Method of controlling fuel injection quantity using lambda sensor and vehicle to which the same is applied

Patent metadata

Field	Value
Publication number	US-10550789-B2
Application number	US-201615380684-A
Country	US
Kind code	B2
Filing date	Dec 15, 2016
Priority date	Aug 23, 2016
Publication date	Feb 4, 2020
Grant date	Feb 4, 2020

Abstract

A method of controlling a fuel injection quantity using a lambda sensor may include performing a lambda deviation learning mode by controlling a lambda deviation, due to a difference between a lambda model value and a lambda sensor measurement value, by a controller during engine combustion in which an engine RPM and a fuel injection quantity are detected, wherein, in the lambda deviation learning mode, a learning map is learned and is then updated by setting a fuel correction quantity depending on the lambda deviation as a learning value, and a fuel injection quantity is determined, in consideration of the fuel correction quantity depending on an RPM and a fuel quantity based on the updated learning map, and is output as an output value, so that the output value is applied to feedback control for a next fuel injection quantity.

First claim

What is claimed is:

1. A method of controlling a fuel injection quantity using a lambda sensor, comprising:
performing a lambda deviation learning mode by controlling a lambda deviation, due to a difference between a lambda model value and a lambda sensor measurement value, by a controller during engine combustion in which an engine RPM and a fuel injection quantity are detected,
wherein, in the lambda deviation learning mode, a learning map is learned and is then updated by setting a fuel correction quantity depending on the lambda deviation as a learning value, and the fuel injection quantity is determined, in consideration of the fuel correction quantity depending on an engine RPM and a fuel quantity based on the updated learning map, and is output as an output value, so that the output value is applied to feedback control for a next fuel injection quantity,
wherein when the lambda deviation is not generated, a lambda deviation non-learning mode is performed by control of the controller, and in the lambda deviation non-learning mode, the fuel injection quantity is determined, in consideration of the lambda sensor measurement value depending on an engine RPM and a fuel quantity based on the learning map, and is output as the output value, so that the output value is applied to feedback control for the next fuel injection quantity, and
wherein the lambda deviation learning mode is performed by converting the lambda deviation output through Proportional Integral Differential (PID) control into the fuel correction quantity, by learning the lambda deviation such that a bin cell is divided into four points surrounding one point, to which the fuel correction quantity is input, by a histogram of oriented gradient in the learning map, and bin values of the four divided points are determined as two-dimensional coordinate values to update the learning map, and by determining a lambda deviation fuel injection quantity such that the bin values of the four points of the bin cell are obtained as two-dimensional coordinate values according to an operating section by a bilinear interpolation in the updated learning map, and one point surrounded by the four points is output as the output value.

2. The method of claim 1 , wherein the lambda deviation is present when the difference obtained by subtracting the lambda model value from the lambda sensor measurement value is greater than zero.

3. The method of claim 1 , wherein the learning the lambda deviation is performed by determining an index such that an associated section of the learning map, in which the one point is present, is determined, by performing normalization for an engine RPM and a fuel quantity in the associated section, by determining a bin value of each of the four points, by calculating a bin function to which the bin value of each of the four points is applied, and by learning and updating the learning map using a determined value of the bin function.

4. The method of claim 3 , wherein the normalization is performed such that the index is set as 1.

5. The method of claim 3 , wherein the bin function is determined by applying an internally dividing method thereto.

6. The method of claim 1 , wherein the determining a lambda deviation fuel injection quantity is performed by determining an index such that the index is divided into an RPM index and a fuel quantity index to determine whether the bin cell, to which a bin value of each of the four points is input, is present in which of RPM and fuel quantity sections of the learning map, by invoking the bin value of each of the four points as a peripheral array value, by performing normalization such that the RPM index and the fuel quantity index are converted into a reference RPM index and a reference fuel quantity index, and by performing determination such that a value of the one point is obtained as the output value by applying the reference RPM index and the reference fuel quantity index to the four points.

7. The method of claim 6 , wherein the normalization is performed such that the index is set as 1.

8. A vehicle comprising:
a system for controlling a fuel injection quantity comprising a sensor measurement unit configured to detect a measured value of a lambda sensor, a lambda deviation calculation unit configured to determine a lambda deviation by comparing the lambda sensor measurement value with a lambda model value and to determine a fuel correction quantity by outputting the determined lambda deviation through Proportional Integral Differential (PID) control, and a learning controller configured to learn a revolution per minute (RPM) and a fuel quantity using the fuel correction quantity by a histogram of oriented gradient, to determine a learning value as an output value corresponding to the fuel correction quantity by a bilinear interpolation depending on the RPM and a fuel quantity in an operating section to which the learning value is applied, and then to apply the output value to feedback control for a next fuel injection quantity.

9. The vehicle of claim 8 , wherein the lambda sensor is provided in an exhaust system from which exhaust gas is discharged to an outside.

10. The vehicle of claim 9 , wherein the lambda deviation determination unit includes a lambda model in which the lambda model value compared with the lambda sensor measurement value is established, a PID governor to output a lambda deviation value, obtained by subtracting the lambda model value from the lambda sensor measurement value, through the PID control, and a converter to determine the fuel injection quantity output through the PID control.

11. The vehicle of claim 10 , wherein the learning controller includes a learning map configured to divide the operating section into an RPM section and a fuel quantity section and to be updated by learning the learning value, and the learning map includes a learning machine to perform the histogram of the oriented gradient and a calculator to perform the bilinear interpolation.

12. The vehicle of claim 9 , wherein the system for controlling the fuel injection quantity is connected to an engine control unit (ECU) to control an engine.

13. The vehicle of claim 12 , wherein the engine ECU performs feedback control of the output value of the system for controlling the fuel injection quantity for the next fuel injection quantity.

Assignees

• Hyundai Motor Co Ltd

Inventors

• YIM Sung-Soon

Classifications

• F02D2041/1409

  using at least a proportional, integral or derivative controller · CPC title

• F02D41/2467 Primary

  for injectors · CPC title

• F02D41/1454

  the characteristics being an oxygen content or concentration or the air-fuel ratio · CPC title

• F02D41/1482

  Integrator, i.e. variable slope · CPC title

• F02D41/1483

  Proportional component · CPC title