Method for sensing closing time of injector using artificial neural network and method for controlling injector using the same

Patent metadata

Field	Value
Publication number	US-11255289-B2
Application number	US-202016887315-A
Country	US
Kind code	B2
Filing date	May 29, 2020
Priority date	Dec 31, 2019
Publication date	Feb 22, 2022
Grant date	Feb 22, 2022

Abstract

A method for sensing a closing time of an injector using an artificial neural network may include: sensing, by a controller, a voltage generated by an injector; performing, by the controller, a preprocess to derive an input matrix using variation characteristics of the voltage; and performing, by the controller, a closing time prediction to derive a closing time of the injector by an artificial neural network model including an input layer including the input matrix, a hidden layer, and an output layer.

First claim

What is claimed is:

1. A method for sensing a closing time of an injector using an artificial neural network, the method comprising:
sensing, by a controller, a voltage generated by an injector;
performing, by the controller, a preprocess to derive an input matrix using variation characteristics of the voltage; and
performing, by the controller, a closing time prediction to derive a closing time of the injector by an artificial neural network model including an input layer including the input matrix, a hidden layer, and an output layer,
wherein the variation characteristics of the voltage are a half-life constant of the voltage, and the half-life constant is a value calculated as:

k
=

-

t

log
2

⁢

V
t

V
0

where, V t is a voltage value V for each measurement point, V 0 is a voltage value at an initial measurement point, k is a half-life constant, and t is a time at a measurement point.

2. The method of claim 1 , wherein performing the preprocess comprises:
calculating half-life constants at a plurality of measurement time points of the voltage in a specific section;
deriving an approximation polynomial for changes in the calculated half-life constants in accordance with the time; and
deriving the input matrix by normalizing respective coefficients of the approximation polynomial.

3. The method of claim 2 , wherein in deriving the approximation polynomial, the coefficients of the approximation polynomial are derived using a normal equation utilizing linear algebra.

4. The method of claim 2 , wherein in performing the closing time prediction, the hidden layer derives a first preparation matrix by multiplying the normalized input matrix by a first weight matrix and adding a first bias matrix to the multiplied matrix, and
the hidden layer derives a first resulting matrix by substituting a transfer function of the following equation for the first preparation matrix,

a
1

=

2

1
+

e

-
2

⁢

n
1

-
1

where, a 1 is the first resulting matrix, and n 1 is the first preparation matrix.

5. The method of claim 4 , wherein in performing the closing time prediction, the output layer calculates a normalized closing time of the injector by multiplying the first resulting matrix by a second weight matrix and adding a bias value to the multiplied matrix, and the output layer calculates a final closing time of the injector by de-normalizing the calculated closing time of the injector.

Assignees

• Hyundai Motor Co Ltd
• Kia Motors Corp
• Hyundai Autoever Corp
• Hyundai Kefico Corp

Inventors

• Oh Se-Do
• Kim Ji-Hye
• Do Hyung-Soo
• Kim Young-Jin
• Lee Je-Hyeuk
• Kim Young-Jae
• Kim Sung-Jae

Classifications

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• Y02T10/40

  Engine management systems · CPC title

• F02D2200/0618

  Actual fuel injection timing or delay, e.g. determined from fuel pressure drop · CPC title

• F02D41/1401

  characterised by the control or regulation method (F02D41/1473, F02D41/1477 take precedence) · CPC title