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Optimization of the engine cold start is critical for gasoline direct injection (GDI) engines to meet increasingly stringent emission regulations, since the emissions during the first 20 seconds of the cold start constitute more than 80% of the hydrocarbon (HC) emissions for the entire EPA FTP75 drive cycle. However, Direct Injection Spark Ignition (DISI) engine cold start optimization is very challenging due to the rapidly changing engine speed, cold thermal environment and low cranking fuel pressure. One approach to reduce HC emissions for DISI engines is to adopt retarded spark so that engines generate high heat fluxes for faster catalyst light-off during the cold idle. This approach typically degrades the engine combustion stability and presents additional challenges to the engine cold start. This paper describes a CFD modeling based approach to address these challenges for the Ford 3.5L V6 EcoBoost engine cold start.

Ford Motor Company is introducing “EcoBoost” gasoline turbocharged direct injection (GTDI) engine technology in the 2010 Lincoln MKS. A logical enhancement of EcoBoost technology is the use of E85 for knock mitigation. The subject of this paper is the optimal use of E85 by using two fuel systems in the same EcoBoost engine: port fuel injection (PFI) of gasoline and direct injection (DI) of E85. Gasoline PFI is used for starting and light-medium load operation, while E85 DI is used only as required during high load operation to avoid knock. Direct injection of E85 (a commercially available blend of ∼85% ethanol and ∼15% gasoline) is extremely effective in suppressing knock, due to ethanol's high inherent octane and its high heat of vaporization, which results in substantial cooling of the charge. As a result, the compression ratio (CR) can be increased and higher boost levels can be used.

This paper describes a CFD modeling based approach to address design challenges in GDI (gasoline direct injection) engine combustion system development. A Ford in-house developed CFD code MESIM (Multi-dimensional Engine Simulation) was applied to the study. Gasoline fuel is multi-component in nature and behaves very differently from the single component fuel representation under various operating conditions. A multi-component fuel model has been developed and is incorporated in MESIM code. To apply the model in engine simulations, a multi-component fuel recipe that represents the vaporization characteristics of gasoline is also developed using a numerical model that simulates the ASTM D86 fuel distillation experimental procedure. The effect of the multi-component model on the fuel air mixture preparations under different engine conditions is investigated. The modeling approach is applied to guide the GDI engine piston designs.

The paper presents a method for the indoor testing of road vehicle suspension systems. A suspension is positioned on a rotating drum which is located in the Laboratory for the Safety of Transport at Politecnico di Milano. Special six-axis load cells have been designed and used for measuring the forces/moments acting at each suspension-chassis joints. The forces/moments, wheel accelerations, displacements are measured up to 100 Hz. Two different types of test can be performed. The tire/wheel unbalance effect on the suspension system behavior (Vibration and Harshness, VH) has been analyzed by testing the suspension system from zero to the vehicle maximum speed on a flat surface and by monitoring the forces transmitted to the chassis. In the second kind of test, the suspension system has been excited as the wheel passes over different cleats fixed on the drum.

In the virtual development process validated simulation models are requested to accurately predict power train vibration and comfort phenomena. Conclusions from refined parameter studies enable to avoid costly tests on rigs and on the road. Thereby, an appropriate modeling approach for specific phenomena has to be chosen to ensure high quality results. But then, parameters for characterizing the dynamic properties of components are often insufficient and have to be roughly estimated in this development stage. This results in a imprecise prediction of power train resonances and in a less conclusive understanding of the considered phenomena. Conclusions for improvements remain uncertain. This paper deals with the two different aspects of model refinement and parameter updating. First an existing power train model (predecessor power train) is analyzed whether the underlying modeling approach can reproduce the physical behavior of the power train dynamics adequately.

The need for significant reduction of fuel consumption and CO₂ emissions has become the major driver for development of new vehicle powertrains today. For the medium term, the majority of new vehicles will retain an internal combustion engine (ICE) in some form. The ICE may be the sole prime mover, part of a hybrid powertrain or even a range extender; in every case potential still exists for improvement in mechanical efficiency of the engine itself, through reduction of friction and of parasitic losses for auxiliary components. A comprehensive approach to mechanical efficiency starts with an analysis of the main contributions to engine friction, based on a measurement database of a wide range of production engines. Thus the areas with the highest potential for improvement are identified. For each area, different measures for friction reduction may be applicable with differing benefits.

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.

In this work a multilevel CFD analysis have been applied for the design of an intake air-box with improved characteristics of noise reduction and fluid dynamic response. The approaches developed and applied for the optimization process range from the 1D to fully 3D CFD simulation, exploring hybrid approaches based on the integration of a 1D model with quasi-3D and 3D tools. In particular, the quasi-3D strategy is exploited to investigate several configurations, tailoring the best trade-off between noise abatement at frequencies below 1000 Hz and optimization of engine performances. Once the best configuration has been defined, the 1D-3D approach has been adopted to confirm the prediction carried out by means of the simplified approach, studying also the impact of the new configuration on the engine performances.

This paper presents a nonlinear control approach to achieve good performances in vehicle path following and collision avoidance when the vehicle is driving under cruise highway conditions. Nonlinear model predictive control (NLMPC) is adopted to achieve online trajectory control based on a simplified vehicle model. GMRES/Continuation algorithm is used to solve the online optimization problem. Simulations show that the proposed controller is capable of tracking the desired path as well as avoiding the obstacles.

The definition of the energy management strategy for a hybrid electric vehicle is a key element to ensure maximum energy efficiency. The ability to optimally manage the on-board energy sources, i.e., fuel and electricity, greatly affects the final energy consumption of hybrid powertrains. In the case of plug-in series-hybrid architectures, such as Range-Extender Electric Vehicles (REEVs), fuel efficiency optimization alone can result in a stressful operation of the range-extender engine with an excessively high number of start/stops. Nonetheless, reducing the number of start/stops can lead to long periods in which the engine is off, resulting in the after-treatment system temperature to drop and higher emissions to be produced at the next engine start.

One of the most important safety issues for automotive engineering is to avoid any fire due to the ignition of flammable liquids, which may result from leaks. Fire risk is a combination of hot temperature, fast vaporisation and accumulation of vapor in a cavity. In IC engines, potentially flammable liquids are fuel and oil. To guarantee safety, flammable liquids must not come into contact with hot parts of the engine. Consequently, shields are designed to guide the flow path of possible leakages and to take any flammable liquid out of the hot areas. Simulation is a great help to optimize the shape of the shield by investigating a large number of possible leakages rapidly. Recent breakthroughs in numerical methods make it possible to apply simulations to industrial design concepts. The employed approach is based on the Lagrangian Smoothed Particle Hydrodynamics (SPH) method.

This paper describes the simulation tool chain serving to design and optimize the transmission of an electric axle drive from concept to final design with respect to NVH. A two-stage transmission of an eAxle is designed from scratch by the initial layout of gears and shafts, including the optimization of gear micro geometry. After the shaft system and bearings are defined, the concept design of the transmission housing is evaluated with the help of a basic topology optimization regarding stiffness and certain eigenfrequencies. In the next step a fully flexible multi-body dynamic (MBD) and acoustic analysis of the transmission is performed using internally calculated excitations due to gear contact and bearing interaction with shaft and gear dynamics for the entire speed and load range. Critical operating conditions in terms of shaft dynamics, structure borne noise and noise radiation are evaluated and selected as target for optimization in the following steps.

The friction estimation at the tire-ground contact is crucial for the active safety of vehicles. Friction estimation is a key problem of vehicle dynamics and the ultimate solution is still unknown. However the proposed approach, based on a simple idea and on a simple hardware, provides an actual solution. The idea is to compare the tire characteristic at a given friction (nominal characteristic) with the actual characteristic that the tire has while running. The comparison among these two characteristics (the nominal one and the actual one) gives the desired friction coefficient. The friction coefficient is expressed in vector form and a number of running parameters are identified. The mentioned comparison is an efficient but complex algorithm based on a mathematical formulation of the tire characteristic. The actual tire characteristic is somehow measured in real time by a relatively simple smart wheel which is able to detect the three forces and the three moments acting at the hub.

A direct trajectory optimization approach is developed to assess the capability of a GTDI-DCT Powertrain, with a Gasoline Turbocharged Direct Injection (GTDI) engine and Dual Clutch Transmission (DCT), to satisfy stringent drivability requirements during launch. The optimization is performed directly on a high fidelity black box powertrain model for which a single simulation of a launch event takes about 8 minutes. To address this challenging problem, an efficient parameterization of the control trajectory using Gaussian kernel functions and a Mesh Adaptive Direct Search optimizer are exploited. The results and observations are reported for the case of clutch torque optimization for launch at normal conditions, at high altitude conditions and at non-zero grade conditions. The results and observations are also presented for the case of simultaneous optimization of multiple actuator trajectories at normal conditions.

This paper proposes a novel probabilistic approach for multidisciplinary design optimization (MDO) under uncertainty, especially for systems with feedback coupled analyses with multiple coupling variables. The proposed approach consists of four components: multidisciplinary analysis, Bayesian network, copula-based sampling, and design optimization. The Bayesian network represents the joint distribution of multiple variables through marginal distributions and conditional probabilities, and updates the distributions based on new data. In this methodology, the Bayesian network is pursued in two directions: (1) probabilistic surrogate modeling to estimate the output uncertainty given values of the design variables, and (2) probabilistic multidisciplinary analysis (MDA) to infer the distributions of the coupling and output variables that satisfy interdisciplinary compatibility conditions.

Beside hard facts as performance, emissions and fuel consumption especially the brand specific attributes such as styling and sound are very emotional, unique selling prepositions. To develop these emotional characters, within the given boundary conditions of the future pass-by regulation, it is necessary to define them at the very beginning of the project and to follow a consequent development process. The following paper shows examples of motorcycle NVH development work on noise cleaning and sound engineering using a hybrid development process combining front loading, simulation and testing. One of the discussed solutions is the investigation of a piston pin offset in combination with a crankshaft offset for the reduction of friction. The optimization of piston slap noise as a result of the piston secondary motion was performed by simulation. As another example a simulation based development was performed for the exhaust system layout.

Process integration and optimization is the key enabler of the Integrated Computational Materials Engineering (ICME) of carbon fiber composites. In this work, automated workflows are developed for two types of composites: Sheet Molding Compounds (SMC) short fiber composites, and multi-layer unidirectional (UD) composites. For SMC, the proposed workflow integrates material processing simulation, microstructure representation volume element (RVE) models, material property prediction and structure preformation simulation to enable multiscale, multidisciplinary analysis and design. Processing parameters, microstructure parameters and vehicle subframe geometry parameters are defined as the design variables; the stiffness and weight of the structure are defined as the responses. For multi-layer UD structure, this work focuses on the discussion of different design representation methods and their impacts on the optimization performance.

Emissions and fuel economy optimization of internal combustion engines is becoming more challenging as the stringency of worldwide emission regulations are constantly increasing. Aggressive transient characteristics of new emission test cycles result in transient operation where the majority of soot is produced for turbocharged diesel engines. Therefore soot optimization has become a central component of the engine calibration development process. Steady state approach for air-fuel ratio limitation calibration development is insufficient to capture the dynamic behavior of soot formation and torque build-up during transient engine operation. This paper presents a novel methodology which uses transient maneuvers to optimize the air-fuel ratio limitation calibration, focusing on the trade-off between vehicle performance and engine-out soot emissions. The proposed methodology features a procedure for determining candidate limitation curves with smoothness criteria considerations.

Because of rising complexity during the automotive product development process, the number of disciplines to be concerned has been significantly increased. Multidisciplinary design optimization (MDO) methodology, which provides an opportunity to integrate each discipline and conduct compromise searching process, is investigated and introduced to achieve the best compromise solution for the automotive industry. To make a better application of MDO, the suitable coupling strategy of different disciplines and efficient optimization techniques for automotive design are studied in this article. Firstly, considering the characteristics of automotive load cases which include many shared variables but rare coupling variables, a multilevel MDO coupling strategy based on enhanced collaborative optimization (ECO) is studied to improve the computational efficiency of MDO problems.

In the last decades numerical simulations have become reliable tools for the design and the optimization of silencers for internal combustion engines. Different approaches, ranging from simple 1D models to detailed 3D models, are nowadays commonly applied in the engine development process, with the aim to predict the acoustic behavior of intake and exhaust systems. However, the acoustic analysis is usually performed under the hypothesis of infinite stiffness of the silencer walls. This assumption, which can be regarded as reasonable for most of the applications, can lose validity if low wall thickness are considered. This consideration is even more significant if the recent trends in the automotive industry are taken into account: in fact, the increasing attention to the weight of the vehicle has lead to a general reduction of the thickness of the metal sheets, due also to the adoption of high-strength steels, making the vibration of the components a non negligible issue.