Development of a Fast Response High Accuracy Virtual Air Flow Meter for Internal Combustion Engine Applications

Liu, Jingping; Xia, Xiaolang; Yong, Wang; Yang, Hanqian

doi:10.1109/icmtma.2011.260

Cited by 2 publications

(3 citation statements)

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“…Fresh charge mass trapped in-cylinder at the end of the gas exchange process is a particularly difficult variable to measure, especially when the throttle position, intake air pressure and engine speed are instantaneously changing. Jingping et al [15] proposed a virtual instrumentation set up consisting of two dynamic pressure transducers to measure pressure fluctuation near the intake and exhaust ports and a descriptor control system. Therefore, the fresh mass and un-swept burnt mass fraction trapped in the cylinder can be calculated throughout the process.…”

Section: Physical Modelsmentioning

confidence: 99%

“…Only the section between the intake and exhaust pressure transducers is modelled which reduced modelling complexity. [15] showing the experimental engine and locations of transducers. U Vaidya et al [16] proposed an explicit formula for the controllability and observability Gramians as a function of actuator and sensor locations and the advection velocity field.…”

Section: Physical Modelsmentioning

confidence: 99%

“…Sensors could then be selected to make a robust information centric configuration, which could make full use of the information available within the architecture. Using these methods, virtual instrumentation, as proposed in the literature [15] [51], could be applied in the information centric configuration. Currently there is no clear consensus of selection criteria for specific sensors/actuators for automotive applications to determine an optimal configuration.…”

Section: Optimisation -What Does It Mean To Be Better?mentioning

confidence: 99%

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Review of Selection Criteria for Sensor and Actuator Configurations Suitable for Internal Combustion Engines

Comissiong¹,

Steffen²

2018

SAE Technical Paper Series

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This literature review considers the problem of finding a suitable configuration of sensors and actuators for the control of an internal combustion engine. It takes a look at the methods, algorithms, processes, metrics, applications, research groups and patents relevant for this topic. Several formal metric have been proposed, but practical use remains limited. Maximal information criteria are theoretically optimal for selecting sensors, but hard to apply to a system as complex and nonlinear as an engine. Thus, we reviewed methods applied to neighboring fields including nonlinear systems and nonminimal phase systems. Furthermore, the closed loop nature of control means that information is not the only consideration, and speed, stability and robustness have to be considered. The optimal use of sensor information also requires the use of models, observers, state estimators or virtual sensors, and practical acceptance of these remains limited. Simple control metrics such as conditioning number are popular, mostly because they need fewer assumptions than closed-loop metrics, which require a full plant, disturbance and goal model. Overall, no clear consensus can be found on the choice of metrics to define optimal control configurations, with physical measures, linear algebra metrics and modern control metrics all being used. Genetic algorithms and multi-criterial optimisation were identified as the most widely used methods for optimal sensor selection, although addressing the dimensionality and complexity of formulating the problem remains a challenge. This review does present a number of different successful approaches for specific applications domains, some of which may be applicable to diesel engines and other automotive applications. For a thorough treatment, non-linear dynamics and uncertainties need to be considered together, which requires sophisticated (non-Gaussian) stochastic models to establish the value of a control architecture.

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Section: Physical Modelsmentioning

confidence: 99%

Section: Physical Modelsmentioning

confidence: 99%

Section: Optimisation -What Does It Mean To Be Better?mentioning

confidence: 99%

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