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A potential route to reduce CO2 emissions from heavy-duty trucks is to combine low-carbon fuels and a hybrid-electric powertrain to maximize overall efficiency. A hybrid electric powertrain can reduce the peak power required from the internal combustion engine, leading to opportunities to reduce the engine size but still meet vehicle performance requirements. Although engine downsizing in the light-duty sector can offer significant fuel economy savings mainly due to increased part-load efficiency, its benefits and downsides in heavy-duty engines are less clear. As there has been limited published research in this area to date, there is a lack of a standardized engine downsizing procedure.

The development and calibration of modern combustion engines is challenging in the area of continuously tightening emission limits and the necessity for meeting real driving emissions regulations. A focus is on the knowledge of the internal engine processes and the determination of pollutants formations in order to predict the engine emissions. A physical model-based development provides an insight into hardly measurable phenomena properties and is robust against changing input data. With increasing modeling depth the required computing capacities increase. As an alternative to physical modeling, data-driven machine learning methods can be used to enable high-performance modeling accuracy. However, these are dependent on the learned data. To combine the performance and robustness of both types of modeling a hybrid application of data-driven and physical models is developed in this paper as a grey box model for the exhaust emission prediction of a commercial vehicle diesel engine.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

Ducted fuel injection (DFI) has been shown to be an effective method to significantly reduce soot formation in mixing controlled compression ignition (MCCI) diesel combustion. This reduction has been demonstrated in both combustion vessels and in an optical engine. The mechanisms driving the soot reduction are to date not fully understood. Optimal duct configurations are also not immediately evident. The objective of this study is to show the effects of two geometric variables, namely distance from fuel injector orifice exit to duct inlet (0.1-6 mm) for a 2x14 mm duct, and duct length variation (8-14 mm) at a given stand-off distance of 0.1 mm. A 138 μm on-axis single-orifice injector operated at 100-250 MPa was used in a heated, continuous flow, constant pressure vessel with optical access.

With the rapid development of artificial intelligence, autonomous driving technology will finally reshape an automotive industry. Although fully autonomous cars are not commercially available to common consumers at this stage, partially autonomous vehicles, which are defined as level 2 and level 3 autonomous vehicles by SAE J3016 standard, are widely tested by automakers and researchers. A typical Human-Machine-Interface (HMI) for a vehicle takes a form to support a human domination role. Although modern driving assistance systems allow vehicles to take over control at certain scenarios, the typical human-machine-interface has not changed dramatically for a long time. With deep learning neural network technologies penetrating into automotive applications, multi-modal communications between a driver and a vehicle can be enabled by a cost-effective solution.

The effects of Variable Valve Timing (VVT) on Homogeneous Charge Compression Ignition (HCCI) engine energy distribution and waste heat recovery are investigated using a fully flexible Electromagnetic Variable Valve Timing (EVVT) system. The experiment is carried out in a single cylinder, 657 cc, port fuel injection engine fueled with n-heptane. Exergy analysis is performed to understand the relative contribution of different loss mechanisms in HCCI engines and how VVT changes these contributions. It is found that HCCI engine brake thermal efficiency, the Combined Heat and Power (CHP) power to heat ratio, the first and the second law efficiencies are improved with proper valve timing. Further analysis is performed by applying the first and second law of thermodynamics to compare HCCI energy and exergy distribution to Spark Ignition (SI) combustion using Primary Reference Fuel (PRF). HCCI demonstrates higher fuel efficiency and power to heat and energy loss ratios compared to SI.

The automotive industry is dramatically changing. Many automotive Original Equipment Manufacturers (OEMs) proposed new prototype models or concept vehicles to promote a green vehicle image. Non-traditional players bring many latest technologies in the Information Technology (IT) industry to the automotive industry. Typical vehicle’s characteristics became wider compared to those of vehicles a decade ago, and they include not only a driving range, mileage per gallon and acceleration rating, but also many features adopted in the IT industry, such as usability, connectivity, vehicle software upgrade capability and backward compatibility. Consumers expect the latest technology features in vehicles as they enjoy in using digital applications in laptops and mobile phones. These features create a huge challenge for a design of a new vehicle, especially for a human-machine-interface (HMI) system.

China has become the world’s largest vehicle market in terms of sales volume. Automobiles sales keep growing in recent years despite the declining economic growth rate. Due to the increasing attention given to the environmental impact, more stringent emission regulations are being drafted to control traditional internal combustion engine emissions. In order to reduce vehicle emissions, environmentally-friendly new-energy vehicles, such as electric vehicles and plug-in hybrid vehicles, are being promoted by government policies. The Chinese government plans to boost sales of new-energy cars to account for about five percent of China’s total vehicle sales. It is well known that more electric and electronic components will be integrated into a vehicle platform during vehicle electrification.

Gasoline direct injection engines can greatly improve the fuel economy, but the idea mixture distribution cannot be easily controlled. In this paper, the linearized instability sheet atomization (LISA) and large eddy simulation (LES) implemented into KIVA-3V code were used to study the gasoline hollow cone spray process for gasoline direct injection (GDI) in a constant volume vessel. The three-dimensional results show that the LISA model can effectively simulate the gasoline hollow cone spray and obtain the string structure compared to the experiment data. And the velocity interpolation method can reduce the grid dependency of spray simulation. Using dense grid (about 8 million cells) in LES and RANS all can obtain the good spray tip penetration and width. Unlike diesel spray, for gasoline spray there are not big difference between the results using LES and RANS. In additional the ambient pressure significantly influence the gasoline spray shape.

In this work, large eddy simulation (LES) with a K-equation subgrid turbulent kinetic energy model is implemented into the CFD code KIVA3V to study the features of liquid fuel spray and combustion using gradually varying grid in a constant volume chamber. The characteristic time-scale combustion model (CTC) incorporating a turbulent timescale is adopted to predict the combustion process and the SHELL auto-ignition model is used to predict auto-ignition. Combustion is also simulated using Parallel Detailed Chemistry with Lu's n-heptane reduced mechanism (58 species), which has been added into the KIVA3V-LES code. The computational results are compared with Sandia experimental data for non-reacting and reacting cases. As a result, LES can capture the complex structure of the spray and temperature distribution as well as the trend of ignition delay and flame lift-off length variations. Better results are obtained using the Parallel Detailed Chemistry than the CTC model.

The growth of auto sales in emerging markets provides a good opportunity for automakers. Cost is a key factor for any automaker to win in an emerging market. This paper analyzes risks and opportunities in a low cost manufacturing environment. The Chinese auto market is used as an example and three categories of risks are analyzed. A typical risk assessment for cost reduction includes the analysis of environment risks, process risks and strategic risks associated with all phases of a product life. In an emerging market, emission regulations are a rapidly-evolving environment variable, since most countries with less regulated emission codes try to catch up with the newly- developed technologies to meet sustainable growth targets. Emission regulations have a huge impact on product design, manufacturing and maintenance in the automotive industry, and hence the related cost reduction must be thoroughly analyzed during risk assessment.

This paper presents a large eddy simulation study of the liquid spray mixing with hot ambient gas in a constant volume vessel under engine-like conditions with the injection pressure of 1500 bar, ambient density 22.8 kg/m₃, ambient temperature of 900 K and an injector nozzle of 0.09 mm. The simulation results are compared with the experiments carried out by Pickett et al., under similar conditions. Under modern direct injection diesel engine conditions, it has been argued that the liquid core region is small and the droplets after atomization are fine so that the process of spray evaporation and mixing with the air is controlled by the heat and mass transfer between the ambient hot gas and central fuel flow. To examine this hypothesis a simple spray breakup model is tested in the present LES simulation. The simulations are performed using an open source compressible flow solver, in OpenFOAM.

Today's engine and combustion process development is closely related to the intake port layout. Combustion, performance and emissions are coupled to the intensity of turbulence, the quality of mixture formation and the distribution of residual gas, all of which depend on the in-cylinder charge motion, which is mainly determined by the intake port and cylinder head design. Additionally, an increasing level of volumetric efficiency is demanded for a high power output. Most optimization efforts on typical homogeneous charge spark ignition (HCSI) engines have been at low loads because that is all that is required for a vehicle to make it through the FTP cycle. However, due to pumping losses, this is where such engines are least efficient, so it would be good to find strategies to allow the engine to operate at higher loads.

Smaller locomotives often use mechanical transmissions instead of diesel-electric drive systems typically used in larger locomotives. This paper discusses how Model Based Design was used to develop the complete drive train control system for a 24 ton sugar cane locomotive. A complete MATLAB Simulink machine model was built to fully test and verify the shift control logic, traction control, vehicle speed limiting, and braking control for this locomotive application before it was commissioned. The model included the engine, torque converter, planetary transmission, drive line, and steel on steel driving surface. Simulation was used to debug all control code and test and refine control strategies so that the initial field commissioning in remote Australia was executed very quickly with minimal engineering support required.

Natural gas is a challenging fuel for HCCI engines because its single-stage ignition and rapid combustion make it difficult to optimize combustion timing over a significant load range. This study investigates direct injection of a pilot quantity of high-cetane fuel near TDC as a range extension and combustion control mechanism for natural gas HCCI engines. The EGR and load range is studied in a supercharged natural gas HCCI engine equipped with external EGR, intake heating and a direct injection system for n-heptane pilot fuel. The operating range and emissions are of primary interest and are compared between both the baseline HCCI engine with variable intake temperature and the direct injected HCCI (DI-HCCI) engine with constant intake temperature. Test results show the EGR and load range at fixed intake temperature can be extended using pilot direct injection.

The objective of this project was to model and study the interior noise in an Off-Highway Truck cab using Statistical Energy Analysis (SEA). The analysis was performed using two different modeling techniques. In the first method, the structural members of the cab were modeled along with the panels and the interior cavity. In the second method, the structural members were not modeled and only the acoustic cavity and panels were modeled. Comparison was done between the model with structural members and without structural members to evaluate the necessity of modeling the structure. Correlation between model prediction of interior sound pressure and test data was performed for eight different load conditions. Power contribution analysis was performed to find dominant paths and 1/3rd octave band frequencies.

A physics-based control-oriented model is developed to dynamically predict cycle-to-cycle combustion timing in transient fueling conditions for Homogeneous Charge Compression Ignition (HCCI) engines. The model simulates the engine cycle from the intake stroke to the exhaust stroke and includes the thermal coupling dynamics caused by the residual gases from one cycle to the next cycle. A residual gas model, a modified knock integral model, a fuel burn rate model, and thermodynamic models for the gas state in combustion and exhaust strokes are incorporated to simulate the engine cycle. The gas exchange process, generated work and completeness of combustion are predicted using semi-empirical correlations. The resulting model is parameterized for the combustion of Primary Reference Fuel (PRF) blends using 5703 simulations from a detailed thermo-kinetic model. Semi-empirical correlations in the model are parameterized using the experimental data obtained from a single-cylinder engine.

The paper describes a novel method for calculating the residual gas fraction and the trapping efficiency in a 2 stroke engine. Assuming one dimensional compressible flow through the inlet and exhaust ports, the method estimates the instantaneous mass flowing in and out from the combustion chamber; later the residual gas fraction and trapping efficiency are estimated combining together the perfect displacement and perfect mixing scavenging models. It is assumed that when the intake port opens, the fresh mixture is pushing out the burned charge without any mixing and after a multiple of the time needed for the largest eddy to perform one rotation, the two gasses are instantly mixed up together and expelled. The result is a very simple algorithm that does not require much computational time and is able to estimate with high level of precision the trapping efficiency and the residual gas fraction in 2 stroke engines.

It has been known from multi-zone simulations that HCCI combustion can be significantly affected by temperature stratification of the in-cylinder gas. With the same combustion timing (i.e. crank angles at 50% heat release, denoted as CA50), large temperature stratification tends to prolong the combustion duration and lower down the in-cylinder pressure-rise-rate. With low pressure-rise-rate HCCI engines can be operated at high load, therefore it is of practical importance to look into more details about how temperature stratification affects the auto-ignition process. It has been realized that multi-zone simulations can not account for the effects of spatial structures of the stratified temperature field, i.e. how the size of the hot and cold spots in the temperature field could affect the auto-ignition process. This question is investigated in the present work by large eddy simulation (LES) method which is capable of resolving the in-cylinder turbulence field in space and time.

Nowadays for small-scale power generation there are electrochemical batteries and mini engines. Many efforts have been done for improving the power density of the batteries but unfortunately the value of 1 MJ/kg seems to be asymptotic. If the energy source is an organic fuel which has an energy density of around 29 MJ/kg with a minimum overall efficiency of only 3.5%, this device would surpass the batteries. This paper is the fifth of a series of publications aimed to study the HCCI combustion process in the milli domain at high engine speed in order to design and develop VIMPA, Vibrating Microengine for Low Power Generation and Microsystems Actuation. Previous studies ranged from general characterization of the HCCI combustion process by using metal and optical engines, to more specific topics for instance the influence of the boundary layer and quenching distance on the quality of the combustion.