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Multiple injections that are necessary to reduce emissions, noise, and vibration of diesel engine have been accomplished by common-rail fuel injection system. Especially a fuel amount of each injection has to be tightly controlled, but actually there is a difference between an actual amount and a target one. Injection period will be determined according to a map in that the relation between injected amount and injection period is stated. But in the case of multiple injections, pressure wave caused by previous injections remain in a common-rail system at the time an injection second or later starts. Therefore, actual amounts injected will be different from target ones. In order to compensate the difference, the method that the influence of pressure wave on fuel amount injected is also investigated and injection period will be corrected is realized in an actual engine control system. Meanwhile, pressure wave propagation in common-rail has been studied.

This study focuses on improvement of the predictive accuracy of empirical engine models using the Model Base Calibration (MBC) method. This research discusses the effects of the number of measurement points on the accuracy of models for different Design of Experiments (DoE) by using a direct-injection 4-cylinder diesel engine. The results show that the predictive accuracy of the models converges on fixed values when the number of measurement points is increased in Latin Hypercube Sampling (LHS) and D-Optimal Design. This is because the probability density distribution of the measurement data has little variation as the number of measurement points increases. Comparing LHS and D-Optimal indicates that D-Optimal displays a higher level of accuracy, it is able to extend the boundary model because of its greater number of measurement points at the boundaries of the boundary model.