Hyundai Motor Company

Kia Motors Corporation

Postech Academy-Industry Foundation

Jong Hyun Park

Man Ju Oh

Jae Woong Kim

Sang Shin Lee

So La Chung

Young Jin Kim

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.

Deep learning-based cooling system temperature prediction apparatus according to physical causality and method therefor

Growing community of inventors

Pohang-si, South Korea

Jong Hyun Park