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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.

Computational models directly derived from data gained increased interest in recent years. Data-driven approaches have brought breakthroughs in different research areas such as image-, video- and audio-processing. Often denoted as Machine Learning (ML), today these approaches are not widely applied in the field of vehicle Noise, Vibration and Harshness (NVH). Works combining ML and NVH mainly discuss the topic with respect to psychoacoustics, traffic noise, structural health monitoring and as improvement to existing numerical simulation methods. Vehicle interior noise is a major quality criterion for today’s automotive customers. To estimate noise levels early in the development process, deterministic system descriptions are created by utilizing time-consuming measurement techniques. This paper examines whether pattern-recognizing algorithms are suitable to conduct the prediction process for a steering system.

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.

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.

In this paper the design of a robust control system for a parallel hybrid drivetrain is presented The drivetram is based on a continuously variable transmission (CVT) and is therefore a highly nonlinear multiple-input-multiple-output system (MIMO-System) Input-Output-Linearization offers the possibility of linearizing and of decoupling the system Since for example the vehicle mass varies with the load and the efficiency of the gearbox depends strongly on the actual working point, an exact linearization of the plant will mostly fail Therefore a robust control algorithm based on sliding mode is used to control the drivetrain

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 increasing complexity of automotive functions which are necessary for improved driving assistance systems and automated driving require a change of common vehicle architectures. This includes new concepts for E/E architectures such as a domain-oriented vehicle network based on powerful Domain Control Units (DCUs). These highly integrated controllers consolidate several applications on different safety levels on the same ECU. Hence, the functions depend on a strictly separated and isolated implementation to guarantee a correct behavior. This requires middleware layers which guarantee task isolation and Quality of Service (QoS) communication have to provide several new features, depending on the domain the corresponding control unit is used for. In a first step we identify requirements for a middleware in automotive DCUs. Our goal is to reuse legacy AUTOSAR based code in a multicore domain controller.

Future emission regulations require further development for internal combustion engines operating on gasoline. To comply with such regulations and simultaneously improve fuel efficiency, major development trends are found in reduced displacements, increased compression ratios and turbocharging. To counteract such engines’ increased tendencies to knocking combustion, direct fuel injection systems are necessarily applied. Compared to standard port fuel injection, direct injection systems cause increased particle emissions. State-of-the-art magnet-driven gasoline direct injectors are capable of realizing various injection events of small injected mass per event and short dwell time between one another. Thereby, they facilitate multiple injection strategies, able to overcome the drawbacks of direct injection systems in relation to exhaust emissions. However, the full potential of multiple injection strategies is not yet taken advantage of.

In motorsports, aerodynamic development processes target to achieve gains in performance. This requires a comprehensive understanding of the prevailing aerodynamics and the capability of analysing large quantities of numerical data. However, manual analysis of a significant amount of Computational Fluid Dynamics (CFD) data is time consuming and complex. The motivation is to optimize the aerodynamic analysis workflow with the use of deep learning architectures. In this research, variants of 3D deep learning models (3D-DL) such as Convolutional Autoencoder (CAE) and U-Net frameworks are applied to flow fields obtained from Reynolds Averaged Navier Stokes (RANS) simulations to transform the high-dimensional CFD domain into a low-dimensional embedding. Consequently, model order reduction enables the identification of inherent flow structures represented by the latent space of the models.

Aging effects such as coking or cavitation in the nozzle of common rail (CR) diesel injectors deteriorate combustion performance. This is of particular relevance when it comes to complying with emission legislation and demonstrates the need for detecting and compensating aging effects during operation. The first objective of this paper is to analyze the influence of worn nozzles on the injection rate. Therefore, measurements of commercial solenoid common rail diesel injectors with different nozzles are carried out using an injection rate analyzer of the Bosch type. Furthermore, a fault model for typical aging effects in the nozzle of the injector is presented together with two methods to detect and identify these effects. Both methods are based on a multi-domain simulation model of the injector. The needle lift, the control piston lift and the pressure in the lower feed line are used for the fault diagnosis.

The thermal and emission efficiency of diesel engines depends to a large extent on the quality of fuel injection. However, over engine lifetime, injection rate and quality will change due to adverse nozzle aging effects, such as coking or cavitation. In this study, we discuss the influences of these effects on injection and heat release rate. The injection rates of previously unused nozzles and a nozzle that had been operated in a vehicle engine were compared in order to clarify the impact of aging effects. The key to the detection of alterations of injection nozzles is the identification of strongly correlating parameters. As a first step, an instrumented injector was set up to measure fuel pressure inside the feed line of the injector and the lift of the control piston. Different nozzles showed a distinguishable control piston motion depending on their different geometric specifications, which also affect the injection rates.

The synthetic fuels dimethyl ether (DME) and polyoxymethylene dimethylether (POMDME or OME) are promising oxygenated fuels to meet the rising challenges of air pollution control, CO2-neutrality, and sustainability. The sootless combustion and high ignitability of DME and OME represent ideal properties for an application in diesel engines. However, recent investigations of oxygenates reported an increase of nanoparticles, which are known to have fatal effects on human’s health. Besides nanoparticles, ongoing discussions about future emission legislation focus on a drastic reduction of NOx. For this reason, the present work investigates different measures to reduce NOx emissions using DME/OME and a paraffinic diesel fuel (PDF) as reference. Different rail pressures, exhaust gas recirculation (EGR) rates, and injection timings are evaluated, considering the effectivity on NOx reduction and the impact on other emissions, especially on nanoparticles.

Bicycle-drawn cargo trailers with an electric drive to enable the transportation of high cargo loads are used as part of the last-mile logistics. Depending on the load, the total mass of a trailer can vary between approx. 50 and 250 kg, potentially more than the mass of the towing bicycle. This can result in major changes in acceleration and braking behavior of the overall system. While existing systems are designed primarily to provide sufficient power, improvements are needed in the powertrain control system in terms of driver safety and comfort. Hence, we propose a novel prototype that allows measurement of the tensile force in the drawbar which can subsequently be used to design a superior control system. In this context, a sinusoidal force input from the cyclist to the trailer according to the cadence of the cyclist is observed. The novelty of this research is to analyze whether torque impulses of the cyclist can be reduced with the help of Model Predictive Control (MPC).

A novel algorithm-based approach is employed in this publication to calculate multiple direct injection patterns for spark ignition engines. The algorithm is verified by investigating the combustion and emission behavior of a single-cylinder research engine. State-of-the-art standard exhaust gas analyzers, a particle counter and an additional FTIR analyzer enable in-depth investigation of engine exhaust gas composition. With the upcoming worldwide pollutant emission targets, the emission limits will be reduced while the test procedures’ requirements to the engine increase. Special attention to the engine-out emissions must be paid during cold-start, during which the aftertreatment system lacks sufficient pollutant emission conversion efficiency. With advanced injection control, the engine-out emissions can be reduced and exhaust aftertreatment heat-up can be accelerated.

Deep neural network models have been widely used for environment perception of intelligent vehicles. However, due to models’ innate probabilistic property, the lack of transparency, and sensitivity to data, perception results have inevitable uncertainties. To compensate for the weakness of probabilistic models, many pieces of research have been proposed to analyze and quantify such uncertainties. For safety-critical intelligent vehicles, the uncertainty analysis of data and models for environment perception is especially important. Uncertainty estimation can be a way to quantify the risk of environment perception. In this regard, it is essential to deliver a comprehensive survey. This work presents a comprehensive overview of uncertainty estimation in deep neural networks for environment perception of intelligent vehicles.

Aging effects such as coking or erosive damage that occur in fuel injection nozzles are known to deteriorate the engine performance. This article proposes an optimization method to compensate for injector aging and to control the combustion behavior over engine lifetime by adapting the injection strategy. First, a control-oriented combustion model is presented, which takes the condition of the injection nozzle into account. In combination with a simulation model of the entire fuel injection system from a previous study, the model is capable of predicting the heat release rate (HRR) at different working conditions. Measurements with a single-cylinder diesel engine were performed, using injectors with modified and aged nozzles, to validate the proposed combustion model and particularly to analyze the influence of injector aging. Using the simulation model, optimal injection strategies were obtained by applying a line search optimization scheme to recover a reference HRR trajectory.

Synthetic fuels for internal combustion engines offer CO2-neutral mobility if produced in a closed carbon cycle using renewable energies. C1-based synthetic fuels can offer high knock resistance as well as soot free combustion due to their molecular structure containing oxygen and no direct C-C bonds. Such fuels as, for example, dimethyl carbonate (DMC) and methyl formate (MeFo) have great potential to replace gasoline in spark-ignition (SI) engines. In this study, a mixture of 65% DMC and 35% MeFo (C65F35) was used in a single-cylinder research engine to determine friction losses in the piston group using the floating-liner method. The results were benchmarked against gasoline (G100). Compared to gasoline, the density of C65F35 is almost 40% higher, but its mass-based lower heating value (LHV) is 2.8 times lower. Hence, more fuel must be injected to reach the same engine load as in a conventional gasoline engine, leading to an increased cooling effect.

In order to predict reality as accurately as possible leads to the fact that numerical models in automotive vibroacoustic problems become increasingly high dimensional. This makes applications with a large number of model evaluations, e.g. optimization tasks or uncertainty quantification hard to solve, as they become computationally very expensive. Engineers are thus faced with the challenge of making decisions based on a limited number of model evaluations, which increases the need for data-efficient methods and reduced order models. In this contribution, variational autoencoders (VAEs) are used to reduce the dimensionality of the vibroacoustic model of a vehicle body and to find a low-dimensional latent representation of the system.

The objective of this study was to reduce pollutant emissions during cold start conditions in a spark-ignited direct injection engine, by exploring the potential of oxygenated fuels. With their high oxygen content and lack of direct C-C bonds, they effectively reduce particle number (PN) and NOx emissions under normal conditions. Methanol was chosen due to its wide availability. As methanol is toxic to humans and associated with cold-start issues, a second promising synthetic fuel was selected to be benchmarked against gasoline, comprising 65 vol% of dimethyl carbonate and 35 vol% of methyl formate (C65F5). Currently, there is a lack of detailed investigations on the cold start performance for both oxygenated fuels utilizing today’s injector capabilities. Spray measurements were caried out in a constant volume chamber to assess the spray of C65F35. Reduced fuel temperature increased spray-penetration length and compromised fast vaporization.

Software complexity of vehicles is constantly growing especially with additional autonomous driving features being introduced. This increases the risk for bugs in the system, when the car is delivered. According to a car manufacturer, more than 90% of availability problems corresponding to Electronic Control Unit (ECU) functionality are either caused by software bugs or they can be resolved by applying software updates to overcome hardware issues. The main concern are sporadic errors which are not caught during the development phase since their trigger condition is too unlikely to occur or is not covered by the tests. For such systems, there is a need of safe and secure infield diagnosis. In this paper we present a tool software architecture with remote access, which facilitates standard read/write access, an efficient channel interface for communication and file I/O, and continuous trace.