Search Results

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.

In spark-ignition engines, fluctuations of the in-cylinder pressure trace and the apparent rate of heat release are usually observed from one cycle to another. These Cycle-to-Cycle Variations (CCV) are affected by the early flame development and the subsequent flame front propagation. The CCV are responsible for engine performance (e.g. fuel consumption) and the knock behavior. The occurrence of the phenomena is unpredictable and the stochastic nature offers challenges in the optimization of engine control strategies. In the present work, CCV are analyzed in terms of their impact on the engine knock behavior and the related efficiency. Target is to estimate the possible fuel consumption savings in steady-state operation and in the drivecycle, when CCV are reduced. Since CCV are immanent on real engines, such a study can only be done by means of simulation.

Efficient integration of mechanics and microelectronics components is nowadays a must within the automotive industry in order to minimize integration risks and support optimization of the entire system. We propose in this work a cross domain co-simulation platform for the efficient analysis of mechatronic systems. The interfacing of two state-of-the-art simulation platforms provides a direct link between the two domains at an early development stage, thus enabling the validation and optimization of the system already during modeling phase. The proposed cross-domain co-simulation is used within our TEODACS project for the analysis of the FlexRay technology. We illustrate using a drive-by-wire use case how the different architecture choices may influence the system.

The electrification of the powertrain is a prerequisite to meet future fuel consumption limits, while the internal combustion engine (ICE) will remain a key element of most production volume relevant powertrain concepts. High volume applications will be covered by electrified powertrains. The range will include parallel hybrids, 48V- or High voltage Mild- or Full hybrids, up to Serial hybrids. In the first configurations the ICE is the main propulsion, requiring the whole engine speed and load range including the transient operation. At serial hybrid applications the vehicle is generally electrically driven, the ICE provides power to drive the generator, either exclusively or supporting a battery charging concept. As the ICE is not mechanically coupled to the drive train, a reduction of the operating range and thus a partial simplification of the ICE is achievable.

In the present work, a scalable simulation methodology is presented that enables the assessment of the impact of SI-engine cycle-to-cycle combustion variations on fuel consumption and hence CO2 emissions on three different levels of modeling depth: in-cylinder, steady-state engine and transient engine and vehicle simulation. On the detailed engine combustion chamber level, a 3D-CFD approach is used to study the impact of the turbulent in-cylinder flow on the cycle-resolved flame propagation characteristics. On engine level, cycle-to-cycle combustion variations are assessed regarding their impact on indicated mean effective pressure, aiming at estimating the possible fuel consumption savings when cyclic variations are minimized. Finally, on the vehicle system level, a combined real-time engine approach with crank-angle resolved cylinder is used to assess the potential fuel consumption savings for different vehicle drivecycle conditions.

In order to meet current and future emission and CO2 targets, an efficient vehicle thermal management system is one of the key factors in conventional as well as in electrified powertrains. Global vehicle simulation is already a well-established tool to support the vehicle development process. In contrast to conventional vehicles, electrified powertrains offer an additional challenge to the thermal conditioning: the durability of E-components is not only influenced by temperature peaks but also by the duration and amplitude of temperature swings as well as temperature gradients within the components during their lifetime. Keeping all components always at the preferred lowest temperature level to avoid ageing under any conditions (driving, parking, etc.) will result in very high energy consumption which is in contradiction to the efficiency targets.

The automotive domain has seen safety engineering at the forefront of the industry’s priorities for the last decade. Therefore, additional safety engineering efforts, design approaches, and well-established safety processes have been stipulated. Today many connected and automated vehicles are available and connectivity features and information sharing are increasingly used. This increases the attractiveness of an attack on vehicles and thus introduces new risks for vehicle cybersecurity. Thus, just as safety became a critical part of the development in the late 20th century, the automotive domain must now consider cybersecurity as an integral part of the development of modern vehicles. Aware of this fact, the automotive industry has, therefore, recently taken multiple efforts in designing and producing safe and secure connected and automated vehicles.

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.

Future demands regarding emissions, fuel consumption and driveability lead to complex engine and power train control systems. The calibration of the increasing number of free parameters in the ECU's contradicts the demand for reduced time in the power train development cycle. This paper will focus on the automatic, unmanned closed loop optimization of driveability quality on a high dynamic engine test bed. The collaboration of three advanced methods will be presented: Objective real time driveability assessment, to predict the expected feelings of the buyers of the car Automatic computer assisted variation of ECU parameters on the basis of statistical methods like design of experiments (DoE). Thus data are measured in an automated process allowing an optimization based on models (e.g. neural networks).

Approaching engineering limits for the thermal design of battery modules requires virtual prototyping and appropriate models with respect to physical depth and computational effort. A multi-scale and multi-domain model describes the electrochemical behavior of a single battery unit cell in 1D+1D at the level of intra-cell phenomena, and it applies a 3D thermal model at module level. Both models are connected within a common vehicle simulation platform. The models are discussed with special emphasis on battery degradation such as solid electrolyte interphase layer formation, decomposition and lithium plating. The performance of the electrochemical model is assessed by discharge cycles and repeated charge/discharge simulations. The thermal module model is compared to CFD reference data and studied with respect to its grid sensitivity.

The present work introduces an innovative mechanistically based 0D spray model which is coupled to a combustion model on the basis of an advanced mixture controlled combustion approach. The model calculates the rate of heat release based on the injection rate profile and the in-cylinder state. The air/fuel distribution in the spray is predicted based on momentum conservation by applying first principles. On the basis of the 2-zone cylinder framework, NOx emissions are calculated by the Zeldovich mechanism. The combustion and emission models are calibrated and validated with a series of dedicated test bed data specifically revealing its capability of describing the impact of variations of EGR, injection timing, and injection pressure. A model based optimization is carried out, aiming at an optimum trade-off between fuel consumption and engine-out emissions. The findings serve to estimate an economic optimum point in the NOx/BSFC trade-off.

The present work introduces a fully integrated real-time (RT) capable engine and vehicle model. The gas path and drive line are described in the time domain of seconds whereas the reciprocating characteristics of an IC engine are reflected by a crank angle resolved cylinder model. The RT engine model is derived from a high fidelity 1D cycle simulation and gas exchange model to support an efficient and consistent transfer of model data like geometries, heat transfer or combustion. The workflow of model calibration and application is outlined and base ECU functionalities for boost pressure, EGR, smoke and idle speed control are applied for transient engine operation. Steady state results of the RT engine model are compared to experimental data and 1D high fidelity simulations for 19 different engine load points. In addition an NEDC (New European Drive Cycle) is simulated and results are evaluated with data from chassis dynamometer measurements.

Today's powertrains are becoming more and more complex due to the increasing number of gear box types requiring gearshift patterns like conventional (equipped with GSI) and automatic-manual transmissions (AT, AMT), double clutch and continuous variable transmissions (DCT, CVT). This increasing variety of gear boxes requires a higher effort for the overall optimization of the powertrain. At the same time, it is necessary to assess the impact of different powertrains and control strategies on CO₂ emissions very early in the development process. The optimization of Gear Shift Patterns (G.S.P.) has to fulfill multiple constraints in terms of objective customers' requirements, like driveability, NVH, performance, emissions and fuel consumption. For these reasons, RENAULT and AVL entered an engineering collaboration in order to develop a dedicated simulation tool: CRUISE GSP.

In a tiered cooling pack, the airflow through the individual heat exchangers is determined by the package and aperture lay out. Each heat exchanger rejects heat as a function of the internal coolant flows, the cooling airflow and the air temperature. In a typical automotive cooling pack, the cooling airflow will be non-uniform in velocity and temperature due to fans, aperture geometry, exterior flows, heat exchangers and recirculation. In a drive cycle, these boundary conditions will change with vehicle operating conditions like vehicle speed, engine speed, ambient temperature, and altitude. These non-uniform conditions on the cooling pack can lead to significant errors when uniform boundary conditions are assumed in a transient simulation. This error is commonly corrected using vehicle test data. A predictive approach, which eliminates the need for correlation vehicle testing, is presented.

Future legislation scenarios as well as stringent CO2 targets, in particular under real driving conditions, will require the introduction of new and additional powertrain technologies. Beside the increasing electrification of the powertrain, it will be essential to utilize the full potential of the Internal Combustion Engine (ICE). There is clearly a competition of new and different ICE-Technologies [1] including VCR. VCR systems are expected to be introduced to a considerable number of next generation turbocharged Spark Ignited (SI) engines in certain vehicle classes. The implementation of Miller or Atkinson cycles is an essential criterion for increased geometric Compression Ratio (CR). The DUAL MODE Variable Compression System (VCS)TM enables a 2-stage variation of the connecting rod length and thus of the compression ratio (CR).

The current trend of highly boosted petrol engines is demanding significant engineering effort on the air intake system development. The package of the air intake system is done early in the programme phase and the main engineering effort have historically been around achieving the system pressure drop targets. The thermal impact of the package is assessed during the vehicle testing phase. This can lead to significant design changes in order to maintain engine performance under all operating conditions late on in the development, driving up cost and programme delays. The highly boosted engine performance is very sensitive to heat pick up of the intake air and therefore requires an optimised system. To be able to support the engine intake design at the early program phases with thermal input, an analytical method has been developed.

In the ever increasing challenge of developing more efficient and less polluting engines, friction reduction is of significant importance and its investigation needs an accurate and reliable measurement technique. The Pressurized Motoring method is one of the techniques used for both friction and heat transfer measurements in internal combustion engines. This method is able to simulate mechanical loading on the engine components similar to the fired conditions. It also allows measurement of friction mean effective pressure (FMEP) with a much smaller uncertainty as opposed to that achieved from a typical firing setup. Despite its advantages, the FMEP measurements obtained by this method are usually criticized over the fact that the thermal conditions imposed in pressurized motoring are far detached from those seen in fired conditions. In light of these considerations, the authors have put forward a modification to the method, employing Argon in place of Air as pressurization medium.

Mechanical friction and heat transfer in internal combustion engines have long been studied through both experimental and numerical simulation. This publication presents a continuation study on a Pressurized Motoring setup, which was presented in SAE paper 2018-01-0121 and found to offer robust measurements at relatively low investment and running cost. Apart from the limitation that the peak in-cylinder pressure occurs around 1 DegCA BTDC, the pressurized motoring method is often criticized on the fact that the gas temperatures in motoring are much lower than that in fired engines, hence might reflect in a different FMEP measurement. In the work presented in SAE paper 2019-01-0930, Argon was used as the pressurization gas due to its high ratio of specific heats. This allowed to achieve higher peak in-cylinder temperatures which close further the gap between fired and motored mechanical friction tests.

In this paper, a recently improved Computational Fluid Dynamics (CFD) methodology for virtual prototyping of the heat treatment of cast aluminum parts, above most of cylinder heads of internal combustion engines (ICE), is presented. The comparison between measurement data and numerical results has been carried out to simulate the real time immersion quenching cooling process of realistic cylinder head structure using the commercial CFD code AVL FIRE®. The Eulerian multi-fluid modeling approach is used to handle the boiling flow and the heat transfer between the heated structure and the sub-cooled liquid. While for the fluid region governing equations are solved for each phase separately, only the energy equation is solved in the solid region. Heat transfer coefficients depend on the boiling regimes which are separated by the Leidenfrost temperature.

Reynolds-averaged Navier-Stokes (RANS) computations of heat transfer involving wall bounded flows at elevated Prandtl numbers typically suffer from a lack of accuracy and/or increased mesh dependency. This can be often attributed to an improper near-wall turbulence modeling and the deficiency of the wall heat transfer models (based on the so called P-functions) that do not properly account for the variation of the turbulent Prandtl number in the wall proximity (y+< 5). As the conductive sub-layer gets significantly thinner than the viscous velocity sub-layer (for Pr >1), treatment of the thermal buffer layer gains importance as well. Various hybrid strategies utilize blending functions dependent on the molecular Prandtl number, which do not necessarily provide a smooth transition from the viscous/conductive sub-layer to the logarithmic region.