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| Temperature Prediction Model of Auto Parts Based on Data Driven |
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| DOI：10.7643/issn.1672-9242.2023.02.014 |
| KeyWord:big data analysis neural network machine learning outdoor exposure test of vehicle |
| Author | Institution |
| LI Huai |
China National Electric Apparatus Research Institute Co., Ltd, Guangzhou , China |
| ZHANG Xiao-dong |
China National Electric Apparatus Research Institute Co., Ltd, Guangzhou , China |
| ZHANG Chuan-hong |
Nanjing University of Aeronautics and Astronautics, Nanjing , China |
| CHEN Xin-xin |
China National Electric Apparatus Research Institute Co., Ltd, Guangzhou , China |
| ZHAO Xue-ru |
China National Electric Apparatus Research Institute Co., Ltd, Guangzhou , China |
| JIE Gan-xin |
China National Electric Apparatus Research Institute Co., Ltd, Guangzhou , China |
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| Abstract: |
| The work aims to predict the temperature changes of the parts of a car in the Phoenix area of the United States based on the test data of the temperature changes of the parts of the car naturally exposed in Turpan, China. With the temperature of the auto parts was taken as the output variable, the key features that affect the temperature changes of the auto parts (test time, atmospheric temperature, solar radiation) were extracted as the input variables. At the same time, a formula was used to correct the solar radiation of parts in different latitudes to eliminate the effects of geographic location. Software such as python were used to build a machine learning model. The test data in Turpan were used to train the model, and then the temperature change of the auto parts in the Phoenix area of the United States was predicted. The prediction results showed that the gradient boosting algorithm model had good generalization ability and prediction accuracy. The average absolute error between the predicted value and the actual value was within 3.3 degrees, and the goodness of fit R2 was greater than 0.90. The BP neural network and random forest algorithm models also had good prediction accuracy. Using the natural exposure test data of a car at a test site in my country could predict the temperature changes of auto parts under the meteorological conditions in similar regions abroad. The research in this work has certain guiding significance for predicting the temperature changes of auto parts under the natural environment of similar regions in other countries based on the results of the natural exposure test of auto parts in China. |
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