Adaptive speed control of BLDC motors for enhanced electric vehicle performance using fuzzy logic
Abstract This study investigates the use of a closed-loop speed control approach based on fuzzy logic for brushless DC (BLDC) motors in Electric Vehicle (EV) applications. The primary objective is to overcome the drawbacks of traditional control techniques by improving dynamic performance, response...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-90957-6 |
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| author | R. Shenbagalakshmi Shailendra Kumar Mittal J. Subramaniyan V. Vengatesan D. Manikandan Krishnaraj Ramaswamy |
| author_facet | R. Shenbagalakshmi Shailendra Kumar Mittal J. Subramaniyan V. Vengatesan D. Manikandan Krishnaraj Ramaswamy |
| author_sort | R. Shenbagalakshmi |
| collection | DOAJ |
| description | Abstract This study investigates the use of a closed-loop speed control approach based on fuzzy logic for brushless DC (BLDC) motors in Electric Vehicle (EV) applications. The primary objective is to overcome the drawbacks of traditional control techniques by improving dynamic performance, response time, and stability under changing load conditions and parameter uncertainty. Nonlinearities, load fluctuations, and transient overshoots are common problems for traditional PID controllers, which results in suboptimal performance of EV propulsion systems. A state-space modelling technique for the BLDC motor is used in this study to address these issues, incorporating a Fuzzy Logic Controller (FLC) for accurate speed control. The superiority of FLC over PID controllers is demonstrated by a comparison study that was verified by simulation and hardware implementation. The results show that FLC produces smooth speed transitions, no overshoot, and zero steady-state error with a settling time of only 0.05s, as in contrast to 0.1s for the PID controller. Under load fluctuations, the FLC’s torque response stays constant at about 1.05 Nm, however the PID controller shows noticeable oscillations and a larger torque ripple. Additionally, FLC guarantees smooth speed regulation throughout a broad range (1500–3000 rpm), greatly increasing motor lifespan and energy efficiency. When compared to the PID controller, the experimental validation shows that FLC performs robustly in real-time EV settings, exhibiting smoother speed transitions, faster disturbance rejection, and improved adaptability. According to these results, FLC is a better option for BLDC motor speed control in EV applications, guaranteeing effective propulsion, less mechanical stress, and more driving stability. As a potential control strategy for upcoming EV technologies, the proposed strategy not only improves energy utilisation but also strengthens system reliability. |
| format | Article |
| id | doaj-art-c614d6f06e774a21929dfe901bd08752 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c614d6f06e774a21929dfe901bd087522025-08-20T02:11:42ZengNature PortfolioScientific Reports2045-23222025-04-0115112210.1038/s41598-025-90957-6Adaptive speed control of BLDC motors for enhanced electric vehicle performance using fuzzy logicR. Shenbagalakshmi0Shailendra Kumar Mittal1J. Subramaniyan2V. Vengatesan3D. Manikandan4Krishnaraj Ramaswamy5Department of Electrical & Electronics Engineering, Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeDepartment of Electrical Engineering, G H Raisoni College of Engineering and ManagementDepartment of Electrical & Electronics Engineering, SRM TRP Engineering CollegeDepartment of Electrical & Electronics Engineering, SRM TRP Engineering CollegeDepartment of Mechanical Engineering, SRM TRP Engineering CollegeDepartment of Mechanical Engineering, College of Engineering and Technology, Dambi Dollo UniversityAbstract This study investigates the use of a closed-loop speed control approach based on fuzzy logic for brushless DC (BLDC) motors in Electric Vehicle (EV) applications. The primary objective is to overcome the drawbacks of traditional control techniques by improving dynamic performance, response time, and stability under changing load conditions and parameter uncertainty. Nonlinearities, load fluctuations, and transient overshoots are common problems for traditional PID controllers, which results in suboptimal performance of EV propulsion systems. A state-space modelling technique for the BLDC motor is used in this study to address these issues, incorporating a Fuzzy Logic Controller (FLC) for accurate speed control. The superiority of FLC over PID controllers is demonstrated by a comparison study that was verified by simulation and hardware implementation. The results show that FLC produces smooth speed transitions, no overshoot, and zero steady-state error with a settling time of only 0.05s, as in contrast to 0.1s for the PID controller. Under load fluctuations, the FLC’s torque response stays constant at about 1.05 Nm, however the PID controller shows noticeable oscillations and a larger torque ripple. Additionally, FLC guarantees smooth speed regulation throughout a broad range (1500–3000 rpm), greatly increasing motor lifespan and energy efficiency. When compared to the PID controller, the experimental validation shows that FLC performs robustly in real-time EV settings, exhibiting smoother speed transitions, faster disturbance rejection, and improved adaptability. According to these results, FLC is a better option for BLDC motor speed control in EV applications, guaranteeing effective propulsion, less mechanical stress, and more driving stability. As a potential control strategy for upcoming EV technologies, the proposed strategy not only improves energy utilisation but also strengthens system reliability.https://doi.org/10.1038/s41598-025-90957-6BLDC motorElectric vehiclesFuzzy logic controllerMembership functionsPID controllerPropulsion system |
| spellingShingle | R. Shenbagalakshmi Shailendra Kumar Mittal J. Subramaniyan V. Vengatesan D. Manikandan Krishnaraj Ramaswamy Adaptive speed control of BLDC motors for enhanced electric vehicle performance using fuzzy logic Scientific Reports BLDC motor Electric vehicles Fuzzy logic controller Membership functions PID controller Propulsion system |
| title | Adaptive speed control of BLDC motors for enhanced electric vehicle performance using fuzzy logic |
| title_full | Adaptive speed control of BLDC motors for enhanced electric vehicle performance using fuzzy logic |
| title_fullStr | Adaptive speed control of BLDC motors for enhanced electric vehicle performance using fuzzy logic |
| title_full_unstemmed | Adaptive speed control of BLDC motors for enhanced electric vehicle performance using fuzzy logic |
| title_short | Adaptive speed control of BLDC motors for enhanced electric vehicle performance using fuzzy logic |
| title_sort | adaptive speed control of bldc motors for enhanced electric vehicle performance using fuzzy logic |
| topic | BLDC motor Electric vehicles Fuzzy logic controller Membership functions PID controller Propulsion system |
| url | https://doi.org/10.1038/s41598-025-90957-6 |
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