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This paper describes a novel approach for modeling an automotive HVAC unit. The model consists of black-box models trained with experimental data from a self-developed measurement setup. It is capable of predicting the temperature and mass flow of the air entering the vehicle cabin at the various air vents. A combination of temperature and velocity sensors is the basis of the measurement setup. A measurement fault analysis is conducted to validate the accuracy of the measurement system. As the data collection is done under fluctuating ambient conditions, a review of the impact of various ambient conditions on the HVAC unit is performed. Correction models that account for the different ambient conditions incorporate these results. Numerous types of black-box models are compared to identify the best-suited type for this approach. Moreover, the accuracy of the model is validated using test drive data.

The Service Bay Diagnostic System (SBDS) will be designed to assist the dealership technician in diagnosing and repairing Ford Motor vehicles. The system hardware will be configured around a Service Bay Computer with mass storage capability and auxiliary service equipment. Major system features include: guided service writer/customer interaction, interactive vehicle diagnostics, information management. capabilities, and an additional aid to identifying intermittent failures through the use of a portable over-the-road data acquisition device. In order to assist the technician in properly diagnosing the causal factor, the Service Bay Computer System will also be enhanced through the use of an expert system knowledge base.

When considering ride comfort and precision there are lots of components in the vehicle suspensions that have influence in this behavior and some ride occurrences (mainly higher frequencies) are rubber bushing responsibility but due their compliance, other vehicle attributes, steering and handling, can be affected. So the correct components tuning can maintain or improve vehicle attributes to address desired brand DNA and vehicle its specific needs. These studies were done considering the elastokinematics of front axle only due need of improve its comfort concerning higher frequencies (impacts and harshness). In addiction, correlation between subjective evaluation and objective data acquisition/post processing is desirable to optimize development time. Based in subjective directional, the activities time was reduced and final configuration reached faster.

This paper proposes a novel algorithm. This algorithm is called Self-Organizing Fuzzy Neural Network (SOFNN). SOFNN revolutionizes how researchers apply control theories, image/signal processing on control systems and other applications. In general, SOFNN is an identification technique that automatically initiates, builds and fine-tunes the required network parameters. SOFNN evaluates required structures without predefined parameters or expressions regarding systems. SOFNN sets out to learn and configure a system's characteristics. Self-constructing and self-tuning features enable SOFNN to handle complex, non-linear, and time-varying systems with higher accuracy, making systems identification easier. SOFNN constructs and fine-tunes the system parameter through two phases. The two phases are the construction and the parameter-tuning phase. The two phases run concurrently allowing SOFNN to identify systems on-line.

The aim of the project is to reliably identify the set of constructive features responsible for the highest noise levels in the interior of motor vehicles. A simulation environment based on artificial intelligence techniques such as neural networks and genetic algorithms has been implemented. We used a system identification approach in order to approximate the functional relationship between the target noise series and the sets of constructive parameters corresponding to the cars. The noise levels were measured with a microphone positioned on the driver''s chair, and corresponded to a variation of the engine rotation of 600-900 rot/min. The database includes 45 different cars, each described by vectors of 67 constructive features.

A flexible, easily configuration data acquisition system was designed and built for detailed studies of the steady state and dynamic properties of lean NOx traps for an engine dynamometer environment. The system is based on the industry standard VXI backplane. The overriding design philosophy was to design and develop a data acquisition system that was user friendly and could be operated easily by engine laboratory technicians, as well as by test engineers. The primary requirements guiding the design were the following: (1) the ability easily to configure, save, recall, modify, and print test configurations. (2) The ability to configure the gain, channel name, and engineering units for each analog channel. (3) The ability to trigger from one analog input channel. (4) A provision for numeric auto-incrementing of data file names. (5) The ability to save data in Excel™ compatible ASCII format. (6) The utilization of off-the shelf VXI hardware.

An experimental study of the performance of a reverse tumble, DISI engine is reported. Specific fuel consumption and engine-out emissions have been investigated for both homogeneous and stratified modes of fuel injection. Trends in performance with varying AFR, EGR, spark and injection timings have been explored. It is shown that neural networks can be trained to describe these trends accurately for even the most complex case of stratified charge operation with exhaust gas recirculation.

A number of advancements have been made in the coulometric sulfur trace instrumental technique for the real-time measurement of engine oil economy. These advancements include modification of the coulometric cell to improve reliability and reproducibility. The instrument has been interfaced with a microcomputer for instrument control as well as data acquisition, storage, and analysis. Studies were undertaken which demonstrate sufficient sensitivity and linearity for determination of engine oil economy at levels better than 10,000 miles/quart. Applications to steady-state engine oil consumption mapping and to instantaneous oil consumption during transient engine cycling are described. These instruments are being produced by an outside supplier for use in various company locations in both the engine production and engine research environments.

Air charge calibration of turbocharged SI gasoline engines with both variable inlet valve lift and variable inlet and exhaust valve opening angle has to be very accurate and needs a high number of measurements. In particular, the modeling of the transition area from unthrottled, inlet valve controlled resp. throttled mode to turbocharged mode, suffers from small number of measurements (e.g. when applying Design of Experiments (DoE)). This is due to the strong impact of residual gas respectively scavenging dominating locally in this area. In this article, a virtual residual gas sensor in order to enable black-box-modeling of the air charge is presented. The sensor is a multilayer perceptron artificial neural network. Amongst others, the physically calculated air mass is used as training data for the artificial neural network.

This paper is the second in the series of documents designed to record the progress of a series of SAE documents - SAE J2836™, J2847, J2931, & J2953 - within the Plug-In Electric Vehicle (PEV) Communication Task Force. This follows the initial paper number 2010-01-0837, and continues with the test and modeling of the various PLC types for utility programs described in J2836/1™ & J2847/1. This also extends the communication to an off-board charger, described in J2836/2™ & J2847/2 and includes reverse energy flow described in J2836/3™ and J2847/3. The initial versions of J2836/1™ and J2847/1 were published early 2010. J2847/1 has now been re-opened to include updates from comments from the National Institute of Standards Technology (NIST) Smart Grid Interoperability Panel (SGIP), Smart Grid Architectural Committee (SGAC) and Cyber Security Working Group committee (SCWG).

This paper describes the design and development of the Ford Driving Simulator. The simulator is a fixed-base device which provides real-time, interactive feedback to the driver through a combination of visual, auditory and tactile cues. The system is comprised of a modular buck, 150° field-of-view visual scene, a steering torque controller, high fidelity dynamics models, and an interactive experimenters station. Data acquisition systems have been developed to capture a broad spectrum of driver performance metrics.

During the fall of 1992, the Michigan Roadside Study was conducted. During this study IM240 tests were conducted on vehicles that had also been emissions tested during on-road operation via two remote sensors that were separated by 100 feet. The use of two remote sensors provided an indication of the short-term real-world emissions variability of a large number of on-road vehicles. This data was used to determine the frequency of flippers, i.e. vehicles that are sometimes high emitters (>4% CO) and at other times low emitters (<2% CO). The data show that the flipper frequency increases for older model year vehicles. Also, the correlations between remote sensor readings of emissions concentrations and IM240 mass emissions rates were determined. The data show that the correlation between remote sensing and IM240 improves with increasing numbers of remote sensing readings. For three remote sensor readings, CO correlates with an r2 of 0.69 and HC correlates with an r2 of 0.54

A highly adaptable fatigue testing computer system is presented for controlling single or multichannel test machines. The system imposes most common varieties of waveforms and also provides time synchronization between channels, such as in the case of variable amplitude biaxial load histories, and monitors various feedback signals for both data acquisition and alarm purposes. The program operates in a real-time Unix system as a separate stand-alone process. Communication with other users or the operator is done only through a reserved common block of shared memory. This feature allows control and monitoring of all tests over the computer network. A user can simply login remotely and check the test or start a data acquisition task from any workstation in the company, and then take the data files and analyze them on other computers. This paper describes the operation of the software, the methodology behind the hardware selection and the software structure.

A robotic sound intensity data acquisition system has been developed to overcome the problems of manual data acquistion. The system has the following performance: Accurate and repeatable positioning of the intensity probe Regular five and six sided rectangular measurement surfaces Irregular measurement surfaces around the complete powerplant, or individual components Continuous scanning or fixed point measurements Probe motion, data measurement, and storage of acoustic and geometric data integrated within the acquisition system database.

Automotive product development requires higher degree of quality upfront engineering, faster CAE turn-around, and integration with other functional requirements. Prediction of potential durability concerns using analytical methods for sheet metal structures subjected to road loads and other customer uses has become very important. A process has been developed to provide design direction based upon peak loads, simultaneous peak loads, and vehicle program analytical or measured loads. It identifies critical loads at each input location and load sets for multiple input locations, filters load time histories, selects critical areas and analyzes for fatigue life. Several case studies have been completed. The results show that the variations are consistent with the accuracies in finite element analysis, road load data acquisition, and fatigue calculation methods.

A new estimator for IC engine A/F ratio is described. A/F ratio is important for engine operation since it determines the quantities of engine emissions, such as HC, CO, NOx, the conversion efficiency of catalyst systems, and the engine combustion stability. The A/F ratio estimator described in this paper is based on a fundamental metric that relies on inducing and detecting crankshaft speed fluctuations caused by modulating the engine's fuel injection pulse widths. Fuel pulse width modulation varies the instantaneous combustion A/F ratio crankshaft velocity. Synchronous measurement of crankshaft velocity provides a metric that, when used with other engine state variables as inputs to a conventional neural network, can accurately estimate A/F ratio. The estimator provides A/F information when a physical sensor is not available.

This case study presents a new automated production noise test for power steering pumps. The test included adaptive noise cancellation, and a neural network implementation. The result mapped the pump acceleration signature into an objective repeatable noise metric. The test algorithm was a distributed DSP architecture designed for real-time measurement and decision processing. It was implemented with no increase in test cycle time. It accomplished the correlation of in-vehicle power steering pump noise to it's vibration characteristics, and retrofitting of accelerometers in place of microphones for acceptance testing.

A hybrid approach to predict road-induced loads in vehicle structures is presented. The technique involves full vehicle dynamic simulation using measured wheel forces, absolute wheel vertical displacements, and steering angle as input. The wheel vertical displacement is derived from the measured wheel acceleration. This approach avoids the use of tire-road interface modeling. It also improves the conventional loads measuring process with minimum instrumentation and data acquisition. Existing load data from a test vehicle is used to validate this approach. Computed component loads show good agreement with measurements.

A microcomputer-based, multichannel data acquisition system has been developed to acquire high frequency transient information typified by, but not limited to, automotive vehicle crash test applications. The system, which has been designed to be mounted on the test vehicle during a vehicle crash, will accommodate up to 240 channels. Each channel is comprised of a stand-alone microcomputer, memory for data storage, signal conditioning for piezoresistive transducers, automatic calibration and zero offsets, and programmable gain amplifier. The microcomputer is based upon a Motorola 6801/68701 microcomputer. The paper describes the design, development, and data processing characteristics of the prototype system.

There is a clearly demonstrated need to develop “real-time” methods for the measurement of diesel vehicle particulate emissions. Optical techniques provide One alternative for such methodology because of the rapid data acquisition times involved and the relatively simple sampling methods that can be used. This report describes two different approaches to this problem. The first, the spectrophone, measures light absorption by the diluted exhaust plume using photoacoustic spectroscopy, and the second, the long pathlength smokemeter, determines total light extinction across the diluted exhaust plume. For the measurement of total mass emissions, both techniques show estimated errors of ±15% for specific vehicles, while for a multi-vehicle diesel fleet the estimated errors are ±20% and ±30% for the long pathlength smokemeter and the spectrophone, respectively.