A novel construction and evaluation framework for driving cycle of electric vehicles based on energy consumption and emission analysis

The driving cycle (DC) is essential for establishing vehicle emission standards, transportation policies, and urban planning. However, existing driving cycles demonstrate poor representativeness and excessive randomness due to the insufficient capture of driving characteristics. Therefore, a novel framework for constructing and evaluating driving cycles of electric vehicles (EVs) based on energy consumption and emissions analysis is proposed to enhance the representativeness of the constructed driving cycles. First, based on road information, an improved dual-chain Markov chain method combined with the self-organizing mapping (SOM) neural network is introduced for clustering and constructing driving cycles. Subsequently, a double-layer evaluation model oriented towards energy consumption and emissions is established through a combination of model-driven and data-driven approaches to select a representative driving cycle (RDC). Finally, comparative experiments are conducted to evaluate the feasibility and scientific validity of the proposed method in multiple dimensions. The results indicate that the driving cycle constructed in this study demonstrates excellent representativeness, with an emission error of 2.04% and a comprehensive characterization parameter (CCP) value of 0.097. This study emphasizes the necessity of incorporating reasonable constraints in the driving cycle construction. This improved representativeness provides a reliable foundation for electric vehicle design and transportation policy development.

• Record URL:
  • https://doi.org/10.1016/j.scs.2024.105951
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2210670724007753
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/22106707
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Guo, Jianhua
  • Xie, Dong
  • Jiang, Yu
  • Li, Yue
• Publication Date: 2024-12-15

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 105951
• Serial:
  • Sustainable Cities and Society
  • Volume: 117
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2210-6707
  • Serial URL: http://www.sciencedirect.com/science/journal/22106707?sdc=2

Subject/Index Terms

• TRT Terms: Driving cycles; Electric vehicles; Energy consumption; Evaluation and assessment; Exhaust gases
• Subject Areas: Energy; Environment; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01939018
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 9 2024 5:02PM