IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i12p4561-d1165735.html
   My bibliography  Save this article

Optimizing Energy Harvesting: A Gain-Scheduled Braking System for Electric Vehicles with Enhanced State of Charge and Efficiency

Author

Listed:
  • Anith Khairunnisa Ghazali

    (Faculty of Engineering & Technology, Multimedia University, Bukit Beruang 75450, Melaka, Malaysia)

  • Mohd Khair Hassan

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Mohd Amran Mohd Radzi

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Azizan As’arry

    (Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Selangor, Malaysia)

Abstract

Recycling braking energy is crucial in increasing the overall energy efficiency of an electric vehicle. Regenerative braking system (RBS) technology makes a significant contribution, but it is quite challenging to design an optimal braking force distribution while ensuring vehicle stability and battery health. In this study, a parallel-distribution braking system that transfers as much energy as possible from the wheel to the battery was investigated. An integrated braking force distribution with gain-scheduling super-twisting sliding mode control (GSTSMC) was proposed to capture the maximum kinetic energy during braking and convert it into electrical energy. Parallel friction and regenerative braking ratios dominate the design of the braking component, which is based on the speed of the vehicle. A GSTSMC was implemented and incorporated into the vehicle dynamics model developed in the ADVISOR environment. Simulation was utilized to rigorously validate the efficacy of the proposed control strategy, ensuring its potential to perform optimally in practical applications. Consideration was given to the vehicle’s slip ratio on dry asphalt to maintain vehicle stability. Simulation results were used to validate the performance of the proposed design in terms of the state of charge (SOC), transmitted energy, motor efficiency, battery temperature, and slip ratio. Based on the results, the proposed control strategy is capable of increasing the SOC value to 54%, overall efficiency to 25.98%, energy transmitted to 14.27%, and energy loss to 87 kJ while considering the vehicle’s speed-tracking ability, battery temperature, and stability.

Suggested Citation

  • Anith Khairunnisa Ghazali & Mohd Khair Hassan & Mohd Amran Mohd Radzi & Azizan As’arry, 2023. "Optimizing Energy Harvesting: A Gain-Scheduled Braking System for Electric Vehicles with Enhanced State of Charge and Efficiency," Energies, MDPI, vol. 16(12), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4561-:d:1165735
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/12/4561/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/12/4561/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kanghyun Nam & Yoichi Hori & Choonyoung Lee, 2015. "Wheel Slip Control for Improving Traction-Ability and Energy Efficiency of a Personal Electric Vehicle," Energies, MDPI, vol. 8(7), pages 1-21, July.
    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. Idris Idris Sunusi & Jun Zhou & Chenyang Sun & Zhenzhen Wang & Jianlei Zhao & Yongshuan Wu, 2021. "Development of Online Adaptive Traction Control for Electric Robotic Tractors," Energies, MDPI, vol. 14(12), pages 1-24, June.
    2. Xudong Zhang & Dietmar Göhlich, 2017. "Integrated Traction Control Strategy for Distributed Drive Electric Vehicles with Improvement of Economy and Longitudinal Driving Stability," Energies, MDPI, vol. 10(1), pages 1-18, January.
    3. Zhenpo Wang & Changhui Qu & Lei Zhang & Jin Zhang & Wen Yu, 2018. "Integrated Sizing and Energy Management for Four-Wheel-Independently-Actuated Electric Vehicles Considering Realistic Constructed Driving Cycles," Energies, MDPI, vol. 11(7), pages 1-22, July.
    4. Valery Vodovozov & Zoja Raud & Eduard Petlenkov, 2021. "Review on Braking Energy Management in Electric Vehicles," Energies, MDPI, vol. 14(15), pages 1-26, July.
    5. Binh-Minh Nguyen & Hung Van Nguyen & Minh Ta-Cao & Michihiro Kawanishi, 2020. "Longitudinal Modelling and Control of In-Wheel-Motor Electric Vehicles as Multi-Agent Systems," Energies, MDPI, vol. 13(20), pages 1-28, October.

    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:16:y:2023:i:12:p:4561-:d:1165735. 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.