FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-130317

Research Article |

Adaptive Speed Control of BLDC Motors Based on Fuzzy Inference System Using a PWM Strategy for Electric Vehicles

Author(s): Bambang Sri Kaloko1*,Abdul Kharis Ismail1,Widyono Hadi1,Gamma Aditya Rahardi1,Dani Hari Tunggal Prasetiyo2

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 13, Issue 2

Publisher : FOREX Publication

Published : 30 September 2025

e-ISSN : 2347-470X

Page(s) : 524-535

DOI: https://doi.org/10.37391/IJEER.130317

Abstract

Along with the development of increasingly advanced technology, innovations continue to develop, one of which is in the field of transportation. In line with the rapid advancement of technology, electric vehicles (EVs) have gained significant attention due to their environmental and performance advantages. Among the EV components, the Brushless Direct Current (BLDC) motor stands out due to its high efficiency and minimal maintenance. This paper proposes a speed control system for BLDC motors based on the Fuzzy Inference System (FIS). The system was evaluated through acceleration, deceleration, and energy efficiency tests over a 2400-meter track. Results indicate that the FIS-based controller achieved smoother speed transitions and higher energy efficiency (380.95 km/kWh) compared to open-loop control (358.2 km/kWh). These findings suggest that FIS can enhance the performance and reliability of electric vehicle drivetrains.

Keywords: Electrical Technology, Brushless Direct Current, Fuzzy Inference System, Energy, Efficiency.




Bambang Sri Kaloko,Electrical Engineering, University of Jember, Jember, Indonesia; Email: kaloko@unej.ac.id

Abdul Kharis Ismail, Electrical Engineering, University of Jember, Jember, Indonesia; Email: abdulkharisismail97@gmail.com

Widyono Hadi, Electrical Engineering, University of Jember, Jember, Indonesia; Email: widyono@unej.ac.id

Gamma Aditya Rahardi, Electrical Engineering, University of Jember, Jember, Indonesia; Email: gamma.rahardi@unej.ac.id

Dani Hari Tunggal Prasetiyo, Department of Mechanical Engineering, University of Jember, Jember, Indonesia; Email: dani.hari@unej.ac.id

    [1] A. Kerem, “Torque estimation of electric vehicle motor using adaptive- network based fuzzy inference systems,” 2021. doi: 10.18245/ijaet.879754.
    [2] A. L. Shuraiji and S. W. Shneen, “Fuzzy Logic Control and PID Controller for Brushless Permanent Magnetic Direct Current Motor: A Comparative Study,” J. Robot. Control, vol. 3, no. 6, pp. 762–768, 2022, doi: 10.18196/jrc.v3i6.15974.
    [3] R. Shenbagalakshmi, S. K. Mittal, J. Subramaniyan, V. Vengatesan, D. Manikandan, and K. Ramaswamy, “Adaptive speed control of BLDC motors for enhanced electric vehicle performance using fuzzy logic,” Sci. Rep., vol. 15, no. 1, pp. 1–22, 2025, doi: 10.1038/s41598-025-90957-6.
    [4] M. A. Awadallah, M. M. Morcos, S. Gopalakrishnan, and T. W. Nehl, “A neuro-fuzzy approach to automatic diagnosis and location of stator inter-turn faults in CSI-fed PM brushless DC motors,” IEEE Trans. Energy Convers., vol. 20, no. 2, pp. 253–259, 2005, doi: 10.1109/TEC.2005.847976.
    [5] K. Premkumar and B. V. Manikandan, “Adaptive Neuro-Fuzzy Inference System based speed controller for brushless DC motor,” Neurocomputing, vol. 138, pp. 260–270, 2014, doi: 10.1016/j.neucom.2014.01.038.
    [6] D. Tian and Z. Huang, “Design of Brushless Direct Current Motor Control System Based on DSP,” IOP Conf. Ser. Earth Environ. Sci., vol. 186, no. 5, 2018, doi: 10.1088/1755-1315/186/5/012048.
    [7] K. Kamalapathi et al., “A hybrid moth-flame fuzzy logic controller based integrated cuk converter fed brushless DC motor for power factor correction,” Electron., vol. 7, no. 11, 2018, doi: 10.3390/electronics7110288.
    [8] M. E. Ahmed Mohamed and Y. Guo, “Design of speed control for brushless DC motor used for electric vehicle based on adaptive neuro-fuzzy inference system,” IOP Conf. Ser. Mater. Sci. Eng., vol. 612, no. 4, 2019, doi: 10.1088/1757-899X/612/4/042066.
    [9] K. Prathibanandhi and R. Ramesh, “Hybrid control technique for minimizing the torque ripple of brushless direct current motor,” Meas. Control (United Kingdom), vol. 51, no. 7–8, pp. 321–335, 2018, doi: 10.1177/0020294018786753.
    [10] M. Subbarao, K. Dasari, S. S. Duvvuri, K. R. K. V. Prasad, B. K. Narendra, and V. B. Murali Krishna, “Design, control and performance comparison of PI and ANFIS controllers for BLDC motor driven electric vehicles,” Meas. Sensors, vol. 31, no. March 2023, p. 101001, 2024, doi: 10.1016/j.measen.2023.101001.
    [11] E. Thorin et al., “Quantitative real-time in situ measurement of gaseous K, KOH and KCl in a 140-kW entrained-flow biomass gasifier,” Proc. Combust. Inst., vol. 39, no. 1, pp. 1337–1345, 2023, doi: 10.1016/j.proci.2022.07.180.
    [12] M. A. A. Aziz et al., “A Review on BLDC Motor Application in Electric Vehicle (EV) using Battery, Supercapacitor and Hybrid Energy Storage System: Efficiency and Future Prospects,” J. Adv. Res. Appl. Sci. Eng. Technol., vol. 30, no. 2, pp. 41–59, 2023, doi: 10.37934/araset.30.2.4159.
    [13] D. Ksetiawan, P. Suprihadi, and B. S. Kaloko, “Field-oriented induction multi-machine drive using single-inverter and diagonal recurrent neural network,” J. Theor. Appl. Inf. Technol., vol. 95, no. 9, pp. 1958–1962, 2017.
    [14] N. Farhan, A. Humod, and F. Hasan, “Field Oriented Control of AFPMSM for Electrical Vehicle Using Adaptive Neuro-Fuzzy Inference System (ANFIS),” Eng. Technol. J., vol. 39, no. 10, pp. 1571–1582, 2021, doi: 10.30684/etj.v39i10.1969.
    [15] Y. I. M. Al Mashhadany, A. K. Abbas, and S. S. Algburi, “Modeling and analysis of brushless DC motor system based on intelligent controllers,” Bull. Electr. Eng. Informatics, vol. 11, no. 6, pp. 2995–3003, 2022, doi: 10.11591/eei.v11i6.4365.
    [16] A. H. Loka, T. Hardianto, and B. S. Kaloko, “Design and construction of control system SEPIC converter for solar panel based on fuzzy logic,” J. Theor. Appl. Inf. Technol., vol. 87, no. 2, pp. 308–315, 2016.
    [17] T. Wang, H. Wang, H. Hu, X. Lu, and S. Zhao, “An adaptive fuzzy PID controller for speed control of brushless direct current motor,” SN Appl. Sci., vol. 4, no. 3, 2022, doi: 10.1007/s42452-022-04957-6.
    [18] A. Varshney, D. Gupta, and B. Dwivedi, "Speed response of brushless DC motor using fuzzy PID controller under varying load condition," Journal of Electrical Systems and Information Technology, vol. 4, no. 2, pp. 310–321, 2017.
    [19] K. Kroiˇcs and A. Bumanis, “BLDC Motor Speed Control with Digital Adaptive PID-Fuzzy Controller and Reduced Harmonic Content,” Energies, vol. 17, no. 3–15, 2024.
    [20] S. R. Begam, L. R. Burthi, and S. R. Depuru, “Regenerative Braking in Electric Vehicles using BLDC motor with Modified Torque and Adaptive-Neuro-Fuzzy-Control,” Prz. Elektrotechniczny, vol. 2024, no. 3, pp. 184–190, 2024, doi: 10.15199/48.2024.03.33.
    [21] K. Premkumar and B. V. Manikandan, “Speed control of Brushless DC motor using bat algorithm optimized Adaptive Neuro-Fuzzy Inference System,” Appl. Soft Comput. J., vol. 32, pp. 403–419, 2015, doi: 10.1016/j.asoc.2015.04.014.
    [22] A. A. Sarhan and A. T. Hafez, “UAV brushless DC motor speed control via adaptive neuro fuzzy inference systems (ANFIS) and self-adaptive PID,” AIAA Scitech 2019 Forum, no. January 2019, 2019, doi: 10.2514/6.2019-1563.
    [23] M. G. Lopez, P. Ponce, L. A. Soriano, A. Molina, and J. J. R. Rivas, “A Novel Fuzzy-PSO Controller for Increasing the Lifetime in Power Electronics Stage for Brushless DC Drives,” IEEE Access, vol. 7, pp. 47841–47855, 2019, doi: 10.1109/ACCESS.2019.2909845.
    [24] A. Intidam et al., “Development and Experimental Implementation of Optimized PI-ANFIS Controller for Speed Control of a Brushless DC Motor in Fuel Cell Electric Vehicles,” Energies, vol. 16, no. 11, 2023, doi: 10.3390/en16114395.
    [25] W. S. Chai, Y. Bao, P. Jin, G. Tang, and L. Zhou, “A review on ammonia, ammonia-hydrogen and ammonia-methane fuels,” Renew. Sustain. Energy Rev., vol. 147, no. May, p. 111254, 2021, doi: 10.1016/j.rser.2021.111254.
    [26] H. R. Jayetileke, W. R. De Mel, and H. U. W. Ratnayake, “Modelling and simulation analysis of the genetic-fuzzy controller for speed regulation of a sensored BLDC motor using MATLAB/SIMULINK,” 2017 IEEE Int. Conf. Ind. Inf. Syst. ICIIS 2017 - Proc., vol. 2018-January, pp. 1–6, 2017, doi: 10.1109/ICIINFS.2017.8300340.
    [27] G. Kaczmarczyk, M. Malarczyk, D. D. Ferreira, and M. Kaminski, “Stable Rules Definition for Fuzzy TS Speed Controller Implemented for BLDC Motor,” Appl. Sci., vol. 14, no. 3, 2024, doi: 10.3390/app14030982.
    [28] I. Anshory, I. Robandi, and M. Ohki, “System Identification of BLDC Motor and Optimization Speed Control Using Artificial Intelligent,” Int. J. Civ. Eng. Technol., vol. 10, no. 7, pp. 1–13, 2019.
    [29] M. R. Ishaque et al., “Fuzzy logic-based duty cycle controller for the energy management system of hybrid electric vehicles with hybrid energy storage system,” Appl. Sci., vol. 11, no. 7, 2021, doi: 10.3390/app11073192.
    [30] C. Krishna Rao, S. K. Sahoo, and F. F. Yanine, “IoT enabled Intelligent Energy Management System employing advanced forecasting algorithms and load optimization strategies to enhance renewable energy generation,” Unconv. Resour., vol. 4, no. August, p. 100101, 2024, doi: 10.1016/j.uncres.2024.100101.

Bambang Sri Kaloko, Abdul Kharis Ismail, Widyono Hadi, Gamma Aditya Rahardi, Dani Hari Tunggal Prasetiyo(2025),Adaptive Speed Control of BLDC Motors Based on Fuzzy Inference System Using a PWM Strategy for Electric Vehicles. IJEER 13(3), 524-535. DOI: 10.37391/IJEER.130317.