A Hybrid MPPT Approach for BLDC Motor Speed Control Using Adaptive PI and Boost Converter

MPPT is crucial for optimizing the efficiency of PV systems. However, conventional methods such as Perturb and Observe (P&O) and Incremental Conductance (I&C) suffer from slow convergence, steady-state oscillations, and failure to track the global maximum power point (GMPP) under partial shading conditions (PSC). To address these limitations, this paper proposes a hybrid MPPT strategy integrating Incremental Conductance (I&C) and spider Monkey Optimization (SMO). The I&C method ensures rapid tracking under uniform irradiance, while the SMO algorithm is activated under PSC to identify the true GMPP, overcoming local maxima issues. The extracted power is regulated using a boost converter to charge a battery, which supplies an inverter-fed Brushless DC (BLDC) motor. A closed-loop PI controller with an adaptive mechanism ensures precise speed control, minimizing torque ripples and enhancing system stability. Simulation results validate the proposed approach, demonstrating higher MPPT efficiency, reduced power loss, and improved motor performance under dynamic conditions. The proposed system enhances the reliability of solar-powered BLDC motor drives, making it a viable solution for electric vehicle and industrial automation applications.Several hybrid MPPT strategies have been explored in literature, including combinations of I&C withHybrid MPPT strategies have been explored, combining I&C with Particle Swarm Optimization (PSO), Genetic Algorithms, and Grey Wolf Optimization (GWO), each addressing various trade-offs between speed and global accuracy. Compared to these, SMO offers a better balance of exploration and convergence control.