New Energy Vehicle Development Influencing Factors and Trend Forecast Exploration

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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Publication Details

Journal
Highlights in Science Engineering and Technology
Published
2024-08-20
DOI
https://doi.org/10.54097/kam2zw56
Primary Topic
Engineering Applied Research
Type
article
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article

New Energy Vehicle Development Influencing Factors and Trend Forecast Exploration

Baoli Li, Kelong Hu
Highlights in Science Engineering and Technology
Engineering Applied Research
article

New Energy Vehicle Development Influencing Factors and Trend Forecast Exploration

Baoli Li, Kelong Hu
article en

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.

Highlights in Science Engineering and TechnologyVol. 112
Guiyang University (CN)
Affordable and clean energy
Openalex Percentile: Top 85%
Engineering Applied Research
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New Energy Vehicle Development Influencing Factors and Trend Forecast Exploration — Baoli Li, Kelong Hu · Highlights in Science Engineering and Technology (2024) | TGRS Research Map | TGRS