Research Article
BibTex RIS Cite
Year 2023, Volume: 41 Issue: 1, 156 - 177, 14.03.2023

Abstract

References

  • [1] Tutelea L, Boldea I. Optimal design of residential brushless d.c. permanent magnet motors with FEM validation. 2007 International Aegean Conference on Electrical Machines and Power Electronics Electromotion (ACEMP’07); 2007 Sept 10-12; Bodrum Turkey: IEEE; 2007. pp. 435–439. [CrossRef]
  • [2] Zarko D, Ban D, Lipo TA. Analytical solution for cogging torque in surface permanent-magnet motors using conformal mapping. IEEE Trans Magn 2007;44:52–64. [CrossRef]
  • [3] Ustun O, Yilmaz M, Gokce C, Karakaya U, Tuncay RN. Energy management method for solar race car design and application. IEEE International Electric Machines and Drives Conference; 2009 May 03-06; Miami, FL: IEEE; 2009. pp. 804–811. [CrossRef]
  • [4] Markovic M, Hodder A, Perriard Y. An analyti-cal determination of the torque–speed and effi-ciency– speed characteristics of a BLDC motor. Energy Conversion Congress and Exposition; 2009 Sept 20-24; San Jose, CA: IEEE; 2009. pp. 168–172.[CrossRef]
  • [5] Zhao L, Ham C, Zheng L, Wu T, Sundaram K, Kapat J, et al. A highly efficient 200000 rpm per-manent magnet motor system. IEEE Trans Magn 2007;43:2528–2530. [CrossRef]
  • [6] Tuncay RN, Ustun O, Yılmaz M, Gokce C. Karakaya U. Design and implementation of an electric drive system for in-wheel motor electric vehicle appli-cations. 7th IEEE Vehicle Power and Propulsion Conference; 2011 Sept 06-09; Chicago, IL: IEEE; 2011. pp. 1–6. [CrossRef]
  • [7] Nair SS, Nalakath S, Dhinagar SJ. Design and analysis of axial flux permanent magnet BLDC motor for automotive applications. IEEE International Electric Machines & Drives Conference (IEMDC’11); 2011 May 15-18; Niagara Falls, Canada: IEEE; 2011. pp. 1615–1618. [CrossRef]
  • [8] Park SJ, Park HW, Lee MH, Harashima F. A new approach for minimum-torque-ripple maximum-efficiency control of BLDC motor. IEEE Trans Ind Electron 2000;47:109–114. [CrossRef]
  • [9] Zarko D, Ban D, Lipo TA. Analytical calculation of magnetic field distribution in the slotted air gap of a surface permanent-magnet motor using com-plex relative air-gap permeance. IEEE Trans Magn 2006;42:1828–1837.
  • [10] Rahim NA, Ping HW, Tadjuddin M. Design of axial flux permanent magnet brushless DC motor for direct drive of electric vehicle. IEEE Power Engineering Society General Meeting; 2007 Jun 24- 28; Tampa, USA: IEEE; 2007. 10290216. [CrossRef]
  • [11] Soong WL, Miller TJE. Field-weakening performance of brushless synchronous AC motor drives. IEE Proc Electr Power Appl 1994;141:331–340. [CrossRef]
  • [12] Wu B. Brushless DC Motor Speed Control. Master Thesis. Dep of Electrical & Computing Engineering, Ryerson University, October, 2001.
  • [13] Premkumar K, Manikandan BV. Bat algorithm opti-mized fuzzy PD based speed controller for brush-less direct current motor. Eng Sci Technol Int J 2016;19:818–840. [CrossRef]
  • [14] Chico A, Brillianto H, Maghfiroh H. Fuzzy logic controller and its application in brushless DC motor (BLDC) in electric vehicle. J Electrical Electronic Inform Commun Technol 2021;3:35−43. [CrossRef]
  • [15] Agrawal S, Vivek S. Particle swarm optimization of BLDC motor with fuzzy logic controller for speed improvement. 2017 8th International Conference on Computing, Communication and Networking Technologies; 2017 Jul 3-5; Delhi, India: IEEE; 2017. 17418417. [CrossRef]
  • [16] Wang H, Li P, Shu Y, Kang D. Double closed loop control for BLDC based on whole fuzzy con-trollers. 2nd IEEE International Conference on Computational Intelligence and Applications; 2017 Sept 08-11; Beijing, China: IEEE; 2017. 17415277. [CrossRef]
  • [17] Davoudkhani IF, Akbari M. Adaptive speed control of brushless DC (BLDC) motor based on interval type-2 logic. 24th Iranian Conference on Electrical Engineering (ICEE); 2016 May 10-12; Shiraz, Iran: IEEE; 2016. 16375523. [CrossRef]
  • [18] Wang ZS, Liu CY, Song XL, Song ZY, Yang ZK. Improved variable universe fuzzy PID application in brushless DC motor speed regulation system. Proceedings of the 2016 International Conference on Machine Learning and Cybernetics; 2016 Jul 10-13; Jeju, South Korea: IEEE; 2016. 16692057. [CrossRef]
  • [19] Hanselman DC. Brushless permanent magnet motor design. 1st ed. New York: McGraw Hill; 1994.
  • [20] Kenjo T, Nagomori S. Permanent Magnet and Brushless DC Motors. 1st ed. Oxford: Oxford University Press; 1985
  • [21] Indra F, Era P, Novie AW. Fuzzy gain scheduling of PID (FGSPID) for speed control three phase induc-tion motor based on indirect field oriented control (IFOC). EMITTER Int J Eng Technol 2016;4:237– 258. [CrossRef]
  • [22] Aydogdu O, Levent ML. Trajectory control of a variable loaded servo system by using fuzzy itera-tive learning PID control. Int J Robot Control Syst 2017;2:170–177.
  • [23] Xu C, Huang D, Huang Y, Gong S. Digital PID con-troller for brushless DC motor based on AVR micro-controller. 2008 IEEE International Conference on Mechatronics and Automation; 2008 Aug 05-08; Takamatsu: IEEE; 2008. pp. 247–252.
  • [24] Tzafestas S, Papanikolopulos NP. Incremental fuzzy expert PID control. IEEE Trans Ind Electron 1990;37:365–371. [CrossRef]
  • [25] Bahadır A. Brushless direct current motor driver design and adaptive control for electric vehicles. Doctoral Thesis. Konya, Turkey: Konya Technical University; 2021.
  • [26] Jaya A, Purwanto E, Fauziah MB, Murdianto FD, Prabowo G, Rusli MR. Design of PID-fuzzy for speed control of brushless DC motor in dynamic electric vehicle to improve steady-state perfor-mance. 2017 International Electronics Symposium on Engineering Technology and Applications (IES-ETA); 2017 Sept 26-27; Surabaya, Indonesia: IEEE; 2017. 17453328. [CrossRef]
  • [27] Aziza Z. ANN-Based Current Controlled BLDC Servo-Motor. 9th European Conference on Power Electronics and Applications, EPE 2001, Graz, 27-29 August, 2001.
  • [28] Bojadziev G, Bojadziev M. Fuzzy Sets, Fuzzy Logic, Applications. River Edge: Word Scientific Publishing; 2000.
  • [29] Vas Peter. Artificial-Intelligence-Based Electrical Machines and Drives. 1st ed. New York: Oxford University Press; 1999.

Modeling of a brushless dc motor driven electric vehicle and its pid-fuzzy control with dSPACE

Year 2023, Volume: 41 Issue: 1, 156 - 177, 14.03.2023

Abstract

In this study, a high power (75 kW) original driver and control algorithm has been developed for an electric passenger vehicle whose features can be used practically. It has been observed that some problems occur in the operation of the developed control algorithms in traction systems operating at high power. In this study, the solution methods of these problems are included. Firstly, the simulation model of an electrical vehicle was obtained by determining the basic parameters for a passenger electric vehicle. Then a brushless DC motor and drive system was determined for the electric vehicle and an original 75 kW DC-AC Converter (Inverter) in accordance with automotive standards has been designed and tested for the Brushless DC motor. Also, in the design and implementation phase, PID and Fuzzy control-based vehicle control software was developed in MATLAB/Simulink environment on the purpose of rapid prototyping and loaded on the DS1401 dSPACE based control system. It has been seen that through rapid prototyping, the appropriate controller development cycle time for the vehicle drastically reduced, which significantly has reduced the research and development costs. In the vehicle control algorithm, speed information is used as a reference input and brake information is used as feedback. The control signal generated by the controller is converted into PWM pulses for each phase and applied to the IGBT driver. These PWM pulses were used to switch the six IGBT power components used in the three-phase full-bridge DC-AC converter. Driving performance at the design stage has been studied for cases of starting, speed, reversal and load failure. Simulation and experimental results demonstrated the effectiveness of the driver and drive control system that were originally developed. When the system response was examined, it was revealed that the fuzzy logic control algorithm presented much better results than the PI and then the PID control algorithm. Simulation results and application results were consistent with each other and the system performance was successfully tested. Many protection circuits have been designed and configured in the system, with the control algorithms developed according to the problems arising in the operation of high-power systems, hardware add-ons for the operation of the high power (75kW) power-train. Safety and security infrastructures have been developed in both hardware and software for the appropriate certification in automotive standards.

References

  • [1] Tutelea L, Boldea I. Optimal design of residential brushless d.c. permanent magnet motors with FEM validation. 2007 International Aegean Conference on Electrical Machines and Power Electronics Electromotion (ACEMP’07); 2007 Sept 10-12; Bodrum Turkey: IEEE; 2007. pp. 435–439. [CrossRef]
  • [2] Zarko D, Ban D, Lipo TA. Analytical solution for cogging torque in surface permanent-magnet motors using conformal mapping. IEEE Trans Magn 2007;44:52–64. [CrossRef]
  • [3] Ustun O, Yilmaz M, Gokce C, Karakaya U, Tuncay RN. Energy management method for solar race car design and application. IEEE International Electric Machines and Drives Conference; 2009 May 03-06; Miami, FL: IEEE; 2009. pp. 804–811. [CrossRef]
  • [4] Markovic M, Hodder A, Perriard Y. An analyti-cal determination of the torque–speed and effi-ciency– speed characteristics of a BLDC motor. Energy Conversion Congress and Exposition; 2009 Sept 20-24; San Jose, CA: IEEE; 2009. pp. 168–172.[CrossRef]
  • [5] Zhao L, Ham C, Zheng L, Wu T, Sundaram K, Kapat J, et al. A highly efficient 200000 rpm per-manent magnet motor system. IEEE Trans Magn 2007;43:2528–2530. [CrossRef]
  • [6] Tuncay RN, Ustun O, Yılmaz M, Gokce C. Karakaya U. Design and implementation of an electric drive system for in-wheel motor electric vehicle appli-cations. 7th IEEE Vehicle Power and Propulsion Conference; 2011 Sept 06-09; Chicago, IL: IEEE; 2011. pp. 1–6. [CrossRef]
  • [7] Nair SS, Nalakath S, Dhinagar SJ. Design and analysis of axial flux permanent magnet BLDC motor for automotive applications. IEEE International Electric Machines & Drives Conference (IEMDC’11); 2011 May 15-18; Niagara Falls, Canada: IEEE; 2011. pp. 1615–1618. [CrossRef]
  • [8] Park SJ, Park HW, Lee MH, Harashima F. A new approach for minimum-torque-ripple maximum-efficiency control of BLDC motor. IEEE Trans Ind Electron 2000;47:109–114. [CrossRef]
  • [9] Zarko D, Ban D, Lipo TA. Analytical calculation of magnetic field distribution in the slotted air gap of a surface permanent-magnet motor using com-plex relative air-gap permeance. IEEE Trans Magn 2006;42:1828–1837.
  • [10] Rahim NA, Ping HW, Tadjuddin M. Design of axial flux permanent magnet brushless DC motor for direct drive of electric vehicle. IEEE Power Engineering Society General Meeting; 2007 Jun 24- 28; Tampa, USA: IEEE; 2007. 10290216. [CrossRef]
  • [11] Soong WL, Miller TJE. Field-weakening performance of brushless synchronous AC motor drives. IEE Proc Electr Power Appl 1994;141:331–340. [CrossRef]
  • [12] Wu B. Brushless DC Motor Speed Control. Master Thesis. Dep of Electrical & Computing Engineering, Ryerson University, October, 2001.
  • [13] Premkumar K, Manikandan BV. Bat algorithm opti-mized fuzzy PD based speed controller for brush-less direct current motor. Eng Sci Technol Int J 2016;19:818–840. [CrossRef]
  • [14] Chico A, Brillianto H, Maghfiroh H. Fuzzy logic controller and its application in brushless DC motor (BLDC) in electric vehicle. J Electrical Electronic Inform Commun Technol 2021;3:35−43. [CrossRef]
  • [15] Agrawal S, Vivek S. Particle swarm optimization of BLDC motor with fuzzy logic controller for speed improvement. 2017 8th International Conference on Computing, Communication and Networking Technologies; 2017 Jul 3-5; Delhi, India: IEEE; 2017. 17418417. [CrossRef]
  • [16] Wang H, Li P, Shu Y, Kang D. Double closed loop control for BLDC based on whole fuzzy con-trollers. 2nd IEEE International Conference on Computational Intelligence and Applications; 2017 Sept 08-11; Beijing, China: IEEE; 2017. 17415277. [CrossRef]
  • [17] Davoudkhani IF, Akbari M. Adaptive speed control of brushless DC (BLDC) motor based on interval type-2 logic. 24th Iranian Conference on Electrical Engineering (ICEE); 2016 May 10-12; Shiraz, Iran: IEEE; 2016. 16375523. [CrossRef]
  • [18] Wang ZS, Liu CY, Song XL, Song ZY, Yang ZK. Improved variable universe fuzzy PID application in brushless DC motor speed regulation system. Proceedings of the 2016 International Conference on Machine Learning and Cybernetics; 2016 Jul 10-13; Jeju, South Korea: IEEE; 2016. 16692057. [CrossRef]
  • [19] Hanselman DC. Brushless permanent magnet motor design. 1st ed. New York: McGraw Hill; 1994.
  • [20] Kenjo T, Nagomori S. Permanent Magnet and Brushless DC Motors. 1st ed. Oxford: Oxford University Press; 1985
  • [21] Indra F, Era P, Novie AW. Fuzzy gain scheduling of PID (FGSPID) for speed control three phase induc-tion motor based on indirect field oriented control (IFOC). EMITTER Int J Eng Technol 2016;4:237– 258. [CrossRef]
  • [22] Aydogdu O, Levent ML. Trajectory control of a variable loaded servo system by using fuzzy itera-tive learning PID control. Int J Robot Control Syst 2017;2:170–177.
  • [23] Xu C, Huang D, Huang Y, Gong S. Digital PID con-troller for brushless DC motor based on AVR micro-controller. 2008 IEEE International Conference on Mechatronics and Automation; 2008 Aug 05-08; Takamatsu: IEEE; 2008. pp. 247–252.
  • [24] Tzafestas S, Papanikolopulos NP. Incremental fuzzy expert PID control. IEEE Trans Ind Electron 1990;37:365–371. [CrossRef]
  • [25] Bahadır A. Brushless direct current motor driver design and adaptive control for electric vehicles. Doctoral Thesis. Konya, Turkey: Konya Technical University; 2021.
  • [26] Jaya A, Purwanto E, Fauziah MB, Murdianto FD, Prabowo G, Rusli MR. Design of PID-fuzzy for speed control of brushless DC motor in dynamic electric vehicle to improve steady-state perfor-mance. 2017 International Electronics Symposium on Engineering Technology and Applications (IES-ETA); 2017 Sept 26-27; Surabaya, Indonesia: IEEE; 2017. 17453328. [CrossRef]
  • [27] Aziza Z. ANN-Based Current Controlled BLDC Servo-Motor. 9th European Conference on Power Electronics and Applications, EPE 2001, Graz, 27-29 August, 2001.
  • [28] Bojadziev G, Bojadziev M. Fuzzy Sets, Fuzzy Logic, Applications. River Edge: Word Scientific Publishing; 2000.
  • [29] Vas Peter. Artificial-Intelligence-Based Electrical Machines and Drives. 1st ed. New York: Oxford University Press; 1999.
There are 29 citations in total.

Details

Primary Language English
Subjects Empirical Software Engineering
Journal Section Research Articles
Authors

Ali Bahadır This is me 0000-0002-8322-2527

Ömer Aydoğdu This is me 0000-0003-0815-0356

Publication Date March 14, 2023
Submission Date May 27, 2021
Published in Issue Year 2023 Volume: 41 Issue: 1

Cite

Vancouver Bahadır A, Aydoğdu Ö. Modeling of a brushless dc motor driven electric vehicle and its pid-fuzzy control with dSPACE. SIGMA. 2023;41(1):156-77.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/