Abstract
Electric Vehicles (EVs) are complex electromechanical systems described by nonlinear models and therefore, their control design and analysis is not an easy task. Double-Squirrel Cage Induction Motor (DSCIM) has the advantages of driving complex loads which require high starting torque and low starting current. This paper presents a speed control comparison between the PI controller and the advanced techniques of control based on the Artificial Neural Networks (ANN) and Fuzzy Logic (FL) in order to be applied for an electric vehicle motorization. The simulation results are numerically validated by using the MATLAB/Simulink universe; they highlight the robustness properties of the different control strategies based on field-oriented control technique.
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References
Makrygiorgou, J. J., & Alexandridis, A. T. (2017). Fuzzy logic control of electric vehicles: Design and analysis concepts. In: 2017 twelfth international conference on ecological vehicles and renewable energies (EVER) (pp. 1–6). IEEE.
Arancibia, A., & Strunz, K. (March 2012). Modeling of an electric vehicle charging station for fast dc charging. In: The IEEE proceedings on industrial electronics vehicle conference (IEVC) (pp. 1–6).
Mediouni, H., El hani, S., & Ouachtouk, I. et al. (2017). Artificial neural networks applied on double squirrel cage induction motor for an electric vehicle motorisation. In: 2017 international conference on electrical and information technologies (ICEIT) (pp. 1–6). IEEE.
Antonino-Daviu, J., Riera-Guasp, M., Pons-Llinares, J., Park, J., Lee, S. B., Yoo, J., et al. (2012). Detection of broken outer-cage bars for double-cage induction motors under the startup transient. IEEE Transactions on Industry Applications, 48(5), 1539–1548.
Gritli, Y., Lee, S. B., Filippetti, F., & Zarri, L. (2014). Advanced diagnosis of outer cage damage in double-squirrel-cage induction motors under time-varying conditions based on wavelet analysis. IEEE Transactions on Industry Applications, 50(3), 1791–1800.
Levi, E. (1997). General method of magnetising flux saturation modelling in d–q axis models of double-cage induction machines. IEE Proceedings-Electric Power Applications, 144(2), 101–109.
Menghal, P. M., & Laxmi, A. J. (Sept 2013). Application of artificial intelligence controller for dynamic simulation of induction motor drives. Asian Power Electronics Journal, 7(1).
Chan, T. F., & Shi, K. (2011). Applied intelligent control of induction motor drives (1st edn.). IEEE Willey Press.
Menghal, P. M., & Laxmi, A. J. (Sept 2013). Application of artificial intelligence controller for dynamic simulation of induction motor drives. Asian Power Electronics Journal, 7(1).
Khammar, F., Debbache, N. E. (2016). Application of artificial intelligence techniques for the control of the asynchronous machine. Journal of Electrical and Computer Engineering, Article IDÂ 8052027, Algeria.
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Mediouni, H., Hani, S.E., Ouachtouk, I., Ouadghiri, M., Aboudrar, I. (2019). Application of Artificial Intelligence Techniques on Double-Squirrel Cage Induction Motor for an Electric Vehicle Motorization. In: El Hani, S., Essaaidi, M. (eds) Recent Advances in Electrical and Information Technologies for Sustainable Development. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-05276-8_16
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DOI: https://doi.org/10.1007/978-3-030-05276-8_16
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