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Research Papers: Internal Combustion Engines

Observer-Based Cylinder Air Charge Estimation for Spark-Ignition Engines

[+] Author and Article Information
Zhe Wang

Department of Automotive Engineering,
Clemson University,
Greenville, SC 29607
e-mail: zwang5@clemson.edu

Qilun Zhu

Department of Automotive Engineering,
Clemson University,
Greenville, SC 29607
e-mail: qilun@clemson.edu

Robert Prucka

Department of Automotive Engineering,
Clemson University,
Greenville, SC 29607
e-mail: rprucka@clemson.edu

Michael Prucka

FCA U.S. LLC,
Auburn Hills, MI 48326
e-mail: michael.prucka@fcagroup.com

Hussein Dourra

FCA U.S. LLC,
Auburn Hills, MI 48326
e-mail: hussein.dourra@fcagroup.com

Contributed by the IC Engine Division of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received February 25, 2017; final manuscript received March 24, 2017; published online May 2, 2017. Editor: David Wisler.

J. Eng. Gas Turbines Power 139(10), 102806 (May 02, 2017) (7 pages) Paper No: GTP-17-1079; doi: 10.1115/1.4036360 History: Received February 25, 2017; Revised March 24, 2017

Spark-ignition engine in-cylinder air charge estimation is important for air-to-fuel ratio (AFR) control, maintaining high after-treatment efficiency, and determination of current engine torque. Current cylinder air charge estimation methodologies generally depend upon either a mass air flow (MAF) sensor or a manifold absolute pressure (MAP) sensor individually. Methods based on either sensor have their own advantages and disadvantages. Some production vehicles are equipped with both MAF and MAP sensors to offer air charge estimation and other benefits. This research proposes several observer-based cylinder air charge estimation methods that take advantage of both MAF and MAP sensors to potentially reduce calibration work while providing acceptable transient and steady-state accuracy with low computational load. This research also compares several common air estimation methods with the proposed observer-based algorithms using steady-state and transient dynamometer tests and a rapid-prototype engine controller. With appropriate tuning, the proposed observer-based methods are able to estimate cylinder air charge mass under different engine operating conditions based on the manifold model and available sensors. Methods are validated and compared based on a continuous tip-in tip-out operating condition.

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References

Figures

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Fig. 1

Intake system model for cylinder air charge estimation

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Fig. 4

Engine operating conditions for algorithm performance “tip-in” test

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Fig. 5

Experimental results of the EKF-based estimation method

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Fig. 6

Variation in VE estimation from the EKF-based method

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Fig. 7

Experimental results of the KF-based estimation method

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Fig. 2

Calculation flow for the integration-based air flow estimation method

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Fig. 3

Schematic diagram of engine control and data acquisition system

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Fig. 8

Transient engine operating conditions used for method comparison

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Fig. 9

Comparison of the proposed estimation algorithms and other methods (in time domain)

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Fig. 10

Error comparison of the proposed estimation algorithms

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Fig. 11

Error% of all proposed estimation methods

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