IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i12p2051-d121551.html
   My bibliography  Save this article

An Improved Continuous-Time Model Predictive Control of Permanent Magnetic Synchronous Motors for a Wide-Speed Range

Author

Listed:
  • Dandan Su

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicle in Beijing, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China)

  • Chengning Zhang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicle in Beijing, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China)

  • Yugang Dong

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicle in Beijing, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China)

Abstract

This paper proposes an improved continuous-time model predictive control (CTMPC) of permanent magnetic synchronous motors (PMSMs) for a wide-speed range, including the constant torque region and the flux-weakening (FW) region. In the constant torque region, the mathematic models of PMSMs in dq-axes are decoupled without the limitation of DC-link voltage. However, in the FW region, the mathematic models of PMSMs in dq-axes are cross-coupled together with the limitation of DC-link voltage. A nonlinear PMSMs mathematic model in the FW region is presented based on the voltage angle. The solving of the nonlinear mathematic model of PMSMs in FW region will lead to heavy computation load for digital signal processing (DSP). To overcome such a problem, a linearization method of the voltage angle is also proposed to reduce the computation load. The selection of transiting points between the constant torque region and FW regions is researched to improve the performance of the driven system. Compared with the proportional integral (PI) controller, the proposed CTMPC has obvious advantages in dealing with systems’ nonlinear constraints and improving system performance by restraining overshoot current under step torque changing. Both simulation and experimental results confirm the effectiveness of the proposed method in achieving good steady-state performance and smooth switching between the constant torque and FW regions.

Suggested Citation

  • Dandan Su & Chengning Zhang & Yugang Dong, 2017. "An Improved Continuous-Time Model Predictive Control of Permanent Magnetic Synchronous Motors for a Wide-Speed Range," Energies, MDPI, vol. 10(12), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2051-:d:121551
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/12/2051/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/12/2051/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiong, Rui & Cao, Jiayi & Yu, Quanqing, 2018. "Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 211(C), pages 538-548.
    2. Xiong, Rui & Yu, Quanqing & Wang, Le Yi & Lin, Cheng, 2017. "A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter," Applied Energy, Elsevier, vol. 207(C), pages 346-353.
    3. Xiong, Rui & Tian, Jinpeng & Mu, Hao & Wang, Chun, 2017. "A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries," Applied Energy, Elsevier, vol. 207(C), pages 372-383.
    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. Rui Xiong & Suleiman M. Sharkh & Xi Zhang, 2018. "Research Progress on Electric and Intelligent Vehicles," Energies, MDPI, vol. 11(7), pages 1-5, July.
    2. Kai Zhou & Min Ai & Yancheng Sun & Xiaogang Wu & Ran Li, 2019. "PMSM Vector Control Strategy Based on Active Disturbance Rejection Controller," Energies, MDPI, vol. 12(20), pages 1-19, October.
    3. Qi Wang & Haitao Yu & Min Wang & Xinbo Qi, 2018. "A Novel Adaptive Neuro-Control Approach for Permanent Magnet Synchronous Motor Speed Control," Energies, MDPI, vol. 11(9), pages 1-21, September.
    4. Cheng-Kai Lin & Jen-te Yu & Hao-Qun Huang & Jyun-Ting Wang & Hsing-Cheng Yu & Yen-Shin Lai, 2018. "A Dual-Voltage-Vector Model-Free Predictive Current Controller for Synchronous Reluctance Motor Drive Systems," Energies, MDPI, vol. 11(7), pages 1-29, July.
    5. Chunyan Li & Fei Guo & Baoquan Kou & Tao Meng, 2021. "Research on the Non-Magnetic Conductor of a PMSM Based on the Principle of Variable Exciting Magnetic Reluctance," Energies, MDPI, vol. 14(2), pages 1-29, January.

    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. Xiong, Rui & Li, Linlin & Li, Zhirun & Yu, Quanqing & Mu, Hao, 2018. "An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 219(C), pages 264-275.
    2. Hong Zhang & Zhuang Xing & Jiajian Song & Qiangqiang Yang, 2018. "Development and Test Application of an Auxiliary Power-Integrated System," Energies, MDPI, vol. 11(1), pages 1-18, January.
    3. Mingchun Liu & Feihong Gu & Juhua Huang & Changjiang Wang & Ming Cao, 2017. "Integration Design and Optimization Control of a Dynamic Vibration Absorber for Electric Wheels with In-Wheel Motor," Energies, MDPI, vol. 10(12), pages 1-23, December.
    4. Xiaopeng Tang & Ke Yao & Boyang Liu & Wengui Hu & Furong Gao, 2018. "Long-Term Battery Voltage, Power, and Surface Temperature Prediction Using a Model-Based Extreme Learning Machine," Energies, MDPI, vol. 11(1), pages 1-16, January.
    5. Zepeng Gao & Sizhong Chen & Yuzhuang Zhao & Jinrui Nan, 2018. "Height Adjustment of Vehicles Based on a Static Equilibrium Position State Observation Algorithm," Energies, MDPI, vol. 11(2), pages 1-26, February.
    6. Bumin Meng & Yaonan Wang & Jianxu Mao & Jianwen Liu & Guochang Xu & Jian Dai, 2018. "Using SoC Online Correction Method Based on Parameter Identification to Optimize the Operation Range of NI-MH Battery for Electric Boat," Energies, MDPI, vol. 11(3), pages 1-20, March.
    7. Aijuan Li & Wanzhong Zhao & Xibo Wang & Xuyun Qiu, 2018. "ACT-R Cognitive Model Based Trajectory Planning Method Study for Electric Vehicle’s Active Obstacle Avoidance System," Energies, MDPI, vol. 11(1), pages 1-21, January.
    8. Chen, Zeyu & Xiong, Rui & Lu, Jiahuan & Li, Xinggang, 2018. "Temperature rise prediction of lithium-ion battery suffering external short circuit for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 213(C), pages 375-383.
    9. Mingjie Zhao & Junhui Shi & Cheng Lin & Junzhi Zhang, 2018. "Application-Oriented Optimal Shift Schedule Extraction for a Dual-Motor Electric Bus with Automated Manual Transmission," Energies, MDPI, vol. 11(2), pages 1-16, February.
    10. Peng Liu & Zhenyu Sun & Zhenpo Wang & Jin Zhang, 2018. "Entropy-Based Voltage Fault Diagnosis of Battery Systems for Electric Vehicles," Energies, MDPI, vol. 11(1), pages 1-15, January.
    11. Yang, Ruixin & Xiong, Rui & He, Hongwen & Mu, Hao & Wang, Chun, 2017. "A novel method on estimating the degradation and state of charge of lithium-ion batteries used for electrical vehicles," Applied Energy, Elsevier, vol. 207(C), pages 336-345.
    12. Xiaogang Wu & Wenwen Shi & Jiuyu Du, 2017. "Multi-Objective Optimal Charging Method for Lithium-Ion Batteries," Energies, MDPI, vol. 10(9), pages 1-18, August.
    13. Mu, Hao & Xiong, Rui & Zheng, Hongfei & Chang, Yuhua & Chen, Zeyu, 2017. "A novel fractional order model based state-of-charge estimation method for lithium-ion battery," Applied Energy, Elsevier, vol. 207(C), pages 384-393.
    14. Yan Bao & Yu Luo & Weige Zhang & Mei Huang & Le Yi Wang & Jiuchun Jiang, 2018. "A Bi-Level Optimization Approach to Charging Load Regulation of Electric Vehicle Fast Charging Stations Based on a Battery Energy Storage System," Energies, MDPI, vol. 11(1), pages 1-21, January.
    15. He, Hongwen & Guo, Xiaoguang, 2018. "Multi-objective optimization research on the start condition for a parallel hybrid electric vehicle," Applied Energy, Elsevier, vol. 227(C), pages 294-303.
    16. Ashleigh Townsend & Rupert Gouws, 2022. "A Comparative Review of Lead-Acid, Lithium-Ion and Ultra-Capacitor Technologies and Their Degradation Mechanisms," Energies, MDPI, vol. 15(13), pages 1-29, July.
    17. Ning Chen & Xu Zhao & Jiayao Chen & Xiaodong Xu & Peng Zhang & Weihua Gui, 2022. "Design of a Non-Linear Observer for SOC of Lithium-Ion Battery Based on Neural Network," Energies, MDPI, vol. 15(10), pages 1-26, May.
    18. Wang, Ju & Xiong, Rui & Li, Linlin & Fang, Yu, 2018. "A comparative analysis and validation for double-filters-based state of charge estimators using battery-in-the-loop approach," Applied Energy, Elsevier, vol. 229(C), pages 648-659.
    19. Li, Zhirun & Xiong, Rui & Mu, Hao & He, Hongwen & Wang, Chun, 2017. "A novel parameter and state-of-charge determining method of lithium-ion battery for electric vehicles," Applied Energy, Elsevier, vol. 207(C), pages 363-371.
    20. Xiong, Rui & Duan, Yanzhou & Cao, Jiayi & Yu, Quanqing, 2018. "Battery and ultracapacitor in-the-loop approach to validate a real-time power management method for an all-climate electric vehicle," Applied Energy, Elsevier, vol. 217(C), pages 153-165.

    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:12:p:2051-:d:121551. 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.