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Review on Braking Energy Management in Electric Vehicles

Author

Listed:
  • Valery Vodovozov

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Zoja Raud

    (Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Eduard Petlenkov

    (Department of Computer Systems, Tallinn University of Technology, 19086 Tallinn, Estonia)

Abstract

The adoption of electric vehicles promises numerous benefits for modern society. At the same time, there remain significant hurdles to their wide distribution, primarily related to battery-based energy sources. This review concerns the systematization of knowledge in one of the areas of the electric vehicle control, namely, the energy management issues when using braking controllers. The braking process optimization is summarized from two aspects. First, the advantageous solutions are presented that were identified in the field of gradual and urgent braking. Second, several findings discovered in adjacent fields of automation are debated as prospects for their possible application in braking control. Following the specific classification of braking methods, a generalized braking system composition is offered, and all publications are evaluated primarily in terms of their energy recovery abilities as a global target. Then, conventional and intelligent classes of braking controllers are compared. In the first category, classic PID, threshold, and sliding-mode controllers are reviewed in terms of their energy management restrictions. The second group relates to the issues of the tire friction-slip identification and braking torque allocation between the hydraulic and electrical brakes. From this perspective, several intelligent systems are analyzed in detail, especially fuzzy logic, neural network, and their numerous associations.

Suggested Citation

  • Valery Vodovozov & Zoja Raud & Eduard Petlenkov, 2021. "Review on Braking Energy Management in Electric Vehicles," Energies, MDPI, vol. 14(15), pages 1-26, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:15:p:4477-:d:600645
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    References listed on IDEAS

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    1. Shu-zhi Gao & Jing Yang & Jie-sheng Wang, 2014. "D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-13, April.
    2. Jakov Topić & Branimir Škugor & Joško Deur, 2019. "Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range," Energies, MDPI, vol. 12(7), pages 1-20, April.
    3. Aritra Ghosh, 2020. "Possibilities and Challenges for the Inclusion of the Electric Vehicle (EV) to Reduce the Carbon Footprint in the Transport Sector: A Review," Energies, MDPI, vol. 13(10), pages 1-22, May.
    4. 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.
    5. Boyi Xiao & Huazhong Lu & Hailin Wang & Jiageng Ruan & Nong Zhang, 2017. "Enhanced Regenerative Braking Strategies for Electric Vehicles: Dynamic Performance and Potential Analysis," Energies, MDPI, vol. 10(11), pages 1-19, November.
    6. Valery Vodovozov & Andrei Aksjonov & Eduard Petlenkov & Zoja Raud, 2021. "Neural Network-Based Model Reference Control of Braking Electric Vehicles," Energies, MDPI, vol. 14(9), pages 1-22, April.
    7. 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.
    8. 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.
    9. 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).
    10. Lee, Henry & Clark, Alex, 2018. "Charging the Future: Challenges and Opportunities for Electric Vehicle Adoption," Working Paper Series rwp18-026, Harvard University, John F. Kennedy School of Government.
    11. Yang Yang & Chang Luo & Pengxi Li, 2017. "Regenerative Braking Control Strategy of Electric-Hydraulic Hybrid (EHH) Vehicle," Energies, MDPI, vol. 10(7), pages 1-18, July.
    12. Sabrine Slama & Ayachi Errachdi & Mohamed Benrejeb, 2019. "Neural Adaptive PID and Neural Indirect Adaptive Control Switch Controller for Nonlinear MIMO Systems," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-11, August.
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    2. Gianfranco Rizzo & Francesco Antonio Tiano & Valerio Mariani & Matteo Marino, 2021. "Optimal Modulation of Regenerative Braking in Through-The-Road Hybridized Vehicles," Energies, MDPI, vol. 14(20), pages 1-15, October.
    3. Giulia Sandrini & Daniel Chindamo & Marco Gadola, 2022. "Regenerative Braking Logic That Maximizes Energy Recovery Ensuring the Vehicle Stability," Energies, MDPI, vol. 15(16), pages 1-43, August.
    4. Md. Sazal Miah & Molla Shahadat Hossain Lipu & Sheikh Tanzim Meraj & Kamrul Hasan & Shaheer Ansari & Taskin Jamal & Hasan Masrur & Rajvikram Madurai Elavarasan & Aini Hussain, 2021. "Optimized Energy Management Schemes for Electric Vehicle Applications: A Bibliometric Analysis towards Future Trends," Sustainability, MDPI, vol. 13(22), pages 1-38, November.
    5. Jacek Caban & Jan Vrabel & Dorota Górnicka & Radosław Nowak & Maciej Jankiewicz & Jonas Matijošius & Marek Palka, 2023. "Overview of Energy Harvesting Technologies Used in Road Vehicles," Energies, MDPI, vol. 16(9), pages 1-32, April.
    6. Deping Wang & Changyang Guan & Junnian Wang & Haisheng Wang & Zhenhao Zhang & Dachang Guo & Fang Yang, 2023. "Review of Energy-Saving Technologies for Electric Vehicles, from the Perspective of Driving Energy Management," Sustainability, MDPI, vol. 15(9), pages 1-17, May.
    7. Agnieszka Dudziak & Jacek Caban & Ondrej Stopka & Monika Stoma & Marie Sejkorová & Mária Stopková, 2023. "Vehicle Market Analysis of Drivers’ Preferences in Terms of the Propulsion Systems: The Czech Case Study," Energies, MDPI, vol. 16(5), pages 1-20, March.

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