Air Flow Measurement in Internal Combustion Engines

1989 ◽  
Author(s):  
C. R. Stone
2006 ◽  
Vol 129 (1) ◽  
pp. 32-40 ◽  
Author(s):  
Matthew A. Franchek ◽  
Patrick J. Buehler ◽  
Imad Makki

Presented is the detection, isolation, and estimation of faults that occur in the intake air path of internal combustion engines during steady state operation. The proposed diagnostic approach is based on a static air path model, which is adapted online such that the model output matches the measured output during steady state conditions. The resulting changes in the model coefficients create a vector whose magnitude and direction are used for fault detection and isolation. Fault estimation is realized by analyzing the residual between the actual sensor measurement and the output of the original (i.e., healthy) model. To identify the structure of the steady state air path model a process called system probing is developed. The proposed diagnostics algorithm is experimentally validated on the intake air path of a Ford 4.6L V-8 engine. The specific faults to be identified include two of the most problematic faults that degrade the performance of transient fueling controllers: bias in the mass air flow sensor and a leak in the intake manifold. The selected model inputs include throttle position and engine speed, and the output is the mass air flow sensor measurement.


Author(s):  
Patrick J. Buehler ◽  
Matthew A. Franchek ◽  
Imad Makki

Presented in this paper is an information synthesis (IS) approach for the mass air flow (MAF) sensor diagnosis on internal combustion engines. An information synthesis solution is attractive for diagnostics since the algorithm automatically calibrates itself, reduces the number of false detections and compresses a large amount of engine health information into the model coefficients. There are three primary parts to information synthesis diagnostics. First, an IS model is used to predict the MAF sensor output based on the engine operating condition. The inputs to this IS model include the throttle position sensor (TPS) and the engine speed sensor information. The second part concerns an online adaptation process that is used to reduce the errors between the IS model output and the actual MAF sensor output. Finally the adapted model coefficients are used to diagnose the sensor as well as identify the source for changes in the sensor characteristics. This proposed solution is experimentally tested and validated on a Ford 4.6 L V-8 fuel injected engine. The specific MAF sensor faults to be identified include sensor bias and a leak in the intake manifold.


2013 ◽  
Vol 760-762 ◽  
pp. 1159-1163
Author(s):  
Gang Li ◽  
Rui Feng Bai ◽  
Ying Hao ◽  
Yu Chen Li

A new technique was devised to deal with real-time fuel consumption measurement in internal-combustion engines, where fuel circulation flow is much larger than the consumption rate. With extensive experiments and error analysis method, the fuel mass flow measurement had been realized by a improved turbine flow sensor with temperature compensation. The real-time accurate fuel consumption measurement had been achieved through data processing and real flow parameter matching method. The results showed that the prototype measurement precision was 1.0 class, satisfying practical measurement requirements.


Author(s):  
Ana Marta Souza ◽  
Hugo de Sanches Souza ◽  
TAYNARA BRITO DA SILVA ◽  
Kaissar Nabbout

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