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This paper presents the application of a proposed fuzzy inference system as part of a stability control design scheme implemented with active steering actuator sets. The fuzzy inference system is used to detect the level of overseer/understeer at the high level and a speed-adaptive activation module determines whether an active front steering, active rear steering, or active 4 wheel steering is suited to improve vehicle handling stability. The resulting model-free system is capable of minimizing the amount of model calibration during the vehicle stability control development process as well as improving vehicle performance and stability over a wide range of vehicle and road conditions. A simulation study will be presented that evaluates the proposed scheme and compares the effectiveness of active front steer (AFS) and active rear steer (ARS) in enhancing the vehicle performance. Both time and frequency domain results are presented.

Hands-free phone use is the most utilized use case for vehicles equipped with infotainment systems with external microphones that support connection to phones and implement speech recognition. Critically then, achieving hands-free phone call quality in a vehicle is problematic due to the extremely noisy nature of the vehicle environment. Noise generated by wind, mechanical and structural, tire to road, passengers, engine/exhaust, HVAC air pressure and flow are all significant contributors and sources of noise. Other factors influencing the quality of the phone call include microphone placement, cabin acoustics, seat position of the talker, noise reduction of the hands-free system, etc. This paper describes the work done to develop procedures and metrics to quantify the effects that influence the hands-free phone call quality.

This paper proposes an approach that characterizes a driver's driving behavior and style in real-time during car-following drives. It uses an online learning of the evolving Takagi-Sugeno fuzzy model combined with the Markov model. The inputs fed into the proposed algorithm are from the measured signals of on-board sensors equipped with current vehicles, including the relative distance sensors for Adaptive Cruise Control feature and the accelerometer for Electronic Stability Control feature. The approach is verified using data collected using a test vehicle from several car-following test trips. The effectiveness of the proposed approach has been shown in the paper.

Cold idle operation of a modern design light duty diesel engine and the effect of multiple pilot injections on stability were investigated. The investigation was initially carried out experimentally at 1000rpm and at −20°C. Benefits of mixture preparation were initially explored by a heat release analysis. Kiva 3v was then used to model the effect of multiple pilots on in-cylinder mixture distribution. A 60° sector of mesh was used taking advantage of rotational symmetry. The combustion system and injector arrangements mimic the HPCR diesel engine used in the experimental investigation. The CFD analysis covers evolutions from intake valve closing to start of combustion. The number of injections was varied from 1 to 4, but the total fuel injected was kept constant at 17mm3/stroke. Start of main injection timing was fixed at 7.5°BTDC.

Available methodologies for model bias identification are mainly regression-based approaches, such as Gaussian process, Bayesian inference-based models and so on. Accuracy and efficiency of these methodologies may degrade for characterizing the model bias when more system inputs are considered in the prediction model due to the curse of dimensionality for regression-based approaches. This paper proposes a copula-based approach for model bias identification without suffering the curse of dimensionality. The main idea is to build general statistical relationships between the model bias and the model prediction including all system inputs using copulas so that possible model bias distributions can be effectively identified at any new design configurations of the system. Two engineering case studies whose dimensionalities range from medium to high will be employed to demonstrate the effectiveness of the copula-based approach.

In an effort to reduce the cost and time associated with bench level automotive electrical and electromagnetic compatibility (EMC) validation tests, a survey was created to request advice from the test labs that perform this testing. The survey focuses particularly on the development of the test plan document and the preparation of the test setup. The survey was sent to a targeted group of individuals with experience in performing this type of testing. The invitees work at laboratories that represent the majority of labs in the world that are authorized to perform component electrical / EMC validation testing for automotive original equipment manufacturers (OEMs). There were a significant number of responses; it is possible that representatives from all of the invited laboratories responded. The survey results provide demographic information about the test labs and their participants.

This paper documents the finite element (FE) analysis of a hot stamping process for high strength aluminum sheet. In this process a 7075 blank, heated above its solvus temperature, was simultaneously die quenched and stamped in a room temperature die to form a B-pillar outer reinforcement. Two modeling approaches have been investigated: an isothermal mechanical model and a non-isothermal coupled thermo-mechanical model. The accuracy of each approach was assessed by comparing the predicted strain and thickness distributions to experimental measurements from a formed panel. The coupled thermo-mechanical model provided the most accurate prediction.

This paper presents the application of model predictive control (MPC) to DOC temperature control during DPF regeneration. The model predictive control approach is selected for its advantage - using a model to optimize control moves over horizon while handling constraints. Due to the slow thermal dynamics of the DOC and DPF, computational bandwidth is not an issue, allowing for more complex calculations in each control loop. The control problem is formulated such that all the engine control actions, other than far post injection, are performed by the existing production engine controller, whereas far post injection is selected as the MPC manipulated variable and DOC outlet temperature as the controlled variable. The Honeywell OnRAMP Design Suite (model predictive control software) is used for model identification, control design and calibration.

New vehicle control algorithms are needed to meet future emissions and fuel economy mandates that are quite likely to require a measurement of ambient specific humidity (SH). Current practice is to obtain the SH by measurement of relative humidity (RH), temperature and barometric pressure with physical sensors, and then to estimate the SH using a fit equation. In this paper a novel approach is described: a system of neural networks trained to estimate the SH using data that already exists on the vehicle bus. The neural network system, which is referred to as a virtual SH sensor, incorporates information from the global navigation satellite system such as longitude, latitude, time and date, and from the vehicle climate control system such as temperature and barometric pressure, and outputs an estimate of SH. The conclusion of this preliminary study is that neural networks have the potential of being used as a virtual sensor for estimating ambient and intake manifold's SH.

Tools are now publicly available that can potentially help a company assess the impact of its water use and risks in relation to their global operations and supply chains. In this paper we describe a comparative analysis of two publicly available tools, specifically the WWF/DEG Water Risk Filter and the WBCSD Global Water Tool that are used to measure the water impact and risk indicators for industrial facilities. By analyzing the risk assessments calculated by these tools for different scenarios that include varying facilities from different industries, one can better gauge the similarities and differences between these water strategy tools. Several scenarios were evaluated using the water tools, and the results are compared and contrasted. As will be shown, the results can vary significantly.

There have been many articles published in the last decade or so concerning the components of an electronic stability control (ESC) system, as well as numerous statistical studies that attempt to predict the effectiveness of such systems relative to crash involvement. The literature however is free from papers that discuss how engineers might develop such systems in order to achieve desired steering, handling, and stability performance. This task is complicated by the fact that stability control systems are very complex and their designs and what they can do have changed considerably over the years. These systems also differ from manufacturer to manufacturer and from vehicle to vehicle in a given maker of automobiles. In terms of ESC hardware, differences can include all the components as well as the addition or absence of roll rate sensors or active steering gears to name a few.

This work presents an application of airborne source path contribution analysis with emphasis on prediction of wideband sounds inside a cabin from measurements made around a stand-alone engine. The heart of the method is a time domain source path receiver technique wherein the engine surface is modeled as a number of source points. Nearfield microphone measurements and transfer functions are used to quantify the source strengths at these points. This acoustic engine model is then used in combination with source-to-receiver transfer functions to calculate sound levels at other positions, such as at the driver's ear position. When combining all the data, the in-cabin engine sound can be synthesized even before the engine is physically installed into the vehicle. The method has been validated using a powertrain structure artificially excited by several shakers playing band-limited noise so as to produce a complicated vibration pattern on the surface.

With a goal to help develop pressure sensor calibration and deployment algorithms using computer simulations, an Arbitrary Lagrangian Eulerian (ALE) approach was adopted in this research to predict the responses of side crash pressure sensors for unitized vehicles. For occupant protection, acceleration-based crash sensors have been used in the automotive industry to deploy restraint devices when vehicle crashes occur. With improvements in the crash sensor technology, pressure sensors that detect pressure changes in door cavities have been developed recently for vehicle crash safety applications. Instead of using acceleration (or deceleration) in the acceleration-based crash sensors, the pressure sensors utilize pressure change in a door structure to determine the deployment of restraint devices. The crash pulses recorded by the acceleration-based crash sensors usually exhibit high frequency and noisy responses.

Modal parameter identification is used to identify those parameters of the model which describe the dynamic properties of a vibration system. Structural dynamic methods and technologies have been used with great success by the automotive industry. Experimental Modal Analysis is the process of determining the modal parameters (modes, natural frequencies, damping factors) of a linear, time-invariant system. One common reason for experimental modal analysis is the verification or correction of the results of the analytical approach. Another reason to extract the modal parameters experimentally is due to its use for future evaluations such as structural modifications. This paper presents the Experimental Modal Analysis of a car in trimmed body configuration which represents a complex system due to the systems attached to it, increasing its damping and measuring noise. Both frequency and time domain system identification methods are studied to obtain the main modes of the structure.

In an attempt to predict the responses of side crash pressure sensors, the Corpuscular Particle Method (CPM) was adopted and enhanced in this research. Acceleration-based crash sensors have traditionally been used extensively in automotive industry to determine the air bag firing time in the event of a vehicle accident. The prediction of crash pulses obtained from the acceleration-based crash sensors by using computer simulations has been very challenging due to the high frequency and noisy responses obtained from the sensors, especially those installed in crash zones. As a result, the sensor algorithm developments for acceleration-based sensors are largely based on prototype testing. With the latest advancement in the crash sensor technology, side crash pressure sensors have emerged recently and are gradually replacing acceleration-based sensor for side impact applications.

An Arbitrary Lagrangian Eulerian (ALE) approach was adopted in this study to predict the responses of side crash pressure sensors in an attempt to assist pressure sensor algorithm development by using computer simulations. Acceleration-based crash sensors have traditionally been used to deploy restraint devises (e.g., airbags, air curtains, and seat belts) in vehicle crashes. The crash pulses recorded by acceleration-based crash sensors usually exhibit high frequency and noisy responses depending on the vehicle's structural design. As a result, it is very challenging to predict the responses of acceleration-based crash sensors by using computer simulations, especially those installed in crush zones. Therefore, the sensor algorithm developments for acceleration-based sensors are mostly based on physical testing.

The electroluminescence, a process where light is emitted by the recombination of electrically accelerating charges (electrons and holes) on a solid state chip, is the basis of light-emitting-diodes (LEDs) technology. Until the early 90s, LEDs were considered light sources of low efficiency. Nowadays, white high power, high efficiency LED's, based upon fluorophores are extremely promising for both vehicular / automotive and machinery usages. Associate with its high efficient light production, the long life capability of these devices keep improving their performance. In this study, three models of LEDs, from two different manufacturers were used.

This paper proposes an approach to use ABS wheel speed sensor signals together with other vehicle state information from a brake control module to detect an unbalanced tire or tires in real-time. The proposed approach consists of two-stage algorithms that mix a qualitative method using band-pass filtering with a quantitative parameter identification using conditional least squares. This two-stage approach can improve the robustness of tire imbalance or imbalances. The proposed approach is verified through vehicle testing and the test results show the effectiveness of the approach.

Most people still remember the introduction of the IBM PC in 1981 and the first Microsoft Windows operating system in 1985. These were the pioneering technologies that started a revolution in automotive test equipment in the service bay. What was once a purely mechanical garage environment where information was published annually in large paper manuals has evolved into a highly technical computing environment. Today vehicle networks link onboard vehicle control systems with diagnostic systems and updated service information is published daily over the Internet. A lot has changed over the last twenty years, and manufacturers of diagnostic test equipment are learning to deal with the constantly evolving computing platforms and host operating systems. This paper traces the history of automotive diagnostic equipment at Ford Motor Company and shares some of the hard lessons learned from the early systems.

In South America and other emerging markets sound package development is limited by the cost and weight of its components. Reaching the right balance between cost and a good NVH performance provides an important competitive advantage, therefore any achieved design opportunities can be replicated to other vehicle lines and markets. In this work the main noise transmission paths are verified by evaluating the contribution of sound package components to noise attenuation in two cases, initially from the tire contact patch through vehicle body to a number of positions within the vehicle interior and, next, from the engine compartment, by placing a High Frequency Sound Source (HFSS) at engine faces to the same vehicle interior positions. The main objective is to optimize sound package distribution and to prioritize which areas should have the sound package reinforced in order to improve Tire and Engine noise reduction.