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When designing an electric vehicle (EV) traction system, overcoming the issues arising from the variations in the battery voltage due to the state of charge (SoC) is critical, which otherwise can lead to a deterioration of the powertrain energy efficiency and overall drive performance. However, systems are typically documented under fixed voltage and temperature conditions, potentially lacking comprehensive specifications that account for these variations across the entire range of the vehicle operating regions. To tackle this challenge, this paper seeks to adjust an optimal DC-link voltage across the complete range of drive operating conditions by integrating a DC-DC converter into the powertrain, thereby enhancing powertrain efficiency. This involves conducting a comprehensive analysis of power losses in the power electronics of a connected converter-inverter system considering the temperature variations, along with machine losses, accounting for variable DC-link voltages.

This paper presents a feedback control strategy to minimize noise during dog clutch engagement in a hybrid transmission. The hybrid transmission contains an internal combustion engine(ICE) and 2 electric motors in P1 and P3 configurations. For efficiency during driving, at high vehicle speeds ICE is connected to wheels, via the dog clutch, hence shifting the vehicle from series to parallel hybrid mode. It is shown by experimental results that if the speed difference between the two sides of the dog clutch is below a certain level the engagement will be without clonk noise. In this paper the designed state feedback Linear Quadratic Integral (LQI) control provides the synchronization torque request to the P1 motor, hence matching the speed of one side of dog clutch with the other under the disturbance from combustion torque of the engine.

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 proposed Euro 7 regulation aims to substantially reduce the NOx emissions to 0.03 g/km, a trend also seen in upcoming China 6b and US EPA regulations. Meeting these stringent requirements necessitates advancements in Urea/Selective Catalytic Reduction (SCR) aftertreatment systems, with the urea deposit formation being a key challenge to its design. It’s proven that Computational Fluid Dynamics (CFD) can be an effective tool to predict Urea deposits. Transient wall temperature prediction is crucial in Urea deposit modeling. Additionally, fully understanding the kinetics of urea decomposition and by-products solidification are also critical in predicting the deposit amount and its location. In this study, we introduce (i) a novel film boiling model (IFPEN-BRT model) and (ii) a new urea by-product solidification model in the CONVERGE CFD commercial solver, and validate the results against the recent experiments.

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.

The transition towards battery electric vehicles (BEVs) has increased the focus of vehicle manufacturers on energy efficiency. Ensuring adequate airflow through the heat exchanger is necessary to climatize the vehicle, at the cost of an increase in the aerodynamic drag. With lower cooling airflow requirements in BEVs during driving, the front air intakes could be made smaller and thus be placed with greater freedom. This paper explores the effects on exterior aerodynamics caused by securing a constant cooling airflow through intakes at various positions across the front of the vehicle. High-fidelity simulations were performed on a variation of the open-source AeroSUV model that is more representative of a BEV configuration. To focus on the exterior aerodynamic changes, and under the assumption that the cooling requirements would remain the same for a given driving condition, a constant mass flow boundary condition was defined at the cooling airflow inlets and outlets.

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.

This paper investigates the gaseous and particulate emissions of a hydrogen powered direct injection spark ignition engine. Experiments were performed over different engine speeds and loads and with varying air- fuel ratio, start of injection and intake manifold pressure. An IAG FTIR system was used to detect and measure a variety of gaseous emissions, which include standard emissions such as NOX and unburned hydrocarbons as well as some non-standard emissions such as formaldehyde, formic acid, and ammonia. The particle number concentration and size distribution were measured using a DMS 500 fast particle analyzer from Cambustion. Particle composition was investigated using ICP analysis as well as a Sunset OC/EC analyzer to determine the soot content and the presence of any unburned engine oil. The results show that NOX emissions range between 0.1 g/kWh for a λ of 2.5 and 10 g/kWh λ of 1.5.

The transport sector is experiencing a shift to zero-carbon powertrains driven by aggressive international policies aiming to fight climate change. Battery electric vehicles (BEVs) will play the main role in passenger car applications, while diversified solutions are under investigation for the heavy-duty sector. Within this framework, Light Commercial Vehicles (LCVs) impact is not negligible and accountable for about 2.5% of greenhouse gas (GHG) emissions in Europe. In this regard, few LCV comparative assessments on green powertrains are available in the scientific literature and justified by the fact that several factors and limitations should be considered and addressed to define optimal powertrain solutions for specific use cases. The proposed research study deals with a comparative numerical assessment of different zero-carbon powertrain solutions for LCV. BEVs are compared to hydrogen-based fuel cells (FC) and internal combustion engines (ICE) powered vehicles.

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.

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.

Heavy-duty vehicles are primarily powered by diesel fuel, emitting CO2 emissions regardless of the exhaust after-treatment system. Contrastingly, a hydrogen engine has the potential to decarbonize the transportation sector as hydrogen is a carbon free, renewable fuel. In this study, a multi-physics 1D simulation tool (GT-Power) is used to model the gas exchange process and performance prediction of a two-stroke hydrogen engine. The aim is to establish a maximum torque-level for a four-stroke hydrogen engine and then utilize different methods for two-stroke modeling to achieve similar torque by optimizing the gas exchange process. A camless engine is used as base, enabling the flexibility to utilize approximately square valve lift profiles. The preliminary step is the GT-Power model validation, which has been done using diesel and hydrogen engines (single-cylinder heavy-duty) experiments at different operating points (871 rpm, 1200 rpm, 1259 rpm, and 1508 rpm).

Gasoline compression ignition shows great potential in reducing NOx and soot emissions with competitive thermal efficiency by leveraging the properties of gasoline fuels and the high compression ratio of compression ignition engines operating air-dilute. Meanwhile, its control becomes challenging due to not only the properties of different gasoline-type fuels but also the impacts of injection strategies on the in-cylinder reactivity. As such, a computationally efficient zero-dimension combustion model can significantly reduce the cost of control development. In this study, a previously developed zero-dimension combustion model for gasoline compression ignition was extended to multiple gasoline-type fuel blends and a port fuel injection/direct fuel injection strategy. Tests were conducted on a 12.4-liter heavy-duty engine with five fuel blends.

In vehicle development, a subjective evaluation of the vehicle’s behavior at high speeds is usually conducted by experienced drivers with the objective of assessing driving stability. To avoid late design changes, it is desirable to predict and resolve perceived instabilities early in the development phase. In this study, a mathematical model is developed from measurements during on-road tests to predict the driver’s ability to identify vehicle instabilities under excitations such as aerodynamic excitations. A vehicle is fitted with add-ons to create aerodynamic excitations and is driven by multiple drivers on a high-speed track. Drivers’ evaluation, responses, cabin motion, and crosswind conditions are recorded. The influence of yaw and roll rates, lateral acceleration, and steering angle at various frequency ranges when predicting the drivers’ evaluation of induced excitation is demonstrated. The drivers’ evaluation of vehicle behavior is influenced by driver-vehicle interactions.

Ammonia is a promising carbon-free alternative fuel for use in combustion systems. The main associated challenges are its relatively low reactivity and high NOx emissions compared to conventional fuels. Therefore, the combustion behaviour of ammonia and ammonia blends still needs to be better understood over a wide range of conditions. To this end, a comprehensive chemical kinetic mechanism C3MechV3.4, which is an update of C3MechV3.3, has been developed for improved predictions of the combustion of ammonia and ammonia blends. C3MechV3.4 has been validated using a wide range of experimental results for pure ammonia and ammonia/hydrogen, ammonia/methanol and ammonia/n-heptane blends. These validations target different data sets including ignition delay times, species profiles measured as a function of time, and/or temperature and laminar flame speeds over a wide range of conditions.

Gasoline knock resistance is characterized by the Research and Motor Octane Number (RON and MON), which are rated on the CFR octane rating engine at naturally aspirated conditions. However, modern automotive downsized boosted spark ignition (SI) engines generally operate at higher cylinder pressures and lower temperatures relative to the RON and MON tests. Using the naturally aspirated RON and MON ratings, the octane index (OI) characterizes the knock resistance of gasolines under boosted operation by linearly extrapolating into boosted “beyond RON” conditions via RON, MON, and a linear regression K factor. Using OI solely based on naturally aspirated RON and MON tests to extrapolate into boosted conditions can lead to significant errors in predicting boosted knock resistance between gasolines due to non-linear changes in autoignition and knocking characteristics with increasing pressure conditions.