Apparatus for predicting performance of power window and method thereof

Patent metadata

Field	Value
Publication number	US-2023398840-A1
Application number	US-202217953950-A
Country	US
Kind code	A1
Filing date	Sep 27, 2022
Priority date	Jun 10, 2022
Publication date	Dec 14, 2023
Grant date	—

Abstract

The present disclosure relates to an apparatus for predicting performance of a power window and a method thereof. The apparatus for predicting performance of a power window may include a memory storage that stores a deep learning model and trained updates thereto and a controller that trains the deep learning model to predict the performance of the power window using a slide resistance of a glass run, a stroke distance of a door glass, a weight of the door glass, a torque of a motor, and a durability of the power window. The system may then predict performance of a target power window based on the deep learning model on which training has been performed.

First claim

What is claimed is:

1 . An apparatus for predicting performance of a power window, comprising:
a memory storage configured to store a deep learning model and trained updates thereto; and a controller configured to: train the deep learning model to predict the performance of a power window using: a slide resistance of a glass run, a stroke distance of a door glass, a weight of the door glass, a torque of a motor, and a durability of the power window; and predict performance of a target power window based on the deep learning model and trained updates thereto.

2 . The apparatus of claim 1 , further comprising:
an input device configured to input: the slide resistance of the glass run, the stroke distance of the door glass, the weight of the door glass, the torque of the motor, and the durability of the power window as real data for the target power window.

3 . The apparatus of claim 2 , wherein the controller is further configured to predict an operating current and an operating time of the motor as the performance of the target power window by inputting the real data to the deep learning model.

4 . The apparatus of claim 2 , wherein the controller is further configured to:
replace the slide resistance of the glass run and the torque of the motor with different values in the real data when the predicted performance of the target power window does not satisfy designer's requirements, and re-perform the predicted performance of the target power window as a re-prediction.

5 . The apparatus of claim 4 , wherein the controller is further configured to:
select training data including values similar to the stroke distance and weight of the door glass in the real data from among a plurality of pieces of training data, and replace the slide resistance of the glass run and the torque of the motor in the real data with a slide resistance of the glass run and a torque of the motor in the selected training data.

6 . The apparatus of claim 4 , wherein the controller is further configured to: determine that the predicted performance of the target power window does not satisfy the designer's requirements when (a) the predicted operating current of the motor is greater than a reference current, and/or (b) the predicted operating time of the motor is greater than a reference time.

7 . The apparatus of claim 4 , further comprising:
an output device configured to output the predicted performance of the target power window.

8 . The apparatus of claim 7 , wherein:
the controller is further configured to output the slide resistance of the glass run and the torque of the motor, the slide resistance of the glass run and the torque of the motor being replaced via the output device when the re-prediction performance of the target power window satisfies the designer's requirements.

9 . The apparatus of claim 1 , wherein the deep learning model is implemented with a Long Short Term Memory (LSTM).

10 . A method for predicting performance of a power window, comprising:
storing, by a memory storage, a deep learning model and trained updates thereto; and training, by a controller, the deep learning model to predict the performance of a power window using: a slide resistance of a glass run, a stroke distance of a door glass, a weight of the door glass, a torque of a motor, and a durability of the power window; and predicting, by the controller, performance of a target power window based on the deep learning model and trained updates thereto.

11 . The method of claim 10 , wherein the predicting of the performance of the target power window step further includes:
receiving, by the controller, the slide resistance of the glass run, the stroke distance of the door glass, the weight of the door glass, the torque of the motor, and the durability of the power window as real data for the target power window; and predicting, by the controller, an operating current and an operating time of the motor as the performance of the target power window by inputting the real data to the deep learning model.

12 . The method of claim 11 , wherein the predicting of the performance of the target power window step further includes:
replacing, by the controller, the slide resistance of the glass run and the torque of the motor with different values in the real data when the predicted performance of the target power window does not satisfy designer's requirements, and re-performing the predicted performance of the target power window as a re-prediction.

13 . The method of claim 12 , wherein the re-performing of the predicted performance of the target power window step further includes:
selecting, by the controller, training data including values similar to the stroke distance and weight of the door glass in the real data from among a plurality of pieces of training data; and replacing, by the controller, the slide resistance of the glass run and the torque of the motor in the real data with a slide resistance of the glass run and a torque of the motor in the selected training data.

14 . The method of claim 12 , wherein the re-performing the predicted performance of the target power window step further includes:
determining, by the controller, that the predicted performance of the target power window does not satisfy the designer's requirements when (a) the predicted operating current of the motor is greater than a reference current, and/or (b) the predicted operating time of the motor is greater than a reference time.

15 . The method of claim 12 , wherein the predicting of the performance of the target power window step further includes outputting, by the controller, the slide resistance of the glass run and the torque of the motor, the slide resistance of the glass run and the torque of the motor being replaced when the re-prediction performance of the target power window satisfies the designer's requirements.

Assignees

• Hyundai Motor Co Ltd
• Kia Corp

Inventors

• Choi Sung Tae

Classifications

• G06N3/08

  Learning methods · CPC title

• G06N3/0442 Primary

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• B60J1/00 Primary

  Windows; Windscreens; Accessories therefor (B60J10/00 takes precedence; air curtains instead of windows B60J9/04 {; sealing strips for windshields B60J10/70; sealing sash guides for sliding window panes B60J10/74; glass partitions inside vehicles to protect occupants against personal attack B60R21/12}) · CPC title

• G06N3/049

  Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs · CPC title

• G06N3/09

  Supervised learning · CPC title