IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i17p7188-d408113.html
   My bibliography  Save this article

Development of Driving Cycle Construction for Hybrid Electric Bus: A Case Study in Zhengzhou, China

Author

Listed:
  • Jiankun Peng

    (School of Transportation, Southeast University, Nanjing 211189, China)

  • Jiwan Jiang

    (School of Transportation, Southeast University, Nanjing 211189, China)

  • Fan Ding

    (School of Transportation, Southeast University, Nanjing 211189, China)

  • Huachun Tan

    (School of Transportation, Southeast University, Nanjing 211189, China)

Abstract

A driving cycle is important to accomplish an accurate depiction of a vehicle’s driving characteristics as the traction motor’s flexible response to stop and start commands. In this paper, the driving cycle construction of an urban hybrid electric bus (HEB) in Zhengzhou, China is developed in which a measurement system integrating global positioning and inertial navigation function is used to acquire driving data. The collected data are then divided into acceleration, deceleration, uniform, and stop fragments. Meanwhile, the velocity fragments are classified into seven state clusters according to their average velocities. A transfer matrix applied to reveal the transfer relationship of velocity clusters can be obtained with statistical analysis. In the third stage, a three-part construction method of driving cycle is designed. Firstly, according to the theory of Markov chain, all the alternative parts that satisfy the construction’s precondition are selected based on the transfer matrix and Monte Carlo method. The Zhengzhou urban driving cycle (ZZUDC) could be determined by comparing the performance measure (PM) values subsequently. Eventually, the method and the cycle are validated by the high correlation coefficient (0.9972) with original data of ZZUDC than that of the other driving cycle (0.9746) constructed with traditional micro-trip and as well by comparing several statistical characteristics of ZZUDC and seven international cycles. Particularly, with around 20.5 L/100 km fuel and approximately 12.8 kwh/100 km electricity consumption, there is a narrow gap between the energy consumption of ZZUDC and WVUCITY, and their characteristics are similar.

Suggested Citation

  • Jiankun Peng & Jiwan Jiang & Fan Ding & Huachun Tan, 2020. "Development of Driving Cycle Construction for Hybrid Electric Bus: A Case Study in Zhengzhou, China," Sustainability, MDPI, vol. 12(17), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:7188-:d:408113
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/17/7188/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/17/7188/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jiankun Peng & Hao Fan & Hongwen He & Deng Pan, 2015. "A Rule-Based Energy Management Strategy for a Plug-in Hybrid School Bus Based on a Controller Area Network Bus," Energies, MDPI, vol. 8(6), pages 1-21, June.
    2. Jie Lin & Debbie A. Niemeier, 2003. "Estimating Regional Air Quality Vehicle Emission Inventories: Constructing Robust Driving Cycles," Transportation Science, INFORMS, vol. 37(3), pages 330-346, August.
    3. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    4. Pang, W. K. & Yang, Z. H. & Hou, S. H. & Leung, P. K., 2002. "Non-uniform random variate generation by the vertical strip method," European Journal of Operational Research, Elsevier, vol. 142(3), pages 595-609, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Kaixuan & Ruan, Jiageng & Li, Tongyang & Cui, Hanghang & Wu, Changcheng, 2023. "The effects investigation of data-driven fitting cycle and deep deterministic policy gradient algorithm on energy management strategy of dual-motor electric bus," Energy, Elsevier, vol. 269(C).
    2. 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).
    3. Zvonimir Dabčević & Branimir Škugor & Jakov Topić & Joško Deur, 2022. "Synthesis of Driving Cycles Based on Low-Sampling-Rate Vehicle-Tracking Data and Markov Chain Methodology," Energies, MDPI, vol. 15(11), pages 1-21, June.
    4. Gnap Jozef & Dočkalik Marek & Dydkowski Grzegorz, 2021. "Examination of the Development of New Bus Registrations with Alternative Powertrains in Europe," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 12(1), pages 147-158, January.
    5. Jia Di Yang & Jason Millichamp & Theo Suter & Paul R. Shearing & Dan J. L. Brett & James B. Robinson, 2023. "A Review of Drive Cycles for Electrochemical Propulsion," Energies, MDPI, vol. 16(18), pages 1-26, September.
    6. Jakov Topić & Branimir Škugor & Joško Deur, 2021. "Synthesis and Feature Selection-Supported Validation of Multidimensional Driving Cycles," Sustainability, MDPI, vol. 13(9), pages 1-21, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ye Yang & Youtong Zhang & Jingyi Tian & Si Zhang, 2018. "Research on a Plug-In Hybrid Electric Bus Energy Management Strategy Considering Drivability," Energies, MDPI, vol. 11(8), pages 1-22, August.
    2. Pengxiang Song & Yulong Lei & Yao Fu, 2020. "Multi-Objective Optimization and Matching of Power Source for PHEV Based on Genetic Algorithm," Energies, MDPI, vol. 13(5), pages 1-20, March.
    3. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    4. Li, Menglin & Yin, Long & Yan, Mei & Wu, Jingda & He, Hongwe & Jia, Chunchun, 2024. "Hierarchical intelligent energy-saving control strategy for fuel cell hybrid electric buses based on traffic flow predictions," Energy, Elsevier, vol. 304(C).
    5. Xiong, Rui & Duan, Yanzhou & Cao, Jiayi & Yu, Quanqing, 2018. "Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle," Applied Energy, Elsevier, vol. 217(C), pages 153-165.
    6. Bizon, Nicu, 2019. "Efficient fuel economy strategies for the Fuel Cell Hybrid Power Systems under variable renewable/load power profile," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    7. Li, Shuangqi & Zhao, Pengfei & Gu, Chenghong & Huo, Da & Zeng, Xianwu & Pei, Xiaoze & Cheng, Shuang & Li, Jianwei, 2022. "Online battery-protective vehicle to grid behavior management," Energy, Elsevier, vol. 243(C).
    8. Du, Guodong & Zou, Yuan & Zhang, Xudong & Kong, Zehui & Wu, Jinlong & He, Dingbo, 2019. "Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    9. Vitale, F. & Rispoli, N. & Sorrentino, M. & Rosen, M.A. & Pianese, C., 2021. "On the use of dynamic programming for optimal energy management of grid-connected reversible solid oxide cell-based renewable microgrids," Energy, Elsevier, vol. 225(C).
    10. Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
    11. Guo, Hongqiang & Sun, Qun & Wang, Chong & Wang, Qinpu & Lu, Silong, 2018. "A systematic design and optimization method of transmission system and power management for a plug-in hybrid electric vehicle," Energy, Elsevier, vol. 148(C), pages 1006-1017.
    12. Du, Guodong & Zou, Yuan & Zhang, Xudong & Liu, Teng & Wu, Jinlong & He, Dingbo, 2020. "Deep reinforcement learning based energy management for a hybrid electric vehicle," Energy, Elsevier, vol. 201(C).
    13. Chen, Zheng & Hu, Hengjie & Wu, Yitao & Zhang, Yuanjian & Li, Guang & Liu, Yonggang, 2020. "Stochastic model predictive control for energy management of power-split plug-in hybrid electric vehicles based on reinforcement learning," Energy, Elsevier, vol. 211(C).
    14. Anselma, Pier Giuseppe, 2022. "Computationally efficient evaluation of fuel and electrical energy economy of plug-in hybrid electric vehicles with smooth driving constraints," Applied Energy, Elsevier, vol. 307(C).
    15. Guo, Hongqiang & Hou, Daizheng & Du, Shangye & Zhao, Ling & Wu, Jian & Yan, Ning, 2020. "A driving pattern recognition-based energy management for plug-in hybrid electric bus to counter the noise of stochastic vehicle mass," Energy, Elsevier, vol. 198(C).
    16. Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    17. Hou, Daizheng & Sun, Qun & Bao, Chunjiang & Cheng, Xingqun & Guo, Hongqiang & Zhao, Ying, 2019. "An all-in-one design method for plug-in hybrid electric buses considering uncertain factor of driving cycles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    18. Zeyu Chen & Jiahuan Lu & Bo Liu & Nan Zhou & Shijie Li, 2020. "Optimal Energy Management of Plug-In Hybrid Electric Vehicles Concerning the Entire Lifespan of Lithium-Ion Batteries," Energies, MDPI, vol. 13(10), pages 1-15, May.
    19. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    20. Haibo Wu & Xingwang Tang & Sichuan Xu & Jiangbin Zhou, 2022. "Research on Energy Saving of PHEV Air Conditioning System Based on Reducing Air Backflow in Underhood," Energies, MDPI, vol. 15(9), pages 1-15, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:7188-:d:408113. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.