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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 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 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.

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).

This paper presents the energy savings of an automated driving control applied to an electric vehicle based on the on-track testing results. The control is a universal speed planner that analytically solves the eco-driving optimal control problem, within a receding horizon framework and coupled with trajectory tracking lower-level controls. The automated eco-driving control can take advantage of signal phase and timing (SPaT) provided by approaching traffic lights via vehicle-to-infrastructure (V2I) communications. At each time step, the controller calculates the accelerator and brake pedal position (APP/BPP) based on the current state of the vehicle and the current and future information about the surrounding environment (e.g., speed limits, traffic light phase).

This paper introduces a new systematic workflow for the rapid evaluation of energy-efficient automated driving controls in real vehicles in controlled laboratory conditions. This vehicle-in-the-loop (VIL) workflow, largely standardized and automated, is reusable and customizable, saves time and minimizes costly dynamometer time. In the first case study run with the VIL workflow, an automated car driven by an energy-efficient driving control previously developed at Argonne used up to 22 % less energy than a conventional control. In a VIL experiment, the real vehicle, positioned on a chassis dynamometer, has a digital twin that drives in a virtual world that replicates real-life situations, such as approaching a traffic signal or following other vehicles.

Expanding upon the authors’ previous work which utilized a GT-Power model of the Cooperative Fuels Research (CFR) engine under Research Octane Number (RON) conditions, this work defines the boundary conditions of the CFR engine under Motored Octane Number (MON) test conditions. The GT-Power model was validated against experimental CFR engine data for primary reference fuel (PRF) blends between 60 and 100 under standard MON conditions, defining the full range of interest of MON for gasoline-type fuels. The CFR engine model utilizes a predictive turbulent flame propagation sub-model, and a chemical kinetic solver for the end-gas chemistry. The validation was performed simultaneously for thermodynamic and chemical kinetic parameters to match in-cylinder pressure conditions, burn rate, and knock point prediction with experimental data, requiring only minor modifications to the flame propagation model from previous model iterations.

Advanced compression ignition (ACI) operation in gasoline direct injection (GDI) engines is a promising concept to reduce fuel consumption and emissions at part load conditions. However, combustion phasing control and the limited operating range in ACI mode are a perennial challenge. In this study the combined impact of fuel properties and engine control strategies in ACI operation are investigated. A design of experiments method was implemented using a three level orthogonal array to determine the sensitivity of engine control parameters on the engine load, combustion noise and stability under low load ACI operation for three RON 98 gasoline fuels, each exhibiting disparate chemical composition. Furthermore, the thermodynamic state of the compression histories was studied with the aid of the pressure-temperature framework.

Reduced-order models typically assume that the flow through the injector orifice is quasi-steady. The current study investigates to what extent this assumption is true and what factors may induce large-scale variations. Experimental data were collected from a single-hole metal injector with a smoothly converging hole and from a transparent facsimile. Gas, likely indicating cavitation, was observed in the nozzles. Surface roughness was a potential cause for the cavitation. Computations were employed using two engineering-level Computational Fluid Dynamics (CFD) codes that considered the possibility of cavitation. Neither computational model included these small surface features, and so did not predict internal cavitation. At steady state, it was found that initial conditions were of little consequence, even if they included bubbles within the sac. They however did modify the initial rate of injection by a few microseconds.

Currently there is a significant research effort being made in gasoline spark/ignition (SI) engines to understand and reduce cycle-to-cycle variations. One of the phenomena that presents this cycle-to-cycle variation is combustion knock, which also happens to have a very stochastic behavior in modern SI engines. Conversely, the CFR octane rating engine presents much more repeatable combustion knock activity. The aim of this study is to assess the impact of fuel composition on the cycle to cycle variation of the pressure and timing of end gas autoignition. The variation of cylinder conditions at the timing of end-gas autoignition (knock point) for a wide selection of cycle ensembles have been analyzed for several constant RON 98 fuels on the CFR engine, as well as in a modern single-cylinder gasoline direct injection (GDI) SI engine operated at RON-like intake conditions.

This study is a continuation of previous work assessing the robustness of a Cummins XPI common rail injection system operating with gasoline-like fuel. All the hardware from the original study was retained except for the high pressure pump head and check valves which were replaced due to cavitation damage. An additional 400 hour NATO cycle was run on the refurbished fuel system to achieve a total exposure time of 800 hours and detect any other significant failure modes. As in the initial investigation, fuel system parameters including pressures, temperatures and flow rates were logged on a test bench to monitor performance over time. Fuel and lubricant samples were taken every 50 hours to assess fuel consistency, metallic wear, and interaction between fuel and oil. High fidelity driving torque and flow measurements were made to compare overall system performance when operating with both diesel and light distillate fuel.

Validation of a vehicle simulation model of the Chevrolet Volt 2016 was conducted. The Chevrolet Volt 2016 is equipped with the new “Voltec” extended-range propulsion system introduced into the market in 2016. The second generation Volt powertrain system operates in five modes, including two electric vehicle modes and three extended-range modes. Model development and validation were conducted using the test data performed on the chassis dynamometer set in a thermal chamber of Argonne National Laboratory’s Advanced Powertrain Research Facility. First, the components of the vehicle, such as the engine, motor, battery, wheels, and chassis, were modeled, including thermal aspects based on the test data. For example, engine efficiency changes dependent on the coolant temperature, or chassis heating or air-conditioning operations according to the ambient and cabin temperature, were applied.

There are many challenges in implementing grid aware plug-in electric vehicle (PEV) charging systems with local load control. New opportunities for innovative load control were created as a result of changes to the 2014 National Electric Code (NEC) about automatic load control definitions for EV charging infrastructure. Stakeholders in optimized dispatch of EV charging assets include the end users (EV drivers), site owner/operators, facility managers and utilities. NEC definition changes allow for ‘over subscription’ of more potential EV charging station load than can be continuously supported if the total load at any time is within the supply system safety limit. Local load control can be implemented via compact submeter(s) with locally hosted control algorithms using direct communication to the managed electric vehicle supply equipment (EVSE).

This work presents a methodology to determine the off-cycle fuel economy benefit of a 2-Layer HVAC system which reduces ventilation and heat rejection losses of the heater core versus a vehicle using a standard system. Experimental dynamometer tests using EPA drive cycles over a broad range of ambient temperatures were conducted on a highly instrumented 2016 Lexus RX350 (3.5L, 8 speed automatic). These tests were conducted to measure differences in engine efficiency caused by changes in engine warmup due to the 2-Layer HVAC technology in use versus the technology being disabled (disabled equals fresh air-considered as the standard technology baseline). These experimental datasets were used to develop simplified response surface and lumped capacitance vehicle thermal models predictive of vehicle efficiency as a function of thermal state.

The fuel spray behavior in the near nozzle region of a gasoline injector is challenging to predict due to existing pressure gradients and turbulences of the internal flow and in-nozzle cavitation. Therefore, statistical parameters for spray characterization through experiments must be considered. The characterization of spray velocity fields in the near-nozzle region is of particular importance as the velocity information is crucial in understanding the hydrodynamic processes which take place further downstream during fuel atomization and mixture formation. This knowledge is needed in order to optimize injector nozzles for future requirements. In this study, the results of three experimental approaches for determination of spray velocity in the near-nozzle region are presented. Two different injector nozzle types were measured through high-speed shadowgraph imaging, Laser Doppler Anemometry (LDA) and X-ray imaging.

Given the importance of the fuel-injection process on the combustion and emissions performance of gasoline direct injected engines, there has been significant recent interest in understanding the fluid dynamics within the injector, particularly around the needle and through the nozzles. The pressure losses and transients that occur in the flow passages above the needle are also of interest. Simulations of these injectors typically use the nominal design geometry, which does not always match the production geometry. Computed tomography (CT) using x-ray and neutron sources can be used to obtain the real geometry from production injectors, but there are trade-offs in using these techniques. X-ray CT provides high resolution, but cannot penetrate through the thicker parts of the injector. Neutron CT has excellent penetrating power but lower resolution.

The performance of Gasoline Direct Injection (GDI) engines is governed by multiple physical processes such as the internal nozzle flow and the mixing of the liquid stream with the gaseous ambient environment. A detailed knowledge of these processes even for complex injectors is very important for improving the design and performance of combustion engines all the way to pollutant formation and emissions. However, many processes are still not completely understood, which is partly caused by their restricted experimental accessibility. Thus, high-fidelity simulations can be helpful to obtain further understanding of GDI injectors. In this work, advanced simulation and experimental methods are combined in order to study the spray characteristics of two different 3-hole GDI injectors.

It is widely understood that cold ambient temperatures negatively impact vehicle system efficiency. This is due to a combination of factors: increased friction (engine oil, transmission, and driveline viscous effects), cold start enrichment, heat transfer, and air density variations. Although the science of quantifying steady-state vehicle component efficiency is mature, transient component efficiencies over dynamic ambient real-world conditions is less understood and quantified. This work characterizes wheel assembly efficiencies of a conventional and electric vehicle over a wide range of ambient conditions. For this work, the wheel assembly is defined as the tire side axle spline, spline housing, bearings, brakes, and tires. Dynamometer testing over hot and cold ambient temperatures was conducted with a conventional and electric vehicle instrumented to determine the output energy losses of the wheel assembly in proportion to the input energy of the half-shafts.

The 4th Workshop of the Engine Combustion Network (ECN) was held September 5-6, 2015 in Kyoto, Japan. This manuscript presents a summary of the progress in experiments and modeling among ECN contributors leading to a better understanding of soot formation under the ECN “Spray A” configuration and some parametric variants. Relevant published and unpublished work from prior ECN workshops is reviewed. Experiments measuring soot particle size and morphology, soot volume fraction (fv), and transient soot mass have been conducted at various international institutions providing target data for improvements to computational models. Multiple modeling contributions using both the Reynolds Averaged Navier-Stokes (RANS) Equations approach and the Large-Eddy Simulation (LES) approach have been submitted. Among these, various chemical mechanisms, soot models, and turbulence-chemistry interaction (TCI) methodologies have been considered.

The primary strength of large eddy simulation (LES) is in directly resolving the instantaneous large-scale flow features which can then be used to study critical flame properties such as ignition, extinction, flame propagation and lift-off. However, validation of the LES results with experimental or direct numerical simulation (DNS) datasets requires the determination of statistically-averaged quantities. This is typically done by performing multiple realizations of LES and performing a statistical averaging among this sample. In this study, LES of n-dodecane spray flame is performed using a well-mixed turbulent combustion model along with a dynamic structure subgrid model. A high-resolution mesh is employed with a cell size of 62.5 microns in the entire spray and combustion regions. The computational cost of each calculation was in the order of 3 weeks on 200 processors with a peak cell count of about 22 million at 1 ms.