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Generation of a driving cycle for battery electric vehicles：A case study of Beijing

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  • Gong, Huiming
  • Zou, Yuan
  • Yang, Qingkai
  • Fan, Jie
  • Sun, Fengchun
  • Goehlich, Dietmar

Abstract

Driving cycle, which is widely adopted as a standard measurement procedure for evaluation of fuel economy, emission and driving range, can facilitate vehicle design and performance evaluation of emerging vehicular technologies. However, all current popular driving cycles are developed based on the operation characteristics of conventional vehicles, while the driving characteristics of battery electric vehicles could be quite different, which means traditional driving cycles may not be suitable for evaluating and improving battery electric vehicles. Thus it is important to develop a new driving cycle, which is consistent with real-world situations of battery electric vehicles for the development of new energy auto industry. In this study, the real-world operation data of battery electric vehicles in Beijing are collected with high frequency and the usage and driving characteristics of battery electric vehicles are analyzed based on the real-world data and compared with several standard cycles, such as New European Driving Cycle, Federal Test Procedure-75, and Japan 10–15. Then the Beijing driving cycle is developed using statistic and Markov chain method. The following evaluation proves the new developed driving cycle represents the real-world driving well, which establishes a solid foundation for accurate performance evaluation of battery electric vehicles at least in Beijing.

Suggested Citation

  • Gong, Huiming & Zou, Yuan & Yang, Qingkai & Fan, Jie & Sun, Fengchun & Goehlich, Dietmar, 2018. "Generation of a driving cycle for battery electric vehicles：A case study of Beijing," Energy, Elsevier, vol. 150(C), pages 901-912.
    • Handle: RePEc:eee:energy:v:150:y:2018:i:c:p:901-912
    DOI: 10.1016/j.energy.2018.02.092
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    References listed on IDEAS

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    Cited by:

    1. Liu, Teng & Tan, Wenhao & Tang, Xiaolin & Zhang, Jinwei & Xing, Yang & Cao, Dongpu, 2021. "Driving conditions-driven energy management strategies for hybrid electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    2. Cui, Yuepeng & Zou, Fumin & Xu, Hao & Chen, Zhihui & Gong, Kuangmin, 2022. "A novel optimization-based method to develop representative driving cycle in various driving conditions," Energy, Elsevier, vol. 247(C).
    3. Xingyue Jiang & Jianjun Hu & Meixia Jia & Yong Zheng, 2018. "Parameter Matching and Instantaneous Power Allocation for the Hybrid Energy Storage System of Pure Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-18, July.
    4. Qiao, Qinyu & Zhao, Fuquan & Liu, Zongwei & He, Xin & Hao, Han, 2019. "Life cycle greenhouse gas emissions of Electric Vehicles in China: Combining the vehicle cycle and fuel cycle," Energy, Elsevier, vol. 177(C), pages 222-233.
    5. Wang, Yue & Li, Keqiang & Zeng, Xiaohua & Gao, Bolin & Hong, Jichao, 2022. "Energy consumption characteristics based driving conditions construction and prediction for hybrid electric buses energy management," Energy, Elsevier, vol. 245(C).
    6. Zhang, Xudong & Zou, Yuan & Fan, Jie & Guo, Hongwei, 2019. "Usage pattern analysis of Beijing private electric vehicles based on real-world data," Energy, Elsevier, vol. 167(C), pages 1074-1085.
    7. Zhang, Jin & Wang, Zhenpo & Liu, Peng & Zhang, Zhaosheng, 2020. "Energy consumption analysis and prediction of electric vehicles based on real-world driving data," Applied Energy, Elsevier, vol. 275(C).
    8. Calearo, Lisa & Marinelli, Mattia & Ziras, Charalampos, 2021. "A review of data sources for electric vehicle integration studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    9. Wu, Ziyang & Wang, Can & Wolfram, Paul & Zhang, Yaxin & Sun, Xin & Hertwich, Edgar, 2019. "Assessing electric vehicle policy with region-specific carbon footprints," Applied Energy, Elsevier, vol. 256(C).
    10. Lijing Zhang & Shuke Fu & Jiali Tian & Jiachao Peng, 2022. "A Review of Energy Industry Chain and Energy Supply Chain," Energies, MDPI, vol. 15(23), pages 1-21, December.
    11. Luin, Blaž & Petelin, Stojan & Al-Mansour, Fouad, 2019. "Microsimulation of electric vehicle energy consumption," Energy, Elsevier, vol. 174(C), pages 24-32.
    12. Zhang, Jin & Wang, Zhenpo & Liu, Peng & Zhang, Zhaosheng & Li, Xiaoyu & Qu, Changhui, 2019. "Driving cycles construction for electric vehicles considering road environment: A case study in Beijing," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    13. Lin, Cheng & Zhao, Mingjie & Pan, Hong & Yi, Jiang, 2019. "Blending gear shift strategy design and comparison study for a battery electric city bus with AMT," Energy, Elsevier, vol. 185(C), pages 1-14.
    14. Cui, Yuepeng & Xu, Hao & Zou, Fumin & Chen, Zhihui & Gong, Kuangmin, 2021. "Optimization based method to develop representative driving cycle for real-world fuel consumption estimation," Energy, Elsevier, vol. 235(C).
    15. Anne Magdalene Syré & Florian Heining & Dietmar Göhlich, 2020. "Method for a Multi-Vehicle, Simulation-Based Life Cycle Assessment and Application to Berlin’s Motorized Individual Transport," Sustainability, MDPI, vol. 12(18), pages 1-26, September.
    16. Guilherme Medeiros Soares de Andrade & Fernando Wesley Cavalcanti de Araújo & Maurício Pereira Magalhães de Novaes Santos & Fabio Santana Magnani, 2020. "Standardized Comparison of 40 Local Driving Cycles: Energy and Kinematics," Energies, MDPI, vol. 13(20), pages 1-20, October.

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