IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i2p204-d89997.html
   My bibliography  Save this article

A Proportional Resonant Control Strategy for Efficiency Improvement in Extended Range Electric Vehicles

Author

Listed:
  • Xiaoyuan Wang

    (Institute of Electrical Engineering & Automation, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China)

  • Haiying Lv

    (Institute of Electrical Engineering & Automation, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China)

  • Qiang Sun

    (College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300384, China)

  • Yanqing Mi

    (Institute of Electrical Engineering & Automation, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China)

  • Peng Gao

    (Institute of Electrical Engineering & Automation, Tianjin University, No. 92 Weijin Road, Tianjin 300072, China)

Abstract

The key to control the range extender generation system is to improve the efficiency and reduce the emissions of the electric vehicle (EV). In this paper, based on the purpose of efficiency optimization, both engine and generator are matched to get a public high efficiency region, and a partial power following control strategy was presented. The engine speed is constant in the defined power range, so the output power regulation of the range extender is only realized by the adjustment of the torque of the generator. Engine speed and generator torque were decoupled. An improved proportional resonant (PR) controller is adopted to achieve fast output power regulation. In order to ensure the response characteristics of the control system and to improve the robustness, the impacts on system’s characteristics and stability caused by PR controller and parameters in the inner-current loop were analyzed via frequency response characteristics. A pre-Tustin with deviation compensation is proposed for PR controller’s discretization. A stable and robust power following control method is obtained for the range extender control system. Finally, simulation and experiment of the proposed control strategy illustrated its feasibility and correctness.

Suggested Citation

  • Xiaoyuan Wang & Haiying Lv & Qiang Sun & Yanqing Mi & Peng Gao, 2017. "A Proportional Resonant Control Strategy for Efficiency Improvement in Extended Range Electric Vehicles," Energies, MDPI, vol. 10(2), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:204-:d:89997
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/2/204/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/2/204/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pérez, Laura V. & Bossio, Guillermo R. & Moitre, Diego & García, Guillermo O., 2006. "Optimization of power management in an hybrid electric vehicle using dynamic programming," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 73(1), pages 244-254.
    2. Yongpeng Shen & Zhendong He & Dongqi Liu & Binjie Xu, 2016. "Optimization of Fuel Consumption and Emissions for Auxiliary Power Unit Based on Multi-Objective Optimization Model," Energies, MDPI, vol. 9(2), pages 1-18, February.
    3. Ming Cheng & Le Sun & Giuseppe Buja & Lihua Song, 2015. "Advanced Electrical Machines and Machine-Based Systems for Electric and Hybrid Vehicles," Energies, MDPI, vol. 8(9), pages 1-24, 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. Dung Vo Tien & Radomir Gono & Zbigniew Leonowicz, 2018. "A Multifunctional Dynamic Voltage Restorer for Power Quality Improvement," Energies, MDPI, vol. 11(6), pages 1-17, May.
    2. Ren, Guizhou & Wang, Jinzhong & Chen, Changlei & Wang, Haoran, 2021. "A variable-voltage ultra-capacitor/battery hybrid power source for extended range electric vehicle," Energy, Elsevier, vol. 231(C).
    3. Wilson Pavon & Esteban Inga & Silvio Simani & Matthew Armstrong, 2023. "Optimal Hierarchical Control for Smart Grid Inverters Using Stability Margin Evaluating Transient Voltage for Photovoltaic System," Energies, MDPI, vol. 16(5), pages 1-16, 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. Haitao Min & Weiyi Sun & Xinyong Li & Dongni Guo & Yuanbin Yu & Tao Zhu & Zhongmin Zhao, 2017. "Research on the Optimal Charging Strategy for Li-Ion Batteries Based on Multi-Objective Optimization," Energies, MDPI, vol. 10(5), pages 1-15, May.
    2. Bảo-Huy Nguyễn & João Pedro F. Trovão & Ronan German & Alain Bouscayrol, 2020. "Real-Time Energy Management of Parallel Hybrid Electric Vehicles Using Linear Quadratic Regulation," Energies, MDPI, vol. 13(21), pages 1-19, October.
    3. Jiajun Liu & Tianxu Jin & Li Liu & Yajue Chen & Kun Yuan, 2017. "Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs," Sustainability, MDPI, vol. 9(10), pages 1-18, October.
    4. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2021. "Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain," Energies, MDPI, vol. 14(23), pages 1-22, November.
    5. Bedatri Moulik & Dirk Söffker, 2015. "Optimal Rule-Based Power Management for Online, Real-Time Applications in HEVs with Multiple Sources and Objectives: A Review," Energies, MDPI, vol. 8(9), pages 1-15, August.
    6. Feng Yu & Ming Cheng & Kwok Tong Chau & Feng Li, 2015. "Control and Performance Evaluation of Multiphase FSPM Motor in Low-Speed Region for Hybrid Electric Vehicles," Energies, MDPI, vol. 8(9), pages 1-19, September.
    7. Sebastian Berhausen & Tomasz Jarek, 2021. "Method of Limiting Shaft Voltages in AC Electric Machines," Energies, MDPI, vol. 14(11), pages 1-19, June.
    8. Zheng, Weiguang & Ma, Mengcheng & Xu, Enyong & Huang, Qibai, 2024. "An energy management strategy for fuel-cell hybrid electric vehicles based on model predictive control with a variable time domain," Energy, Elsevier, vol. 312(C).
    9. Rui Tu & Hui Yang & Heyun Lin & Hanlin Zhan & Di Wu & Minghu Yu & Liang Chen & Wenjie Chen, 2022. "Investigation of a Novel Consequent-Pole Flux-Intensifying Memory Machine," Energies, MDPI, vol. 15(15), pages 1-15, July.
    10. Pozna, Claudiu & Troester, Fritz & Precup, Radu-Emil & Tar, József K. & Preitl, Stefan, 2009. "On the design of an obstacle avoiding trajectory: Method and simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(7), pages 2211-2226.
    11. Enang, Wisdom & Bannister, Chris, 2017. "Modelling and control of hybrid electric vehicles (A comprehensive review)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1210-1239.
    12. Sun, Chao & Sun, Fengchun & He, Hongwen, 2017. "Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 1644-1653.
    13. Yuanxi Chen & Weinong Fu & Shuangxia Niu & Sigao Wang, 2023. "A Torque-Enhanced Magnetic-Geared Machine with Dual-Series-Winding and Its Design Approach for Electric Vehicle Powertrain," Sustainability, MDPI, vol. 15(6), pages 1-17, March.
    14. Jingxia Wang & Yusheng Hu & Ming Cheng & Biao Li & Bin Chen, 2020. "Bidirectional Coupling Model of Electromagnetic Field and Thermal Field Applied to the Thermal Analysis of the FSPM Machine," Energies, MDPI, vol. 13(12), pages 1-15, June.
    15. Tobias Nüesch & Philipp Elbert & Michael Flankl & Christopher Onder & Lino Guzzella, 2014. "Convex Optimization for the Energy Management of Hybrid Electric Vehicles Considering Engine Start and Gearshift Costs," Energies, MDPI, vol. 7(2), pages 1-23, February.
    16. Chaoying Xia & Cong Zhang, 2015. "Power Management Strategy of Hybrid Electric Vehicles Based on Quadratic Performance Index," Energies, MDPI, vol. 8(11), pages 1-16, November.
    17. Constantijn Romijn & Tijs Donkers & John Kessels & Siep Weiland, 2017. "Real-Time Distributed Economic Model Predictive Control for Complete Vehicle Energy Management," Energies, MDPI, vol. 10(8), pages 1-28, July.
    18. Tian, Xiang & Cai, Yingfeng & Sun, Xiaodong & Zhu, Zhen & Xu, Yiqiang, 2019. "An adaptive ECMS with driving style recognition for energy optimization of parallel hybrid electric buses," Energy, Elsevier, vol. 189(C).
    19. Ruan, Shumin & Ma, Yue & Yang, Ningkang & Xiang, Changle & Li, Xunming, 2022. "Real-time energy-saving control for HEVs in car-following scenario with a double explicit MPC approach," Energy, Elsevier, vol. 247(C).
    20. Wenping Chai & Thomas A. Lipo & Byung-il Kwon, 2018. "Design and Optimization of a Novel Wound Field Synchronous Machine for Torque Performance Enhancement," Energies, MDPI, vol. 11(8), pages 1-15, August.

    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:10:y:2017:i:2:p:204-:d:89997. 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.