IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i21p4082-d280451.html
   My bibliography  Save this article

Step-by-Step Small-Signal Modeling and Control of a Light Hybrid Electric Vehicle Propulsion System

Author

Listed:
  • Carmen Raga

    (Power Electronics System Group, Universidad Carlos III de Madrid, 28911 Leganes, Spain)

  • Antonio Lázaro

    (Power Electronics System Group, Universidad Carlos III de Madrid, 28911 Leganes, Spain)

  • Andrés Barrado

    (Power Electronics System Group, Universidad Carlos III de Madrid, 28911 Leganes, Spain)

  • Alberto Martín-Lozano

    (Power Electronics System Group, Universidad Carlos III de Madrid, 28911 Leganes, Spain)

  • Isabel Quesada

    (Power Electronics System Group, Universidad Carlos III de Madrid, 28911 Leganes, Spain)

Abstract

This paper develops step-by-step a complete electric model of a light hybrid electric vehicle propulsion system. This model includes the vehicle mass, the radius and mass of the wheels, the aerodynamic profile of the vehicle, the electric motor and the motor drive, among other elements. Each element of the model is represented by a set of equations, which lead to getting an equivalent electric circuit. Based on this model, the outer and inner loop compensators of the motor drive control circuit are designed to provide stability and a fast dynamic response to the system. To achieve this, the steady-state equations and the small-signal model of the equivalent electric circuit are also obtained. Furthermore, as these elements are the main load of the power distribution system of the fully electric and light hybrid electric vehicle, the input impedance model of the set composed of the input filter, the motor drive, the motor, and the vehicle is presented. This input impedance is especially useful to get the system stability of the entire power distribution system.

Suggested Citation

  • Carmen Raga & Antonio Lázaro & Andrés Barrado & Alberto Martín-Lozano & Isabel Quesada, 2019. "Step-by-Step Small-Signal Modeling and Control of a Light Hybrid Electric Vehicle Propulsion System," Energies, MDPI, vol. 12(21), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4082-:d:280451
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/21/4082/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/21/4082/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wei, Zhen & Xu, John & Halim, Dunant, 2017. "HEV power management control strategy for urban driving," Applied Energy, Elsevier, vol. 194(C), pages 705-714.
    2. Guo, Qingbo & Zhang, Chengming & Li, Liyi & Gerada, David & Zhang, Jiangpeng & Wang, Mingyi, 2017. "Design and implementation of a loss optimization control for electric vehicle in-wheel permanent-magnet synchronous motor direct drive system," Applied Energy, Elsevier, vol. 204(C), pages 1317-1332.
    3. Feroldi, Diego & Carignano, Mauro, 2016. "Sizing for fuel cell/supercapacitor hybrid vehicles based on stochastic driving cycles," Applied Energy, Elsevier, vol. 183(C), pages 645-658.
    4. Carmen Raga & Andres Barrado & Henry Miniguano & Antonio Lazaro & Isabel Quesada & Alberto Martin-Lozano, 2018. "Analysis and Sizing of Power Distribution Architectures Applied to Fuel Cell Based Vehicles," Energies, MDPI, vol. 11(10), pages 1-30, 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. Md Ragib Ahssan & Mehran Ektesabi & Saman Gorji, 2020. "Gear Ratio Optimization along with a Novel Gearshift Scheduling Strategy for a Two-Speed Transmission System in Electric Vehicle," Energies, MDPI, vol. 13(19), pages 1-24, September.

    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. Carmen Raga & Andres Barrado & Antonio Lazaro & Alberto Martin-Lozano & Isabel Quesada & Pablo Zumel, 2018. "Influence of the Main Design Factors on the Optimal Fuel Cell-Based Powertrain Sizing," Energies, MDPI, vol. 11(11), pages 1-22, November.
    2. Zhang, Chaoyu & Zhang, Chengming & Li, Liyi & Guo, Qingbo, 2021. "Parameter analysis of power system for solar-powered unmanned aerial vehicle," Applied Energy, Elsevier, vol. 295(C).
    3. Haris, Muhammad & Hasan, Muhammad Noman & Qin, Shiyin, 2021. "Early and robust remaining useful life prediction of supercapacitors using BOHB optimized Deep Belief Network," Applied Energy, Elsevier, vol. 286(C).
    4. Kurnia, Jundika C. & Sasmito, Agus P. & Shamim, Tariq, 2017. "Performance evaluation of a PEM fuel cell stack with variable inlet flows under simulated driving cycle conditions," Applied Energy, Elsevier, vol. 206(C), pages 751-764.
    5. Andrzej Łebkowski, 2018. "Design, Analysis of the Location and Materials of Neodymium Magnets on the Torque and Power of In-Wheel External Rotor PMSM for Electric Vehicles," Energies, MDPI, vol. 11(9), pages 1-23, August.
    6. Desantes, J.M. & Novella, R. & Pla, B. & Lopez-Juarez, M., 2021. "Impact of fuel cell range extender powertrain design on greenhouse gases and NOX emissions in automotive applications," Applied Energy, Elsevier, vol. 302(C).
    7. He, Hongwen & Wang, Yunlong & Han, Ruoyan & Han, Mo & Bai, Yunfei & Liu, Qingwu, 2021. "An improved MPC-based energy management strategy for hybrid vehicles using V2V and V2I communications," Energy, Elsevier, vol. 225(C).
    8. Kim, Joonsuk & Chun, Kwang Min & Song, Soonho & Baek, Hong-Kil & Lee, Seung Woo, 2018. "Hydrogen effects on the combustion stability, performance and emissions of a turbo gasoline direct injection engine in various air/fuel ratios," Applied Energy, Elsevier, vol. 228(C), pages 1353-1361.
    9. Piotr Dukalski & Roman Krok, 2021. "Selected Aspects of Decreasing Weight of Motor Dedicated to Wheel Hub Assembly by Increasing Number of Magnetic Poles," Energies, MDPI, vol. 14(4), pages 1-27, February.
    10. Li, Guidan & Yang, Zhe & Li, Bin & Bi, Huakun, 2019. "Power allocation smoothing strategy for hybrid energy storage system based on Markov decision process," Applied Energy, Elsevier, vol. 241(C), pages 152-163.
    11. Zhichao Zhao & Lu Li & Yang Ou & Yi Wang & Shaoyang Wang & Jing Yu & Renhua Feng, 2023. "A Comparative Study on the Energy Flow of Electric Vehicle Batteries among Different Environmental Temperatures," Energies, MDPI, vol. 16(14), pages 1-15, July.
    12. Abdeldjalil Djouahi & Belkhir Negrou & Boubakeur Rouabah & Abdelbasset Mahboub & Mohamed Mahmoud Samy, 2023. "Optimal Sizing of Battery and Super-Capacitor Based on the MOPSO Technique via a New FC-HEV Application," Energies, MDPI, vol. 16(9), pages 1-18, May.
    13. Yang Gao & Changhong Liu & Yuan Liang & Sadegh Kouhestani Hamed & Fuwei Wang & Bo Bi, 2022. "Minimizing Energy Consumption and Powertrain Cost of Fuel Cell Hybrid Vehicles with Consideration of Different Driving Cycles and SOC Ranges," Energies, MDPI, vol. 15(17), pages 1-12, August.
    14. Zhu, Xiaoyong & Fan, Deyang & Xiang, Zixuan & Quan, Li & Hua, Wei & Cheng, Ming, 2019. "Systematic multi-level optimization design and dynamic control of less-rare-earth hybrid permanent magnet motor for all-climatic electric vehicles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    15. Guo, Peng & Li, Yongjian & Lin, Zhiwei & Li, Yating & Su, Peng, 2023. "Characterization and calculation of losses in soft magnetic composites for motors with SVPWM excitation," Applied Energy, Elsevier, vol. 349(C).
    16. Kong, Yan & Xu, Nan & Zhang, Yuanjian & Sui, Yan & Ju, Hao & Liu, Heng & Xu, Zhe, 2021. "Acquisition of full-factor trip information for global optimization energy management in multi-energy source vehicles and the measure of the amount of information to be transmitted," Energy, Elsevier, vol. 236(C).
    17. Cha, Kyoung-Soo & Kim, Dong-Min & Jung, Young-Hoon & Lim, Myung-Seop, 2020. "Wound field synchronous motor with hybrid circuit for neighborhood electric vehicle traction improving fuel economy," Applied Energy, Elsevier, vol. 263(C).
    18. Alessandro Serpi & Mario Porru, 2019. "Modelling and Design of Real-Time Energy Management Systems for Fuel Cell/Battery Electric Vehicles," Energies, MDPI, vol. 12(22), pages 1-21, November.
    19. Zhu, Jianyun & Chen, Li & Wang, Xuefeng & Yu, Long, 2020. "Bi-level optimal sizing and energy management of hybrid electric propulsion systems," Applied Energy, Elsevier, vol. 260(C).
    20. Molina, S. & Novella, R. & Pla, B. & Lopez-Juarez, M., 2021. "Optimization and sizing of a fuel cell range extender vehicle for passenger car applications in driving cycle conditions," Applied Energy, Elsevier, vol. 285(C).

    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:12:y:2019:i:21:p:4082-:d:280451. 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.