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The main function of mobile air conditioning system in a vehicle is to provide the thermal comfort to the occupants sitting inside the vehicle at all environmental conditions. The function of ducts is to get the sufficient airflow from the HVAC system and distribute the airflow evenly throughout the cabin. In this paper, the focus is to optimize the rear passenger floor duct system to meet the target requirements through design for six sigma (DFSS) methodology. Computational fluid dynamics analysis (CFD) has been used extensively to optimize system performance and shorten the product development time. In this methodology, a parametric modeling of floor duct design using the factors such as crossectional area, duct length, insulation type, insulation thickness and thickness of duct were created using CATIA. L12 orthogonal design array matrix has been created and the 3D CFD analysis has been carried out individually to check the velocity and temperature.

Dimensional Engineering concentrates effort in the early design phases to meet the dimensional build objectives in automotive production. Design optimization tools include tolerance stack up, datum optimization, datum coordination, dimensional control plans, and measurement plans. These tools are typically based on the assumption that parts are rigid and tooling dimensions are perfect. These assumptions are not necessarily true in automotive assemblies of compliant sheet metal parts on high volume assembly lines. To address this issue, Finite Element Analysis (FEA) has been increasingly used to predict the behavior of imperfect and deformable parts in non-nominal tooling. This paper demonstrates an application of this approach. The complete analysis is divided into three phases. The first phase is a nominal design gravity analysis to validate the nominal design and tooling.

This research proposes a control system for Spark Ignition (SI) engines with external Exhaust Gas Recirculation (EGR) based on model predictive control and a disturbance observer. The proposed Economic Nonlinear Model Predictive Controller (E-NMPC) tries to minimize fuel consumption for a number of engine cycles into the future given an Indicated Mean Effective Pressure (IMEP) tracking reference and abnormal combustion constraints like knock and combustion variability. A nonlinear optimization problem is formulated and solved in real time using Sequential Quadratic Programming (SQP) to obtain the desired control actuator set-points. An Extended Kalman Filter (EKF) based observer is applied to estimate engine states, combining both air path and cylinder dynamics. The EKF engine state(s) observer is augmented with disturbance estimation to account for modeling errors and/or sensor/actuator offset.

An automobile door is one vital commodity which has its role in vehicle’s function, strength, safety, dynamics and aesthetic parameters. The door system comprises of individual components and sub-assemblies such as door upper, bolster, armrest, door main panel, map-pocket, handle, speaker and tweeter grille. Among them, armrest is an integral part which provides function and also takes care of some safety parameter for the customers. The basic function of an armrest is to provide ergonomic relief to occupant for resting his hand. Along with this, it also facilitates occupant safety during a side impact collision by absorbing the energy and not imparting the reactive force on occupant. Thus an armrest has evolved as a feature of passive safety. The armrest design should be stiff enough to withstand required elbow load condition with-in the acceptable deflection criteria. On the other hand, armrest has to absorb the dynamic force by deflecting proportionally to the side impact load.

This paper focuses on an inherent problems of active damping control prevalent in contemporary hybrid torque controls. Oftentimes, a supervisory torque controller utilizes simplified system models with minimal system states representation within the optimization problem, often not accounting for nonlinearities and stiffness. This is motivated by enabling the generation of the optimum torque commands with minimum computational burden. When inherent lash and stiffness of the driveline are not considered, the resulting command can lead to vibrations and oscillations in the powertrain, reducing performance and comfort. The paper proposes a Linear Quadratic Integral (LQI)-based compensator to be integrated downstream the torque supervisory algorithm, which role is to shape transient electric machine torques, compensating for the stiffness and backlash present in the vehicle while delivering the driver-requested wheel torque.

In order to meet LEV III, EURO 6C and Beijing 6 emission levels, Original Equipment Manufacturers (OEMs) can potentially implement unique aftertreatment systems solutions which meet the varying legislated requirements. The availability of various washcoat substrates and PGM loading and ratio options, make selection of an optimum catalyst system challenging, time consuming and costly. Design for Six Sigma (DFSS) methodologies have been used in industry since the 1990s. One of the earliest applications was at Motorola where the methodology was applied to the design and production of a paging device which Consumer Reports called “virtually defect-proof”.[1] Since then, the methodology has evolved to not only encapsulate complicated “Variation Optimization” but also “Design Optimization” where multiple factors are in play. In this study, attempts are made to adapt the DFSS concept and methodology to identify and optimize a catalyst for diesel applications.

Spark ignition engines utilize catalytic converters to reform harmful exhaust gas emissions such as carbon monoxide, unburned hydrocarbons, and oxides of nitrogen into less harmful products. Aftertreatment devices require the use of expensive catalytic metals such as platinum, palladium, and rhodium. Meanwhile, tightening automotive emissions regulations globally necessitate the development of high-performance exhaust gas catalysts. So, automotive manufactures must balance maximizing catalyst performance while minimizing production costs. There are thousands of different recipes for catalytic converters, with each having a different effect on the various catalytic chemical reactions which impact the resultant tailpipe gas composition. In the development of catalytic converters, simulation models are often used to reduce the need for physical parts and testing, thus saving significant time and money.

Current generation passenger vehicles are built with several electronic sensors and modules which are required for the functioning of passive safety systems. These sensors and modules are mounted on the vehicle body at locations chosen to meet safety functionality requirements. They are mounted on pillars or even directly on panels based on specific packaging requirements. The body panel or pillar poses local structural resonances and its dynamic behavior can directly affect the functioning of these sensors and modules. Hence a specific inertance performance level at the mounting locations is required for the proper functioning of those sensors and modules. Drive point modal frequency response function (FRF) analysis, at full vehicle model for the frequency range up to 1000 Hz, is performed using finite element method (FEM) and verified against the target level along with test correlation.

To comply with the stringent fuel consumption requirements, many automobile manufacturers have launched vehicle electrification programs which are representing a paradigm shift in vehicle design. Looking specifically at powertrain calibration, optimization approaches were developed to help the decision-making process in the powertrain control. Due to computational power limitations the most common approach is still the use of powertrain calibration tables in a rule-based controller. This is true despite the fact that the most common manual tuning can be quite long and exhausting, and with the optimal consumption behavior rarely being achieved. The present work proposes a simulation tool that has the objective to automate the process of tuning a calibration table in a powertrain model. To achieve that, it is first necessary to define the optimal reference performance.

Design for Six Sigma (DFSS) is an essential tool and methodology for innovation projects to improve the product design/process and performance. This paper aims to present an application of the DFSS Taguchi Method for an automotive/vehicle component. High-Pressure Vacuum Assist Die Casting (HPVADC) technology is used to make Cast Aluminum Front Shock Tower. During the vehicle life, Shock Tower transfers the road high impact loads from the shock absorber to the body structure. Proving Ground (PG) and washout loads are often used to assess part strength, durability life and robustness. The initial design was not meeting the strength requirement for abusive washout loads. The project identified eight parameters (control factors) to study and to optimize the initial design. Simulation results confirmed that all eight selected control factors affect the part design and could be used to improve the Shock Tower's strength and performance.

Two methods of assessing the similarity of a set of impact test signals have been proposed and used in the literature, which are cumulative variance-based and cross correlation-based. In this study, a normalized formulation unites these two approaches by establishing a relationship between the normalized cumulative variance metric (v), an overall similarity metric, and the normalized magnitude similarity metric (m) and shape similarity metric (s): v=1 − m · s. Each of these ranges between 0 and 1 (for the practical case of signals acquired with the same polarity), and they are independent of the physical unit of measurement. Under generally satisfied conditions, the magnitude similarity m is independent of the relative time shifts among the signals in the set; while the shape similarity s is a function of these.

Automotive companies are studying to add extra value in their vehicles by enhancing powertrain sound quality. The objective is to create a brand sound that is unique and preferred by their customers since quietness is not always the most desired characteristic, especially for high-performance products. This paper describes the process of developing a brand powertrain sound for a high-performance vehicle using the DFSS methodology. Initially the customer's preferred sound was identified and analyzed. This was achieved by subjective evaluations through voice-of-customer clinics using vehicles of similar specifications. Objective data were acquired during several driving conditions. In order for the design process to be effective, it is very important to understand the relationship between subjective results and physical quantities of sound. Several sound quality metrics were calculated during the data analysis process.

Many chassis and powertrain components in the transportation and automotive industry experience multi-axial cyclic service loading. A thorough load-history leading to durability damage should be considered in the early vehicle production steps. The key feature of rubber fatigue analysis discussed in this study is how to define local critical location strain time history based on nominal and complex load time histories. Material coupon characterization used here is the crack growth approach, based on fracture mechanics parameters. This methodology was utilized and presented for a truck engine mount. Temperature effects are not considered since proving ground (PG) loads are generated under isothermal high temperature and low frequency conditions without high amounts of self-heating.

Automotive AC systems are typically either single unit or dual unit systems, while the dual unit systems have an additional rear evaporator. The refrigerant evaporates inside these heat exchangers by taking heat and condensing the moisture from the recirculated or fresh air that is being pushed into the car cabin by air blowers. This incoming cold air in turn brings the cabin temperature and humidity to a level that is comfortable for the passengers. These HVAC units have their own thermal expansion valve to set the refrigerant flow, but both are connected to the main AC refrigerant loop. The airflows, however, are controlled independently for front and rear unit that can affect the temperature and amount of air coming into the cabin from each location and consequently the overall cabin cool-down performance.

NHTSA issued the FMVSS 226 ruling in 2011. It established test procedures to evaluate countermeasures that can minimize the likelihood of a complete or partial ejection of vehicle occupants through the side windows during rollover or side impact events. One of the countermeasures that may be used for compliance of this safety ruling is the Side Airbag Inflatable Curtain (SABIC). This paper discusses how three key phases of the optimization strategy in the Design for Six Sigma (DFSS), namely, Identify; Optimize and Verify (I_OV), were implemented in CAE to develop an optimized concept SABIC with respect to the FMVSS 226 test requirements. The simulated SABIC is intended for a generic SUV and potentially also for a generic Truck type vehicle. The improved performance included: minimization of the test results variability and the optimization of the ejection mitigation performance of the SABIC.

Upper frame deflection of automobile doors is a key design attribute that influences structural integrity and door seal performance as related to NVH. This is a critical customer quality perception attribute and is a key enabler to ensure wind noise performance is acceptable. This paper provides an overview of two simulation methodologies to predict door upper frame deflection. A simplified simulation approach using point loads is presented along with its limitations and is compared to a new method that uses CFD tools to estimate aerodynamic loads on body panels at various vehicle speeds and wind directions. The approach consisted of performing external aerodynamic CFD simulation and using the aerodynamic loads as inputs to a CAE simulation. The details of the methodology are presented along with results and correlation to experimental data from the wind tunnel.

Advances in diesel engine and catalyst technologies have enabled light passenger vehicles in meeting the most stringent Tier 3/LEV III emission levels and durability requirements. The advancements in diesel aftertreatment catalyst technology have made catalysts more susceptible to low levels of impurities, typically referred to as poisons. Published studies over the last two decades, have shown a significant impact on the performance of catalysts, to the presence of sulfur and other inorganics in fuels and oils. The design of an aftertreatment system (ATS) typically sets limits for lubricant and fuel quality, specific to the geographical region and availability of certain level of regulated fuels. In this study, we investigate a real-world aged diesel vehicle which exhibited deterioration in tailpipe emissions, beyond levels targeted during engineering development.

The Three Way Catalyst (TWC) is an effective pollutant conversion system widely used in current production vehicles to satisfy emissions regulations. A TWC’s conversion efficiency degrades over time due to chemical and/or thermal mechanisms causing the catalyst to age. This reduction in conversion efficiency must be accounted for to ensure full useful life emissions compliance. This paper presents an experimental study of the aging impact on TWC performance. Four TWCs differentiated by their age, given in terms of miles driven, were tested. It is shown that the dynamics of oxygen storage are substantially affected by aging of the TWC. A previously developed physics-based oxygen storage model [1] is subsequently used to incorporate the effect of aging on the total Oxygen Storage Capacity (OSC). Parameter identification results for the different age catalysts show that total oxygen storage capacity decreases substantially with aging and is insensitive to operating conditions.

This paper focuses on the conjugate heat transfer analysis of an I4 engine, and discusses optimization of the coolant passages in engine coolant jackets. Direct Optimization approach integrates an optimizer with the numerical solver. This method of optimization is compared with a metamodel-based optimization in which a metamodel is generated to aid in finding an optimal design. The direct optimization and metamodel approaches are compared in terms of their accuracy, and execution time.

As electrification of powertrains is progressing, diversification of hybrid powertrains increases. This generally imposes the challenge for a supervisory controller of how to optimally control the torque of the electric machine(s). Architectures, which have at least one belt driven electric machine, are an essential part of the portfolio. This paper describes a strategy on how to include the losses of the belt device in the determination of optimal electric machine torque command. It first depicts a physics-based method for controlling optimal electric machine torque command for systems without a belt connected electric machine. This method considers the constraints of the electric machine(s) as well as the power limitations from the electric devices, which supply power to the motors.