Adaptive hybrid MPPT strategy for PEM fuel cells using type 2 fuzzy logic tuned with lightning search and whale optimization

Abstract

Proton exchange membrane fuel cells (PEMFCs) encounter critical efficiency constraints arising from their inherently nonlinear electrochemical and power characteristics under dynamically fluctuating environmental and load conditions, such as abrupt temperature and pressure variations. Conventional MPPT techniques, including perturb & observe (P&O) and conductance increment (INC) algorithms are often plagued by suboptimal convergence dynamics, local minima entrapment, and insufficient real-time adaptability, resulting in significant power loss and system instability. To overcome these limitations, this study introduces an advanced MPPT framework founded on an Interval Type-2 Fuzzy Logic Controller (IT2FLC) optimized through a hybrid Lightning Search Algorithm and Whale Optimization Algorithm (LSA–WOA). The hybrid LSA–WOA meta-optimizer augments the controller's global exploration efficiency, mitigating local entrapment while dynamically tuning six key IT2FLC parameters to ensure optimal response adaptability. The proposed controller integrates a dual-layer inference mechanism that synergistically processes instantaneous power deviation and its rate of change, enabling self-regulated real-time adjustments. A high-fidelity PEMFC circuit model is developed to simulate internal voltage dynamics across a broad operational envelope temperature (273–400 K) and pressure (1–5 atm) conditions, with power regulation achieved through a Zeta DC-DC converter. The proposed method is rigorously validated under three test scenarios: steady-state conditions (343 K, 1 atm), rapid temperature fluctuations, and abrupt pressure changes. A comparative simulation study was conducted to evaluate the performance of the proposed method against several benchmark controllers, including FL, ANFIS, PSO, and GJOA-PI-PD. The results confirm its superiority, demonstrating faster transient responses and enhanced steady-state stability. Under nominal conditions, the proposed MPPT achieves 99.98% tracking efficiency with a rise time of 0.0801 s, a 5% settling time of 0.0818 s, and a residual steady-state error of merely 0.1010 W. Under dynamic perturbations, efficiency attains 99.99% with minimal oscillatory behavior and ultrafast convergence, demonstrating exceptional robustness. This work establishes a substantial advancement in PEMFC MPPT control by fusing the uncertainty-handling resilience of interval type-2 fuzzy logic with the global optimization proficiency of LSA–WOA, thereby enhancing energy extraction, control stability, and reliability in real-world renewable energy systems subject to stochastic environmental and load variations.