Using the MPPT Approach to Improve the Performance of the Photovoltaic Pumping System Powered by a Brushless Motor BLDC Controlled by an Innovative Predictive Technique (MPC)
https://doi-001.org/1025/17611412499359
Akkari Nadia 1, Naceur Sonia 2, Ikhlef Malika 3
1 Department of Electrical Engineering Engineering, University of Batna 2 Batna, Algeria. Email:
2 Department of Electrical Engineering University of Kasdi Merbah Ouargla 03000, Algeria. Email:
3 Department of Electrical Engineering Engineering, University of Batna 2 Batna, Algeria. Email:
Received: 12/06/2025 ; Accepted: 16/09/2025
Abstract
The industry has a strong need for Brushless DC (BLDC) motors because of their extended lifespan, high torque, speed stability, and straightforward design. As a result, regulating this kind of motor is very difficult. As an alternative, these motors can be employed to improve industrial performance.
BLDC motor speed, generated electromagnetic torque, and stator currents can all be more effectively managed by Model Predictive Control (MPC). The MPC control strategy’s accuracy, stability, responsiveness, and resilience are assessed with the use of these metrics.
A brushless motor or other variable source combined with an MPPT (Maximum Power Point Tracking) system is a crucial technical solution to guarantee steady, maximized power production, which improves the motor’s overall performance and the system’s energy efficiency.
Given the significance of brushless motors and the MPC control technique, as was previously noted, we thought of utilizing these benefits for use in distant locales, especially those that depend on renewable energy sources like solar and wind power.
Keywords: BLDC Motor, Hall sensor, Six-step inverter, Back EMF, MPC Control, Maximum power point (MPPT) photovoltaic pumping system, Perturb and observer P &O.
1. Introduction
A three-phase synchronous motor without brushes, the brushless motor includes electronically powered windings in the stator and permanent magnets in the rotor. Because of this design, the rotor can rotate with dynamic and fine control over speed, torque, and position thanks to precise electrical commutation that creates a revolving magnetic field [1], [2], [3]. Because of its advanced electronic control and brushless technology, the brushless motor combines precision, performance, efficiency, and longevity [4]. Because of these characteristics, it is a popular option for contemporary sectors that demand great performance, cost management, and dependability. Using a dynamic model of the motor, the predictive control (MPC) is a sophisticated closed-loop control technique used with brushless motors to forecast future behavior over a predetermined time horizon [5], [6], [7], [8].
At each control interval, MPC solves an optimization problem to identify the best control inputs that reduce tracking error and control effort while adhering to physical limitations like speed, voltage, and current limits. With this receding horizon technique, the controller may adjust in real time to disturbances and nonlinear system dynamics by applying only the first control action before re-optimizing at the subsequent stage. By combining speed and current control, explicitly managing limits, and anticipating system behavior, MPC achieves more precise, reliable, and effective speed regulation of brushless motors than conventional control techniques. This technique is appropriate for high-performance applications in robotics and electric drives since it enhances performance by lowering torque ripple and guaranteeing adherence to operating limitations [9], [10],. Because it improves performance, this approach is suitable for high-performance applications in robotics and electric drives [11], [12].
Combining an MPPT with a brushless motor that is driven by a solar panel or another variable source is a crucial technical solution to guarantee steady, dependable, and maximum power, which improves the motor’s overall performance and the system’s energy efficiency.
2. The fundamentals of BLDC motor operation and driving Therefore, the construction design of all brushless motors is the same: a stationary stator that supports the coils (usually three-phase). It is made up of excitation coils, usually three or six. Although they can also be joined in delta and a mobile rotor, which is where the permanent magnets are glued, these are most frequently connected in stars [13], [14]. The existence of a mobile contact between the brushes and collector in conventional DC motors has significant disadvantages (maintenance, commutation difficulty, etc.). Therefore, we tried to swap them out with an electronic switch rather than a mechanical one [15], [16]. Hall effect sensors are sensors that detect the position of the rotor, which is information necessary for controlling a BLDC motor [17], [18]. The number of these sensors is three sensors placed at the stator level or a special disc (immobile) and which are offset from each other by either 60° or 120°.
2.1 BLDC motor model
The governing equations for the operation of a BLDC motor typically include equations for torque, current, voltage, and motor speed.3.1 Voltage inverter Control
The three-phase inverter is connected to the BLDC motor.Figure4. Only two switches from two separate phase windings are switched on at a time; with the other switch from the other phase winding turned off, in order to ensure the BLDC motor operates and is controlled properly. The BLDC motor is therefore powered by an inverter that operates on a different principle than regular AC motors [19], [20]. In order to guarantee that the current passes via the proper two-phase windings, the three-phase inverter serves as a control circuit for the BLDC motor. Utilizing semiconductor switches, usually IGBTs or MOSFETs, the inverter generates the necessary phase currents by alternating the current passing through the motor windings [21], [22], [23]. Assuming that the Hall sensors provide the signals 1 0 0 at the beginning of the BLDC, this indicates that the rotor is between 0° and 60°, and therefore S1 and S4 switches must be closed (Fig. 4). When these switches are closed, phase 1 (Van) receives a positive voltage and phase 2 (Vbn) receives a negative voltage; phase 3 (Vcn = 0 V) receives no voltage. Consequently, the field created by the currents Ia and Ib from phases 1 and 2 drives the rotor. Next, the Hall sensors generate the signals 1 0 0 (60°-120°), and from these signals, the converter orders the closure of switches S1 and S6. This causes a negative current to flow in phase 3 and another positive current in phase 1. The resulting field from these two currents drives the rotor. Then, the rotor passes thru the angles.
3.2 MPC Controller structures: Electronic Hall effect sensors are used to measure the rotor’s position. The motor windings create a magnetic field that determines the motor speed and electromagnetic torque, which may be controlled by altering the voltage across the motor terminals. The voltage determines how fast the motor runs. The method of MPC predictives speed control regulates the necessary speed. The controller receives as input the difference between the necessary and actual speeds. The duty cycle of the trigger pulses, which translates into the required voltage change to sustain the target speed, is managed by the MPC controller based on this data [28], [29].
3.4. SIMULINK block model of brushless Motor powered by a photovoltaic system using predictive control MPC:
With the help of Matlab/Simulink, the simulation model (as shown in Fig.6) can be set up to verify the control strategies. The subsystem blocks of BLDC motor, IGBT inverter, reference current and speed controller are created respectively based on their own characteristics. The block presented in figure 7 permits to detect EMF By operating the hall sensor.
4 Configuring the GPV system
4.1 Architectural photovoltaic Generator
The photovoltaic electricity generator (PVG) converts solar energy into direct current using photovoltaic modules, which are often installed in series-parallel configurations. A DC-
DC converter, usually a Boost converter with a Maximum Power Point Tracking (MPPT) algorithm, handles the generated DC power to optimize energy extraction from solar irradiation [30], [31].
4.2 Selection of parameters for photovoltaic system (PVS) A solar energy source with a peak power of 5 kW is selected for the 2 kW brushless motor. The power of (PVS) array is slightly higher than that of the motor so that the system’s performance is not affected by losses related to converters and motors. The parameters of the (PVS) network are selected according to Table 2. The equivalent characteristic model of the (PVS)module is shown in figure 5, [32], [33]. The output current of a photovoltaic cell is expressed in the following mathematical form:
7. Conclusion
With the load (photovoltaic pump), a straightforward control technique has been suggested to enhance the system’s overall performance under changes in the irradiation level and the motor’s reference speed during starting. The control method that is being offered is primarily dependent on the three-phase inverter and the boost converter’s operation being managed. Furthermore, in order to improve the system’s dependability and efficiency and to lessen the issues related to Hall effect sensors which are essential for brushless motors a speed control method has been proposed and implemented.
Using the suggested control technique, the system’s performance characteristics were examined and analyzed in two distinct operating modes based on the degree of irradiation variation (1000 W/m2-800 W/m2-1000 W/m2). We see that the P&O perturbation and observation MPPT technique has been used to control the boost converter’s duty cycle, maximizing the solar arrays utilization and raising the pump flow rate as a result. But in the second mode of operation, where the speed is variable (between 1000 and 2000 rpm), the motor speed was controlled to run at its nominal value while the boost converter’s duty cycle remained constant. Even though the irradiation level was changed, the simulation results demonstrate that the pump flow rate was raised in the first mode and maintained at its nominal value in the second mode. The results also show that the suggested PV pumping system’s overall performance, efficiency, and dependability have improved, confirming the efficacy of the sensorless control method that was provided. It has been demonstrated that the BLDC motor is a good option for the automotive sector since it is more efficient, has a better power density, and has a wider speed range than other motor types. For use in remote locations, especially those that depend on renewable energy sources like solar and wind power, we suggested utilizing the system’s total performance due to these benefits.
References
[1] SudhanshuMitra, R.,Nayak,S., Prakash, R. ”Modeling and simulation of BLDC motor using Matlab/Simulink environment”. International Research Journal of Engineering and Technology (IRJET), 2(8): 199-203.2015
[2] MalothPurnalal, Sunil Kumar TK,”Development of Mathematical Model and Speed Control of BLDC Motor”.International Journal of Electrical and Electronics Engineers (IJEEE), 2015 Volume 07, Pp. 271-280..
[3] Hwang, C.C., Li, P.L., Liu, C.T. and Chen, C., “Design and analysis of a brushless DC motor for applications in robotics”.IET electric power applications, pp.385-38 .2012.
[4] Balogh Tibor,Viliam Fedak, and Frantisek Durovsky., “Modeling and Simulation of the BLDC Motor in MATLAB”GUI,in Proc of the IEEE Fifth International Conference on Fuzzy Systems and Knowledge Discovery, US, pp. 1403-1407, 2011.
[5] Caseiro, L.M.A.; Mendes, A.M.S.; Cruz, S.M.A. “Dynamically Weighted Optimal Switching Vector Model Predictive Control of Power Converters”. IEEE Trans. Ind. Electron., 66, 1235–1245.2019.
[6] Xia, K.; Ye, Y.; Ni, J.; Wang, Y.; Xu, P.”Model Predictive Control Method of Torque Ripple Reduction for BLDC Motor”. IEEE Trans. Magn., 56, 1–6. 2020.
[7] Darba, A.; De Belie, F.; D’Haese, P.; Melkebeek, J.A. “Improved Dynamic Behavior in BLDC Drives Using Model Predictive Speed and Current Control”. IEEE Trans. Ind. Electron. 63, 728–740.2016.
[8] Wen, H.; Yin, J. “A Duty Cycle Based Finite-Set Model Predictive Direct Power Control for BLDC Motor Drives”. In Proceedings of the IECON The 46th Annual Conference of the IEEE Industrial Electronics Society, Singapore, 18–21 pp. 821–825. October 2020.
[9] Ubare, P.; Ingole, D.; Sonawane, D. “Nonlinear Model Predictive Control of BLDC Motor with State Estimation”. IFAC-Papers On Line 54, 107–112, 2021
[10] Mohammd Taher, S.; Halvaei Niasar, A.; Abbas Taher, S. “A New MPC-based Approach for Torque Ripple Reduction in BLDC Motor Drive”. In Proceedings of the 2021 12th Power Electronics,
Drive Systems, and Technologies Conference (PEDSTC), ; pp. 1–6 Tabriz, Iran, 2–4 February 2021.
[11] Azab, M. “A finite control set model predictive control scheme for single-phase grid-connected inverters”. Renew. Sustain. Energy Rev. 135, 110131.2021,
[12] D. Almeida, P.M.; Valle, R.L.; Barbosa, P.G.; Montagner, V.F.; Cuk, V.; Ribeiro, P.F. “Robust Control of a Variable-Speed BLDC Motor Drive”. IEEE J. Emerg. Sel. Top. Ind. Electron. 2, 32–41, 2021.
[13] Singh, B. and Kumar, R.,”Simple brushless DC motor drive for solar photovoltaic array fed water pumping system”. IET Power Electronics, 9(7), pp.1487-1495, 2016.
[14] Xia, C.L, “Permanent magnet brushless DC motor drives and controls”. John Wiley & Sons ,2012.
[15] Bist, V. and Singh, B., A unity “Power factor bridge lessisolated Cuk converter-fed brushless DC motor drive”. IEEE Transactions on Industrial Electronics, 62(7), pp.4118-4129, 2015.
[16] Shakouhi, S.M., Mohamadian, M. and Afjei, E., “Torque ripple minimisation control method for a four-phase brushless DC motor with non-ideal back-electromotive force”. IET Electric Power Applications, 7(5), pp.360-368, 2013.
[17] Lee, Y. Can. J “A New Method to Minimize Overall Torque Ripple in the Presence of Phase Current Shift Error for Three-Phase BLDC Motor Drive”.. Electr. Comput. Eng.,42, 225–231, 2019
[18] Jin, H.; Liu, G.; Li, H.; Zhang, H. “Closed-Loop Compensation Strategy of Commutation Error for Sensorless Brushless DC Motors with Nonv deal Asymmetric Back-EMF”. IEEE Trans. Power Electron. 36, 11835–11846, 2021
[19] Caseiro, L.M.A.; Mendes, A.M.S.; Cruz, S.M.A. “Dynamically Weighted Optimal Switching Vector Model Predictive Control of Power Converters” IEEE Trans. Ind. Electron, 66, 1235–1245, . 2019
[20] Azab, M. “High performance decoupled active and reactive power control for three-phase grid-tied inverters using model predictive control”. Prot. Control. Mod. Power Syst, 2021
[21] Aguilera, R.P.; Acuna, P.; Konstantinou, G.; Vazquez, S.; Leon, J.I. Basic Control Principles in Power Electronics: Analog and Digital Control Design; Chapter 2; Blaabjerg, F., Ed.; “Control of Power Electronic Converters and Systems”, Academic Press Cambridge, MA, USA, 2018.
[22] Ayetul Gelen, Saffet Asyasun, “Realization of Power Electronic Converter Based DC Motor Speed Control Method Using MATLAB Simulink” International Journal of Engineering Education, Vol.25,pp. 33-41,2009
[23] Nitin Sandhya, Ramesh C. Kumhar, Prakash Sundaram, Pankaj Kumar, Shah igal,“Reliability of Permanent Magnet Brushless D.C. Drives Using IGBT’S”,IJIRSET, Vol.2, pp.772-780, March 2013.
[24] Sang-Hoon Kim, Electric Motor Control, DC, AC, and BLDC Motors, ElSEVIER, 2017.
[25] R. M. Pindoriya, S. Rajendran and P. J. Chauhan, “Speed Control of BLDC Motor using PWM Technique,” International Journal of Advance Engineering and Research Development (IJAERD) ETCEE- 2014 Issue, March
[26] Killi M, Samanta S. “An Adaptive Voltage Sensor Based MPPT for Photovoltaic Systems with SEPIC Converter including Steady State and Drift Analysis”. IEEE Trans. on Power Electron; 64(1): 104- 114.2015; 62(12): 7609 – 7619, 2017
[27] Priyadarshi N, Kumar V,Yadav K, Vardia M. “An Experimental Study on Zeta buck-boost converter for Application in PV system. Hand book of distributed generation”. DOI10.1007/978-3-319-51343- 013.
[28] Coelho, R.F., Dos Santos, W.M. and Martins, D.C.November. “Influence of power converters on PV maximum power point tracking efficiency”. In 10th IEEE/IAS International Conference on Industry Applications (pp. 1-8). IEEE, 2012.
[29] Kumar, R., Singh, B.: “Solar PV array fed Cuk converter-VSI controlled BLDC motor drive for water pumping”. Sixth IEEE Power India Int. Conf. (PIICON), 5–7 December 2014
[30] G. N. Tiwari and Swapnil Dubey, ”Fundamentals of photovoltaic modules and their applications,” RSC Energy Series No. 2, 2010.
[31] Huan-Liang Tsai, Ci-Siang Tu, and Yi-Jie Su, “Development of generalized photovoltaic model using matlab/simulink,” Proceedings of the World Congress on Engineering and Computer Science, , San Francisco, USA, pp. 846-85, October 2008.
[32] Ashwini Kumari, P. et al. “Adaptive RAO ensembled dichotomy technique for the accurate parameters extraction of solar PV system”.Sci. Rep. 14(1), 12920 (2024).
[33] Mohd Saifuzam Jamri and Tan Chee Wei, ”Modeling and control of a photovoltaic energy system using the state-space averaging technique,” American Journal of Applied Sciences 7 (5), pp. 682-691, 2010.
[34] Kamran, M.; Mudassar, M.; Fazal, M.R.; Asghar, M.U.; Bilal, M.; Asghar, R. “Implementation of improved Perturb & Observe MPPT technique with confined search space for standalone photovoltaic system.” J. King Saud Univ. Eng. Sci., 32, 432–441, 2020.
[35] Alshareef, M.; Lin, Z.; Ma, M.; Cao, W. Accelerated particle swarm optimization for photovoltaic maximum power point tracking
under partial shading conditions. Energies, 12, 623, 2019.
[36] Ahmed, J. and Salam, Z., A modified P&O maximum power point tracking method with reduced steady-state oscillation and improved tracking efficiency. IEEE Transactions on Sustainable Energy, 7(4), pp.1506-1515. 2016.
[37] Subudhi, B. and Pradhan, R., A comparative study on maximum power point tracking techniques for photovoltaic power systems. IEEE Transactions on sustainable energy, 4(1), pp.89-98, 2013.
[38] M. Nabil, S. M. Allam and E. M. Rashad, “Performance Improvement of a Photovoltaic Pumping System Using a Synchronous Reluctance Motor,” Electric Power Components and Systems, Vol. 41, No. 4, pp. 447-464. February 2013,
[39] Coelho, R.F., Dos Santos, W.M. and Martins, D.C., November. Influence of power converters on PV maximum power point tracking efficiency. In 2012 10th IEEE/IAS International Conference on Industry Applications (pp. 1-8). IEEE, 2012.
[40] Rik De Doncker, Duco W.J and Pulle André Veltma “Advanced Electrical Drives Analysis, Modeling, Control” Springer Science+Business Media B.V. 2011.
[41] Jose Rodriguez and Patricio Cortes” Predictive control of power converter and electrical drives” Universidad Tecnica Federico Santa Maria, Valparaiso, Chile This edition first published 2012
2012, John Wiley & Sons, Ltd
[42] Periasamy, P., Jain, N.K. and Singh, I.P.,”A review on development of photovoltaic water pumping system”. Renewable and Sustainable Energy Reviews, 43, pp.918-925. 2015.
[4] Djeriou, S.; Kheldoun, A.; Mellit, A. “Efficiency Improvement in Induction Motor-Driven Solar Water Pumping System Using Golden Section Search Algorithm”. Arab. J. Sci. Eng., 43, 3199–3211,2018.
[44] Kumar, R.; Singh, B. “Single Stage Solar PV Fed Brushless DC Motor Driven Water Pump”. IEEE J. Emerg. Sel. Top. Power Electron, 5, 1377–1385,2017.