Neural Network-Based Optimization of PMBLDC Motor Performance for Enhanced Energy Efficiency and Management in Renewable Energy Systems
Musa Mohammed GUJJA, Dahaman ISHAK, Muhammad Najwan HAMIDI, Two Leong TIANG
Abstract. Lately, artificial intelligence has begun to revolutionise how we manage and optimize electrical machines, particularly in renewable energy setups. Permanent Magnet Brushless DC (PMBLDC) motors are very popular, widely used for operating the solar pumps, wind turbines, and electric vehicles, due to their robustness, efficiency, and requiring less maintenance. However, PMBLDC machines have limitations such as nonlinearities, control errors, and sometimes instability, which can impact their overall performance. In this paper, we propose a Fitting Neural Network (FNN) based control method to overcome these challenges. The simulation results indicate that the proposed technique performs better in comparison with the conventional methods, with a significant improvement. The energy efficiency is higher to about 18.6%, while the settling time is 67.5% faster. The overshoot and undershoot are minimized, showing a reduction of 98.3% and 84.27%, respectively. It is noted that the proposed control method can learn and adapt effectively to obtain better performance, particularly in the area of renewable energy systems. Therefore, introducing these AI models not only improves the system but also produces smarter and more efficient PMBLDC motor operations for maintainable power.
Keywords
PMBLDC Motor, Neural Network, Renewable Energy, AI Optimization, Machine Learning, Energy Efficiency, Smart Energy Management
Published online 4/25/2026, 8 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Musa Mohammed GUJJA, Dahaman ISHAK, Muhammad Najwan HAMIDI, Two Leong TIANG, Neural Network-Based Optimization of PMBLDC Motor Performance for Enhanced Energy Efficiency and Management in Renewable Energy Systems, Materials Research Proceedings, Vol. 64, pp 920-927, 2026
DOI: https://doi.org/10.21741/9781644904091-114
The article was published as article 114 of the book Energy Futures
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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