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The internal combustion engine contains several actuators to control engine performance and emissions. These are controlled within the engine ECU and follow a specific operating strategy to achieve objectives such as NOx reduction and fuel economy. However, these two goals are conflicting and a compromise is required. The operating state depends on system constraints such as engine speed, load, temperature levels, and aftertreatment system efficiency. This results in constantly changing target values to stay within the defined limits, especially the legal emission limits. The conventional approach is to use multiple operating modes. Each mode represents a specific compromise and is activated accordingly. Multiple modes are required to meet emissions regulations under all required conditions, which increases the calibration effort. This new control approach uses an artificial neural network to replace the conventional multiple mode approach.

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.

Current developments in automotive industry such as hybrid powertrains and the continuously increasing demands on emission control systems, are pushing complexity still further. Validation of such systems lead to a huge amount of test cases and hence extreme testing efforts on the road. At the same time the pressure to reduce costs and minimize development time is creating challenging boundaries on development teams. Therefore, it is of utmost importance to utilize testing and validation prototypes in the most efficient way. It is necessary to apply high levels of instrumentation and collect as much data as possible. And a streamlined data pipeline allows the fleet managers to get new insights from the raw data and control the validation vehicles as well as the development team in the most efficient way. In this paper we will demonstrate a data-driven approach for validation testing.

Alcohol-based fuels are a viable alternative to fossil fuels for powering vehicles. As a drop-in fuel, an oxygenated fuel blend containing the C8 alcohol 2-ethylhexanol (isomer of octanol), hydrotreated vegetable oil (HVO) and rapeseed methyl ester (RME) can reduce soot and NOx emissions whilst maintaining engine performance. However, fuel injection strategy significantly affects combustion and hence has been investigated with a view to reducing emissions whilst maintaining engine efficiency. In a single cylinder light-duty compression ignition research engine, the effect of different injection strategies (main, main/post, double pre/main, double pre/main/post injection) and EGR levels (0%, 19%) on specifically NOx, soot emissions and particle size distribution was investigated for three different fuels: fossil diesel fuel, HVO and the oxygenated blend. The blend was designed to have diesel-like combustion properties (cetane number of 52) and had an oxygen content of 5.4% by mass.

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 introduction of a physics-based zero-dimensional stochastic reactor model combined with tabulated chemistry enables the simulation-supported development of future compression-ignited engines. The stochastic reactor model mimics mixture and temperature inhomogeneities induced by turbulence, direct injection and heat transfer. Thus, it is possible to improve the prediction of NOx emissions compared to common mean-value models. To reduce the number of designs to be evaluated during the simulation-based multi-objective optimization, genetic algorithms are proven to be an effective tool. Based on an initial set of designs, the algorithm aims to evolve the designs to find the best parameters for the given constraints and objectives. The extension by response surface models improves the prediction of the best possible Pareto Front, while the time of optimization is kept low.

Most stringent emission legislations have been implemented in all major markets to improve air quality across the past years. This effects the product cost of the vehicles which is considered being critical and needs to be minimized. India suffers from bad air quality and countermeasures have been defined. One being the implementation of similar emission standards than EU VI. By doing so, India takes a large step going from the currently effective BS IV directly to BS VI. Emission reduction is currently mainly handled by the usage of EGR, thus no engine aftertreatment system has been applied. BS VI will require an aftertreatment (EAS) concept with several catalysts and corresponding control system. India is a very cost sensitive market and a carry over of solutions from the EU needs to be evaluated carefully and new approaches need to be found.

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.

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.

The use of state of the art simulation tools for effective front-loading of the calibration process is essential to support the additional efforts required by the new Real Driving Emission (RDE) legislation. The process needs a critical model validation where the correlation in dynamic conditions is used as a preliminary insight into the bounds of the representation domain of engine mean values. This paper focuses on the methodologies for correlating dynamic simulations with emissions data measured during dynamic vehicle operation (fundamental engine parameters and gaseous emissions) obtained using dedicated instrumentation on a diesel vehicle, with a particular attention for oxides of nitrogen NOx specie. This correlation is performed using simulated tests run within AVL’s mean value engine and engine aftertreatment (EAS) model MoBEO (Model Based Engine Optimization).

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.

A novel 0-D Probability Density Function (PDF) based approach for the modelling of Diesel combustion using tabulated chemistry is presented. The Direct Injection Stochastic Reactor Model (DI-SRM) by Pasternak et al. has been extended with a progress variable based framework allowing the use of a pre-calculated auto-ignition table. Auto-ignition is tabulated through adiabatic constant pressure reactor calculations. The tabulated chemistry based implementation has been assessed against the previously presented DI-SRM version by Pasternak et al. where chemical reactions are solved online. The chemical mechanism used in this work for both, online chemistry run and table generation, is an extended version of the scheme presented by Nawdial et al. The main fuel species are n-decane, α-methylnaphthalene and methyl-decanoate giving a size of 463 species and 7600 reactions.

Emissions of nitrogen oxides (NOx) from heavy-duty diesel engines are subject to more stringent environmental legislation. Selective catalytic reduction (SCR) over metal ion-exchanged zeolites is in this connection an efficient method to reduce NOx. Understanding durability of the SCR catalyst is crucial for correct design of the aftertreatment system. In the present paper, thermal and chemical ageing of Fe-BEA as NH3-SCR catalyst is studied. Experimental results of hydrothermal ageing, and chemical ageing due to phosphorous and potassium exposure are presented. The catalyst is characterized by flow reactor experiments, nitrogen physisorption, DRIFTS, XRD, and XPS. Based on the experimental results, a multisite kinetic model is developed to describe the activity of the fresh Fe-BEA catalyst.

The continuously decreasing emission limits lead to a growing importance of exhaust aftertreatment in Diesel engines. Hence, methods for achieving a rapid catalyst light-off after engine cold start and for maintaining the catalyst temperature during low load operation will become more and more necessary. The present work evaluates several valve timing strategies concerning their ability for doing so. For this purpose, simulations as well as experimental investigations were conducted. A special focus of simulation was on pointing out the relevance of exhaust temperature, mass flow and enthalpy for these thermomanagement tasks. An increase of exhaust temperature is beneficial for both catalyst heat-up and maintaining catalyst temperature. In case of the exhaust mass flow, high values are advantageous only in case of a catalyst heat-up process, while maintaining catalyst temperature is supported by a low mass flow.

A key element to achieving vehicle emission certification for most light-duty vehicles using spark-ignition engine technology is prompt catalyst warming. Emission mitigation largely does not occur while the catalyst is below its “light-off temperature”, which takes a certain time to achieve when the engine starts from a cold condition. If the catalyst takes too long to light-off, the vehicle could fail its emission certification; it is necessary to minimize the catalyst warm up period to mitigate emissions as quickly as possible. One technique used to minimize catalyst warm up is to calibrate the engine in such a way that it delivers high temperature exhaust. At idle or low speed/low-load conditions, this can be done by retarding spark timing with a corresponding increase in fuel flow rate and / or leaning the mixture. Both approaches, however, encounter limits as combustion stability degrades and / or nitrogen oxide emissions rise excessively.

In order to avoid the high CO and HC emissions associated with low temperature when using high levels of EGR, partially premixed combustion is an interesting possibility. One way to achieve this combustion mode is to increase the ignition delay by adjusting the inlet valve closing timing, and thus the effective compression ratio. The purpose of this study was to investigate experimentally the possibilities of using late and early inlet valve closure to reduce NOx emissions without increasing emissions of soot or unburned hydrocarbons, or fuel consumption. The effect of increasing the swirl number (from 0.2 to 2.5) was also investigated. The combustion timing (CA50) was kept constant by adjusting the start of injection and the possibilities of optimizing combustion using EGR and high injection pressures were investigated. Furthermore, the airflow was kept constant for a given EGR level.

This paper presents a numerical study on the impact of washcoat diffusion on the overall conversion performance of catalytic converters. A comprehensive transient 1D pore diffusion reaction model is embedded in state-of-the-art 1D and 3D catalytic converter models. The pore diffusion model is discussed with its model equations and the applied diffusive transport approaches are summarized. The diffusion reaction model is validated with the help of two available analytical solutions. The impact of basic washcoat characteristics such as pore diameters or thickness on overall conversion performance is investigated by selected 1D+1D calculations. This model is also used to highlight the impact of boundary layer transfer, pore diffusion and reaction on the overall converter conversion performance. The interaction of pore diffusion and flow non-uniformities is demonstrated by 3D+1D CFD simulations.