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In this article, a virtual sensor for the estimation of the injected pilot mass in-cycle is proposed. The method provides an early estimation of the pilot mass before its combustion is finished. Furthermore, the virtual sensor can also estimate pilot masses when its combustion is incomplete. The pilot mass estimation is conducted by comparing the calculated heat release from in-cylinder pressure measurements to a model of the vaporization delay, ignition delay, and the combustion dynamics. A new statistical approach is proposed for the detection of the start of vaporization and the start of combustion. The discrete estimations, obtained at the start of vaporization and the start of combustion, are optimally combined and integrated in a Kalman Filter that estimates the pilot mass during the vaporization and combustion. The virtual sensor was programmed in a field programmable gate array (FPGA), and its performance tested in a Scania D13 Diesel engine.

On-engine surge detection could help in reducing the safety margin towards surge, thus allowing higher boost pressures and ultimately low-end torque. In this paper, experimental data from a truck turbocharger compressor mounted on the engine is investigated. A short period of compressor surge is provoked through a sudden, large drop in engine load. The compressor housing is equipped with knock accelerometers. Different signal treatments are evaluated for their suitability with respect to on-engine surge detection: the signal root mean square, the power spectral density in the surge frequency band, the recently proposed Hurst exponent, and a closely related concept optimized to detect changes in the underlying scaling behavior of the signal. For validation purposes, a judgement by the test cell operator by visual observation of the air filter vibrations and audible noises, as well as inlet temperature increase, are also used to diagnose surge.