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

IF 10.5 1区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Sustainable Cities and Society Pub Date : 2024-11-12 DOI:10.1016/j.scs.2024.105951

Jianhua Guo, Dong Xie, Yu Jiang, Yue Li

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引用次数: 0

Abstract

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.

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基于能耗和排放分析的新型电动汽车驾驶周期构建与评估框架

驾驶循环（DC）对于制定车辆排放标准、交通政策和城市规划至关重要。然而，现有的驾驶循环由于对驾驶特征的捕捉不足，表现出代表性差、随机性过大等问题。因此，本文提出了一种基于能耗和排放分析的新型电动汽车（EV）驾驶循环构建和评估框架，以增强所构建的驾驶循环的代表性。首先，基于道路信息，引入改进的双链马尔可夫链方法，结合自组织映射（SOM）神经网络，对驾驶周期进行聚类和构建。随后，通过模型驱动和数据驱动相结合的方法，建立了以能耗和排放为导向的双层评价模型，以选择具有代表性的驾驶循环（RDC）。最后，通过对比实验从多个维度评价了所提方法的可行性和科学性。结果表明，本研究构建的驾驶循环具有出色的代表性，排放误差为 2.04%，综合表征参数（CCP）值为 0.097。这项研究强调了在构建驾驶循环时加入合理约束的必要性。代表性的提高为电动汽车设计和交通政策制定提供了可靠的基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Sustainable Cities and Society Social Sciences-Geography, Planning and Development

CiteScore

22.00

自引率

13.70%

发文量

810

审稿时长

27 days

期刊介绍： Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including: 1. Smart cities and resilient environments; 2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management; 3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management); 4. Energy efficient, low/zero carbon, and green buildings/communities; 5. Climate change mitigation and adaptation in urban environments; 6. Green infrastructure and BMPs; 7. Environmental Footprint accounting and management; 8. Urban agriculture and forestry; 9. ICT, smart grid and intelligent infrastructure; 10. Urban design/planning, regulations, legislation, certification, economics, and policy; 11. Social aspects, impacts and resiliency of cities; 12. Behavior monitoring, analysis and change within urban communities; 13. Health monitoring and improvement; 14. Nexus issues related to sustainable cities and societies; 15. Smart city governance; 16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society; 17. Big data, machine learning, and artificial intelligence applications and case studies; 18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems. 19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management; 20. Waste reduction and recycling; 21. Wastewater collection, treatment and recycling; 22. Smart, clean and healthy transportation systems and infrastructure;