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This works presents a real-time capable simulation model for dual fuel operated engines. The computational performance is reached by an optimized filling and emptying modeling approach applying tailored models for in-cylinder combustion and species transport in the gas path. The highly complex phenomena taking place during Diesel and gasoline type combustion are covered by explicit approaches supported by testbed data. The impact of the thermodynamic characteristics induced by the different fuels is described by an appropriate set of transport equations in combination with specifically prepared property databases. A thermodynamic highly accurate 6-species approach is presented. Additionally, a 3-species and a 1-species transport approach relying on the assumption of a lumped fuel are investigated regarding accuracy and computational performance. The comparison of measured and simulated pressure and temperature traces shows very good agreement.

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.

This paper describes the findings of a design, simulation and test study into how to reduce particulate number (Pn) emissions in order to meet EU6c legislative limits. The objective of the study was to evaluate the Pn potential of a modern 6-cylinder engine with respect to hardware and calibration when fitted to a full size SUV. Having understood this capability, to redesign the combustion system and optimise the calibration in order to meet an engineering target value of 3×1011 Pn #/km using the NEDC drive cycle. The design and simulation tasks were conducted by JLR with support from AVL. The calibration and all of the vehicle testing was conducted by AVL, in Graz. Extensive design and CFD work was conducted to refine the inlet port, piston crown and injector spray pattern in order to reduce surface wetting and improve air to fuel mixing homogeneity. The design and CFD steps are detailed along with the results compared to target.

Over the past decade there have been significant advances made in the technology used to engineer Powertrain Sound Quality into automobiles. These have included exhaust system technologies incorporating active and semi-active valves, intake system technologies involving passive and direct feedback devices, and technologies aimed at tuning the structure-borne content of vehicle interior sound. All of these technologies have been deployed to complement the traditional control of NVH issues through the enhancement of Powertrain Sound Quality. The aim of this paper is to provide an historical review of the recent industry-wide advances made in these technologies and to provide the author's perspective on what issues have been addressed and what opportunities have been delivered.

The modern power train calibration process is characterized by shorter development cycles and a reduced number of prototypes. However, simultaneously exhaust aftertreatment and emission testing is becoming increasingly more sophisticated. The introduction of predictive simulation tools that represent the complete power train can likely contribute to improving the efficiency of the calibration process using an integral model based workflow. Engine models, which are purely based on complex physical principles, are usually not capable of real-time applications, especially if the simulation is focused on transient emission optimization. Methods, structures and the realization of a global dynamic real-time model are presented in this paper, combining physical knowledge and experimental models and also static and dynamic sub-structures. Such a model, with physical a priori information embedded in the model structure, provides excellent generalization capability.

Today's calibration process for ECU functions is often based on a wide variety of proprietary tools and individual expert knowledge of calibration engineers. Automatic calibration with an industrialized tool chain provides high potential to reduce testbed time, calibration time and project costs. Based on an efficient measurement procedure in combination with an offline calibration methodology the capability is validated, e.g. for calibrating the ECU function “Air Charge Determination” for SI engines. In this article the implementation, in a series production project of a major OEM, is shown. The whole workflow - which can also be applied to other calibration tasks - will be described in detail. Presented here will be how General Motors Corporation (GM) is able to speed up the calibration of the ECU functions, whilst maintaining at least the same quality of calibration as before, by the use of this tool chain.

At the moment the documentation of failure inhibition matrices and the fault path management for different controller types and different vehicle projects are mainly maintained manually in individual Excel tables. This is not only time consuming but also gives a high potential for fault liability. In addition there is also no guarantee that the calibration of these failure inhibition matrices and its fault path really works. Conflicting aims between costs, time and fault liability require a new approach for the calibration, documentation and testing of failure inhibition matrices and the complete Diagnostic System Management (DSM) calibration. The standardization and harmonization of the Diagnostic System Management calibration for different calibration projects and derivates is the first step to reduce time and costs. Creating a master calibration for the conjoint fault paths and labels provides a significant reduction of efforts.

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).

A prototype multi-hole diesel injector operating with n-heptane fuel from a high-pressure common rail system is used in a high-pressure and high-temperature test rig capable of reaching 1100 Kelvin and 150 bar under different oxygen concentrations. A novel optical set-up capable of visualizing the soot cloud evolution in the fuel jet from 30 to 85 millimeters from the nozzle exit with the high-speed color diffused back illumination technique is used as a result of the insertion of a high-pressure window in the injector holder opposite to the frontal window of the vessel. The experiments performed in this work used one wavelength provide information about physical of the soot properties, experimental results variating the operational conditions show the reduction of soot formation with an increase in injection pressure, a reduction in ambient temperature, a reduction in oxygen concentration or a reduction in ambient density.

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).

Hardware-in-the-loop (HIL) simulations have long been used to test electronic control units (ECUs) and software in car manufacturers. It provides an effective platform to the rapid development process of the ECU control algorithms and accommodates the added complexity of the plant under control. Accurate Model based HIL simulation (AMHIL) is considered as a most efficient and cost effective way for exploration of new designs and development of new products, particularly in calibration and parameterization of vehicle stability controllers. The work presented in the paper is to develop a mathematical model of a windscreen wiper system for the purpose of conducting HIL vehicle test and eventually to replace the real component with the model for cost cutting and improved test efficiency. The model is developed based on the electro-mechanical engineering principles.

Wall flow particulate filters have been used as a standard exhaust aftertreatment device for many years. The interaction of particulate matter (PM) regeneration and catalytically supported reactions strongly depends on the given operating conditions. Temperature, species concentration and mass flow cause a change from advective to diffusive-controlled flow conditions and influence the rate controlling dominance of individual reactions. A transient 1D+1D model is presented considering advective and diffusive transport phenomena. The reaction scheme focuses on passive PM conversion and catalytic oxidation of NO. The model is validated with analytical references. The impact of back-diffusion is explored simulating pure advective and combined advective diffusive species transport. Rate approaches from literature are applied to investigate PM conversion at various operating conditions.

A calibration and validation workflow will be presented in this paper, which utilizes common static global models for fuel consumption, NOx and soot. Due to the applicability for warm-up tests, e. g. New European Driving Cycle (NEDC), the models need to predict the temperature influence and will be fitted with measuring data from a conditioned engine test bed. The applied model structure - consisting of a number of global data-based sub-models - is configured especially for the requirements of multi-injection strategies of common rail systems. Additionally common global models for several constant coolant water temperature levels are generated and the workflow tool supports the combination and segmentation of global nominal map with temperature correction maps for seamless and direct ECU setting.

Complexity of electronics and embedded software systems in automobiles has been increasing over the years. This necessitates the need for an effective and exhaustive development and validation process in order to deliver fault free vehicles at reduced time to market. Model-based Product Engineering (MBPE) is a new process for development and validation of embedded control software. The process is generic and defines the engineering activities to plan and assess the progress and quality of the software developed for automotive applications. The MBPE process is comprised of six levels (one design level and five verification and validation levels) ranging from the vehicle requirements phase to the start of production. The process describes the work products to be delivered during the course of product development and also aligns the delivery plan to overall vehicle development milestones.

Simulation of local flow structures in the A-pillar/side glass region of bluff SUV geometries, typical of Land Rover vehicles, presents a considerable challenge. Features such as relatively tight A-pillar radii and upright windscreens produce flows that are difficult to simulate. However, the usefulness of aerodynamics simulations in the early assessment of wind noise depends particularly on the local accuracy obtained in this region. This paper extends work previously published by the author(1) with additional data and analysis. An extended review of the relevant published literature is also provided. Then the degree to which a commercial Lattice-Boltzman solver (Exa PowerFLOW™) is currently able to capture both the local flow structure and surface pressure distribution (both time averaged and unsteady) is evaluated. Influential factors in the simulation are shown to be spatial resolution, turbulence and boundary layer modelling.

Dynamometer (dyno) durability testing plays a significant role in reliability and durability assessment of commercial engines. Frequently, durability test procedures are based on warranty history and corresponding component failure modes. Evolution of engine designs, operating conditions, electronic control features, and diagnostic limits have created challenges to historical-based testing approaches. A physics-based methodology, known as Load Matrix, is described to counteract these challenges. The technique, developed by AVL, is based on damage factor models for subsystem and component failure modes (e.g. fatigue, wear, degradation, deposits) and knowledge of customer duty cycles. By correlating dyno test to field conditions in quantifiable terms, such as customer equivalent miles, more effective and efficient durability test suites and test procedures can be utilized. To this end, application of Load Matrix to a heavy-duty diesel engine is presented.

Due to increasing complexity of engine electronic systems, there is a demand to handle the often more than 10,000 calibration data automatically. Establishing optimized start of injection and EGR tables of a TC DI Diesel engine by conventional methods takes about two weeks of intensive calibration work. By automatic map calibration, this task can be handled in less than 20 hours automatically, with no staff required during optimization. The benefits of automatic calibration therefore are reduced costs and faster response to any changes in parameters, even with complex multidimensional engine calibration problems. The paper describes the optimization method as well as the experimental work on the test stand that produces the results.

The automotive industry is racing to introduce some degree of hybridization into their product ranges. Since the term "hybrid vehicle" can cover a wide range of differing technologies and drivetrain topologies, this has led to a plethora of vehicles that call them "hybrid." This poses an interesting challenge for marketers to differentiate these vehicles from the incumbents. However, it is not just the marketers who are faced with challenges, the developers of such hybrid drivetrains are faced with a rise in technical complexity due to the wide range of operating modes hybridization introduces. As propulsive torque is being generated in more than one place in a hybrid vehicle, the transitions from conventional drive to electrically supported drive bring with them complex aspects of multi-dimensional system control. The challenge is to be able to implement hybrid technology in an existing drivetrain, while adapting the existing components as required.

This paper documents some of the findings from a joint JLR and AVL project which was conducted at the JLR Gaydon test facility in the UK. A testing and development efficiency concept is presented and test data quality is identified as a key factor. In support of this methods are proposed to correctly measure and set targets for data quality with high confidence. An illustrative example is presented involving a Diesel passenger car calibration process which requires response surface models (RSMs) of key engine measured quantities e.g. engine-out emissions and fuel consumption. Methods are proposed that attempt to quantify the relationships between RSM statistical model quality metrics, test data variability measures and design of experiment (DOE) formulation. The methods are tested using simulated and real test data.

Over the past few years, the fuel mass measurement gained in importance to record the consumed fuel mass and the specific fuel consumption [g/kWh] with high accuracy. Measuring instruments, such as positive displacement meters, methods based on the burette or the Wheatstone bridge mass flow meter measure either the volumetric flow and a temperature-dependant fuel density correction is necessary or they have old technology and therefore poor accuracy and repeatability. A new-generation Coriolis sensor featuring an ideal measurement range for engine test beds but still with flow depending pressure drop has been integrated in a fuel meter to ensure that no influence is given to the engine behaviour for example after engine load change. The new Coriolis meter offers better accuracy and repeatability, gas bubble venting and easy test bed integration. For returnless fuel injection systems the fuel system supplies the fuel pressure.