Temperature-Adaptive Fuel Cell MPP Tracking by Using Drone Squadrons Optimisation with Inductive SC DC-DC Converter

The growing demand for clean and reliable energy has accelerated the adoption of renewable energy sources for various applications, including electric vehicles. However, conventional solar-based renewable energy systems often fail to deliver stable and uniform power output, making them unsuitable for hydrogen-based electric vehicle applications. Fuel cell technology, particularly Polymer Electrolyte Fuel Cell systems, has emerged as a promising alternative due to their continuous power delivery, low operating temperature, fast response, and compact design. Despite these advantages, fuel cells exhibit highly nonlinear power characteristics, and their output voltage is typically insufficient for direct use in automotive applications — two critical challenges that remain inadequately addressed in the existing literature. To overcome these limitations, this paper proposes a Variable Step Drone Squadron Optimization-based Maximum Power Point Tracking technique to linearize the nonlinear power output of the fuel cell and achieve fast, accurate tracking with minimal iterations. The proposed approach is benchmarked against several established methods, including Modified Perturb & Observe, Incremental Conductance with Step Change, Multilayer Neural Network, Radial Basis Function Neural Network, and Fuzzy Logic with Adaptive Step controllers. Furthermore, a novel Inductive Network Switched Capacitor Converter is developed to boost the fuel cell's low output voltage to levels suitable for electric vehicle consumers. The proposed system is validated through MATLAB-based simulations and further verified experimentally using a programmable DC supply, demonstrating superior performance in tracking speed, voltage gain, and overall efficiency.