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Beside the main trend technologies such as downsizing, down speeding, external exhaust gas recirculation, and turbocharging in combination with Miller cycles, the optimization of the mechanical efficiency of gasoline engines is an important task in meeting future CO2 emission targets. Friction in the piston assembly is responsible for up to 45% of the total mechanical loss in a gasoline engine. Therefore, optimizing piston assembly friction is a valuable approach in improving the total efficiency of an internal combustion engine. The form honing process enables new specific shapes of the cylinder liner surface. These shapes, such as a conus or bottle neck, help enlarge the operating clearance between the piston assembly and the cylinder liner, which is one of the main factors influencing piston assembly friction.

Development processes for modern combustion engines already make substantial use of more or less sophisticated simulation approaches. The enhancement of computational resources additionally allows the increasing use of simulation tools in terms of time-consuming three-dimensional CFD approaches. In particular, the preliminary estimation of feasible operating ranges and strategies requires a vast multitude of single simulations. Here, multi-zone simulation approaches incorporate the advantages of comparably short simulation durations. Nevertheless, the combination with more detailed sub-models allows these rather simple modeling approaches to offer considerable insight into relevant engine operation phenomena. In the context of combustion process development, this paper describes a phenomenological model approach for the prediction of operating point characteristics of a dual-fuel pilot injection combustion process.

To meet LEV and EU Stage III emission requirements, it is necessary for new catalytic converters to be designed which exceed light-off temperature as quickly as possible. The technical solutions are secondary air injection, active heating systems such as the electrically heated catalytic converter, and the close coupled catalytic converter. Engine control functions are extensively used to heat the converter and will to play a significant role in the future. The concept of relocating the converter to a position close to the engine in an existing vehicle involves new conflicts. Examples include the space requirements, the thermal resistance of the catalytic coating and high temperature loads in the engine compartment.

Although hydrogen ICE engines have existed in one sort or another for many years, the testing of fuel consumption by way of exhaust emissions is not yet a proven method. The current consumption method for gasoline- and diesel-fueled vehicles is called the Carbon-Balance method, and it works by testing the vehicle exhaust for all carbon-containing components. Through conservation of mass, the carbon that comes out as exhaust must have gone in as fuel. Just like the Carbon-Balance method for gas and diesel engines, the new Hydrogen-Balance equation works on the principle that what goes into the engine must come out as exhaust components. This allows for fuel consumption measurements without direct contact with the fuel. This means increased accuracy and simplicity. This new method requires some modifications to the testing procedures and CVS (Constant Volume Sampling) system.

The optimization of the vaporization and mixture formation process is of great importance for the development of modern gasoline direct injection (GDI) engines, because it influences the subsequent processes of the ignition, combustion and pollutant formation significantly. In consequence, the subject of this work was the development of a measurement technique based on the laser induced exciplex fluorescence (LIF), which allows the two dimensional visualization and quantification of the in-cylinder air/fuel ratio. A tracer concept consisting of benzene and triethylamine dissolved in a non-fluorescent base fuel has been used. The calibration of the equivalence ratio proportional LIF-signal was performed directly inside the engine, at a well known mixture composition, immediately before the direct injection measurements were started.

With increasing levels of driving automation, the perception provided by automotive environment sensors becomes highly safety relevant. A correct assessment of the sensors’ perception reliability is therefore crucial for ensuring the safety of the automated driving functionalities. There are currently no standardized procedures or guidelines for demonstrating the perception reliability of the sensors. Engineers therefore face the challenge of setting up test procedures and plan test drive efforts. Null Hypothesis Significance Testing has been employed previously to answer this question. In this contribution, we present an alternative method based on Bayesian parameter inference, which is easy to implement and whose interpretation is more intuitive for engineers without a profound statistical education. We show how to account for different environmental conditions with an influence on sensor performance and for statistical dependence among perception errors.

Improving fuel efficiency while meeting relevant emission limits set by emissions legislation is among the main objectives of engine development. Simultaneously the development costs and development time have to be steadily reduced. For these reasons, the high demands in terms of quality and validity of measurements at the engine test bench are continuously rising. This paper will present a new methodology for efficient testing of an industrial combustion engine in order to improve the process of decision making for combustion-relevant component setups. The methodology includes various modules for increasing measurement quality and validity. Modules like stationary point detection to determine steady state engine behavior, signal quality checks to monitor the signal quality of chosen measurement signals and plausibility checks to evaluate physical relations between several measurement signals ensure a high measurement quality over all measurements.

This study discusses model-based injection rate estimation in common rail diesel injectors exhibiting aging phenomena. Since they result in unexpected injection behavior, aging effects like coking or cavitation may impair combustion performance, which justifies the need for new modeling and estimation approaches. To predict injection characteristics, a simulation model for the bottom section of the injector is introduced, with a main focus on modeling the hydraulic components. Using rail pressure and control piston lift as inputs, a reduced model is then derived in state-space representation, which may be used for the application of an observer in hardware-in-the-loop (HIL) environments. Both models are compared and validated with experimental data, with which they show good agreement. Aging effects and nozzle wear, which result in model uncertainties, are considered using a fault model in combination with an extended Kalman filter (EKF) observer scheme.

Due to its molecular structure, methane provides several advantages as fuel for internal combustion engines. To cope with nitrogen oxide emissions high levels of excess air are beneficial, which on the other hand deteriorates the flammability and combustion duration of the mixture. One approach to meet these challenges and ensure a stable combustion process are fuelled prechambers. The flow and combustion processes within these prechambers are highly influenced by the position, orientation, number and overall cross-sectional area of the orifices connecting the prechamber and the main combustion chamber. In the present study, a water-cooled single cylinder test engine with a displacement volume of 0.5 l is equipped with a methane-fuelled prechamber. To evaluate influences of the aforementioned orifices several prechambers with variations of the orientation and number of nozzles are used under different operating conditions of engine speed and load.

A local Gaussian process regression approach is presented, which allows to model nonlinearities of internal combustion engines more accurate than global Gaussian process regression. By building smaller models, the prediction of local system behavior improves significantly. In order to predict a value, the algorithm chooses the nearest training points. The number of chosen training points depends on the intensity of estimated nonlinearity. After determining the training points, a model is built, the prediction performed and the model discarded. The approach is demonstrated with a benchmark system and air charge test bed measurements. The measurements are taken from a turbocharged SI gasoline engine with both variable inlet valve lift and variable inlet and exhaust valve opening angle. The results show how local Gaussian process regression outmatches global Gaussian process regression concerning model quality and nonlinearities in particular.

Air charge calibration of turbocharged SI gasoline engines with both variable inlet valve lift and variable inlet and exhaust valve opening angle has to be very accurate and needs a high number of measurements. In particular, the modeling of the transition area from unthrottled, inlet valve controlled resp. throttled mode to turbocharged mode, suffers from small number of measurements (e.g. when applying Design of Experiments (DoE)). This is due to the strong impact of residual gas respectively scavenging dominating locally in this area. In this article, a virtual residual gas sensor in order to enable black-box-modeling of the air charge is presented. The sensor is a multilayer perceptron artificial neural network. Amongst others, the physically calculated air mass is used as training data for the artificial neural network.

Natural gas and especially biogas combustion can be seen as one of the key technologies towards climate-neutral energy supply. With its extensive availability, biogas is amongst the most important renewable energy sources in the present energy mix. Today, the use of gaseous fuels is widely established, for example in cogeneration units for combined heat and power generation. In contrast to conventional spark plug ignition, the combustion can also be initialized by a pilot injection. In order to further increase engine efficiency, this article describes the process for a targeted optimization of the pilot fuel injection. One of the crucial points for a more efficient dual fuel combustion process, is to optimize the amount of pilot injection in order to increase overall engine efficiency, and therefore decrease fuel consumption. In this connection, the injection system plays a key role.

Apart from the reduction of engine-out emissions from the powerplant, the development of an efficient and reliable catalytic converter heating system is an important task of automotive engineering in the future to meet standards that will require reduction of cold start emissions. Carrying out a comprehensive study in this field, BMW has tested and evaluated possible solutions to this challenge. In additon to the electrically heated catalytic converter (E-cat) and the afterburner chamber, an incorporated burner system would meet the requirement for fast catalyst light-off in the future, particularly in the case of larger engines.

Existing electronic combustion engine control systems only guarantee a desired air-to-fuel-ratio λ in stationary operation. In order to achieve the desired λ also in in-stationary use of the engine, it is necessary to use new-technology-based control systems. Artificial Intelligence provides methods to cope with difficulties like wide operation range, unknown nonlinearities and time delay. We will propose a strategy for control of a Spark Ignition Engine to determine the mass of air inside the combustion chambers with the highest accuracy. Since Neural Networks are universal approximators for multidimensional nonlinear static functions they can be used effectively for identification and compensation purposes of unknown nonlinearities in closed control loops.

The production of the BMW ALPINA B12 5.7 with Switch-Tronic transmission provides the markets of Europe and Japan with an exclusive, luxury-orientated, high performance limited series limousine. This is the first vehicle worldwide to be fitted with the progressive exhaust gas aftertreatment technology known as the Electrically Heated Catalyst (EHC), in which the effectiveness of the power utilized is increased significantly by an alternating heating process for both catalytic converters. Only since this achievement has the implementation of the EHC been viable without extensive modification to the battery and alternator. With this exhaust gas aftertreatment concept, the emissions of this high performance vehicle will fall to less than half the maximum permissible for compliance with 1996 emission standards.

In order to reduce emissions of spare ignition engines using a three way catalyst, a stoichiometric air-fuel ratio must be guaranteed in stationary and transient operation of the engine. This aim can be reached by using a specific feed-forward structure for the control of the paths of air and fuel based on identification abilities of Artificial Intelligence. As approximators for multidimensional nonlinear static functions we will use specific Neural Networks (NN) together with sophisticated stability-proven learning structures. The acquired knowledge within the NN determines our control action mainly through using feed-forward structures. Our investigations are based on the so-called mean-value-modelling approach of SI engines; it is our aim to present this strategy.

The real-time concentrations of benzene, toluene, xylene, trimethyl-benzene and naphthalene in vehicle exhaust have been monitored during the FTP-cycle with a time-resolution of 20 ms and a sensitivity of 50 ppb. Using a laser mass spectrometer, the aromatic hydrocarbons in unconditioned exhaust gas at sampling positions behind the exhaust valve, before and behind the catalytic converter have been analyzed. The comparison of the emissions sampled before and behind the catalytic converter reveals the effect of dealkylation of the aromatic hydrocarbons in the catalytic converter. Whereas most of the aromatic hydrocarbons are burned in the hot catalytic converter, however, bursts of aromatic hydrocarbons are released at transient motor operation. In these moments, which can be attributed to phases of closed throttle valve and very low engine load at gear changes, a significant part of the C1-, C2- and C3- benzenes has been converted into benzene.

Vehicle thermal management (VTM) simulations are becoming increasingly important in the development phase of a vehicle. These simulations help in predicting the thermal profiles of critical components over a drive cycle. They are usually done using two methodologies: (1) Solving every aspect of the heat transfer, i.e., convection, radiation, and conduction, in a single solver (Conjugate Heat Transfer) or (2) Simulating convection using a fluid solver and computing the other two mechanisms using a separate thermal solver (Co-simulation). The first method is usually computationally intensive, while the second one isn’t. This is because Co-simulation reduces the load of simulating all heat transfer mechanisms in a single code. This is one of the reasons why the Co-simulation method is widely used in the automotive industry. Traditionally, the methods developed for Co-simulation processes are load case specific.

In the near future, pollutant and GHG emission regulations in the transport sector will become increasingly stringent. For this reason, there are many studies in the field of internal combustion research that investigate alternative fuels, one example being oxygenated fuels. Additionally, the design of engine components needs to be optimized to improve the thresholds of clean combustion and thus reduce particulates. Simulations based on PRiME 3D® for dynamic behaviors inside the piston ring group provide a guideline for experimental investigation. Gas flows into the combustion chamber are controlled by adjusting the piston ring design. A direct comparison of regular and synthetic fuels enables to separate the emissions caused by oil and fuel. This study employed a mixture of dimethyl carbonate (DMC) and methyl formate (MeFo).