Data processing: vehicles – navigation – and relative location – Vehicle control – guidance – operation – or indication – With indicator or control of power plant
Reexamination Certificate
2001-11-05
2004-03-23
Dolinar, Andrew M. (Department: 2856)
Data processing: vehicles, navigation, and relative location
Vehicle control, guidance, operation, or indication
With indicator or control of power plant
C701S114000, C073S001340, C123S488000
Reexamination Certificate
active
06711491
ABSTRACT:
BACKGROUND OF INVENTION
1. Technical Field
The present invention relates to engine control systems, and more particularly concerns a mass airflow sensor calibration scheme for pulsating oscillatory flow systems such as reciprocating internal combustion engines.
2. Background
Most internal combustion engines use a mass airflow (MAF) sensor to measure airflow, because it has a fast response and communicates fresh air information directly. Air fuel ratio control for internal combustion engines is directly dependent upon an accurate measurement of mass airflow. MAF sensors work on the principal of a hot-wire anemometer. MAF sensors are heat transfer-based sensors using constant current or constant temperature principals. Thus, as the mass flow of air passing over the hot-wire increases, so does the heat transfer between the wire and the air, resulting in a lower wire temperature and correspondingly higher electrical resistance. The sensor circuitry automatically compensates the current associated with the wire that is reduced by the increased resistance. In this way, the MAF sensor senses the local fluid velocity at the point where the MAF sensor is located.
To indicate the flow rate, the representative velocity and velocity distribution must be known. In the unidirectional, constant flow or semi-constant flow situation, both the airflow velocity and distribution are functions of the Reynolds number. Thus, the MAF sensor signal is calibrated at varied constant steady flow rates using sensor output voltage mean values or modified mean values that are strongly correlated with Reynolds numbers. This procedure provides good accuracy in measuring constant unidirectional flow (DC flow), since the airflow is steady, and the hot-wire boundary layer remains unchanged. Hence, the heat transfer on the hot-wire surface can be described by the equation Nu=f(Re, Pr), where Re represents the Reynolds number and Pr represents the media property such as the Prandtl number (kinematic viscosity divided by thermal diffusivity). Similarly, the flow rate is a function of the MAF sensor voltage.
For internal combustion engine applications, the actual airflow in the induction system is not a unidirectional or DC flow. Engine airflow pulsates due to piston movement, valve events, and in the case of gaseous fuel such as hydrogen or CNG, fuel injection events. Even when the engine speed (RPM) is stable, the airflow velocity is oscillatory. Resulting pulsating flow creates boundary layer disturbances that steady flow heat transfer correlations cannot accurately describe. One exception is when engine operation occurs under heavily throttled conditions such that the flow pulsations dampen out sufficiently to allow constant flow heat transfer to adequately approximate the boundary layer physics. Only under severely throttled conditions, however, do DC flow-calibrated MAF sensors provide reasonable accuracy.
When the engine throttle angle is greater than 25°, or under unthrottled conditions such as in lean burn gasoline engines and diesel engines, the airflow oscillation effects can result in large MAF sensor errors due to the sensor calibration being based upon the mean signal value. If the airflow signal error is too great, most engine control systems ignore the MAF sensor signal and operate under open loop control or estimate airflow by other methods which can result in higher emissions and reduced performance.
Accordingly, there exists a need for an improved mass airflow sensor calibration methodology for pulsating, oscillating flow systems such as reciprocating internal combustion engines and gaseous-fueled engine systems, in particular.
SUMMARY OF INVENTION
The present invention overcomes the drawbacks associated with prior art mass airflow sensors through the provision of a new method of measuring internal combustion engine pulsating flow based upon oscillation flow and heat transfer theory. In this regard, the present invention presents an oscillation-flow heat transfer model of airflow characterized by three parameters: a mean Reynolds number, a dynamic Reynolds number indicative of frequency or engine speed, and a dimensionless amplitude indicating the extent to which the airflow pulsation flow deviates from the mean velocity. The present MAF sensor calibration scheme is then implemented as a function of the mean sensor signal, the ratio of the maximum sensor signal to the mean sensor signal, and the engine speed. For oscillatory flow, the engine speed is indicative of how often the boundary layer is disturbed, while the ratio is indicative of how much the boundary layer is disturbed.
In one embodiment, a method for calibrating an output signal of a mass airflow (MAF) sensor in operative communication with an induction system of an internal combustion engine system is provided. The method comprises the steps of determining an engine speed value, determining a maximum MAF output signal value at the engine speed value, detecting a mean MAF output signal value at the engine speed oscillation ratio, and correlating the MAF output signal with an airflow rate value as a function of the MAF output signal value, mean MAF output signal value and engine speed.
One advantage of the present invention is that it is applicable to all internal combustion engines including diesel engines and gaseous-fueled engine systems.
Another advantage of the present invention is that it reduces MAF sensor signal error due to airflow oscillation.
Other advantages of the invention will become apparent upon reading the following detailed description and appended claims, and upon reference to the accompanying drawings.
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Kabat Daniel Michael
Tang Xiaoguo
Dolinar Andrew M.
Ford Global Technologies LLC
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