Ion Current Comparison in Small, Fast Running Gasoline Engines for Non-Automotive Applications
Published October 30, 2018 by SAE International in United States
Downloadable datasets for this paper availableAnnotation of this paper is available
Small engines for non-automotive applications include 2-stroke and 4-stroke gasoline engine concepts which have a reduced number of sensors due to cost and packaging constraints. In order to cope with future emission regulations, more sophisticated engine control and monitoring becomes mandatory. Therefore, a cost-effective way has to be found to gain maximum information from the existing sensors and actuators. Due to an increasing bio-fuel share in the market, the detection of bio-fuel content is necessary to guarantee a stable combustion by adapting the injection and ignition control strategy.
Meaningful information about the combustion can be retrieved from combustion chamber ion current measurements. This paper proposes a general overview of combustion process monitoring in different engine concepts by measuring the ion current during combustion. Actually, the ion current measurement technique is not yet established in the automotive sector due to the presence of other more accurate and less signal analysis intense sensors as the oxygen and knock sensor. But in small non-automotive applications the ion current could be beneficial for a dynamic control of the engine, due to its cost-efficient measurement hardware solution.
During the research both two- and four-stroke engines are tested in different operating points and fuel blends resulting in a wide general knowledge of the measuring principle and signal properties. Furthermore, a correlation study between signal properties and engine parameters is given in order to extract a stable control variable suitable for the computational power of such engine ECUs.
CitationBasso, R., Gruber, G., Piecha, P., Schacht, H. et al., "Ion Current Comparison in Small, Fast Running Gasoline Engines for Non-Automotive Applications," SAE Technical Paper 2018-32-0077, 2018, https://doi.org/10.4271/2018-32-0077.
Data Sets - Support Documents
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