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New 12 V/48 V power net architectures are potential solutions to close the gap between customer needs and legislative requirements. In order to exploit their potential, an increased effort is needed for functional implementation and hardware integration. Shifting of development tasks to earlier phases (frontloading) is a promising solution to streamline the development process and to increase the maturity level at early stages. This study shows the potential of the frontloading of development tasks by implementing a virtual 48 V mild hybridization in an engine-in-the-loop (EiL) setup. Advanced simulation technics like functional mock-up interface- (FMI) based co-simulation are utilized for the seamless integration of the real-time (RT) simulation models and allow a modular simulation framework as well as a decrease in development time.

Lean burn gasoline spark-ignition engines can support the reduction of CO2 emissions for future hybrid passenger cars. Very high efficiencies and very low NOx raw emissions can be achieved, if relative air/fuel ratios λ of 2 and above can be reached. The biggest challenge here is to assure a reliable ignition process and to enhance the fuel oxidation in order to achieve a short burn duration and a good combustion stability. This article aims at introducing an innovative combustion system fully optimized for ultra-lean operation and very high efficiency. Thereto, a new cylinder head concept has been realized with high peak firing pressure capability and with a low surface-to-volume ratio at high compression ratios. 1D and 3D simulations have been performed to optimize the compression ratio, charge motion and intake valve lift. Numerical calculations also supported the development of the ignition system.

Physics-based models in a closed-loop feedback control of a premixed charge compression ignition (PCCI) engine can improve the combustion efficiency and potentially reduce harmful NOx and soot emissions. A stand-alone multi-zone combustion model has been proposed in the literature using a physics-based mixing approach. The scalar dissipation rate emerged as the determining parameter in the model for mixing among different zones in the mixture fraction space. However, the calculation of the scalar dissipation rate depends on three approaches: three-dimensional computational fluid dynamics (3-D CFD) combustion simulations based on representative interactive flamelet (RIF) model, tabulation, or an empirical algebraic model of the scalar dissipation rate fitted for the given operating conditions of the engine. While the 3-D CFD approach provides accurate results, it is computationally too expensive to use the multi-zone model in closed-loop control.

New combustion processes require an understanding of the highly three-dimensional flow field to effectively decrease fuel consumption and pollutant emission. Due to the complex spatial character of the flow the knowledge of the development of the flow in an extended volume is necessary. Previous investigations were able to visualize the discrete three-dimensional flow field through multi-plane stereoscopic PIV. In this study, cycle resolved tomographic particle-image velocimetry measurement have been performed to obtain a fully resolved representation of the three-dimensional flow structures at each instant. The analysis is based on the measurements at 80°, 160°, and 240° after top dead center(atdc) such that the velocity distributions at the intake, the end of the intake, and the compression stroke at an engine speed of 1,500 rpm are discussed in detail.

Modern automotive development evolves beyond artificial intelligence for highly automated driving, and toward an interconnected manifold of data-driven development processes. Widely used analytical system modelling struggles with rising system complexity, invoking approaches through data-driven system models. We consider these as key enablers for further improvements in accuracy and development efficiency. However, literature and industry have yet to thoroughly discuss the relevance and methods along the vehicle development cycle. We emphasize the importance of data-driven system models in their distinct types and applications along the developing process, from pre-development to fleet operation. Data-driven models have proven in other works to be fast approximators, of high accuracy and adaptive, in contrast to physics-based analytical approaches across domains.

The noise performance of fully electric vehicles is essential to ensure that they gain market acceptance. This can be a challenge for several reasons. Firstly, there is no masking from the internal combustion engine. Next, there is pressure to move to cost-efficient motor designs such as Switched Reluctance Motors, which have worse vibro-acoustic behaviour than their Permanent Magnet counterparts. Finally, power-dense, higher speed motors run closer fundamental frequency to the structural resonances of the system [1]. Experience has shown that this challenge is frequently not met. Reputable suppliers have designed and developed their “quiet” subsystems to state of the art levels, only to discover that the assembled E-powertrain is unacceptably noisy. The paper describes the process and arising results for the noise simulation of the complete powertrain.

This article discusses highly flexible and accurate physics-based mean value modeling (MVM) for internal combustion engines and its wide applicability towards virtual vehicle calibration. The requirement to fulfill the challenging Real Driving Emissions (RDE) standards has significantly increased the demand for precise engine models, especially models regarding pollutant emissions and fuel economy. This has led to a large increase in effort required for precise engine modeling and robust model calibration. Two best-practice engine modeling approaches will be introduced here to satisfy these requirements. These are the exclusive MVM approach, and a combination of MVM and a Design of Experiments (DOE) model for heterogeneous multi-domain engine systems.

In order to reduce pollutant and CO2 emissions and fulfill future legislative requirements, powertrain electrification is one of the key technologies. In this context, especially 48V technologies offer an attractive cost to CO2 reduction ratio. 48V mild hybrid powertrains greatly benefit from additional electric intake air compression (E-Charging) and direct torque assist by an electric machine (E-Boosting). Both systems significantly improve the transient engine behavior while reducing the low end torque drawbacks of extreme downsizing and downspeeding. Since E-Charging and E-Boosting have different characteristics concerning transient torque response and energy efficiency, application of model predictive control (MPC) is a particularly suitable method to improve the operating strategy of these functions. MPC requires fast running real-time capable models that are challenging to develop for systems with pronounced nonlinearities.

Knock control is one of the most vital functions for safe and fuel-efficient operation of gasoline engines. However, all knock control strategies rely on accurate knock detection to operate the engine close to the optimal set point. Knock detection is usually calibrated on the engine test bench, requiring the engine to run with knocking combustion in a time-consuming multi-stage campaign. Model-based calibration significantly reduces calibration loops on the test bench. However, this method requires a large effort in building and validating the model, which is often limited by the lack of function documentation, available measurements or hardware representation. As the software models are often not available, function structures vary between manufacturers and sub model functions are often documented as black boxes. Hence, using the model-based approach is not always possible.

On the way to emission-free mobility, future fuels must be CO2 neutral. To achieve this, synthetic fuels are being developed. In order to better assess the effects of the new fuels on the engine process, simulation models are being developed that reproduce the chemical and physical properties of these fuels. In this paper, the fuel DMC+ is examined. DMC+ (a mixture of dimethyl carbonate (DMC) and methyl formate (MeFo) mainly, characterized by the lack of C-C Bonds and high oxygen content) offers advantages with regard to evaporation heat, demand of oxygen and knock resistance. Furthermore, its combustion is almost particle free. With the aid of modern 0D/1D simulation methods, an assessment of the potential of DMC+ can be made. It is shown that the simulative conversion of a state-of-the-art gasoline engine to DMC+ fuel offers advantages in terms of efficiency in many operating points even if the engine design is not altered.

Multiple-injection strategies are widely used in DI diesel engines. However, the interaction of the injection pulses is not yet fully understood. In this work, a split injection into a combustion vessel is studied by multiple optical imaging diagnostics. The vessel provides quiescent high-temperature, high-pressure ambient conditions. A common-rail injector which is equipped with a three-hole nozzle is used. The spray is visualized by Mie scattering. First and second stage of ignition are probed by formaldehyde laser-induced fluorescence (LIF) and OH* chemiluminescence imaging, respectively. In addition formation of soot is characterized by both laser-induced incandescence (LII) and natural luminosity imaging, showing that low-sooting conditions are established. These qualitative diagnostics yield ensemble-averaged, two-dimensional, time-resolved distributions of the corresponding quantities.

The reduction of harmful emissions is a key challenge in fighting climate change and global warming. Besides battery electric vehicles (BEVs), improved engine efficiency and alternate fuels, such as e-fuels or biofuels, can improve the emission budget of the transportation sector. Pred ictive simulations can be utilized as these avoid relying on slow manufacturing processes and expensive experiments. As the properties of alternative fuels can change drastically compared to classical fuels, even engine parameters, such as the mass flow rate, need to be reevaluated and optimized. However, simulation frameworks often rely on mass flow rates as input quantity, and hence, a prediction is impossible. This paper gives accurate pressure-based boundary conditions for multiphase systems and focuses on equations of state (EOS) employed in homogeneous equilibrium models (HEMs). Additionally, a dual-density approach is introduced to correct modeling errors that are intrinsic to a particular EOS.

The growing number of passenger car variants and derivatives in all global markets, their high degree of software differentiability caused by regionally different legislative regulations, as well as pronounced market-specific customer expectations require a continuous optimization of the entire vehicle development process. In addition, ever stricter emission standards lead to a considerable increase in powertrain hardware and control complexity. Also, efforts to achieve market and brand specific multistep adjustable drivability characteristics as unique selling proposition, rapidly extend the scope for calibration and testing tasks during the development of powertrain control units. The resulting extent of interdependencies between the drivability calibration and other development and calibration tasks requires frontloading of development tasks.

Direct injection (DI) of compressed natural gas (CNG) is a promising technology to increase the indicated thermal efficiency of internal combustion engines (ICE) while reducing exhaust emissions and using a relatively low-cost fuel. However, design and analysis of DI-CNG engines are challenging because supersonic gas jet emerging from the DI injector results in a very complex in-cylinder flow field containing shocks and discontinuities affecting the fuel-air mixing. In this article, numerical simulations are used supported by validation to investigate the direct gas injection and its influence on the flow field and mixing in an optically accessible ICE. The simulation approach involves computation of the in-nozzle flow with highly accurate Large-Eddy Simulations, which are then used to obtain a mapped boundary condition. The boundary condition is applied in Unsteady Reynolds Averaged Navier-Stokes simulations of the engine to investigate the in-cylinder velocity and mixing fields.

Premixed charged compression ignition (PCCI) is an advanced combustion strategy, which has the potential to achieve ultra-low nitrogen oxide and soot emissions at high thermal efficiencies. PCCI combustion is characterized by a complex nonlinear chemical-physical process, which indicates that a physical description involves significant development times and also high computation cost. This paper presents a method to use cylinder pressure data and engine operations parameters for prediction of PCCI engine emissions by unsupervised learning and nonlinear identification techniques. The proposed method first uses principal component analysis (PCA) to reduce the dimension of the cylinder-pressure data. Based on the PCA analysis, a multi-input multi-out model was developed for nitrogen oxide and soot emission prediction by multi-layer perceptron (MLP) neural network.

The calibration of Engine Control Units (ECUs) for road vehicles is challenged by stringent legal and environmental regulations, coupled with short development cycles. The growing number of vehicle variants, although sharing similar engines and control algorithms, requires different calibrations. Additionally, modern engines feature increasingly number of adjustment variables, along with complex parallel and nested conditions within the software, demanding a significant amount of measurement data during development. The current state-of-the-art (White Box) model-based ECU calibration proves effective but involves considerable effort for model construction and validation. This is often hindered by limited function documentation, available measurements, and hardware representation capabilities. This article introduces a model-based calibration approach using Neural Networks (Black Box) for two distinct ECU functional structures with minimal software documentation.

This paper will present a multi-domain (electrical and thermal) model of a three phase voltage source converter and its implementation in Modelica language. An averaged model is utilised for the electrical domain, and a power balance method is used for linking the DC and AC sides. The thermal domain focuses in deriving the converter losses by deriving the analytical equations of the space vector modulation to derive a function for the duty cycle of each converter leg. With this, the conduction and switching losses are calculated for the individual switches and diodes, without having to model their actual switching behaviour. The model is very fast to simulate, as no switching events are needed, and allows obtaining the simulation of the electrical and thermal behaviour in the same simulation package..

In this work, a transport and mixing model that calculates mixing in thermodynamic phase space was derived and validated. The mixing in thermodynamic multizone space is consistent to the one in the spatially resolved physical space. The model is developed using a turbulent channel flow as simplified domain. This physical domain of a direct numerical simulation (DNS) is divided into zones based on the quantitative value of transported scalars. Fluxes between the zones are introduced to describe mixing from the transport equation of the probability density function based on the mixing process in physical space. The mixing process of further scalars can then be carried out with these fluxes instead of solving additional transport equations. The relationship between the exchange flux in phase space and the concept of scalar dissipation are shown and validated by comparison to DNS results.

Auto transformer rectifier units (ATRUs) are commonly used in aircraft applications such as electric actuation for harmonic mitigation due to their high reliability and relative low cost. However, those components and the magnetic filter components associated to it are the major contributors to the overall size and weight of the system. Optimization of the magnetic components is essential in order to minimize weight and size, which are major market drivers in aerospace industry today. This requires knowledge of the harmonic content of the current. This can be obtained by simulation, but the process is slow. In order to enable fast and efficient design space exploration of optimal solutions, an algebraic calculation process is proposed in this paper for multi-pulse ATRUs (e.g. 12-pulse and 18-pulse rectifiers), starting from existing solution proposed for 6 pulse rectifier in the literature.

The influence of two oxygenated tailor-made fuels on soot formation and oxidation in an optical single cylinder research diesel engine has been studied. For the investigation a planar laser-induced incandescence (PLII) measurement technique was applied to the engine in order to detect and evaluate the planar soot distribution for the two bio fuels within a laser light sheet. Furthermore the OH* chemiluminescence and broad band soot luminosity was visualized by high speed imaging to compare the ignition and combustion behavior of tested fuels: Two C8 oxygenates, di-n-butylether (DNBE) and 1-octanol. Both fuels have the same molecular formula but differ in their molecular structure. DNBE ignites fast and burns mostly diffusive while 1-octanol has a low cetane number and therefore it has a longer ignition delay but a more homogeneous mixture at time of ignition. The two bio fuels were finally compared to conventional diesel fuel.