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In electric fuel injection (EFI) systems the intake air pressure is used as system load signal for calculating injection and ignition parameters together with engine speed. Part of an EFI system for motorcycles is a throttle body module with integrated pressure sensor. As motorcycle systems require smaller components than automotive applications the target for engineering is to minimize the component size and still fulfill other system requirements. Therefore the pressure sensor sampling point should be as close as possible to the throttle shaft to reduce the module size but with a sufficient distance to avoid signal distortion by unsteady flow. This paper describes how to find a suitable sampling position by combining static bench testing, dynamic vehicle testing and CFD analysis.

In this paper, an investigation is made to the friction clutch engagement control of automotive AMT systems based on a nonlinear dynamic model with double inputs. According to friction torque transmission characteristics during clutch engagement, an equivalent, fully controllable and linearized model and the feedback linearization control are derived from the original system with nonlinearities via homomorphic transforms. By the resulting mathematical modeling, computer simulations are made both for the original nonlinear and feedback linearized systems with incorporation of ordinary PID controllers to follow ideal vehicle dynamic responses. It has been shown by comparison between the two sets of numerical results that the feedback linearization control designed for the nonlinear system is of fine accuracy and robustness in model tracking behaviors of clutch engagements.

A flat neural network is designed for the on-line state prediction of engine. To reduce the computational cost of weight matrix, a fast recursive algorithm is derived according to the pseudoinverse formula of a partition matrix. Furthermore, the forgetting factor approach is introduced to improve predictive accuracy and robustness of the model. The experiment results indicate that the improved neural network is of good accuracy and strong robustness in prediction, and can apply for the on-line prediction of nonlinear multi input multi output systems like vehicle engines.