Optimizing brushless direct current wheel motors design using a novel bio-inspired algorithm

Brushless Direct Current (BLDC) wheel motors are the mainstay in robotics, electric cars, and many other technologies with high requirements of efficiency, space, and accuracy. Optimization is very difficult to achieve, as there is no easy balancing among the prime drivers of efficiency, torque ripple, and heat endurance. These shortfalls are overcome by introducing Adaptive Emperor Penguin Aquila Optimizer (AEPAO), a novel bio-inspired optimization strategy. This method successfully optimizes exploration and exploitation in the design space by combining Aquila Optimization (AO) and Adaptive Emperor Penguin (AEP) optimization methods. By leveraging the strengths of both algorithms, AEPAO provides effective and adaptive searching within the solution space and thus improves motor performance. A detailed BLDC wheel motor model was constructed, including key design parameters like material properties, magnet topography, and stator winding configurations. AEPAO-based optimization was implemented in MATLAB and tested systematically using standard benchmark algorithms. The performance metrics included minimization of torque ripple, efficiency maximization, and thermal stability. The proposed solution provided better results, with a remarkably high efficiency of 98.59% and a better reduction in torque ripple than traditional optimization techniques. The AEPAO strategy also showed superior thermal stability, thereby providing increased motor lifespan and reliability. The results identified the strength of AEPAO in optimizing BLDC motor design, providing a strong and efficient tool for maximizing motor performance. The adaptive capability of the algorithm enables dynamic adjustments during optimization, providing adaptability under different operating conditions. The use of bio-inspired principles in AEPAO improves computational effectiveness as well as introduces a new paradigm in the solution of complex engineering design challenges. The work introduces a new paradigm for optimizing BLDC motors that holds promises for future technologies in electric mobility, robotics, and industrial automation. AEPAO paves the way for the next-generation high-performance BLDC motors to gain wider acceptance in future applications through efficient motor optimization and mitigation of performance limitations.