DOI: https://doi.org/10.14710/ijred.2021.18596

Movement of a Solar Electric Vehicle Controlled by ANN-based DTC in Hot Climate Regions

1Department of Renewable Energy and Hydrocarbure, African University Ahmed Draia - Adrar, Adrar, 01000,, Algeria

2Faculty of Sciences and Technology, University of Bechar, Algeria

3Laboratory of Smart Grid Renewable Energy (SGRE), Faculty of Technology, Department of Electrical Engineering, Bechar University, B.P. 417, 0800, Algeria

Received: 20 Apr 2018; Revised: 14 Sep 2020; Accepted: 3 Oct 2020; Available online: 6 Oct 2020; Published: 1 Feb 2021.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2021 The Authors. Published by CBIORE
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
Vehicle autonomy presents the most complex problem for modern commercialized solar electric vehicle (SEV) propulsion systems. The power supplied by electric vehicles’ batteries is limited by the state of charge, the type of battery, and its level of technological development. This study’s aim was to resolve the problem of energy variation at several velocities and under different road topology conditions. Several works related to the use of fuzzy logic confirm that classical regulators have such advantages over fuzzy regulators as short processing times and mathematical precision. Therefore, the hybrid power source is presented as the best solution for energy management, and it is composed of a solar panel (PV) and a nickel metal hydride battery. The PV system is connected to the SEV via a boost converter that is controlled using the maximum power point tracking technique. In this paper, we used an intelligent PI regulator for direct torque control, which introduced a certain degree of intelligence into the regulation strategy. Indeed, this approach of associating the PI regulator with the fuzzy rules-composed supervisor allowed us to take advantage of both the PI’s mathematical precision and the adaptability, flexibility, and simplicity of fuzzy linguistic formalism. Because of its dynamic capabilities, an adaptive PI regulator was substituted to achieve high speeds and a satisfactorily vigorous performance while quickly compensating for the disturbances that were expected to possibly take place on the regulation chain. The present study’s results confirm that the proposed control approach increased the utility of SEV autonomy under several speed variations. Moreover, the industry’s future offerings must take the option of hybrid power management into consideration during this type of vehicle’s manufacturing phase
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Keywords: Solar Photovoltaic (PV); MMPT; Artificial Neural network; Buck Boost; DC-DC converter

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Section: Original Research Article
Language : EN
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  2. Ammari, C., Hamouda, M., & Makhloufi, S. (2017). Sizing and optimization for hybrid central in South Algeria based on three different generators. International Journal of Renewable Energy Development, 6(3), 263-272. doi: 10.14710/ijred.6.3.263-272
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  4. Brahim, G., & Abdelfatah, N. (2012). A novel 4WD electric vehicle control strategy based on direct torque control space vector modulation technique. Intelligent Control and Automation, 2012
  5. Chiu, H. J., & Lin, L. W. (2006). A bidirectional DC–DC converter for fuel cell electric vehicle driving system. IEEE Transactions on Power Electronics, 21(4), 950-958; doi: 10.1109/TPEL.2006.876863
  6. Devi, S. R., Arulmozhivarman, P., Venkatesh, C., & Agarwal, P. (2016). Performance comparison of artificial neural network models for daily rainfall prediction. International Journal of Automation and computing, 13(5), 417-427; https://doi.org/10.1007/s11633-016-0986-2
  7. Gao, M., & He, S. (2008, October). Self-adapting fuzzy-PID control of variable universe in the non-linear system. In 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA) (Vol. 1, pp. 473-478). IEEE
  8. Gasbaoui, B., Nasri, A., Laoufi, A., & Mouloudi, Y. (2013). 4 WD Urban Electric Vehicle Motion Studies Based on MIMO Fuzzy Logic Speed Controller. International Journal of Control and Automation, 6(1), 105-118
  9. Javed, K., Ashfaq, H., & Singh, R. (2018). An Improved MPPT Algorithm to Minimize Transient and Steady State Oscillation Conditions for Small SPV Systems. International Journal of Renewable Energy Development, 7(3). doi: 10.14710/ijred.7.3.191-197
  10. Kumar, C. H., & Rao, R. S. (2016). A novel global MPP tracking of photovoltaic system based on whale optimization algorithm. International Journal of Renewable Energy Development, 5(3); doi: 10.14710/ijred.5.3.225-232
  11. Mebarki, B., Allaoua, B., Draoui, B., & Belatrache, D. (2017). Study of the energy performance of a PEM fuel cell vehicle. International Journal of Renewable Energy Research (IJRER), 7(3), 1395-1402
  12. Moreno, J., Dixon, J., & Ortuzar, M. (2006). “Energy management system for an electric vehicle, using ultra capacitors and neural networks. IEEE Transactions on Industrial Electronics, 53(2), 614-623
  13. Ouledali, O., Meroufel, A., Wira, P., & Bentouba, S. (2019). Genetic Algorithm Tuned PI Controller on PMSM Direct Torque Control. Algerian Journal of Renewable Energy and Sustainable Development, 1(2), 204-211. doi.org/10.46657/ajresd.2019.1.2.10
  14. Prelas, M. A. (2009). Energy Resources and Systems: Volume 1: Fundamentals and Non-Renewable Resources. Springer Netherlands
  15. Reddy, Y. S., Vijayakumar, M., & Reddy, T. B. (2007). Direct torque control of induction motor using sophisticated lookup tables based on neural networks. AIML Journal, 7(1), 9-15
  16. Sasikumar, S., & Harinandan, L. (2014). Automatic power management and monitoring system for electric vehicles. Int. J. Technol. Enhancem. Emerging Eng. Res, 2(4), 134-137
  17. Shen, J. M., Jou, H. L., Wu, J. C., & Wu, K. D. (2014). Single-phase three-wire grid-connected power converter with energy storage for positive grounding photovoltaic generation system. International Journal of Electrical Power & Energy Systems, 54, 134-143; https://doi.org/10.1016/j.ijepes.2013.07.002
  18. Tahiri, F., Bekraoui, F., Boussaid, I., Ouledali, O., & HARROUZ, A. (2019). Direct Torque Control (DTC) SVM Predictive of a PMSM Powered by a photovoltaic source. Algerian Journal of Renewable Energy and Sustainable Development, 1(01), 1-7; https://doi.org/10.46657/ajresd.2019.1.1.1
  19. Yang, Y. P., & Lo, C. P. (2008). Current distribution control of dual directly driven wheel motors for electric vehicles. Control Engineering Practice, 16(11), 1285-1292; https://doi.org/10.1016/j.conengprac.2008.02.005
  20. Zhang, Q., & Yin, Y. (2003). Analysis and evaluation of bidirectional DC/DC converter. Journal of Power Technology, 1(4), 331-338;

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