IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v301y2024ics0360544224014725.html
   My bibliography  Save this article

Cooperative optimization of energy recovery and braking feel based on vehicle speed prediction under downshifting conditions

Author

Listed:
  • Zhou, Xiaochuan
  • Wu, Gang
  • Wang, Chunyan
  • Zhang, Ruijun
  • Shi, Shuaipeng
  • Zhao, Wanzhong

Abstract

Regenerative braking can effectively recover vehicle kinetic energy, but its energy conversion efficiency is low under low-speed conditions, and there is also the problem of premature exit from energy recovery due to insufficient reverse electromotive force. The cooperation of gearbox gears can increase the speed range for the motor to recover energy, but unreasonable shifting will cause fluctuations in braking force and affect the consistency of the braking feel. Therefore, this paper aims to collaboratively optimize energy recovery and braking force fluctuations during gear shifting. Firstly, based on the model of braking system and transmission, the influence of shift strategy on braking impact and energy recovery is studied. In view of the challenge of determining a shift strategy with uncertain target braking speeds, a speed prediction model reconstructed by the support vector regression (SVR) model and the hybrid nonlinear autoregressive neural network (NAR) is proposed. On the basis of NAR-SVR speed prediction, the coupling effect of braking impact force and energy recovery efficiency is considered, and the collaborative optimization of regenerative braking torque and shift time is solved through a multi-objective cuckoo search algorithm. The hardware-in-the-loop test results verified that under high-speed conditions, the braking energy recovery rate of the proposed strategy was increased by 47.06 %, and the peak braking impact was reduced by 61.4 %. This research can provide a reference for the brake downshift optimization strategy and regenerative braking research of vehicles with non-decoupled electro-hydraulic composite braking systems.

Suggested Citation

  • Zhou, Xiaochuan & Wu, Gang & Wang, Chunyan & Zhang, Ruijun & Shi, Shuaipeng & Zhao, Wanzhong, 2024. "Cooperative optimization of energy recovery and braking feel based on vehicle speed prediction under downshifting conditions," Energy, Elsevier, vol. 301(C).
    • Handle: RePEc:eee:energy:v:301:y:2024:i:c:s0360544224014725
    DOI: 10.1016/j.energy.2024.131699
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224014725
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.131699?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Perugu, Harikishan & Collier, Sonya & Tan, Yi & Yoon, Seungju & Herner, Jorn, 2023. "Characterization of battery electric transit bus energy consumption by temporal and speed variation," Energy, Elsevier, vol. 263(PC).
    2. Tang, Qingsong & Yang, Yang & Luo, Chang & Yang, Zhong & Fu, Chunyun, 2022. "A novel electro-hydraulic compound braking system coordinated control strategy for a four-wheel-drive pure electric vehicle driven by dual motors," Energy, Elsevier, vol. 241(C).
    3. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
    4. Li, Liang & Wang, Xiangyu & Xiong, Rui & He, Kai & Li, Xujian, 2016. "AMT downshifting strategy design of HEV during regenerative braking process for energy conservation," Applied Energy, Elsevier, vol. 183(C), pages 914-925.
    5. 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).
    6. He, Hongwen & Wang, Chen & Jia, Hui & Cui, Xing, 2020. "An intelligent braking system composed single-pedal and multi-objective optimization neural network braking control strategies for electric vehicle," Applied Energy, Elsevier, vol. 259(C).
    7. Jing Lian & Shuang Liu & Linhui Li & Xuanzuo Liu & Yafu Zhou & Fan Yang & Lushan Yuan, 2017. "A Mixed Logical Dynamical-Model Predictive Control (MLD-MPC) Energy Management Control Strategy for Plug-in Hybrid Electric Vehicles (PHEVs)," Energies, MDPI, vol. 10(1), pages 1-18, January.
    8. Li, Liang & Li, Xujian & Wang, Xiangyu & Song, Jian & He, Kai & Li, Chenfeng, 2016. "Analysis of downshift’s improvement to energy efficiency of an electric vehicle during regenerative braking," Applied Energy, Elsevier, vol. 176(C), pages 125-137.
    9. Li, Shicheng & Xu, Lin & Du, Xiaofang & Wang, Nian & Lin, Feng & Abdelkareem, Mohamed A.A., 2023. "Combined single-pedal and low adhesion control systems for enhanced energy regeneration in electric vehicles: Modeling, simulation, and on-field test," Energy, Elsevier, vol. 269(C).
    10. Chengqun, Qiu & Wan, Xinshan & Wang, Na & Cao, Sunjia & Ji, Xinchen & Wu, Kun & Hu, Yaoyu & Meng, Mingyu, 2023. "A novel regenerative braking energy recuperation system for electric vehicles based on driving style," Energy, Elsevier, vol. 283(C).
    11. Zhang, Junjiang & Yang, Yang & Hu, Minghui & Yang, Zhong & Fu, Chunyun, 2021. "Longitudinal–vertical comprehensive control for four-wheel drive pure electric vehicle considering energy recovery and ride comfort," Energy, Elsevier, vol. 236(C).
    12. Lee, Gwangryeol & Song, Jingeun & Han, Jungwon & Lim, Yunsung & Park, Suhan, 2023. "Study on energy consumption characteristics of passenger electric vehicle according to the regenerative braking stages during real-world driving conditions," Energy, Elsevier, vol. 283(C).
    13. Congcong Li & Guirong Zhuo & Chen Tang & Lu Xiong & Wei Tian & Le Qiao & Yulin Cheng & Yanlong Duan, 2023. "A Review of Electro-Mechanical Brake (EMB) System: Structure, Control and Application," Sustainability, MDPI, vol. 15(5), pages 1-38, March.
    Full references (including those not matched with items on IDEAS)

    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. Yang, Chao & Sun, Tonglin & Wang, Weida & Li, Ying & Zhang, Yuhang & Zha, Mingjun, 2024. "Regenerative braking system development and perspectives for electric vehicles: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 198(C).
    2. Li, Shicheng & Xu, Lin & Du, Xiaofang & Wang, Nian & Lin, Feng & Abdelkareem, Mohamed A.A., 2023. "Combined single-pedal and low adhesion control systems for enhanced energy regeneration in electric vehicles: Modeling, simulation, and on-field test," Energy, Elsevier, vol. 269(C).
    3. Sun, Xilei & Fu, Jianqin, 2024. "Many-objective optimization of BEV design parameters based on gradient boosting decision tree models and the NSGA-III algorithm considering the ambient temperature," Energy, Elsevier, vol. 288(C).
    4. Chengqun, Qiu & Wan, Xinshan & Wang, Na & Cao, Sunjia & Ji, Xinchen & Wu, Kun & Hu, Yaoyu & Meng, Mingyu, 2023. "A novel regenerative braking energy recuperation system for electric vehicles based on driving style," Energy, Elsevier, vol. 283(C).
    5. Lipeng, Zhang & Xin, Liu & Shuaishuai, Liu & Haoran, Guo & Kaixin, Shi, 2024. "Low energy consumption traction control for centralized and distributed dual-mode coupling drive electric vehicle on split ramps," Energy, Elsevier, vol. 289(C).
    6. Xiaoping Li & Junming Zhou & Wei Guan & Feng Jiang & Guangming Xie & Chunfeng Wang & Weiguang Zheng & Zhijie Fang, 2023. "Optimization of Brake Feedback Efficiency for Small Pure Electric Vehicles Based on Multiple Constraints," Energies, MDPI, vol. 16(18), pages 1-20, September.
    7. 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).
    8. Zhichao Zhao & Lu Li & Yang Ou & Yi Wang & Shaoyang Wang & Jing Yu & Renhua Feng, 2023. "A Comparative Study on the Energy Flow of Electric Vehicle Batteries among Different Environmental Temperatures," Energies, MDPI, vol. 16(14), pages 1-15, July.
    9. Maria Cieśla & Piotr Nowakowski & Mariusz Wala, 2024. "The Impact of Variable Ambient Temperatures on the Energy Efficiency and Performance of Electric Vehicles during Waste Collection," Energies, MDPI, vol. 17(17), pages 1-21, August.
    10. Antonia Tamborrino & Claudio Perone & Filippo Catalano & Giacomo Squeo & Francesco Caponio & Biagio Bianchi, 2019. "Modelling Energy Consumption and Energy-Saving in High-Quality Olive Oil Decanter Centrifuge: Numerical Study and Experimental Validation," Energies, MDPI, vol. 12(13), pages 1-20, July.
    11. Sun, Xilei & Fu, Jianqin, 2024. "Experiment investigation for interconnected effects of driving cycle and ambient temperature on bidirectional energy flows in an electric sport utility vehicle," Energy, Elsevier, vol. 300(C).
    12. Yang Yang & Xiaolong He & Yi Zhang & Datong Qin, 2018. "Regenerative Braking Compensatory Control Strategy Considering CVT Power Loss for Hybrid Electric Vehicles," Energies, MDPI, vol. 11(3), pages 1-15, February.
    13. Qi, Lingfei & Wu, Xiaoping & Zeng, Xiaohui & Feng, Yan & Pan, Hongye & Zhang, Zutao & Yuan, Yanping, 2020. "An electro-mechanical braking energy recovery system based on coil springs for energy saving applications in electric vehicles," Energy, Elsevier, vol. 200(C).
    14. Dimitrios Rimpas & Stavrοs D. Kaminaris & Dimitrios D. Piromalis & George Vokas & Konstantinos G. Arvanitis & Christos-Spyridon Karavas, 2023. "Comparative Review of Motor Technologies for Electric Vehicles Powered by a Hybrid Energy Storage System Based on Multi-Criteria Analysis," Energies, MDPI, vol. 16(6), pages 1-24, March.
    15. Zhao, Mingjie & Shi, Junhui & Lin, Cheng, 2019. "Optimization of integrated energy management for a dual-motor coaxial coupling propulsion electric city bus," Applied Energy, Elsevier, vol. 243(C), pages 21-34.
    16. Harasis, Salman & Khan, Irfan & Massoud, Ahmed, 2024. "Enabling large-scale integration of electric bus fleets in harsh environments: Possibilities, potentials, and challenges," Energy, Elsevier, vol. 300(C).
    17. Yang, Jian & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin & Zhao, Qinghai & Meng, Zewen, 2021. "Research on driving control strategy and Fuzzy logic optimization of a novel mechatronics-electro-hydraulic power coupling electric vehicle," Energy, Elsevier, vol. 233(C).
    18. Nan, Sirui & Tu, Ran & Li, Tiezhu & Sun, Jian & Chen, Haibo, 2022. "From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus," Energy, Elsevier, vol. 261(PA).
    19. Duggal, Angel Swastik & Singh, Rajesh & Gehlot, Anita & Gupta, Lovi Raj & Akram, Sheik Vaseem & Prakash, Chander & Singh, Sunpreet & Kumar, Raman, 2021. "Infrastructure, mobility and safety 4.0: Modernization in road transportation," Technology in Society, Elsevier, vol. 67(C).
    20. Muhammad Khalid, 2019. "A Review on the Selected Applications of Battery-Supercapacitor Hybrid Energy Storage Systems for Microgrids," Energies, MDPI, vol. 12(23), pages 1-34, November.

    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:eee:energy:v:301:y:2024:i:c:s0360544224014725. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.