Invention Grant
- Patent Title: Deep learning-based cooling system temperature prediction apparatus according to physical causality and method therefor
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Application No.: US16824907Application Date: 2020-03-20
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Publication No.: US11795973B2Publication Date: 2023-10-24
- Inventor: Jae Woong Kim , Sang Shin Lee , So La Chung , Man Ju Oh , Young Jin Kim , Jong Hyun Park
- Applicant: HYUNDAI MOTOR COMPANY , KIA MOTORS CORPORATION , POSTECH Research and Business Development Foundation
- Applicant Address: KR Seoul
- Assignee: HYUNDAI MOTOR COMPANY,KIA MOTORS CORPORATION,POSTECH ACADEMY-INDUSTRY FOUNDATION
- Current Assignee: HYUNDAI MOTOR COMPANY,KIA MOTORS CORPORATION,POSTECH ACADEMY-INDUSTRY FOUNDATION
- Current Assignee Address: KR Seoul; KR Seoul; KR Pohang-si
- Agency: Morgan, Lewis & Bockius LLP
- Priority: KR 20190087170 2019.07.18
- Main IPC: F04D29/58
- IPC: F04D29/58 ; F04D13/06 ; B60H1/00 ; G06N3/084

Abstract:
A deep learning-based cooling system temperature prediction apparatus has an artificial neural network modeled by connecting a plurality of artificial neural network submodels each including an input layer, a hidden layer, and an output layer is used. A pump flow speed, a cooling water flow rate, a battery inlet cooling water temperature, a motor inlet cooling water temperature, a radiator outlet cooling water temperature, a battery temperature, and a motor temperature are predicted by inputting at least one of a predetermined control variable, an environment variable, or a time variable to the plurality of artificial neural network submodels in accordance with a physical causality. A number of the plurality of artificial neural network submodels and the control variables or environment variables that are sequentially input to each submodel depend on divisional control and integral control of the cooling system.
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