IDEAS home Printed from https://ideas.repec.org/a/hin/complx/4071743.html
   My bibliography  Save this article

Energy Recovery Strategy Numerical Simulation for Dual Axle Drive Pure Electric Vehicle Based on Motor Loss Model and Big Data Calculation

Author

Listed:
  • Huiyuan Xiong
  • Xionglai Zhu
  • Ronghui Zhang

Abstract

Aiming at the braking energy feedback control in the optimal energy recovery of the two-motor dual-axis drive electric vehicle (EV), the efficiency numerical simulation model based on the permanent magnet synchronous motor loss was established. At the same time, under different speed and braking conditions, based on maximum recovery efficiency and data calculation of motor system, the optimization motor braking torque distribution model was established. Thus, the distribution rule of the power optimization for the front and rear electric mechanism was obtained. This paper takes the Economic Commission of Europe (ECE) braking safety regulation as the constraint condition, and finally, a new regenerative braking torque distribution strategy numerical simulation was developed. The simulation model of Simulink and CarSim was established based on the simulation object. The numerical simulation results show that under the proposed strategy, the average utilization efficiency of the motor system is increased by 3.24% compared with the I based braking force distribution strategy. Moreover, it is 9.95% higher than the maximum braking energy recovery strategy of the front axle. Finally, through the driving behavior of the driver obtained from the big data platform, we analyze how the automobile braking force matches with the driver’s driving behavior. It also analyzes how the automobile braking force matches the energy recovery efficiency. The research results in this paper provide a reference for the future calculation of braking force feedback control system based on big data of new energy vehicles. It also provides a reference for the modeling of brake feedback control system.

Suggested Citation

  • Huiyuan Xiong & Xionglai Zhu & Ronghui Zhang, 2018. "Energy Recovery Strategy Numerical Simulation for Dual Axle Drive Pure Electric Vehicle Based on Motor Loss Model and Big Data Calculation," Complexity, Hindawi, vol. 2018, pages 1-14, August.
    • Handle: RePEc:hin:complx:4071743
    DOI: 10.1155/2018/4071743
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2018/4071743.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2018/4071743.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2018/4071743?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. B Yu & Z-Z Yang & J-X Xie, 2011. "A parallel improved ant colony optimization for multi-depot vehicle routing problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 183-188, January.
    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. Yang, Yang & He, Qiang & Fu, Chunyun & Liao, Shuiping & Tan, Peng, 2020. "Efficiency improvement of permanent magnet synchronous motor for electric vehicles," Energy, Elsevier, vol. 213(C).
    2. Yang, Lu & Xie, Pengli & Bi, Chongke & Zhang, Ronghui & Cai, Bowen & Shao, Xiaowei & Wang, Rongben, 2020. "Household power consumption pattern modeling through a single power sensor," Renewable Energy, Elsevier, vol. 155(C), pages 121-133.
    3. He, Qiang & Yang, Yang & Luo, Chang & Zhai, Jun & Luo, Ronghua & Fu, Chunyun, 2022. "Energy recovery strategy optimization of dual-motor drive electric vehicle based on braking safety and efficient recovery," Energy, Elsevier, vol. 248(C).
    4. Ying Lyu & Xuenan Sun & Hong Chu & Bingzhao Gao, 2020. "Improvement of Battery Life and Energy Economy for Electric Vehicles with Two-Speed Transmission," Energies, MDPI, vol. 13(13), pages 1-20, July.
    5. Jie Hu & Wentong Cao & Feng Jiang & Lingling Hu & Qian Chen & Weiguang Zheng & Junming Zhou, 2023. "Study on Multi-Objective Optimization of Power System Parameters of Battery Electric Vehicles," Sustainability, MDPI, vol. 15(10), pages 1-23, May.
    6. Huiyuan Xiong & Huan Liu & Jian Ma & Yuelong Pan & Ronghui Zhang, 2021. "An NN-Based Double Parallel Longitudinal and Lateral Driving Strategy for Self-Driving Transport Vehicles in Structured Road Scenarios," Sustainability, MDPI, vol. 13(8), pages 1-16, April.
    7. Hong, Jichao & Wang, Zhenpo & Zhang, Tiezhu & Yin, Huaixian & Zhang, Hongxin & Huo, Wei & Zhang, Yi & Li, Yuanyuan, 2019. "Research on integration simulation and balance control of a novel load isolated pure electric driving system," Energy, Elsevier, vol. 189(C).
    8. Guang Chen & Tonghai Jiang & Meng Wang & Xinyu Tang & Wenfei Ji, 2020. "Design and model checking of timed automata oriented architecture for Internet of thing," International Journal of Distributed Sensor Networks, , vol. 16(5), pages 15501477209, May.
    9. Yang Yang & Qiang He & Yongzheng Chen & Chunyun Fu, 2020. "Efficiency Optimization and Control Strategy of Regenerative Braking System with Dual Motor," Energies, MDPI, vol. 13(3), pages 1-21, February.

    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. Md. Anisul Islam & Yuvraj Gajpal, 2021. "Optimization of Conventional and Green Vehicles Composition under Carbon Emission Cap," Sustainability, MDPI, vol. 13(12), pages 1-20, June.
    2. Schmidt, Carise E. & Silva, Arinei C.L. & Darvish, Maryam & Coelho, Leandro C., 2023. "Time-dependent fleet size and mix multi-depot vehicle routing problem," International Journal of Production Economics, Elsevier, vol. 255(C).
    3. Li, Junsong & Rong, Gang & Feng, Yiping, 2015. "Request selection and exchange approach for carrier collaboration based on auction of a single request," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 84(C), pages 23-39.
    4. Wang, Yuan & Lei, Linfei & Zhang, Dongxiang & Lee, Loo Hay, 2020. "Towards delivery-as-a-service: Effective neighborhood search strategies for integrated delivery optimization of E-commerce and static O2O parcels," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 38-63.
    5. Yiwei Fan & Gang Wang & Xiaoling Lu & Gaobin Wang, 2019. "Distributed forecasting and ant colony optimization for the bike-sharing rebalancing problem with unserved demands," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-26, December.
    6. Junlong Zhang & William Lam & Bi Chen, 2013. "A Stochastic Vehicle Routing Problem with Travel Time Uncertainty: Trade-Off Between Cost and Customer Service," Networks and Spatial Economics, Springer, vol. 13(4), pages 471-496, December.
    7. Grigorios D. Konstantakopoulos & Sotiris P. Gayialis & Evripidis P. Kechagias, 2022. "Vehicle routing problem and related algorithms for logistics distribution: a literature review and classification," Operational Research, Springer, vol. 22(3), pages 2033-2062, July.
    8. Schryen, Guido, 2020. "Parallel computational optimization in operations research: A new integrative framework, literature review and research directions," European Journal of Operational Research, Elsevier, vol. 287(1), pages 1-18.
    9. Weiheng Zhang & Yuvraj Gajpal & Srimantoorao. S. Appadoo & Qi Wei, 2020. "Multi-Depot Green Vehicle Routing Problem to Minimize Carbon Emissions," Sustainability, MDPI, vol. 12(8), pages 1-19, April.
    10. José M. Ferrer & M. Teresa Ortuño & Gregorio Tirado, 2020. "A New Ant Colony-Based Methodology for Disaster Relief," Mathematics, MDPI, vol. 8(4), pages 1-23, April.
    11. Wan Fang & Guo Haixiang & Li Jinling & Gu Mingyun & Pan Wenwen, 2021. "Multi-objective Emergency Scheduling for Geological Disasters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(2), pages 1323-1358, January.
    12. Baozhen Yao & Chao Chen & Xiaolin Song & Xiaoli Yang, 2019. "Fresh seafood delivery routing problem using an improved ant colony optimization," Annals of Operations Research, Springer, vol. 273(1), pages 163-186, February.
    13. Zong, Fang & Yu, Ping & Tang, Jinjun & Sun, Xiao, 2019. "Understanding parking decisions with structural equation modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 408-417.

    More about this item

    Statistics

    Access and download statistics

    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:hin:complx:4071743. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.