New Energy Vehicle Development Influencing Factors and Trend Forecast Exploration

Kelong Hu; Baoli Li

doi:10.54097/kam2zw56

Authors

Kelong Hu
Baoli Li

DOI:

https://doi.org/10.54097/kam2zw56

Keywords:

New Energy Electric Vehicles, Multiple Linear Regression Analysis, Pearson Correlation Coefficient, Time Series Forecasting ARIMA Algorithm.

Abstract

The rapid development of new energy electric vehicles (EVs) stems from the urgent need to solve the problems of environmental pollution and energy consumption of traditional fuel vehicles. In this study, four main factors affecting the development of new energy electric vehicles in China are selected from the official open source website: the number of charging piles, energy density, vehicle sales and market share. Through multiple linear regression analysis, Pearson's correlation coefficient and scatter plot analysis, it is found that the number of charging piles and energy density are the main influencing factors. In addition, the ARIMA algorithm is applied to predict the development trend of new energy electric vehicles in China in the next ten years. Finally, the negative correlation between new energy electric vehicles and traditional energy vehicles is revealed by comparing the development data of global new energy and traditional energy vehicles from 2011 to 2023, combined with scatter plot analysis.

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