Abstract
Pressure is one of the essential variables to give information about engine condition and monitoring. Direct recording of this signal is complex and invasive, while angular velocity can be measured. Nonetheless, the challenge is to predict the cylinder pressure using the shaft kinematics accurately. In this paper, a time-delay neural network (TDNN), interpreted as a finite pulse response (FIR) filter, is proposed to estimate the in-cylinder pressure of a single-cylinder internal combustion engine (ICE) from fluctuations in shaft angular velocity. The experiments are conducted over data obtained from an ICE operating in 12 different states by changing the angular velocity and load. The TDNN’s delay is adjusted to get the highest possible correlation-based score. Our methodology can predict pressure with an R2 , avoiding complicated pre-processing steps.
Highlights
The accelerated development of internal combustion engines in recent years has enhanced the complexity of their composing elements
The time delay (TDNN complexity represented by the number of units K), the engine velocity, and the load are studied
The TRPM value marks the time delay matching the fundamental period as a reference value to analyze the prediction assessment
Summary
The accelerated development of internal combustion engines in recent years has enhanced the complexity of their composing elements. HCCI demands a complex control of the combustion process that the pressure measuring can alleviate [4,5] As another application, cylinder pressure measuring revealed the influence of fuel borne additives on ternary fuel blend operated in single-cylinder diesel engines [6]. Another study analyzed the effect of fuel injection pressure on a diesel engine run with butanol-diesel blend by measuring the in-cylinder pressure signal [7]. Such a signal allowed correlating the advancing pilot injection timing on the engine performance, combustion, and emissions under high loads with variation pilot injection timing [8]. Researchers put effort into implementing reliable pressure monitoring for accurately modeling the engine behavior [9]
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