within Co-Optima help address emissions challenges with mixing controlled compression ignition (i.e., conventional diesel combustion) and enable advanced compression ignition modes suitable for implementation in a diesel engine. Additionally, the potential synergies of these Co-Optima technologies in hybrid vehicle applications in the medium- and heavy-duty sector was also investigated. In this work, vehicles system were simulated using the Autonomie software tool for quantifying the benefits of Co-Optima engine technologies for medium-duty trucks. A Class 6 delivery truck with a 6.7 L diesel engine was used for simulations over representative real-world and certification drive cycles with four different powertrains to investigate fuel economy, criteria emissions, and performance.

This study presents experimental and numerical examination of directly injected (DI) propane and iso-octane, surrogates for liquified petroleum gas (LPG) and gasoline, respectively, at various engine like conditions with the overall objective to establish the baseline with regards to fuel delivery required for future high efficiency DI-LPG fueled heavy-duty engines. Sprays for both iso-octane and propane were characterized and the results from the optical diagnostic techniques including high-speed Schlieren and planar Mie scattering imaging were applied to differentiate the liquid-phase regions and the bulk spray phenomenon from single plume behaviors. The experimental results, coupled with high-fidelity internal nozzle-flow simulations were then used to define best practices in CFD Lagrangian spray models.

This paper examines, for several major markets, the fuel savings achievable with advanced engine technologies as “drop-in” substitutions for existing engines, as well as from increased electric hybridization of the powertrain. Key segments of light duty vehicles in major automotive markets including the US, China, EU, Japan, India, and Saudi Arabia were examined. Representative vehicles for each market were simulated using advanced vehicle modeling tools and evaluated on the relevant local regulatory cycle or cycles. In all cases, to ensure meaningful results, the performance of a given vehicle was maintained as engine and powertrain technology was varied through appropriate resizing of powertrain components. In total, 4 engine technologies and 5 powertrain architectures were simulated for 5 different markets.

Connected and automated technologies poised to change the way vehicles operate are starting to enter the mainstream market. Methods to accurately evaluate these technologies, in particular for their impact on safety and energy, are complex due to the influence of static and environmental factors, such as road environment and traffic scenarios. Therefore, it is important to develop modeling and testing frameworks that can support the development of complex vehicle functionalities in a realistic environment. Microscopic traffic simulations have been increasingly used to assess the performance of connected and automated vehicle technologies in traffic networks. In this paper, we propose and apply an evaluation method based on a combination of microscopic traffic simulation (AIMSUN) and a chassis dynamometer-based vehicle-in-the-loop environment, developed at Argonne National Laboratory.

The objective of this study was to evaluate the fuel saving potential of various hybrid powertrain architectures for medium and heavy duty vehicles. The relative benefit of each powertrain was analyzed, and the observed fuel savings was explained in terms of operational efficiency gains, regenerative braking benefits from powertrain electrification and differences in vehicle curb weight. Vehicles designed for various purposes, namely urban delivery, utility, transit, refuse, drayage, regional and long haul were included in this work. Fuel consumption was measured in regulatory cycles and various real world representative cycles. A diesel-powered conventional powertrain variant was first developed for each case, based on vehicle technical specifications for each type of truck. Autonomie, a simulation tool developed by Argonne National Laboratory, was used for carrying out the vehicle modeling, sizing and fuel economy evaluation.

National concerns over energy consumption and emissions from the transportation sector have prompted regulatory agencies to implement aggressive fuel economy targets for light-duty vehicles through the U.S. National Highway Traffic Safety Administration/Environmental Protection Agency (EPA) Corporate Average Fuel Economy (CAFE) program. Automotive manufacturers have responded by bringing competitive technologies to market that maximize efficiency while meeting or exceeding consumer performance and comfort expectations. In a collaborative effort among Toyota Motor Corporation, Argonne National Laboratory (ANL), and the National Renewable Energy Laboratory (NREL), the real-world savings of one such technology is evaluated. A commercially available Toyota Highlander equipped with two-phase cold storage technology was tested at ANL’s chassis dynamometer testing facility.

Automated driving functionalities delivered through Advanced Driver Assistance System (ADAS) have been adopted more and more frequently in consumer vehicles. The development and implementation of such functionalities pose new challenges in safety and functional testing and the associated validations, due primarily to their high demands on facility and infrastructure. This paper presents a rather unique Vehicle-in-the-Loop (VIL) test setup and methodology compared those previously reported, by combining the advantages of the hardware-in-the-loop (HIL) and traditional chassis dynamometer test cell in place of on-road testing, with a multi-agent real-time simulator for the rest of test environment.

Achieving robust ignitability for compression ignition of diesel engines at cold conditions is traditionally challenging due to insufficient fuel vaporization, heavy wall impingement, and thick wall films. Gasoline compression ignition (GCI) has shown the potential to offer an enhanced NOx-particulate matter tradeoff with diesel-like fuel efficiency, but it is unknown how the volatility and reactivity of the fuel will affect ignition under very cold conditions. Therefore, it is important to investigate the impact of fuel physical and chemical properties on ignition under pressures and temperatures relevant to practical engine operating conditions during cold weather. In this paper, 0-D and 3-D computational fluid dynamics (CFD) simulations of GCI combustion at cold conditions were performed.

The U.S. Department of Energy, Vehicle Technologies Office (U.S. DOE-VTO) has been developing more energy-efficient and environmentally friendly highway transportation technologies that would enable the United States to burn less petroleum on the road. System simulation is an accepted approach for evaluating the fuel economy potential of advanced (future) technology targets. U.S. DOE-VTO defines the targets for advancement in powertrain technologies (e.g., engine efficiency targets, battery energy density, lightweighting, etc.) Vehicle system simulation models based on these targets have been generated in Autonomie, reflecting the different EPA classifications of vehicles for different advanced timeframes as part of the DOE Benefits and Scenario (BaSce) Analysis. It is also important to evaluate the progress of these component technical targets compared to existing technologies available in the market.

As emerging automated vehicle technology is making advances in safety and reliability, engineers are also exploring improvements in energy efficiency with this new paradigm. Powertrain efficiency receives due attention, but also impactful is finding ways to reduce driving losses in coordinated-driving scenarios. Efforts focused on simulation to quantify road load improvements require a sufficient amount of background validation work to support them. This study uses a practical approach to directly quantify road load changes by testing the coordinated driving of two vehicles on a test track at various speeds (64, 88, 113 km/h) and vehicle time gaps (0.3 to 1.3 s). Axle torque sensors were used to directly measure the load required to maintain steady-state speeds while following a lead vehicle at various gap distances.