IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i16p4212-d399090.html
   My bibliography  Save this article

Real-Time Processor-in-Loop Investigation of a Modified Non-Linear State Observer Using Sliding Modes for Speed Sensorless Induction Motor Drive in Electric Vehicles

Author

Listed:
  • Mohan Krishna Srinivasan

    (Department of Electrical and Electronics Engineering, Alliance College of Engineering and Design, Alliance University, Bangalore 562 106, India)

  • Febin Daya John Lionel

    (SELECT, Vellore Institute of Technology, Chennai 600127, India)

  • Umashankar Subramaniam

    (Renewable Energy Lab, College of Engineering, Prince Sultan University, Riyadh 11586, Saudi Arabia)

  • Frede Blaabjerg

    (CORPE, Department of Energy Technology, Aalborg University, 9000 Aalborg, Denmark)

  • Rajvikram Madurai Elavarasan

    (Electrical and Automotive parts Manufacturing unit, AA Industries, Chennai 600123, India)

  • G. M. Shafiullah

    (Discipline of Engineering and Energy, Murdoch University, Murdoch 6150, Australia)

  • Irfan Khan

    (Marine Engineering Technology Department in a joint appointment with Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA)

  • Sanjeevikumar Padmanaban

    (Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark)

Abstract

Tracking performance and stability play a major role in observer design for speed estimation purpose in motor drives used in vehicles. It is all the more prevalent at lower speed ranges. There was a need to have a tradeoff between these parameters ensuring the speed bandwidth remains as wide as possible. This work demonstrates an improved static and dynamic performance of a sliding mode state observer used for speed sensorless 3 phase induction motor drive employed in electric vehicles (EVs). The estimated torque is treated as a model disturbance and integrated into the state observer while the error is constrained in the sliding hyperplane. Two state observers with different disturbance handling mechanisms have been designed. Depending on, how they reject disturbances, based on their structure, their performance is studied and analyzed with respect to speed bandwidth, tracking and disturbance handling capability. The proposed observer with superior disturbance handling capabilities is able to provide a wider speed range, which is a main issue in EV. Here, a new dimension of model based design strategy is employed namely the Processor-in-Loop. The concept is validated in a real-time model based design test bench powered by RT-lab. The plant and the controller are built in a Simulink environment and made compatible with real-time blocksets and the system is executed in real-time targets OP4500/OP5600 (Opal-RT). Additionally, the Processor-in-Loop hardware verification is performed by using two adapters, which are used to loop-back analog and digital input and outputs. It is done to include a real-world signal routing between the plant and the controller thereby, ensuring a real-time interaction between the plant and the controller. Results validated portray better disturbance handling, steady state and a dynamic tracking profile, higher speed bandwidth and lesser torque pulsations compared to the conventional observer.

Suggested Citation

  • Mohan Krishna Srinivasan & Febin Daya John Lionel & Umashankar Subramaniam & Frede Blaabjerg & Rajvikram Madurai Elavarasan & G. M. Shafiullah & Irfan Khan & Sanjeevikumar Padmanaban, 2020. "Real-Time Processor-in-Loop Investigation of a Modified Non-Linear State Observer Using Sliding Modes for Speed Sensorless Induction Motor Drive in Electric Vehicles," Energies, MDPI, vol. 13(16), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:16:p:4212-:d:399090
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/16/4212/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/16/4212/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jing Tang & Yongheng Yang & Frede Blaabjerg & Jie Chen & Lijun Diao & Zhigang Liu, 2018. "Parameter Identification of Inverter-Fed Induction Motors: A Review," Energies, MDPI, vol. 11(9), pages 1-21, August.
    2. Mohan Krishna S. & Febin Daya J.L. & Sanjeevikumar Padmanaban & Lucian Mihet-Popa, 2017. "Real-Time Analysis of a Modified State Observer for Sensorless Induction Motor Drive Used in Electric Vehicle Applications," Energies, MDPI, vol. 10(8), pages 1-23, 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. Carmenza Moreno Roa & Adolfo Andrés Jaramillo Matta & Juan David Bastidas Rodríguez, 2020. "Stochastic Search Technique with Variable Deterministic Constraints for the Estimation of Induction Motor Parameters," Energies, MDPI, vol. 13(1), pages 1-21, January.
    2. S. Usha & C. Subramani & Sanjeevikumar Padmanaban, 2019. "Neural Network-Based Model Reference Adaptive System for Torque Ripple Reduction in Sensorless Poly Phase Induction Motor Drive," Energies, MDPI, vol. 12(5), pages 1-25, March.
    3. Eklas Hossain & Ron Perez & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Frede Blaabjerg & Vigna K. Ramachandaramurthy, 2017. "Sliding Mode Controller and Lyapunov Redesign Controller to Improve Microgrid Stability: A Comparative Analysis with CPL Power Variation," Energies, MDPI, vol. 10(12), pages 1-24, November.
    4. Kang Miao Tan & Vigna K. Ramachandaramurthy & Jia Ying Yong & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Frede Blaabjerg, 2017. "Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling," Energies, MDPI, vol. 10(11), pages 1-21, November.
    5. Fuad Un-Noor & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Mohammad Nurunnabi Mollah & Eklas Hossain, 2017. "A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development," Energies, MDPI, vol. 10(8), pages 1-84, August.
    6. Mladen Vučković & Vladimir Popović & Djura Oros & Veran Vasić & Darko Marčetić, 2021. "Low Voltage Induction Motor Traction Drive Self-Commissioning Technique with the Advanced Measured Signal Processing Procedure," Energies, MDPI, vol. 14(6), pages 1-18, March.
    7. Sergio Saponara & Lucian Mihet-Popa, 2019. "Energy Storage Systems and Power Conversion Electronics for E-Transportation and Smart Grid," Energies, MDPI, vol. 12(4), pages 1-9, February.
    8. Martin Ćalasan & Mihailo Micev & Ziad M. Ali & Ahmed F. Zobaa & Shady H. E. Abdel Aleem, 2020. "Parameter Estimation of Induction Machine Single-Cage and Double-Cage Models Using a Hybrid Simulated Annealing–Evaporation Rate Water Cycle Algorithm," Mathematics, MDPI, vol. 8(6), pages 1-29, June.
    9. Ondrej Lipcak & Filip Baum & Jan Bauer, 2021. "Influence of Selected Non-Ideal Aspects on Active and Reactive Power MRAS for Stator and Rotor Resistance Estimation," Energies, MDPI, vol. 14(20), pages 1-19, 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:13:y:2020:i:16:p:4212-:d:399090. 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.