Application of Artificial Intelligence Methods to Modeling of Injector Needle Movement in Diesel Engine

The paper presents analysis of injector needle movement in diesel engine. A methodology for obtaining models of this movements was described. The values of injector needle lift were recorded for the engine running at full load, powered by diesel fuel. The models were built using two computational intelligence methods: genetic-fuzzy system and regression trees. The analysis of transparency and accuracy of the obtained models was conducted. The proposed models can be applied to estimation of amount of fuel injected during the engine work cycle.