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The foremost aim of the work presented in this paper is to improve fuel economy and decrease CO2 emissions by reducing the aerodynamic drag of passenger vehicles. In vehicle development, computer aided engineering (CAE) methods have become a development driver tool rather than a design assessment tool. Exploring and developing the capabilities of current CAE tools is therefore of great importance. An efficient method for vehicle shape optimization has been developed using recent years' advancements in neural networks and evolutionary optimization. The proposed method requires the definition of design variables as the only manual work. The optimization is performed on a solver approximation instead of the real solver, which considerably reduces computation time. A database is generated from simulations of sampled configurations within the pre-defined design space. The database is used to train an artificial neural network which acts as an approximation to the simulations.

A structured, semi-automatic method for reducing a high-fidelity engine model to a fast running one has been developed. The principle of this method rests on the fact that, under certain assumptions, the computationally expensive components of the simulation can be substituted with simpler ones. Thus, the computation speed increases substantially while the physical representation of the engine is retained to a large extent. The resulting model is not only suitable for fast running simulations, but also usable and updatable in later stages of the development process. The thrust of the method is that the calibration of the fast running components is achieved by use of automatically selected neural networks. Two illustrative examples demonstrate the methodology. The results show that the methodology achieves substantial increase in computation speed and satisfactory accuracy.

This article describes an expert system for objective rating of subjective characteristics like driving comfort. The system uses radial basis neural networks that can be trained on any dynamic properties, for example acceleration. Training and retraining can be done in real-time. The system includes a measure of the reliability of automatic judgement, which can be used to signal when new training may be necessary. The article shows in detail how the system has been used to automatically judge gearshift comfort for automatic transmission. Tests indicate that the system's accuracy and consistency are as good as one of Volvo's best experts.