公开(公告)号：US12228303B2

公开(公告)日：2025-02-18

申请号：US17566587

申请日：2021-12-30

Applicant: POSTECH RESEARCH AND BUSINESS DEVELOPMENT FOUNDATION

Inventor： Ye Eun Jang ,  Young Jin Kim

IPC: F24F11/64 ,  F24F11/47 ,  G05B13/02 ,  G06N3/08 ,  F24F110/12 ,  F24F110/32 ,  F24F130/20 ,  F24F140/20 ,  F24F140/50 ,  F24F140/60

Abstract: The present disclosure provides a heating, ventilation, and air conditioning (HVAC) system including interconnected artificial neural networks trained for respective subsystems that are required for building temperature control. The HVAC system includes: an air conditioning sensor units installed in or outside a building to detect environmental data; an HVAC device configured to supply thermal energy into an inner space of the building using input power; and a predictive controller configured to generate operational data based on the environmental data and control the HVAC device by adjusting the input power.

公开(公告)号：US20210018016A1

公开(公告)日：2021-01-21

申请号：US16824907

申请日：2020-03-20

Applicant: HYUNDAI MOTOR COMPANY ,  KIA MOTORS CORPORATION ,  POSTECH Research and Business Development Foundation

Inventor： Jae Woong Kim ,  Sang Shin Lee ,  So La Chung ,  Man Ju Oh ,  Young Jin Kim ,  Jong Hyun Park

IPC: F04D29/58 ,  F04D13/06

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.

公开(公告)号：US11795973B2

公开(公告)日：2023-10-24

申请号：US16824907

申请日：2020-03-20

Applicant: HYUNDAI MOTOR COMPANY ,  KIA MOTORS CORPORATION ,  POSTECH Research and Business Development Foundation

Inventor： Jae Woong Kim ,  Sang Shin Lee ,  So La Chung ,  Man Ju Oh ,  Young Jin Kim ,  Jong Hyun Park

IPC: F04D29/58 ,  F04D13/06 ,  B60H1/00 ,  G06N3/084

CPC classification number: F04D29/588 ,  B60H1/0073 ,  F04D13/068 ,  G06N3/084 ,  F05B2270/303 ,  F05B2270/309 ,  F05B2270/335 ,  F05B2270/707 ,  F05B2270/709

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.