Enhancing power efficiency in BLDC motor drives for drones using multiview learning with hybrid optimization algorithms
Abstract This study explores power efficiency enhancements in BLDC motor drives for drone applications, taking cues from the foraging behavior of the electric eel and gooseneck barnacle optimization. Employing multi-view learning techniques, it evaluates various performance metrics such as torque sp...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-13420-6 |
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| author | Anushree Gopalakrishnan Rani Thottungal |
| author_facet | Anushree Gopalakrishnan Rani Thottungal |
| author_sort | Anushree Gopalakrishnan |
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| description | Abstract This study explores power efficiency enhancements in BLDC motor drives for drone applications, taking cues from the foraging behavior of the electric eel and gooseneck barnacle optimization. Employing multi-view learning techniques, it evaluates various performance metrics such as torque speed and power, revealing substantial improvements over the existing methods. Fitness improvement rates per iteration for Eel Foraging (0.05) and Gooseneck Barnacle Optimization (0.04) demonstrate their superior efficiency over standard evolutionary algorithms (0.02–0.10). Analyzing torque speed with control further highlights the effectiveness of Eel Foraging and Gooseneck Barnacle Optimization through Multi-View Learning. At a speed of 2000 rpm, the torque generated by Eel Foraging was 15 Nm, whereas the Gooseneck Barnacle Optimization yielded 14 Nm. This difference underscores the nuanced optimization strategies employed by each method. Additionally, the power rating selection process, based on ideal torque-speed characteristics, provides practical insights. For instance, at a torque of 3.5 Nm, the intermittent power rating for both Eel Foraging and Gooseneck Barnacle Optimization is 265 W, aligned with their efficiency-driven design principles. Utilizing these characteristics as a reference, this study defines optimal performance benchmarks for future BLDC motor-drive designs. Through mathematical optimization techniques, the positions were adjusted based on the fitness values and previous states. This process ensures that Eel Foraging and Gooseneck Barnacle Optimization remain adaptable to changing conditions, with solutions consistently improving over iterations. Moreover, the evaluation of fitness impacts provides critical feedback for further refinement, driving the optimization process forward. |
| format | Article |
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| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
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| spelling | doaj-art-4a53dee44dcf4950b8f83c32ba6a61112025-08-20T03:45:49ZengNature PortfolioScientific Reports2045-23222025-07-0115112610.1038/s41598-025-13420-6Enhancing power efficiency in BLDC motor drives for drones using multiview learning with hybrid optimization algorithmsAnushree Gopalakrishnan0Rani Thottungal1Department of Electrical and Electronics Engineering, Kumaraguru College of TechnologyDepartment of Electrical and Electronics Engineering, Kumaraguru College of TechnologyAbstract This study explores power efficiency enhancements in BLDC motor drives for drone applications, taking cues from the foraging behavior of the electric eel and gooseneck barnacle optimization. Employing multi-view learning techniques, it evaluates various performance metrics such as torque speed and power, revealing substantial improvements over the existing methods. Fitness improvement rates per iteration for Eel Foraging (0.05) and Gooseneck Barnacle Optimization (0.04) demonstrate their superior efficiency over standard evolutionary algorithms (0.02–0.10). Analyzing torque speed with control further highlights the effectiveness of Eel Foraging and Gooseneck Barnacle Optimization through Multi-View Learning. At a speed of 2000 rpm, the torque generated by Eel Foraging was 15 Nm, whereas the Gooseneck Barnacle Optimization yielded 14 Nm. This difference underscores the nuanced optimization strategies employed by each method. Additionally, the power rating selection process, based on ideal torque-speed characteristics, provides practical insights. For instance, at a torque of 3.5 Nm, the intermittent power rating for both Eel Foraging and Gooseneck Barnacle Optimization is 265 W, aligned with their efficiency-driven design principles. Utilizing these characteristics as a reference, this study defines optimal performance benchmarks for future BLDC motor-drive designs. Through mathematical optimization techniques, the positions were adjusted based on the fitness values and previous states. This process ensures that Eel Foraging and Gooseneck Barnacle Optimization remain adaptable to changing conditions, with solutions consistently improving over iterations. Moreover, the evaluation of fitness impacts provides critical feedback for further refinement, driving the optimization process forward.https://doi.org/10.1038/s41598-025-13420-6BLDC motor drivesPower efficiencyDrone applicationsElectric eel foragingGooseneck barnacle optimizationMulti-view learning |
| spellingShingle | Anushree Gopalakrishnan Rani Thottungal Enhancing power efficiency in BLDC motor drives for drones using multiview learning with hybrid optimization algorithms Scientific Reports BLDC motor drives Power efficiency Drone applications Electric eel foraging Gooseneck barnacle optimization Multi-view learning |
| title | Enhancing power efficiency in BLDC motor drives for drones using multiview learning with hybrid optimization algorithms |
| title_full | Enhancing power efficiency in BLDC motor drives for drones using multiview learning with hybrid optimization algorithms |
| title_fullStr | Enhancing power efficiency in BLDC motor drives for drones using multiview learning with hybrid optimization algorithms |
| title_full_unstemmed | Enhancing power efficiency in BLDC motor drives for drones using multiview learning with hybrid optimization algorithms |
| title_short | Enhancing power efficiency in BLDC motor drives for drones using multiview learning with hybrid optimization algorithms |
| title_sort | enhancing power efficiency in bldc motor drives for drones using multiview learning with hybrid optimization algorithms |
| topic | BLDC motor drives Power efficiency Drone applications Electric eel foraging Gooseneck barnacle optimization Multi-view learning |
| url | https://doi.org/10.1038/s41598-025-13420-6 |
| work_keys_str_mv | AT anushreegopalakrishnan enhancingpowerefficiencyinbldcmotordrivesfordronesusingmultiviewlearningwithhybridoptimizationalgorithms AT ranithottungal enhancingpowerefficiencyinbldcmotordrivesfordronesusingmultiviewlearningwithhybridoptimizationalgorithms |