Fiber-optic pressure sensors for internal combustion engines

1994 ◽  
Vol 33 (7) ◽  
pp. 1315 ◽  
Author(s):  
R. A. Atkins ◽  
J. H. Gardner ◽  
W. N. Gibler ◽  
C. E. Lee ◽  
M. D. Oakland ◽  
...  
2017 ◽  
Vol 15 (4) ◽  
pp. 28-39
Author(s):  
A. Tanev ◽  
P. Mitsev ◽  
T. Lazarova

Abstract This paper presents novel green automotive platinum sensing technology together with pressure sensors design principles and applications. In recent years, worldwide emissions legislation has been introduced and is rapidly becoming more stringent. With alternative vehicular propulsion methods far from becoming mainstream reality, leading automotive providers have intensified efforts in the direction of reducing the harmful footprint of their products. This is being accomplished via smaller, appropriately designed internal combustion engines, necessitating an increased and higher-performance sensor content per vehicle. This paper elaborates on temperature sensor application in automotive exhaust gas performance sensing and as well as pressure sensors in different challenging automotive applications with very high pressure levels.


Author(s):  
Yue-Yun Wang ◽  
Ibrahim Haskara

Engine exhaust backpressure is a critical parameter in the calculation of the volumetric efficiency and exhaust gas recirculation flow of an internal combustion engine. The backpressure also needs to be controlled to a presetting limit under high speed and load engine operating conditions to avoid damaging a turbocharger. In this paper, a method is developed to estimate exhaust pressure for internal combustion engines equipped with variable geometry turbochargers. The method uses a model-based approach that applies a coordinate transformation to generate a turbine map for the estimation of exhaust pressure. This estimation can substitute for an expensive pressure sensor, thus saving significant cost for production vehicles. On the other hand, for internal combustion engines that have already installed exhaust pressure sensors, this estimation can be used to generate residual signals for model-based diagnostics. Cumulative sum algorithms are applied to residuals based on multiple sensor fusion, and with the help of signal processing, the algorithms are able to detect and isolate critical failure modes of a turbocharger system.


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