International Journal of Frontiers in Engineering Technology, 2024, 6(1); doi: 10.25236/IJFET.2024.060103.
Ye Zhu1, Zhicheng Lian2, Yikun Chen3, Chengguan Wei4, Runze Yu5, Junchang Chen6
1Textile College, Donghua University, Shanghai, China
2School of Big Data, Guangzhou City University of Technology, Guangzhou, China
3Department of Economic and Management, Zhanjiang Preschool Education College, Zhanjiang, China
4School of Economics and Management, Communication University of China, Beijing, China
5College of Chemical and Biological Engineering, Shandong University of Science and Technology, Qingdao, China
6School of Fire Protection Engineering, China People's Police University, Langfang, China
This article provides a comprehensive and in-depth look at the development of America's new energy electric vehicle industry, employing multiple mathematical modeling techniques to analyze in depth various key aspects of the industry's development. First, we focus on the market acceptance and distribution of NEV sales. In order to improve the generalization ability of the model and prevent overfitting, the data normal distribution check and possible data transformation are carried out. Then we explore the main factors affecting the development of new energy vehicles by calculating the Pearson correlation coefficient between the data. Next, we calculate the evaluation scores of new energy vehicles in different years by constructing TOPSIS model. Finally, we introduce how the LSTM model solves the gradient disappearance or explosion problem of traditional RNNS when dealing with long sequences. The analysis includes the decrease of MSE and RMSE values of the test set and the training set, which shows that the model performs well in the training process and the performance of the test set is also improved.
New Energy Electric Vehicle, Pearson Correlation Coefficient, TOPSIS, LSTM
Ye Zhu, Zhicheng Lian, Yikun Chen, Chengguan Wei, Runze Yu, Junchang Chen. The Influence Model of New Energy Vehicle Development Based on Correlation Analysis. International Journal of Frontiers in Engineering Technology (2024), Vol. 6, Issue 1: 15-21. https://doi.org/10.25236/IJFET.2024.060103.
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