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Author:

Miao, Yang (Miao, Yang.) | Qiu, Zaihui (Qiu, Zaihui.) | Zhang, Meng (Zhang, Meng.) | He, Baofeng (He, Baofeng.) | Zhang, Xiaolu (Zhang, Xiaolu.) | Li, Miao (Li, Miao.)

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EI Scopus

Abstract:

The automotive industry plays very significant role in Chinese industry. but higher energy consumption and higher pollution accompanying with the vehicle's industry chain as well. natural gas for vehicles has the characteristics of cleanness, economy and reliability. Therefore, the hybrid vehicle with both advantages of natural gas and electricity has great potential. However, it is unclear that the promotion of natural gas / electric hybrid vehicles (NGEHV) could really achieve energy saving and emission reduction. In this paper, consider all stages vehicle production, transportation, use, maintenance and scrap recovery, the possession is decomposed into three aspects: resource attributes, energy attributes and environmental attributes. An composition analysis method of NGEHV is constructed. A life cycle assessment(LCA), in the view of Cradle-to-Grave, of environmental impact for natural gas / electric hybrid vehicles is established. Based on BP neural network, prediction information of traditional vehicles and NGEHV on various environmental factors and costs are obtained. It could provide scientific basis for the sustainable advancement and decision-making for government. © Published under licence by IOP Publishing Ltd.

Keyword:

Neural networks Life cycle Natural gas Decision making Energy utilization Energy conservation Emission control Sustainable development Gases Hybrid vehicles Automotive industry

Author Community:

  • [ 1 ] [Miao, Yang]Faculty of Materials and Manufacturing, Beijing University of Technology, Pingleyuan, China
  • [ 2 ] [Miao, Yang]Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, China
  • [ 3 ] [Qiu, Zaihui]Faculty of Materials and Manufacturing, Beijing University of Technology, Pingleyuan, China
  • [ 4 ] [Zhang, Meng]Faculty of Materials and Manufacturing, Beijing University of Technology, Pingleyuan, China
  • [ 5 ] [He, Baofeng]Faculty of Materials and Manufacturing, Beijing University of Technology, Pingleyuan, China
  • [ 6 ] [Zhang, Xiaolu]Faculty of Materials and Manufacturing, Beijing University of Technology, Pingleyuan, China
  • [ 7 ] [Li, Miao]School of the Mechanical and Electrical Engineering, Beijing Polytechnic College, China

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Source :

ISSN: 1742-6588

Year: 2022

Issue: 1

Volume: 2198

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 20

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