Prediction and control of carbon emissions of electric vehicles based on BP neural network under carbon neutral background

Zhengxian Chen; Linkun Liu; Conghu Li

doi:10.1117/12.2615330

15 October 2021 Prediction and control of carbon emissions of electric vehicles based on BP neural network under carbon neutral background

Zhengxian Chen, Linkun Liu, Conghu Li

Author Affiliations +

Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 1193304 (2021) https://doi.org/10.1117/12.2615330
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China

Abstract

With the rapid development of carbon neutral concept, various car companies have gradually listed it as the main research object, which is of great significance for the development of the automobile industry. At the same time, although electric vehicles have no carbon emissions in use, they still have large carbon emissions throughout their life cycle. In this paper, in the context of the carbon neutralization goal of the world, we study the actual carbon emission control of electric vehicles, explore a method of carbon emission management and prediction of electric vehicles based on BP neural network learning algorithm, and realizes prediction before volume production and analysis of carbon emissions while the car in use. Through the construction of neural network and prediction model, the precise management of electric emissions can provide significant reference to existing car companies.

Citation Download Citation

Zhengxian Chen, Linkun Liu, and Conghu Li "Prediction and control of carbon emissions of electric vehicles based on BP neural network under carbon neutral background", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 1193304 (15 October 2021); https://doi.org/10.1117/12.2615330

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available