IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v6y2013i12p6455-6475d31337.html
   My bibliography  Save this article

A Combined Cooperative Braking Model with a Predictive Control Strategy in an Electric Vehicle

Author

Listed:
  • Hongqiang Guo

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Hongwen He

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Fengchun Sun

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

Abstract

Cooperative braking with regenerative braking and mechanical braking plays an important role in electric vehicles for energy-saving control. Based on the parallel and the series cooperative braking models, a combined model with a predictive control strategy to get a better cooperative braking performance is presented. The balance problem between the maximum regenerative energy recovery efficiency and the optimum braking stability is solved through an off-line process optimization stream with the collaborative optimization algorithm (CO). To carry out the process optimization stream, the optimal Latin hypercube design (Opt LHD) is presented to discrete the continuous design space. To solve the poor real-time problem of the optimization, a high-precision predictive model based on the off-line optimization data of the combined model is built, and a predictive control strategy is proposed and verified through simulation. The simulation results demonstrate that the predictive control strategy and the combined model are reasonable and effective.

Suggested Citation

  • Hongqiang Guo & Hongwen He & Fengchun Sun, 2013. "A Combined Cooperative Braking Model with a Predictive Control Strategy in an Electric Vehicle," Energies, MDPI, vol. 6(12), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:6:y:2013:i:12:p:6455-6475:d:31337
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/6/12/6455/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/6/12/6455/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Abdelsalam Ahmed Abdelsalam & Shumei Cui, 2012. "A Fuzzy Logic Global Power Management Strategy for Hybrid Electric Vehicles Based on a Permanent Magnet Electric Variable Transmission," Energies, MDPI, vol. 5(4), pages 1-24, April.
    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, 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. Jiangyi Lv & Hongwen He & Wei Liu & Yong Chen & Fengchun Sun, 2019. "Vehicle Velocity Estimation Fusion with Kinematic Integral and Empirical Correction on Multi-Timescales," Energies, MDPI, vol. 12(7), pages 1-24, April.
    3. Changran He & Guoye Wang & Zhangpeng Gong & Zhichao Xing & Dongxin Xu, 2018. "A Control Algorithm for the Novel Regenerative–Mechanical Coupled Brake System with by-Wire Based on Multidisciplinary Design Optimization for an Electric Vehicle," Energies, MDPI, vol. 11(9), pages 1-18, September.
    4. 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.
    5. Hanwu Liu & Yulong Lei & Yao Fu & Xingzhong Li, 2020. "An Optimal Slip Ratio-Based Revised Regenerative Braking Control Strategy of Range-Extended Electric Vehicle," Energies, MDPI, vol. 13(6), pages 1-21, March.
    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. Yu-Huei Cheng & Ching-Ming Lai, 2017. "Control Strategy Optimization for Parallel Hybrid Electric Vehicles Using a Memetic Algorithm," Energies, MDPI, vol. 10(3), pages 1-21, March.

    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. Qiwei Xu & Jing Sun & Lingyan Luo & Shumei Cui & Qianfan Zhang, 2016. "A Study on Magnetic Decoupling of Compound-Structure Permanent-Magnet Motor for HEVs Application," Energies, MDPI, vol. 9(10), pages 1-16, October.
    2. Andrea Bonfiglio & Damiano Lanzarotto & Mario Marchesoni & Massimiliano Passalacqua & Renato Procopio & Matteo Repetto, 2017. "Electrical-Loss Analysis of Power-Split Hybrid Electric Vehicles," Energies, MDPI, vol. 10(12), pages 1-17, December.
    3. Hongwen He & Chao Sun & Xiaowei Zhang, 2012. "A Method for Identification of Driving Patterns in Hybrid Electric Vehicles Based on a LVQ Neural Network," Energies, MDPI, vol. 5(9), pages 1-18, September.
    4. Jixiang Fan & Jiangyan Zhang & Tielong Shen, 2015. "Map-Based Power-Split Strategy Design with Predictive Performance Optimization for Parallel Hybrid Electric Vehicles," Energies, MDPI, vol. 8(9), pages 1-23, September.
    5. Chun-Liang Liu & Yi-Shun Chiu & Yi-Hua Liu & Yeh-Hsiang Ho & Shu-Syuan Huang, 2013. "Optimization of a Fuzzy-Logic-Control-Based Five-Stage Battery Charger Using a Fuzzy-Based Taguchi Method," Energies, MDPI, vol. 6(7), pages 1-20, July.
    6. Javier Solano & Diego Jimenez & Adrian Ilinca, 2020. "A Modular Simulation Testbed for Energy Management in AC/DC Microgrids," Energies, MDPI, vol. 13(16), pages 1-23, August.
    7. Teng Liu & Yuan Zou & Dexing Liu & Fengchun Sun, 2015. "Reinforcement Learning–Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle," Energies, MDPI, vol. 8(7), pages 1-18, July.

    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:jeners:v:6:y:2013:i:12:p:6455-6475:d:31337. 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.