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The transportation sector still depends on conventional engines in many countries as the alternative technologies are not mature enough to reduce carbon footprints in society. The four-stroke diesel engines, primarily used for heavy-duty applications, need either high intake boosting or a large bore to produce higher torque and power output. There is an alternative where a four-stroke engine operated in two-stroke mode with the help of a fully flexible variable valve actuation (VVA) system can achieve similar power density without raising the intake boosting or engine size. A fully flexible VVA is required to control the valve events (lift, timing, and durations) independently so that the four-stroke events can be completed in one cycle. In this study, 1D-3D CFD coupled simulations were performed to develop a gas exchange process for better air entrapment in the cylinder and evacuate the exhaust products simultaneously.

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 shift toward electrification and limitations in battery electric vehicle technology have led to high demand for hybrid vehicles (HEVs) that utilize a battery and an internal combustion engine (ICE) for propulsion. Although HEVs enable lower fuel consumption and emissions compared to conventional vehicles, they still require combustion of fuels for ICE operation. Thus, emissions from hybrid vehicles are still a major concern. Engine starts are a major source of emissions during any driving event, especially before the three-way catalyst (TWC) reaches its light-off temperature. Since the engine is subjected to multiple starts during most driving events, it is important to mitigate and better understand the impact of these emissions. In this study, experiments were conducted to analyze engine starts in a hybrid powertrain on different experimental setup.

An electrically heated catalyst (EHC) in the three-way catalyst (TWC) aftertreatment system of a gasoline internal combustion engine (ICE) provides cold engine start exhaust pollutant emission reduction potential. The EHC can be started before switching on the ICE, thereby offering the possibility to pre-heat (PRH) the TWC, in the absence of exhaust flow. The EHC can also provide post engine start heat (PSH) when the heat is accompanied by exhaust mass flow over the TWC. A mixed heating strategy (MXH) comprises both PRH and PSH. All three strategies are evaluated under a range of engine start variations using an ICE-exhaust aftertreatment (EATS) simulation framework. It is driven by an engine speed-torque requested trace, with an engine-out emissions model focused on cold-start, engine heating and catalyst heating engine measures and a physics- based EATS with EHC model.

The high-pressure isobaric combustion has been proposed as the most suitable combustion mode for the double compre4ssion expansion engine (DCEE) concept. Previous experimental and simulation studies have demonstrated an improved efficiency compared to the conventional diesel combustion (CDC) engine. In the current study, isobaric combustion was achieved using a single injector with multiple injections. Since this concept involves complex phenomena such as spray to spray interactions, the computational models were extensively validated against the optical engine experiment data, to ensure high-fidelity simulations. The considered optical diagnostic techniques are Mie-scattering, fuel tracer planar laser-induced fluorescence (PLIF), and natural flame luminosity imaging. Overall, a good agreement between the numerical and experimental results was obtained.

The three-way-catalyst (TWC) is an essential part of the exhaust aftertreatment system in spark-ignited powertrains, converting nearly all toxic emissions to harmless gasses. The TWC’s conversion efficiency is significantly temperature-dependent, and cold-starts can be the dominating source of emissions for vehicles with frequent start/stops (e.g. hybrid vehicles). In this paper we develop a thermal TWC model and calibrate it with experimental data. Due to the few number of state variables the model is well suited for fast offline simulation as well as subsequent on-line control, for instance using non-linear state-feedback or explicit MPC. Using the model could allow an on-line controller to more optimally adjust the engine ignition timing, the power in an electric catalyst pre-heater, and/or the power split ratio in a hybrid vehicle when the catalyst is not completely hot.

The automotive industry makes extensive use of virtual models to increase efficiency during the development stage. The complexity of such virtual models increases with the complexity of the process that they describe, and thus new methods for their development are constantly evaluated. Among many others, data-driven techniques and machine learning offer promising results, creating deep neural networks that map complex input-output relations. This work aims at comparing the performance of two different neural network architectures for the estimation of the engine state and emissions (flow fuel, NOx and soot). More specifically, Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) will be evaluated in terms of performance, using different techniques to increase the model generalization. During the learning stage data from different engine cycles are fed to the neural networks.

A method for measuring apparent soot particle size and concentration in turbulent combusting diesel jets with elevated and inhomogeneous optical density is presented and discussed. The method is based on the combination of quasi-simultaneous Laser Induced Incandescence (LII), Elastic Scattering (ELS) and Light Extinction (LE) measurements exhibiting a high potential for spatially resolved measurements of carbonaceous particles in flames and residual gases at a given instant. The method evaluates the LII signal by calculating the laser fluence across the flame and compensating for signal trapping, allowing measurements where laser extinction between the flame borders reaches values up to 90%. The method was implemented by measuring particle size and concentration in the middle sagittal axis of optically dense, combusting diesel jets at a certain time after the start of combustion.

The effects of using n-butanol, n-octanol, fossil Diesel, hydrotreated vegetable oil (HVO), and blends of these fuels on spray penetration, flame and soot characteristics were investigated in a high-pressure high-temperature constant volume combustion chamber designed to mimic a heavy duty Diesel engine. Backlight illumination was used to capture liquid and vapor phase spray images with a high-speed camera. The flame lift-off length (LOL) and ignition delay were determined by analyzing OH* chemiluminescence images. Laser extinction diagnostics were used to measure the spatially and temporally resolved soot volume fraction. The spray experiments were performed by injecting fuels under non-combusting (623 K) and combusting (823 K) conditions at a fixed ambient air density of 26 kg/m3. A Scania 0.19 mm single straight hole injector and Scania XPI common rail fuel supply system were used to produce injection pressures of 120 MPa and 180 MPa.

Maximum fuel injection pressures for GDI engines is expected to increase due to positive effects on emissions and engine-efficiency. Current GDI injectors have maximum operating pressures of 35 MPa, but higher injection pressures have yielded promising reductions in particle number (PN) and improved combustion stability. However, the mechanisms responsible for these effects are poorly understood, and there have been few studies on fuel sprays formed at high injection pressures. This paper summarizes experimental studies on the properties of sprays formed at high injection pressures. The results of these experiments can be used as inputs for CFD simulations and studies on combustion behavior, emissions formation, and combustion system design. The experiments were conducted using an injection rate meter and optical methods in a constant volume spray chamber. Injection rate measurements were performed to determine the injectors’ flow characteristics.

Look ahead information can be used to improve the powertrain’s fuel consumption while efficiently controlling exhaust emissions. A passenger car propelled by a Euro 6d capable diesel engine is studied. In the conventional approach, the diesel powertrain subsystem control is rule based. It uses no information of future load requests but is operated with the objective of low engine out exhaust emission species until the Exhaust After-Treatment System (EATS) light off has occurred, even if fuel economy is compromised greatly. Upon EATS light off, the engine is operated more fuel efficiently since the EATS system is able to treat emissions effectively. This paper presents a supervisory control structure with the intended purpose to operate the complete powertrain using a minimum of fuel while improving the robustness of exhaust emissions.

The present paper aims at developing an innovative procedure to create a one-dimensional (1D) real-time capable simulation model for a heavy-duty diesel engine. The novelty of this approach is the use of the top-level engine configuration, test cell measurement data, and manufacturer maps as opposite to common practice of utilizing a detailed 1D engine model. The objective is to facilitate effective model adjustments and hence further increase the application of Hardware-in-the-Loop (HiL) simulations in powertrain development. This work describes the development of Fast Running Model (FRM) in GT-SUITE simulation software. The cylinder and gas-path modeling and calibration are described in detail. The results for engine performance and exhaust emissions produced satisfactory agreement with both steady-state and transient experimental data.

In order to meet emissions and power requirements, modern engine design has evolved in complexity and control. The cost and time restraints of calibration and testing of various control strategies have made virtual testing environments increasingly popular. Using Hardware-in-the-Loop (HiL), Volvo Penta has built a virtual test rig named VIRTEC for efficient engine testing, using a model simulating a fully instrumented engine. This paper presents an innovative Artificial Neural Network (ANN) based model for engine simulations in HiL environment. The engine model, herein called Artificial Neural Network Engine (ANN-E), was built for D8-600 hp Volvo Penta engine, and directly implemented in the VIRTEC system. ANN-E uses a combination of feedforward and recursive ANNs, processing 7 actuator signals from the engine management system (EMS) to provide 30 output signals.

In engine research and development there are often different engine parameters that produce similar effects on the end-point results. When calibrating modern engines, a huge number of parameters needs to be set, which also includes compensation parameters for model imperfections. In this context, simpler, more robust, and physically based models should be beneficial both for calibration work load and powertrain performance. In this study, we present an experimental methodology that uses intermediate (“intrinsic”) variables instead of engine parameters. By using simple thermodynamic models, the engine parameters EGR, IVC, and PBoost could be translated into oxygen concentration, temperature and gas density at the start of injection. The reason for this transformation of data is to “move” the Design of Experiment (DoE) closer to the situation of interest (i.e. the combustion) and to be able to construct simpler and more physically based models.

Thermodynamic power cycles have been shown to provide an excellent method for waste heat recovery (WHR) in internal combustion engines. By capturing and reusing heat that would otherwise be lost to the environment, the efficiency of engines can be increased. This study evaluates the maximum power output of different cycles used for WHR in a heavy duty Diesel engine with a focus on working fluid selection. Typically, only high temperature heat sources are evaluated for WHR in engines, whereas this study also considers the potential of WHR from the coolant. To recover the heat, four types of power cycles were evaluated: the organic Rankine cycle (ORC), transcritical Rankine cycle, trilateral flash cycle, and organic flash cycle. This paper allows for a direct comparison of these cycles by simulating all cycles using the same boundary conditions and working fluids.

Laws concerning emissions from heavy duty (HD) internal combustion engines are becoming increasingly stringent. New engine technologies are needed to satisfy these new requirements and to reduce fossil fuel dependency. One way to achieve both objectives can be to partially replace fossil fuels with alternatives that are sustainable with respect to emissions of greenhouse gases, particulates and nitrogen oxides (NOx). A suitable candidate is methanol. The aim of the study presented here was to investigate the possible advantages of combusting methanol in a heavy duty Diesel engine. Those are, among others, lower particulate emissions and thereby bypassing the NOx-soot trade-off. Because of methanol’s poor auto-ignition properties, Diesel was used as an igniting sources and both fuels were separately direct injected. Therefore, two separate standard common rail Diesel injection systems were used together with a newly designed cylinder head and adapted injection nozzles.

Reducing emissions and improving efficiency are major goals of modern internal combustion engine research. The use of biomass-derived fuels in Diesel engines is an effective way of reducing well-to-wheels (WTW) greenhouse gas (GHG) emissions. Moreover, partially premixed combustion (PPC) makes it possible to achieve very efficient combustion with low emissions of soot and NOx. The objective of this study was to investigate the effect of using alcohol/Diesel blends or neat alcohols on emissions and thermal efficiency during PPC. Four alcohols were evaluated: n-butanol, isobutanol, n-octanol, and 2-ethylhexanol. The alcohols were blended with fossil Diesel fuel to produce mixtures with low cetane numbers (26-36) suitable for PPC. The blends were then tested in a single cylinder light duty (LD) engine. To optimize combustion, the exhaust gas recirculation (EGR) level, lambda, and injection strategy were tuned.

Even though substantial improvements have been made for the lean NOx trap (LNT) catalyst in recent years, the durability still remains problematic because of the sulfur poisoning and sintering of the precious metals at high operating temperatures. Hence, commercial LNT catalysts were aged and tested in order to investigate their performance and activity degradation compared to the fresh catalyst, and establish a proper correlation between the aging methods used. The target of this study is to provide useful information for regeneration strategies and optimize the catalyst management for better performance and durability. With this goal in mind, two different aging procedures were implemented in this investigation. A catalyst was vehicle-aged in the vehicle chassis dynamometer for 100000 km, thus exposed to real conditions. Whereas, an accelerated aging method was used by subjecting a fresh LNT catalyst at 800 °C for 24 hours in an oven under controlled conditions.

Many new combustion concepts are currently being investigated to further improve engines in terms of both efficiency and emissions. Examples include homogeneous charge compression ignition (HCCI), lean stratified premixed combustion, stratified charge compression ignition (SCCI), and high levels of exhaust gas recirculation (EGR) in diesel engines, known as low temperature combustion (LTC). All of these combustion concepts have in common that the temperatures are lower than in traditional spark ignition or diesel engines. To further improve and develop combustion concepts for clean and highly efficient engines, it is necessary to develop new computational tools that can be used to describe and optimize processes in nonstandard conditions, such as low temperature combustion.

Today numerical models are a major part of the diesel engine development. They are applied during several stages of the development process to perform extensive parameter studies and to investigate flow and combustion phenomena in detail. The models are divided by complexity and computational costs since one has to decide what the best choice for the task is. 0D models are suitable for problems with large parameter spaces and multiple operating points, e.g. engine map simulation and parameter sweeps. Therefore, it is necessary to incorporate physical models to improve the predictive capability of these models. This work focuses on turbulence and mixing modeling within a 0D direct injection stochastic reactor model. The model is based on a probability density function approach and incorporates submodels for direct fuel injection, vaporization, heat transfer, turbulent mixing and detailed chemistry.