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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.

This research investigates the energy savings achieved through eco-driving controls in connected and automated vehicles (CAVs), with a specific focus on the influence of powertrain characteristics. Eco-driving strategies have emerged as a promising approach to enhance efficiency and reduce environmental impact in CAVs. However, uncertainty remains about how the optimal strategy developed for a specific CAV applies to CAVs with different powertrain technologies, particularly concerning energy aspects. To address this gap, on-track demonstrations were conducted using a Chrysler Pacifica CAV equipped with an internal combustion engine (ICE), advanced sensors, and vehicle-to-infrastructure (V2I) communication systems, compared with another CAV, a previously studied Chevrolet Bolt electric vehicle (EV) equipped with an electric motor and battery.

The Argon Power Cycle (APC) is a novel zero-emission closed-loop argon recirculating engine cycle which has been developed by Noble Thermodynamics Systems, Inc. It provides a significant gain in indicated thermal efficiency of the reciprocating engine by breathing oxygen and argon rather than air. The use of argon, a monatomic gas, greatly increases the specific heat ratio of the working fluid, resulting in a significantly higher ideal Otto cycle efficiency. This technology delivers a substantial improvement in reciprocating engine performance, maximizing the energy conversion of fuel into useful work. Combined Heat and Power (CHP) operating under the APC represents a promising solution to realize a net-zero-carbon future, providing the thermal energy that hard-to-electrify manufacturing processes need while at the same time delivering clean, dispatchable, and efficient power.

The internal combustion engine (ICE) has long dominated the heavy-duty sector by using liquid fossil fuels such as diesel but global commitments by countries and OEMs to reduce lifecycle carbon dioxide (CO2) emissions has garnered interest in alternative fuels like hydrogen. Hydrogen is a unique gaseous fuel that contains zero carbon atoms and has desired thermodynamic properties of high energy density per unit mass and high flame speeds. However, there are challenges related to its adoption to the heavy-duty sector as a drop-in fuel replacement for compression ignition (CI) diesel combustion given its high autoignition resistance. To overcome this fundamental barrier, engine manufacturers are exploring dual fuel combustion engines by substituting a fraction of the diesel fuel with hydrogen which enables fuel flexibility when there is no infrastructure and retrofittability to existing platforms.

As the mobility being developed becomes more complex and numerous, it is becoming difficult and inefficient to apply current vehicle-test-based development. To overcome this, research on combining test and simulation models has been actively conducted to perform objective and subjective evaluations more accurately and efficiently in the advance stage without a vehicle over the years. At first, test models for various systems such as tire, suspension and body were made compatible with simulation models by using various methodologies such as blocked forces, FBS decoupling, and Virtual Point Transformation (VPT). The second step was to objectively estimate road noise by using FBS coupling with system models and to deeply analyze transfer paths and system’s sensitivity. The results were verified by comparing with what was measured and analyzed on vehicle.

To properly compare and contrast the environmental performance of one vehicle technology against another, it is necessary to consider their production, operation, and end-of-life fates. Since 1995, Argonne’s GREET® life cycle analysis model (Greenhouse gases, Regulated Emissions, and Energy use in Technologies) has been annually updated to model and refine the latest developments in fuels and materials production, as well as vehicle operational and composition characteristics. Updated cradle-to-grave life cycle analysis results from the model’s latest release are described for a wide variety of fuel and powertrain options for U.S. light-duty and medium/heavy-duty vehicles. Light-duty vehicles include a passenger car, sports utility vehicle (SUV), and pick-up truck, while medium/heavy-duty vehicles include a Class 6 pickup-and-delivery truck, Class 8 day-cab (regional) truck, and Class 8 sleeper-cab (long-haul) truck.

The autoignition chemistry of fuels depends on the pressure, temperature, and time history that the fuel-air mixture experiences during the compression stroke. While piezoelectric pressure transducers offer excellent means of pressure measurement, temperature measurements are not commonly available and must be estimated. Even if the pressure and temperature at the intake and exhaust ports are measured, the residual gas fraction (RGF) within the combustion chamber requires estimation and greatly impacts the temperature of the fresh charge at intake valve closing. This work replaced the standard D1 Detonation Pickup of a CFR engine with a rapid sampling valve to allow for in-cylinder gas sampling at defined crank-angle times during the compression stroke. The extracted cylinder contents were captured in an emissions sample bag and its composition was subsequently analyzed in an AVL i60 emissions bench.

During icing wind tunnel experiments, the calibration process of the spray nozzle and aerothermal systems introduces experimental uncertainty that can potentially compromise the reliability of the test results. Therefore, performing sensitivity analysis (SA) or uncertainty quantification (UQ) studies is not only essential to determine the influence of uncertainties on the ice shape and aerodynamic performance but also crucial to identify the most significant icing parameter uncertainty. However, given the wide range of icing envelopes, it is not practical to conduct SA and UQ by experimental method because a lot of evaluations are required for SA and UQ study. In this study, we addressed these challenges by using a deep learning-based reduced-order modeling technique.

Diagonalized alternating-direction implicit (DADI) method is implemented in the Eulerian hyperbolic droplet solver, ICEPAC, for efficient high-order accurate analysis of aircraft icing. Detailed techniques for implementing the DADI method considering hyperbolicity characteristics are discussed. For the Eulerian droplet equation system to be strictly hyperbolic, additional source terms regarding artificial droplet pressure are included. Validations of the present implicit solver are conducted using two- and three-dimensional steady benchmark tests: NACA0012 airfoil, NACA23012 airfoil, and a swept wing. Also, the oscillating airfoil SC2110 case was analyzed to verify the robustness and efficiency of the proposed solver. In addition, the computational cost of the current implicit solver is considerably lower than that of the explicit multi-stage solver.

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.

This investigation utilizes a correlated fluid-structure interaction (FSI) model of the torque converter and clutch assembly to perform a pseudo transient clutch engagement at steady state operating conditions. The pseudo transient condition consists of a series of nine steady state simulations that transition the torque converter clutch from fully released to near full lockup at a constant input torque and output speed representative of a highway cruising speed. The flow and pressured field of the torque converter torus and clutch are solved using a CFD model and then passed along to a transient structural model to determine the torque capacity of the lockup clutch. Bulk property assumptions regarding the friction material, deformation of the clutch plate, and deflection of supporting structures were made to simplify the model setup, run time, and solution convergence.

Internal combustion engines will continue to be the leading power-train in the heavy-duty, on-highway sector as technologies like hydrogen, fuel cells, and electrification face challenges. Natural gas (NG) engines offer several advantages over diesel engines including near zero particle matter (PM) emissions, lower NOx emissions, lower capital and operating costs, availability of vast domestic NG resources, and lower CO2 emissions being the cleanest burning of all hydrocarbons (HC). The main limitation of this type of engine is the lower efficiency compared to diesel counterparts. Addressing the limitations (knock and misfire) for achieving diesel-like efficiencies is key to accomplishing widespread adoption, especially for the US market. With the aim to achieve high brake thermal efficiency (BTE), three (3) computational fluid dynamics (CFD) optimized pistons with three different compression ratios (CR) have been tested.

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.

Extreme Fast Charging (XFC) infrastructure is crucial for an increase in electric vehicle (EV) adoption. However, an unmanaged implementation may lead to negative grid impacts and huge power costs. This paper presents an optimal energy management strategy to utilize grid-connected Energy Storage Systems (ESS) integrated with XFC stations to mitigate these grid impacts and peak demand charges. To achieve this goal, an algorithm that controls the charge and discharge of ESS based on an optimal power threshold is developed. The optimal power threshold is determined to carry out maximum peak shaving for given battery size and SOC constraints.

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.

This paper presents a stability monitoring algorithm with a combined slip tire model for maximized cornering speed of high-speed autonomous driving. It is crucial to utilize the maximum tire force with maintaining a grip driving condition in cornering situations. The model-free cruise controller has been designed to track the desired acceleration. The lateral motion has been regulated by the sliding mode controller formulated with the center of percussion. The controllers are suitable for minimizing the behavior errors. However, the high-level algorithm is necessary to check whether the intended motion is inside of the limit boundaries. In extreme diving conditions, the maximum tire force is limited by physical constraints. A combined slip tire model has been applied to monitor vehicle stability. In previous studies, vehicle stability was evaluated only by vehicle acceleration.

This paper presents a methodology of trajectory planning for the surrounding-aware lane change maneuver of autonomous vehicles based on a data-driven method. The lateral motion is planned by sampling candidate patterns which are defined based on quintic polynomial functions over time. Based on the cost evaluation among the sampled candidates, the optimal lateral motion pattern is selected as a reference and tracked by the controller. The longitudinal motion is planned and controlled using Model Predictive Control (MPC) which is an optimal control method designed considering the surrounding traffic information. To realize the lane change motion similar to the human driving behavior in the surrounding traffic situation, the human driving pattern is modeled in the form of motion parameters and considered in planning the lateral and longitudinal motion.

This paper describes a hierarchical motion planning and control framework for overtaking maneuvers under racing circumstances. Unlike urban or highway autonomous driving conditions, race track driving requires longer prediction and planning horizons in order to respond to upcoming corners at high speed. In addition, the subject vehicle should determine the optimal action among possible driving modes when opponent vehicles are present. In order to meet these requirements and secure real time performance, a hierarchical architecture for decision making, motion planning, and control for an autonomous racing vehicle is proposed. The supervisor determines whether the subject vehicle should stay behind the preceding vehicle or overtake, and its direction when overtaking. Next, a high level trajectory planner generates the desired path and velocity profile in a receding horizon fashion.