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A full vehicle NVH Simulator has been developed to provide a realistic interactive in-car environment where a subject can experience multi-modal stimuli ( accurately reproduced sound and vibration as well as visual ) whilst either driving or being driven. This paper describes its use in learning about the strategies subjects employ during sound evaluations, and how this information can help optimise decision making during product development. It is possible to understand how subjects assess the sound of vehicles, both in the way that they drive the vehicle and importantly which elements of the sound character have greatest influence on their evaluation of the vehicle. It is also possible to compare the strategies employed by NVH engineers, company decision-makers and non-experts such as customers.

To improve the effectiveness and efficiency of decision making during vehicle development programmes, new interactive NVH simulation tools have been introduced. For their optimum use during the target setting of NVH attributes new methodologies need to be established. A valuable step in achieving this is to further understand opinion forming processes during on-road appraisals. To achieve this however new approaches to capture the events of an appraisal are needed. This paper reports on the first steps taken towards developing these new approaches and demonstrates how attitudes towards vehicle sound quality and driver behavior can vary depending on the personality traits of the assessor and their role within the OEM. This insight will provide a benchmark to compare how expert assessors and customers make decisions.

For a typical communications C-language software stack, its size in terms of ROM and RAM will be dependent upon the network properties such as number of nodes, schedules, messages and signals. A lot of this information is part of a more detailed design and during architecture selection only signal and nodal information will be available. Messages and schedule information will be part of a much more detailed part of the design process. The objective of the study described in this paper is to ascertain whether ROM and RAM requirements can be estimated from only node and signal information only as this is the information that tends to be available at the very beginning of the electrical architecture design process. Historical data from a LIN design and its associated communications stack is statistically analysed and used to develop a methodology for ROM and RAM requirement estimation.

The Controller Area Network (CAN) has seen enormous success in automotive body and powertrain control systems, as well as industrial automation systems using higher layer protocols such as CANopen and DeviceNet. Now, the CAN standard ISO11898 is being extended to Time Triggered CAN (TTCAN) to address the safety critical needs of first generation drive-by-wire systems. However, their successful development depends upon the availability of silicon and software support, and appropriate development & analysis tools. Warwick Control Technologies and the University of Warwick are tasked with prototyping a TTCAN analyser within the European Union Media+ project Silicon Systems for Automotive Electronics (SSAE) consortium, and with funding from the British Department of Trade and Industry (DTI). This paper briefly outlines the current status of both CAN & TTCAN technology and describes the requirements of a TTCAN analyser over that of a traditional CAN analyser.

The popularity of CAN (Controller Area Network) in the production vehicles is well established. As a result, CAN has been developed for use in many non-automotive applications. This gave rise to the development of an open higher layer CAN protocol known as DeviceNet. With the popularity of DeviceNet for Automation Systems, this technology has drastically decreased in cost. Although DeviceNet is quite complex to develop, it easier to implement than SAE J1939 due to the large number of commercial off-the-shelf product that is available. Also, there are many configuration and diagnostic tools available by the same means. There are more than 300 vendors of DeviceNet product. Researchers at the University of Warwick have built a vehicle demonstrator using CAN/DeviceNet modules. This paper will illustrate the ease of vehicle system integration utilising this popular technology.

Sound quality targets for new vehicles are currently specified by jury evaluation techniques based upon listening studies in a sound laboratory. However, jury testing is costly, time consuming and at present there are no methods to include customer expectations or brand requirements. This paper describes a neural computing approach that is being developed to generate knowledge and tools to enable objective measures of a product's sound to be converted into a prediction of the subjective impression of potential customers without carrying out the traditional jury evaluation tests.

Complexity of electronics and embedded software systems in automobiles has been increasing over the years. This necessitates the need for an effective and exhaustive development and validation process in order to deliver fault free vehicles at reduced time to market. Model-based Product Engineering (MBPE) is a new process for development and validation of embedded control software. The process is generic and defines the engineering activities to plan and assess the progress and quality of the software developed for automotive applications. The MBPE process is comprised of six levels (one design level and five verification and validation levels) ranging from the vehicle requirements phase to the start of production. The process describes the work products to be delivered during the course of product development and also aligns the delivery plan to overall vehicle development milestones.

A comprehensive general purpose engine simulation model has been successfully developed. This paper reports on an investigation undertaken to compare the accuracy and computational efficiency of four alternative methods for modelling the gas flow and performance in internal combustion engines. The comparison is based on the filling-and-emptying method, the acoustic method, the Lax-Wendroff two-stage difference method and the Harten-Lax-Leer upstream method, using a unified treatment for the boundary conditions. The filling-and-emptying method is the quickest method among these four methods, giving performance predictions with reasonably good accuracy, and is suitable for simulating engines using not highly tuned gas exchange systems. Based on the linearized Euler equations, the acoustic method is capable of describing time-varying pressure distributions along a pipe.

All new European vehicles face strict drive-by noise regulations. It would help vehicle designers if they could predict drive-by noise given parameters available early in the design process. The large amount of data from previous tests suggests a new approach, using neural networks. This paper introduces neural networks and describes how to apply them to the prediction problem. The selection of suitable inputs and amount of data required is discussed. The problem can be simplified by first predicting vehicle performance. Interim results for a vehicle performance neural network are presented. Further work towards a drive-by noise neural network is proposed.