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Fuzzy Logic Control for Motor Drive Performance Improvement in EV Applications

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Intelligent Control and Smart Energy Management

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 181))

Abstract

Automatic control of electric vehicles (EVs) is challenging due to the presence of system parameter uncertainty and large variations of resistant load. On the other hand, human drivers, without any knowledge of vehicle dynamic model and control, can properly deal with these challenges, thanks to the experiences acquired via training and practice. As a consequence, human expertise-based intelligent controllers are of interest for EVs, in which fuzzy logic controller (FLC) is a promising candidate considering its model-free essence with soft-computing techniques offering flexibility and robustness to the control system. This chapter proposes an FLC for speed control of AC electrical motors including induction motor (IM) and interior permanent magnet (IPM) synchronous motor in EV applications. The FLC membership functions and rules are designed with value normalization that allows the developed controller able to be flexible when applied to a wide range of speed control applications. The proposed FLC is numerically validated via an EV model with system parameters based on a practical off-road vehicle platform of our laboratory. Critical testing scenarios are employed including vehicle mass variations and rolling resistance force due to different load-carrying and road conditions. The results reveal that regardless of these uncertainty and load fluctuations, the speed error is kept within a bound of 2.5% comparing to the nominal speed of 40 km/h. The merit and flexibility of the proposed FLC have been discussed in comparison with the traditional PI controller and also with simulation on other platforms, i-MiEV of Mitsubishi in our lab. Moreover, thanks to its normalized design, the proposed FLC is not limited to the studied EVs, but can be applied to other e-mobility systems.

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Acknowledgements

We would like to express our sincerest gratitude to Dr. Bảo-Huy Nguyê~n, postdoctoral researcher at e-TESC Lab., Department of Electrical and Computer Engineering, University of Sherbrooke, Canada, for his time contributing the simulations in Sects. 6.2 and 7.2. We also express our deepest thanks to Prof. João Pedro F. Trovão at e-TESC Lab., Department of Electrical and Computer Engineering, University of Sherbrooke, Sherbrooke, QC, J1K 2R1, Canada, for his discussion and his great support of the electric vehicle model used in this study.

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Authors and Affiliations

  1. e-TESC Lab., University of Sherbrooke, Sherbrooke, QC, Canada

    Minh C. Ta

  2. CTI Lab. for EVs, School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Hanoi, Vietnam

    Minh C. Ta & Thanh Vo-Duy

  3. Department of Advanced Science and Technology, Toyota Technological Institute, Nagoya, Japan

    Binh-Minh Nguyen

Authors
  1. Minh C. Ta
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  2. Binh-Minh Nguyen
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  3. Thanh Vo-Duy
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Corresponding author

Correspondence to Minh C. Ta .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, Université de Sherbrooke, Sherbrooke, QC, Canada

    Maude Josée Blondin

  2. Department of Electrical and Computer Engineering, Université de Sherbrooke, Sherbrooke, QC, Canada

    João Pedro Fernandes Trovão

  3. Department of Electronics, Carleton University, Ottawa, ON, Canada

    Hicham Chaoui

  4. Department of Industrial & Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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Ta, M.C., Nguyen, BM., Vo-Duy, T. (2022). Fuzzy Logic Control for Motor Drive Performance Improvement in EV Applications. In: Blondin, M.J., Fernandes Trovão, J.P., Chaoui, H., Pardalos, P.M. (eds) Intelligent Control and Smart Energy Management. Springer Optimization and Its Applications, vol 181. Springer, Cham. https://doi.org/10.1007/978-3-030-84474-5_13

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