IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v7y2014i7p4614-4628d38415.html
   My bibliography  Save this article

A Neural Network Combined Inverse Controller for a Two-Rear-Wheel Independently Driven Electric Vehicle

Author

Listed:
  • Duo Zhang

    (School of Electrical and Information Engineering, University of Jiangsu, Zhenjiang 212013, China)

  • Guohai Liu

    (School of Electrical and Information Engineering, University of Jiangsu, Zhenjiang 212013, China)

  • Wenxiang Zhao

    (School of Electrical and Information Engineering, University of Jiangsu, Zhenjiang 212013, China)

  • Penghu Miao

    (School of Electrical and Information Engineering, University of Jiangsu, Zhenjiang 212013, China)

  • Yan Jiang

    (School of Electrical and Information Engineering, University of Jiangsu, Zhenjiang 212013, China)

  • Huawei Zhou

    (School of Electrical and Information Engineering, University of Jiangsu, Zhenjiang 212013, China)

Abstract

Vehicle active safety control is attracting ever increasing attention in the attempt to improve the stability and the maneuverability of electric vehicles. In this paper, a neural network combined inverse (NNCI) controller is proposed, incorporating the merits of left-inversion and right-inversion. As the left-inversion soft-sensor can estimate the sideslip angle, while the right-inversion is utilized to decouple control. Then, the proposed NNCI controller not only linearizes and decouples the original nonlinear system, but also directly obtains immeasurable state feedback in constructing the right-inversion. Hence, the proposed controller is very practical in engineering applications. The proposed system is co-simulated based on the vehicle simulation package CarSim in connection with Matlab/Simulink. The results verify the effectiveness of the proposed control strategy.

Suggested Citation

  • Duo Zhang & Guohai Liu & Wenxiang Zhao & Penghu Miao & Yan Jiang & Huawei Zhou, 2014. "A Neural Network Combined Inverse Controller for a Two-Rear-Wheel Independently Driven Electric Vehicle," Energies, MDPI, vol. 7(7), pages 1-15, July.
  • Handle: RePEc:gam:jeners:v:7:y:2014:i:7:p:4614-4628:d:38415
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/7/7/4614/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/7/7/4614/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guoqing Xu & Weimin Li & Kun Xu & Zhibin Song, 2011. "An Intelligent Regenerative Braking Strategy for Electric Vehicles," Energies, MDPI, vol. 4(9), pages 1-17, September.
    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. Xiang Liu & Min Xu & Mian Li, 2015. "New TA Index-Based Rollover Prevention System for Electric Vehicles," Energies, MDPI, vol. 8(3), pages 1-24, March.
    2. Xiufan Liang & Yiguo Li & Xiao Wu & Jiong Shen, 2018. "Nonlinear Modeling and Inferential Multi-Model Predictive Control of a Pulverizing System in a Coal-Fired Power Plant Based on Moving Horizon Estimation," Energies, MDPI, vol. 11(3), pages 1-27, 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. López, I. & Ibarra, E. & Matallana, A. & Andreu, J. & Kortabarria, I., 2019. "Next generation electric drives for HEV/EV propulsion systems: Technology, trends and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    2. Wasbari, F. & Bakar, R.A. & Gan, L.M. & Tahir, M.M. & Yusof, A.A., 2017. "A review of compressed-air hybrid technology in vehicle system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 935-953.
    3. Emilia M. Szumska & Rafał S. Jurecki, 2021. "Parameters Influencing on Electric Vehicle Range," Energies, MDPI, vol. 14(16), pages 1-23, August.
    4. Jingang Guo & Xiaoping Jian & Guangyu Lin, 2014. "Performance Evaluation of an Anti-Lock Braking System for Electric Vehicles with a Fuzzy Sliding Mode Controller," Energies, MDPI, vol. 7(10), pages 1-18, October.
    5. Jiangbo Wang & Kai Liu & Toshiyuki Yamamoto, 2017. "Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations," Energies, MDPI, vol. 10(1), pages 1-12, January.
    6. Jinhyun Park & In Gyu Jang & Sung-Ho Hwang, 2018. "Torque Distribution Algorithm for an Independently Driven Electric Vehicle Using a Fuzzy Control Method: Driving Stability and Efficiency," Energies, MDPI, vol. 11(12), pages 1-22, December.
    7. Petronilla Fragiacomo & Francesco Piraino & Matteo Genovese & Lorenzo Flaccomio Nardi Dei & Daria Donati & Michele Vincenzo Migliarese Caputi & Domenico Borello, 2022. "Sizing and Performance Analysis of Hydrogen- and Battery-Based Powertrains, Integrated into a Passenger Train for a Regional Track, Located in Calabria (Italy)," Energies, MDPI, vol. 15(16), pages 1-20, August.
    8. 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).
    9. Jinhyun Park & Houn Jeong & In Gyu Jang & Sung-Ho Hwang, 2015. "Torque Distribution Algorithm for an Independently Driven Electric Vehicle Using a Fuzzy Control Method," Energies, MDPI, vol. 8(8), pages 1-25, August.
    10. Seung-Yun Baek & Yeon-Soo Kim & Wan-Soo Kim & Seung-Min Baek & Yong-Joo Kim, 2020. "Development and Verification of a Simulation Model for 120 kW Class Electric AWD (All-Wheel-Drive) Tractor during Driving Operation," Energies, MDPI, vol. 13(10), pages 1-15, May.
    11. Xiang Liu & Min Xu & Mian Li, 2015. "New TA Index-Based Rollover Prevention System for Electric Vehicles," Energies, MDPI, vol. 8(3), pages 1-24, March.
    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. Branimir Škugor & Joško Petrić, 2018. "Optimization of Control Variables and Design of Management Strategy for Hybrid Hydraulic Vehicle," Energies, MDPI, vol. 11(10), pages 1-24, October.
    14. Maksymilian Mądziel & Tiziana Campisi & Artur Jaworski & Giovanni Tesoriere, 2021. "The Development of Strategies to Reduce Exhaust Emissions from Passenger Cars in Rzeszow City—Poland. A Preliminary Assessment of the Results Produced by the Increase of E-Fleet," Energies, MDPI, vol. 14(4), pages 1-21, February.
    15. Wu, Fuliang & Bektaş, Tolga & Dong, Ming & Ye, Hongbo & Zhang, Dali, 2021. "Optimal driving for vehicle fuel economy under traffic speed uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 175-206.
    16. Serrano, José Ramón & García, Antonio & Monsalve-Serrano, Javier & Martínez-Boggio, Santiago, 2021. "High efficiency two stroke opposed piston engine for plug-in hybrid electric vehicle applications: Evaluation under homologation and real driving conditions," Applied Energy, Elsevier, vol. 282(PA).
    17. 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).
    18. Liu, Kai & Wang, Jiangbo & Yamamoto, Toshiyuki & Morikawa, Takayuki, 2018. "Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption," Applied Energy, Elsevier, vol. 227(C), pages 324-331.
    19. Ilya Kulikov, 2019. "Model Analysis of Electrically Driven Vehicles by Means of Unknown Input Observers," Energies, MDPI, vol. 12(12), pages 1-17, June.
    20. Emilia M. Szumska & Rafał Jurecki, 2022. "The Analysis of Energy Recovered during the Braking of an Electric Vehicle in Different Driving Conditions," Energies, MDPI, vol. 15(24), pages 1-16, December.

    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:7:y:2014:i:7:p:4614-4628:d:38415. 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.