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Due to the integration of many interacting subsystems like hybrid vehicle management, energy management, distance management, etc. into the VCU platform the design steps for function development and calibration become more and more complex. This makes an aid necessary to relieve the development. Therefore, the aim of the proposed simulation-based development and calibration design is to improve the time-and-cost consuming development stages of modern VCU platforms. A simulation-based development framework is shown on a complex function development and calibration case study using an advanced powertrain concept with a plug-in hybrid electric vehicle (PHEV) concept with two electrical axles. Presenter Thomas Boehme, IAV GmbH

This paper describes the joint development by Tenneco and Pi Shurlok of a complete diesel engine aftertreatment system for controlling particulate matter emissions. The system consists of a DOC, DPF, sensors, controller and an exhaust fuel injection system to allow active DPF regeneration. The mechanical components were designed for flow uniformity, low backpressure and component durability. The overall package is intended as a complete PM control system solution for OEMs, which does not require any significant additions to the OEM's engine control strategies and minimizes integration complexity. Thus, to make it easier to adapt to different engine platforms, ranging from small off-road vehicle engines to large locomotive engines, model-based control algorithms were developed in preference to map-based controls.

Durability/reliability design of products, such as auto exhaust systems, is essentially based on the observation of test data and the accurate interpretation of these data. Therefore, test planning and related data analysis are critical to successful engineering designs. To facilitate engineering applications, testing and data analysis methods have been standardized over the last decades by several standard bodies such as the American Society for Testing and Materials (ASTM). However, over the last few years, several effective testing and data analysis methods have been developed, and the existing standard procedures need to be updated to incorporate the new observations, knowledge, and consensus. In this paper, the common practices and the standard test planning and data analysis procedures are reviewed first. Subsequently, the recent development in accelerated testing, equilibrium based data fitting, design curve construction, and Bayesian statistical data analysis is presented.

The recent advance in the development of various hybrid vehicle technologies comes along with the need of establishing optimal energy management strategies, in order to minimize both fuel economy and pollutant emissions, while taking into account an increasing number of state and control variables, depending on the adopted hybrid architecture. One of the objectives of this research was to establish benchmarking performance, in terms of fuel economy, for real time on-board management strategies, such as ECMS (Equivalent Consumption Minimization Strategy), whose structure has been implemented in a SIMULINK model for different hybrid vehicle concepts.

Strength and fatigue life prediction is very difficult for stamped structural steel parts because the manufacturing process alters the localized material properties. Traditional tensile tests cannot be used to obtain material properties due to size limitations. Because of this, FEA predictions are most often “directional” at best. In this paper an improved prediction methodology is suggested. With a material library developed from standard homogenous test specimens, or even textbook material property tables, localized strength and plastic strain numbers can be inferred from localized hardness tests(1). The new method, using standard ABAQUS static analysis (not commercial fatigue analysis software with many unknowns), is shown to be very accurate. This paper compares the new process FEA strength and fatigue life predictions to laboratory test results using statistical confidence intervals.

In the near future, emissions legislation will become more and more restrictive for direct injection SI engines by adopting a stringent limitation of particulate number emissions in late 2017. In order to cope with the combustion system related challenges coming along with the introduction of this new standard, Hitachi Automotive Systems Ltd., Hitachi Europe GmbH and IAV GmbH work collaboratively on demonstrating technology that allows to satisfy EU6c emissions limitations by application of Hitachi components dedicated to high pressure injection (1). This paper sets out to describe both the capabilities of a new high pressure fuel system improving droplet atomization and consequently mixture homogeneity as well as the process of utilizing the technology during the development of a demonstrator vehicle called DemoCar. The Hitachi system consists of a fuel pump and injectors operating under a fuel pressure of 30 MPa.

Vehicle exhaust components and systems under fatigue loading often show multiple failure modes, which should be treated, at least theoretically, with rigorous advanced bi-modal and multi-modal statistical theories and approaches. These advanced methods are usually applied to mission-critical engineering applications such as nuclear and aerospace, in which large amounts of test data are often available. In the automotive industry, however, the sample size adopted in the product validation is usually small, thus the bi-modal and multi-modal phenomena cannot be distinguished with certainty.

In the Big Data era, the capability in statistical and probabilistic data characterization, data pattern identification, data modeling and analysis is critical to understand the data, to find the trends in the data, and to make better use of the data. In this paper the fundamental probability concepts and several commonly used probabilistic distribution functions, such as the Weibull for spectrum events and the Pareto for extreme/rare events, are described first. An event quadrant is subsequently established based on the commonality/rarity and impact/effect of the probabilistic events. Level of measurement, which is the key for quantitative measurement of the data, is also discussed based on the framework of probability. The damage density function, which is a measure of the relative damage contribution of each constituent is proposed. The new measure demonstrates its capability in distinguishing between the extreme/rare events and the spectrum events.

This paper investigates an optimal design of a diesel engine and after-treatment systems for a series hybrid electric forklift application. A holistic modeling approach is developed in GT-Suite® to establish a model-based hardware definition for a diesel engine and an after-treatment system to accurately predict engine performance and emissions. The used engine model is validated with the experimental data. The engine design parameters including compression ratio, boost level, air-fuel ratio (AFR), injection timing, and injection pressure are optimized at a single operating point for the series hybrid electric vehicle, together with the performance of the after-treatment components. The engine and after-treatment models are then coupled with a series hybrid electric powertrain to evaluate the performance of the forklift in the standard VDI 2198 drive cycle.

In order to reduce the negative health effects associated with engine pollutants, environmental problems caused by combustion engine emissions and satisfy the current strict emission standards, it is essential to better understand and simulate the emission formation process. Further development of emission model, improves the accuracy of the model-based optimization approach, which is used as a decisive tool for combustion system development and engine-out emission reduction. The numerical approaches for emission simulation are closely coupled to the combustion model. Using a detailed emission model, considering the 3D mixture preparation simulation including, chemical reactions, demands high computational effort. Phenomenological combustion models, used in 1D approaches for model-based system optimization can deliver heat release rate, while using a two-zone approach can estimate the NOx emissions.

As a consequence of reduced fuel consumption, direct injection gasoline engines have already prevailed against port fuel injection. However, in-cylinder fuel homogenization strongly depends on charge motion and injection strategies and can be challenging due to the reduced available time for mixture formation. An insufficient homogenization has generally a negative impact on the combustion and therefore also on efficiency and emissions. In order to reach the targets of the intensified CO2 emission reduction, further increase in efficiency of SI engines is essential. In this connection, 0D/1D simulation is a fundamental tool due to its application area in an early stage of development and its relatively low computational costs. Certainly, inhomogeneities are still not considered in quasi dimensional combustion models because the prediction of mixture formation is not included in the state of the art 0D/1D simulation.

After the realization of very low exhaust gas emissions and corresponding OBD requirements to fulfill Euro VI and Tier 4 legislation, the focus in heavy-duty powertrain development is on the reduction of fuel consumption and thus CO₂ emissions again. Besides this, the total vehicle operation costs play another major role. A holistic view of the overall powertrain system including the combustion process, exhaust gas aftertreatment, energy recuperation and energy storage is necessary in order to obtain the best possible system for a given application. A management system coordinating the energy flow between the different subsystems while guaranteeing low exhaust emissions plays a major part in operating such complex architectures under optimal conditions.

Zero-dimensional heat release rate models have the advantage of being both easy to handle and computationally efficient. In addition, they are capable of predicting the effects of important engine parameters on the combustion process. In this study, a zero-dimensional combustion model based on physical and chemical sub-models for local processes like injection, spray formation, ignition and combustion is presented. In terms of injection simulation, the presented model accounts for a phenomenological nozzle flow model considering the nozzle passage inlet configuration and an approach for modeling the characteristics of the Diesel spray and consequently the mixing process. A formulation for modeling the effects of intake swirl flow pattern, squish flow and injection characteristics on the in-cylinder turbulent kinetic energy is presented and compared with the CFD simulation results.

A physico-chemical model of a Cu-zeolite SCR/DPF-system involving NH₃ storage and SCR reactions as well as soot oxidation reactions with NO₂ has been developed and validated based on fundamental experimental investigations on synthetic gas test bench. The goal of the work was the quantitative modeling of NOx and NH₃ tailpipe emissions in transient test cycles in order to use the model for concept design analysis and the development of control strategies. Another focus was put on the impact of soot on SCR/DPF systems. In temperature-programmed desorption experiments, soot-loaded SCR/DPF filters showed a higher NH₃ storage capacity compared to soot-free samples. The measured effect was small, but could affect the NH₃ slip in vehicle applications. A bimodal desorption characteristic was measured for different adsorption temperatures and heating rates.

The strong demand for diesel fuel is producing a surplus of gasoline fractions in Europe. Despite new vehicles using less energy, the rising volume of traffic will lead to more diesel being consumed. European legislation demands that renewable fuels cover 10% of energy consumed in the transport sector. The present strategy of dividing biofuels in equal shares between diesel and gasoline does not help to improve this situation. It seems reasonable not only to add FAME but also ethanol to diesel. Unfortunately, fuel blends containing ethanol cannot be used in existing cars without hardware modifications. This is because of ethanol's characteristics and well-known from the experience gathered with gasoline cars. As such, the first part of this study investigates material compatibility, focusing on corrosion and changes to the mechanical properties of the materials used in diesel engines.

One of the main challenges in developing cost-effective diesel particulate filters is to guarantee a thermally safe regeneration under all possible conditions on the road. Uncontrolled regenerations occur when the soot reaction rate is so high that the cooling effect of the incoming exhaust gas is insufficient to keep the temperature below the required limit for material integrity. These conditions occur when the engine switches to idle while the filter is already hot enough to initiate soot oxidation, typically following engine operation at high torque and speed or active filter regeneration. The purpose of this work is to investigate engine management techniques to reduce the reaction rate during typical failure mode regenerations. A purely experimental investigation faces many difficulties, especially regarding measurement accuracy, repeatability in filter soot loading, and repeatability in the regeneration protocol.

Flow fields and fuel distribution play a critical role in developing the combustion process inside the cylinders of piston engines. This has prompted the development of measurement and diagnostic capabilities including laser techniques like Doppler Global Velocimetry (DGV). The paper provides an overview of the basics of DGV and the type of results that can be obtained. It also includes a short comparison to Particle Image Velocimetry (PIV) which is a popular alternative method. Furthermore, it is shown that DGV can be used simultaneously in combination with droplet distribution visualization inside cylinders based on Mie scattering.

Diesel burners introduce combustion of diesel fuel to raise exhaust gas temperature to Diesel Oxidization Catalyst (DOC) light-off or Diesel Particulate Filter (DPF) regeneration conditions, thereby eliminating the need of engine measures such as post-injections. Such diesel combustion requirement nevertheless poses challenges to burner development especially in combustion control and risk mitigation of DPF material failure. In particular, burner design must satisfy good soot distribution and heat distribution at DPF front face after meeting minimum requirements of ignition, heat release, and backpressure. In burner development, Computational Fluid Dynamics (CFD) models have been developed based on commercial codes for burner thermal and flow management with capability of predicting comprehensive physical and chemical phenomena including turbulence induced mixing, fuel injection, fuel droplet transport, diesel combustion, radiation, conjugate heat transfer and etc.

Study and design of the lifetime durability of mechanical components in an automotive exhaust system becomes a challenging task today for engineers. During the investigation, both experimental tests and finite element simulations are used for the investigation under dynamic engine excitations. For the dynamic finite element analysis, the experimental system must be simplified as a linear mathematic model and real boundary conditions are idealized. Due to this simplification, the dynamic behavior of the finite element model may strongly deviate from that in operational conditions. To gain insight into the dynamic behavior of exhaust systems from simulations, the finite model must be modified based on experimental results. As known the harmonic response is related to modal shapes and engine loads. Therefore, modifications of the finite element model can be done from these two aspects.

Internal combustion engines with lean homogeneous charge and auto-ignition combustion of gasoline fuels have the capability to significantly reduce fuel consumption and realize ultra-low engine-out NOx emissions. Group research of Volkswagen AG has therefore defined the Gasoline Compression Ignition combustion (GCI®) concept. A detailed investigation of this novel combustion process has been carried out on test bench engines and test vehicles by group research of Volkswagen AG and IAV GmbH Gifhorn. Experimental results confirm the theoretically expected potential for improved efficiency and emissions behavior. Volkswagen AG and IAV GmbH will utilize a highly flexible externally supercharged variable valve train (VVT) engine for future investigations to extend the understanding of gas exchange and EGR strategy as well as the boost demands of gasoline auto-ignition combustion processes.