| Summary: | Automated gear-shifts are critical procedures for the driveline as they are demanded to work as fast and accurate as possible. The torque control of a driveline is especially important for the driver’s feeling of driveability. In the case of gear-shifts and torque control in general, the boost pressure is key to achieve good response and thereby a fast gear-shift. An experimental study is carried out to investigate the phenomena of boost pressure drop during gear-shift and gather data for the modelling work. Results confirm the stated fact on the influence of boost pressure drop on gear-shift completion time and also indicate a clear linear dependence between initial boost pressure and the following pressure drop. A dynamic predictive model of the engine is developed with focus on implementation in a heavy duty truck, considering limitations computational complexity and calibration need between truck configurations. The resulting approach is based on a mean value modelling scheme that uses engine control system parameters and functions when possible. To be able to be predictive, a model for demanded torque and engine speed during the gear-shift is developed as reference inputs to the simulation. The simulation is based on a filling and emptying process throughout the engine dynamics, and yields final values of several engine variables such as boost pressure. The model is validated and later evaluated in comparison to measurements gathered in test vehicle experiments and in terms of robustness to input and model deviations. Computer simulations yield estimations of the boost pressure drop within acceptable limits. Consid- ering estimations used prior to this thesis the performance is good. Input deviations and modelling inaccuracies are found to inflict significant but not devastating deviations to the model output, possibly more over time with ageing of hardware taken into account. Final implementation in a heavy duty truck ecu is carried out with results indicating that the current implementation of the module is relatively computationally heavy. At the time of ending the thesis it is not possible to analyse its performance further, and it is suggested that the module is optimized in terms of computational efficiency.
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