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Diesel engines have been used in large vehicles, locomotives and ships as more efficient alternatives to the gasoline engines. They have also been used in small passenger vehicle applications, but have not been as popular as in other applications until recently. The two main factors that kept them from becoming the major contender in the small passenger vehicle applications were the low power outputs and the noise levels. A combination of improved mechanical technologies such as multiple injection, higher injection pressure, and advanced electronic control has mostly mitigated the problems associated with the noise level and changed the public notion of the Diesel engine technology in the latest generation of common-rail designs. The power output of the Diesel engines has also been improved substantially through the use of variable geometry turbines combined with the advanced fuel injection technology.

The Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model incorporated fuel economy and electricity use of alternative fuel/vehicle systems simulated by the Powertrain System Analysis Toolkit (PSAT) to conduct a well-to-wheels (WTW) analysis of energy use and greenhouse gas (GHG) emissions of plug-in hybrid electric vehicles (PHEVs). Based on PSAT simulations of the blended charge depleting (CD) operation, grid electricity accounted for a share of the vehicle’s total energy use ranging from 6% for PHEV 10 to 24% for PHEV 40 based on CD vehicle mile traveled (VMT) shares of 23% and 63%, respectively. Besides fuel economy of PHEVs and type of on-board fuel, the type of electricity generation mix impacted the WTW results of PHEVs, especially GHG emissions.

EGR coolers are effective to reduce NOx emissions from diesel engines due to lower intake charge temperature. EGR cooler fouling reduces heat transfer capacity of the cooler significantly and increases pressure drop across the cooler. Engine coolant provided at 40–90 C is used to cool EGR coolers. The presence of a cold surface in the cooler causes particulate soot deposition and hydrocarbon condensation. The experimental data also indicates that the fouling is mainly caused by soot and hydrocarbons. In this study, a 1-D model is extended to simulate particulate soot and hydrocarbon deposition on a concentric tube EGR cooler with a constant wall temperature. The soot deposition caused by thermophoresis phenomena is taken into account the model. Condensation of a wide range of hydrocarbon molecules are also modeled but the results show condensation of only heavy molecules at coolant temperature.

Boosted Homogeneous Charge Compression Ignition (HCCI) has been modeled and has demonstrated the potential to extend the engine's upper load limit. A commercially available engine simulation software (GT-PowerÖ) coupled to the University of Michigan HCCI combustion and heat transfer correlations was used to model a 4-cylinder boosted HCCI engine with three different boosting configurations: turbocharging, supercharging and series turbocharging. The scope of this study is to identify the best boosting approach in order to extend the HCCI engine's operating range. The results of this study are consistent with the literature: Boosting helps increase the HCCI upper load limit, but matching of turbochargers is a problem. In addition, the low exhaust gas enthalpy resulting from HCCI combustion leads to high pressures in the exhaust manifold increasing pumping work. The series turbocharging strategy appears to provide the largest load range extension.

This paper introduces a Hydraulic Linear Engine (HLE) concept and describes a model to simulate instantaneous engine behavior. The United States Environmental Protection Agency has developed an HLE prototype as an evolution of their previous six-cylinder, four-stroke, free-piston engine (FPE) hardware. The HLE design extracts work hydraulically, in a fashion identical to the initial FPE, and is intended for use in a series hydraulic hybrid vehicle. Unlike the FPE, however, the HLE utilizes a crank for improved timing control and increased robustness. Preliminary experimental results show significant speed fluctuations and cylinder imbalance that require careful controls design. This paper also introduces a model of the HLE that exhibits similar behavior, making it an indispensible tool for controls design. Further, the model's behavior is evaluated over a range of operating conditions currently unobtainable by the experimental setup.

This paper includes analysis results for the control strategy of the Peugeot 3008 Hybrid4, a diesel-electric hybrid vehicle, under different thermal conditions. The analysis was based on testing results obtained under the different thermal conditions in the Advanced Powertrain Research Facility (APRF) at Argonne National Laboratory (ANL). The objectives were to determine the principal concepts of the control strategy for the vehicle at a supervisory level, and to understand the overall system behavior based on the concepts. Control principles for complex systems are generally designed to maximize the performance, and it is a serious challenge to determine these principles without detailed information about the systems. By analyzing the test results obtained in various driving conditions with the Peugeot 3008 Hybrid4, we tried to figure out the supervisory control strategy.

Manufacturers have been considering various technology options to improve vehicle fuel economy. Some of the most promising technologies are related to vehicle electrification. To evaluate the benefits of vehicle electrification to support the 2017-2025 CAFE regulations, a study was conducted to simulate many of the most common electric drive powertrains currently available on the market: 12V Micro Hybrid Vehicle (start/stop systems), Belt-integrated starter generator (BISG), Crank-integrated starter generator (CISG), Full Hybrid Electric Vehicle (HEV), PHEV with 20-mile all-electric range (AER) (PHEV20), PHEV with 40-mile AER (PHEV40), Fuel-cell HEV and Battery Electric vehicle with 100-mile AER (EV100). Different vehicle classes were also analyzed in the study process: Compact, Midsize, Small SUV, Midsize SUV and Pickup. This paper will show the fuel displacement benefit of each powertrain across vehicle classes.

Dual-fuel combustion using liquid fuels with differing reactivity has been shown to achieve low-temperature combustion with moderate peak pressure rise rates, low soot and NOx emissions, and high indicated efficiency. Varying fractions of gasoline-type and diesel-type fuels enable operation across a range of low- and mid-load operating conditions. Expanding the operating range to cover the full operating range of a heavy-duty diesel engine, while maintaining the efficiency and emissions benefits, is a key objective. With dissimilar properties of the two utilized fuels lying at the heart of the dual-fuel concept, a tool for enabling this load range expansion is altering the properties of the two test fuels - this study focuses on altering the reactivity of the diesel fuel component. Tests were conducted on a 13L six-cylinder heavy-duty diesel engine modified to run dual-fuel combustion with port gasoline injection to supplement the direct diesel injection.

Low temperature combustion through in-cylinder blending of fuels with different reactivity offers the potential to improve engine efficiency while yielding low engine-out NOx and soot emissions. A Navistar MaxxForce 13 heavy-duty compression ignition engine was modified to run with two separate fuel systems, aiming to utilize fuel reactivity to demonstrate a technical path towards high engine efficiency. The dual-fuel engine has a geometric compression ratio of 14 and uses sequential, multi-port-injection of a low reactivity fuel in combination with in-cylinder direct injection of diesel. Through control of in-cylinder charge reactivity and reactivity stratification, the engine combustion process can be tailored towards high efficiency and low engine-out emissions. Engine testing was conducted at 1200 rpm over a load sweep.

Effectively obtaining physical parameters for vehicle dynamic model is the key to successfully performing any computer-based dynamic analysis, control strategy development or optimization. For a spring and lump mass vehicle model, which is a type of vehicle model widely used, its physical parameters include sprung mass, unsprung mass, inertial properties of the sprung mass, stiffness and damping coefficient of suspension and tire, etc. To minimize error, the paper proposes a method to estimate these parameters from vehicle modal parameters which are in turn obtained through full-car dynamic testing. To verify its effectiveness, a visual vehicle with a set of given parameters, build in the Adams(Automatic Dynamic Analysis of Mechanical Systems)/Car environment, is used to perform the dynamic testing and provide the testing data for the parameter estimation.

In recent years gasoline compression ignition (GCI) has been shown to offer an attractive combination of low criteria pollutants and high efficiency. However, enabling GCI across the full engine load map poses several challenges. At high load, the promotion of partial premixing of air and fuel is challenging due to the diminished ignition-delay characteristics at high temperatures, while under low load operations, maintaining combustion robustness is problematic due to the low reactivity of gasoline. Variable valve actuation (VVA) offers a means of addressing these challenges by providing flexibility in effective compression ratio. In this paper, the effects of VVA were studied at high loads in a prototype heavy-duty GCI engine using a gasoline research octane number (RON) 93 at a geometric compression ratio (CR) of 15.7. Both late intake valve closing (LIVC) and early intake valve closing (EIVC) strategies were analyzed as a measure to reduce the effective compression ratio.

This research numerically investigated the combustion process and exhaust emissions from a light-duty Gasoline Compression Ignition (GCI) engine operating at low load as well as medium load conditions using a commercial computational fluid dynamic (CFD) software Converge. The fuel injection strategies and thermal boundary conditions effects were examined to produce locally stratified and globally lean partially premixed compression ignition (PPCI) combustion. The effects of fuel injection pressure, number of injections, and the quantity of fuel injected in each pulse were examined and optimized for emissions and fuel consumption (FC) under the design constraints of 180 bar peak cylinder pressure (PCP) and 10 bar/° CA maximum pressure rise rate (MPRR).

For electrified vehicles, understanding the impact of temperature on vehicle control and performances becomes more important than before because the vehicle might consume more energy than conventional vehicles due to lack of the engine waste heat. Argonne has tested many advanced vehicles and analyzed the vehicle level control based on the test data. As part of its ongoing effort, Toyota Prius Plug-in Hybrid was tested in thermal environmental chamber, and the vehicle level control and performances are analyzed by observing the test results. The analysis results show that the control of the Plug-in Hybrid Electric Vehicle (PHEV) is similar with Prius Hybrid Electric Vehicle (HEV) when the vehicle is under a charge sustaining mode, and the vehicle tries to consume the electric energy first under a charge depleting mode.

Plug-in Hybrid Electric Vehicles (PHEVs) have demonstrated the potential to provide significant reduction in fuel use across a wide range of dynamometer test driving cycles. Companies and research organizations are involved in numerous research activities related to PHEVs. One of the current unknowns is the impact of driving behavior and standard test procedure on the true benefits of PHEVs from a worldwide perspective. To address this issue, five different PHEV powertrain configurations (input split, parallel, series, series-output split and series-parallel), implemented on vehicles with different all-electric ranges (AERs), were analyzed on three different standard cycles (i.e., Urban Dynamometer Driving Schedule, Highway Fuel Economy Test, and New European Driving Cycle). Component sizes, manufacturing cost, and fuel consumption were analyzed for a midsize car in model year 2020 through the use of vehicle system simulations.

Objective tools that can assess the demands associated with in-vehicle human machine interfaces (HMIs) could assist automotive engineers designing safer interaction. This paper presents empirical evidence supporting one objective assessment approach, which compares the demand associated with in-vehicle tasks to the demand associated with “benchmarking” or “comparison tasks”. In the presented study, there were two types of benchmarking tasks-a modified surrogate reference task (SuRT) and a delayed digit recall task (n-back task) - representing different levels of visual demand and cognitive demand respectively. Twenty-four participants performed these two types of benchmarking tasks as well as two radio tasks while driving a vehicle on a closed-loop test track. Response measures included physiological (heart rate), glance metrics, driving performance (steering entropy) and subjective workload ratings.

A computational fluid dynamics (CFD) guided combustion system optimization was conducted for a heavy-duty compression-ignition engine with a gasoline-like fuel that has an anti-knock index (AKI) of 58. The primary goal was to design an optimized combustion system utilizing the high volatility and low sooting tendency of the fuel for improved fuel efficiency with minimal hardware modifications to the engine. The CFD model predictions were first validated against experimental results generated using the stock engine hardware. A comprehensive design of experiments (DoE) study was performed at different operating conditions on a world-leading supercomputer, MIRA at Argonne National Laboratory, to accelerate the development of an optimized fuel-efficiency focused design while maintaining the engine-out NOx and soot emissions levels of the baseline production engine.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC).

The regulatory requirements to lower both greenhouse gases and criteria pollutants from heavy duty engines are driving new perspectives on the interaction between fuels and engines. Fuels that lower the burden on engine manufacturers to reach these goals may be of particular interest. Naphtha, a fuel with a higher volatility than diesel, but with the ability to be burned under traditional mixing-controlled combustion conditions is one such fuel. The higher volatility promotes fuel-air mixing and when combined with its typically lower aromatic content, leads to reduced soot emissions when compared directly to diesel. Naphtha also has potential to be less energy-intensive at the refinery level, and its use in transportation applications can potentially reduce CO2 emissions on a well-to-wheels basis.

Advanced internal combustion engines, although generally more efficient than conventional combustion engines, often encounter limitations in multi-cylinder applications due to variations in the combustion process. This study leverages experimental data from an inline 6-cylinder heavy-duty dual fuel engine equipped with a fully-flexible variable intake valve actuation system to study cylinder-to-cylinder variations in power production. The engine is operated with late intake valve closure timings in a dual-fuel combustion mode featuring a port-injection and a direct-injection fueling system in order to improve fuel efficiency and engine performance. Experimental results show increased cylinder-to-cylinder variation in IMEP as IVC timing moves from 570°ATDC to 610°ATDC, indicating an increasingly uneven fuel distribution between cylinders.

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.