A signal processing algorithm for compensating for back flow effect in intake air mass measurement in internal combustion engines

Aono, Toshihiro; Kowatari, Takehiko

doi:10.1109/cca.2004.1387237

Cited by 1 publication

(2 citation statements)

References 2 publications

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“…It was based on a linearized input-output model, with a suitable transfer function for Simulink, as well as a state space model. Furthermore, Aono et al [40] proposed a signal processing algorithm for compensating for the back flow effect in intake air mass measurement in internal combustion engines. This used frequency characteristics of a signal from an air flow sensor.…”

Section: Applicationsmentioning

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: Applicationsmentioning

confidence: 99%

“…McKelvey [38] Crankshaft mounted torque sensor Balau [39] Electromagnetic valve actuator for automatic transmission Aono [40] Signal Processing technique for back flow effect…”

Section: Authors Applicationsmentioning

confidence: 99%

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

Comissiong¹,

Steffen²

2018

SAE Technical Paper Series

View full text Add to dashboard Cite

show abstract

A signal processing algorithm for compensating for back flow effect in intake air mass measurement in internal combustion engines

Cited by 1 publication

References 2 publications

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

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

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