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The requirement of “zero defects” is rapidly finding its way as a “standard” of quality in numerous quarters. This phrase has great psychological appeal, and is often taken literally at all levels in an organization even though quality motivation may be the intention. It is common to believe that when zero defects are found in the sample, this must be the case for “all the rest” as well. In this paper the technical side of “zero defects” is examined. We look at the statistics of zero defects and show what is implied about lot or process quality when zero defects is the actual sample outcome. The focus is on attribute measurements and includes some special cases where a significant measurement error exists and cases where a Bayesian statistical analysis may be appropriate.

In general for Vehicle-to-Vehicle (V2V) communication, message authentication is performed on every received wireless message by conducting verification for a valid signature, and only messages that have been successfully verified are processed further. In V2V safety communication, there are a large number of vehicles and each vehicle transmits safety messages frequently; therefore the number of received messages per second would be large. Thus authentication of each and every received message, for example based on the IEEE 1609.2 standard, is computationally very expensive and can only be carried out with expensive dedicated cryptographic hardware. An interesting observation is that most of these routine safety messages do not result in driver warnings or control actions since we expect that the safety system would be designed to provide warnings or control actions only when the threat of collision is high.

Oil pumpability in passenger car gasoline engines was well-characterized by an ASTM program and by individual researchers in the 1970's and early 1980's. Oil pumpability in diesel engines however, was not investigated to any significant extent until the mid-1980's. This study was initiated to define the performance of several commercial viscosity modifiers in different formulations containing 3 detergent-inhibitor (DI) additive packages and 4 basestock types. The test oils were run at -18°C (0°F) in a Cummins NTC-400 diesel engine. The results, when statistically analyzed, indicated that a new, second generation olefin copolymer (OCP) viscosity modifier had better performance than a first generation OCP and, furthermore, had performance equal to a polymethacrylate (PMA) viscosity modifier. The analysis also showed that one DI/base stock combination had a significant effect on performance.

In the modern and fast growing automotive sector, reliability & durability are two terms of utmost importance along with weight & cost optimization. Therefore it is important to explore new technology which has less weight, low manufacturing cost and better strength. The new technology developed always seek for a quick, cost effective and reliable methodology for its design validation so that any modification can be made by identifying the failures. This paper presents the rig level test methodology to validate and to correlate the CAE derived strain levels, life cycle & failure mode of newly developed light weight stabilizer link for EV Bus suspension

Understanding customer expectations is critical to satisfying customers. Holding customer clinics is one approach to set winning targets for the engineering functional measures to drive customer satisfaction. In these clinics, customers are asked to operate and interact with vehicle systems or subsystems such as doors, lift gates, shifters, and seat adjusters, and then rate their experience. From this customer evaluation data, engineers can create customer loss or preference functions. These functions let engineers set appropriate targets by balancing risks and benefits. Statistical methods such as cumulative customer loss function are regularly applied for such analyses. In this paper, a new approach based on the Taguchi method is proposed and developed. It is referred to as Taguchi Customer Loss Function (TCLF).

TFC/IW, total fuel consumption divided by inertia weight is reported with other engineering variables for recent EPA data for industry passenger cars and truck. TFC/IW is used in comparisons between gasoline and diesel engines, 49 States and California, passenger cars and trucks. The California fuel economy penalty due to more stringent emissions standards is discussed. The relationship between TFC/IW and ton miles per gallon is shown. Special attention is focused on 4 cylinder gasoline powered vehicles in 49 States passenger car fleet. The use of TFC/IW to answer the question, ‘What Changed?’ when comparing the fuel economies of two fleets is described.

The SHIFT-MATE is a dashboard mounted computer based device that cues a truck driver to shift more efficiently. Through electronic circuitry, key vehicle parameters are monitored, computed, then via graphic display, instructs the driver when to shift for improved fuel economy. The theory of operation is described in the text.

Estimation of soot deposited on a wall flow type DPF, is a vital information to ensure safe and efficient DPF management. Accuracy in determining mass of soot present inside the DPF ensures a correct regeneration management strategy in-terms of fuel efficiency and DPF safety considering soot overloading and too frequent regenerations. It also ensures an efficient detection of anomalies in the PM filtration mandated by the BSVI/EURO VI legislation as a part of On-board diagnostics. Classical approach of determining soot present inside DPF involves monitoring increase in pressure drop. Real time usage of such a model is limited by the inaccuracy of measuring pressure drop at low exhaust flows. Hence, contemporary engine controllers use pressure drop based models as a failsafe and estimate DPF soot loading by modelling soot release rate due to engine combustion and the rate at which it is oxidized.

In automotive electronics on-board diagnostics does the fault diagnosis and reporting. It provides the level of robustness required for the control electronics against various faults. The amount of diagnostic information available via on board diagnostics are depends on the type of vehicle. Pre-supply fuel pump is the component in the common rail hydraulic system. It pumps the fuel from the fuel tank to the inlet valve of the high pressure fuel pump. Electronic control unit synchronizes its operation with high pressure fuel pump. A dedicated driver module in the ECU controls the operation of pre-supply fuel pump. The driver module consist of an ASIC with internal voltage, current monitoring modules for the fault diagnosis and the pre-drivers to control external HS and LS power stages. The software part of the OBD programmed in the internal memory of the ASIC. The “Rds_on” of the power MOSFETs are used for the fault detection purpose.

A large number of testing procedures have been developed to ensure vehicle safety in common and extreme driving situations. However, these conventional testing procedures are insufficient for testing autonomous vehicles. They have to handle unexpected scenarios with the same or less risk a human driver would take. Currently, safety related systems are not adequately tested, e.g. in collision avoidance scenarios with pedestrians. Examples are the change of pedestrian behaviour caused by interaction, environmental influences and personal aspects, which cannot be tested in real environments. It is proposed to use augmented reality techniques. This method can be seen as a new (Augmented) Pedestrian in the Loop testing procedure.

A survey of the in-service fuel consumption of passenger vehicles and derivatives in the Australian fleet was carried out in 1984-85. Seven hundred and four owners across Australia took part in the survey. Vehicle owners reported by questionnaire the amount of fuel used during four tank fills of normal operation, the distance travelled, and other details of the operating circumstances. The survey shows a clear downward trend in the fuel consumption of the Australian passenger fleet. The data also provides comparisons of actual fuel consumption obtained on the road, with laboratory derived values for fuel consumption. Vehicles in a sub-set of 40 were fitted with fuel flow meters during the survey and tested to Australian Standard 2077 for fuel consumption. The questionnaire method is shown to be a valid and accurate technique for determining in-service fuel consumption.

The current research was aimed at determining the most effective way to use alternative renewable feedstock to power a diesel engine. Mimusops elengi, a new and novel biofuel was recognized for this current study, which is widely available in the south of India. The investigation was conducted on B20 volume basis (20% Mimusops elengi methyl ester blended with 80% diesel). Furthermore, it was recognized that when the performance characteristics were traded off, the emission magnitude has slightly higher. To address the diesel engine pollution, an oxygenated nano additive like titanium oxide was dissipated only with the fuel blend at distinct mass fractions of 25 parts per million (ppm) with differing injection pressures of 180 bar, 200 bar, 220 bar, and 240 bar. The tests were created using a statistical programme known as design of experiments, which is purely based on Taguchi and response surface methodology.

Combustion concepts for future SI engines try to meet CO2-emission commitments and legislation all over the world. Where the Diesel engine has an advantage by principle, the efficiency of the SI engine has to be improved significantly, while of course the exhaust emissions must not become worse. An approach is to reduce the gas exchange losses using fully variable valve trains on the intake side of the combustion engine. OptiVent is a patented new way of controlling the mass air flow in the cylinder of a combustion engine using opening valves during the compression phase of a four stroke engine. This technology regards a wider range of variability on the valvetrain components of the engine especially for opening the valves more than one time during a cycle. On the other hand it is necessary to combine this technology with direct injection to avoid fuel losses in the exhaust system and raising the exhaust hydrocarbon emission of the engine.

A multi vector design tool to accurately predict instrument panel obscuration was developed to insure that critical legal displays in vehicles are not obscured. The concept provides for a computer generated light source shaped to replicate the human eyes. The light source is then projected onto a 3D math based arrangement and the resultant shadows are visible on the instrument panel surface and its displays. Design studios require criteria for the placement of the instrument cluster gages and displays, various controls, switches, and steering column stalks before an interior theme can be completed. Therefore, instrument panel obscuration and visibility must be determined early in the design process. The obscured areas are a function of the instrument panel surface, steering wheel rim, hub, spokes, and the location of the driver's eyes. This light source method allows engineers and designers the ability to quickly determine obscured areas.

Vehicle dynamics is a discipline of mechanical engineering that benefited of significant improvements thanks to the progress of computational engineering. Vehicle dynamics engineers are using CAE for the development of a vehicle with MBS and FEA. The concurrent use of these two technologies is a standard in the automotive industry. However the current simulation process is not fully efficient because local geometrical and material nonlinearities are not accurately modeled in classical MBS software. This paper introduces a methodology for vehicle dynamics simulation integrating MBS capabilities in one single nonlinear FEA environment enabling an accurate modeling of nonlinearity in vehicles.

This paper describes the application of statistical techniques known as Design of Experiments (D.O.E.) to efficiently use the results of numerical analysis data in order to improve the configuration of automotive systems. The general framework of these techniques is presented in a format aiming at the design engineer as their end user. Besides, a case study is presented with the purpose of illustrating their practical use. The first step of the case study is to build predictive models for the behaviour of the automotive system being developed by means of the Response Surface Method (RSM), using the proper D.O.E. options. Once these predictive models are available, automatic numerical optimization algorithms are used to improve the responses of interest for given operating conditions. Finally, the automotive systems are robust designed taking into account that the operating conditions vary randomly.

An two-dimensional axial symmetrical finite volume model will be introduced for the calculation of catalytic converters. It is able to predict transient temperatures and conversion rates in different converter systems according to the driving conditions. Input data are the mass-flow rate, the converter inlet temperature and the raw emissions. The performance of this model is demonstrated on an Indian motorcycle application. Cold start behavior and peak temperatures are investigated. This model has proven to be an effective tool for the preselection of an optimal cost effective catalyst system.

Affordable, efficient and durable catalytic converters for the two and three wheeler industry in developing countries are required to reduce vehicle emissions and to participate in a cleaner and healthier environment. As a contribution Continental Emitec started a comprehensive testing program with a state of the art 180 cc Bharat Stage (BS) III Indian motorcycle. The program consists of testing the state of the art of Metallic substrates with structured foils with various catalyst sizes and positions (original or close coupled). The publication presents a short literature survey and the results of the investigation with a big catalyst volume mounted in underfloor position as well as in close coupled position, gained over the World-wide harmonized Motorcycle Test Cycle, considering the two possible vehicle classifications of this motorcycle, Sub-Class 2.1 and Sub-Class 2.2.

The goal of this project was to investigate how to make driver distraction state classification more efficient by applying selected machine learning techniques to existing datasets. The data set used in this project included both overt driver behavior measures (e.g., lane keeping and headway measures) and indices of internal cognitive processes (e.g., driver situation awareness responses) collected under four distraction conditions, including no-distraction, visual-manual distraction only, cognitive distraction only, and dual distraction conditions. The baseline classification method that we employed was a support vector machine (SVM) to first identify driver states of visual-manual distraction and then to identify any cognitive-related distraction among the visual-manual distraction cases and other non-visual manual distraction cases.