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On-Board Fuel Identification using Artificial Neural Networks
Abstract:
On-board fuel identification is important to ensure engine safe operation, similar power output, fuel economy and emissions levels when different fuels are used. Real-time detection of physical and chemical properties of the fuel requires the development of identifying techniques based on a simple, non-intrusive sensor. The measured crankshaft speed signal is already available on series engine and can be utilized to estimate at least one of the essential combustion parameters such as peak pressure and its location, rate of cylinder pressure rise and start of combustion, which are an indicative of the ignition properties of the fuel.
Using a dynamic model of the crankshaft numerous methods have been previously developed to identify the fuel type but all with limited applications in terms of number of cylinders and computational resources for real time control.
The purpose of the current work is to overcome these limitations and to present how Artificial Neural Networks expand the capability of utilizing engine speed signal for fuel identification by using main combustion characteristics such as firing peak cylinder pressure and peak pressure rise rate.