Modeling of a Fuel Cell with Boost Converter Using Artificial Neural Network

The adoption of fuel cell technology as a sustainable renewable energy solution because it delivers efficient, clean energy conversion. There is a need to develop an effective Maximum Power Point Tracking (MPPT) algorithm to reach maximum power extraction from fuel cell systems. This paper introduces an improved Artificial Neural Network (ANN)-based Maximum Power Point Tracking (MPPT) control method for fuel cell systems whose boost converter forms an integral part of their operation. The proposed ANN-based MPPT utilizes simulation-determined fuel cell operating datasets for training because it surpasses Perturb and Observe (P&O) and Incremental Conductance (IC) methods regarding adaptive power tracking accuracy. The MPPT system based on ANN achieved in MATLAB/Simulink simulations shows improved performance by reaching convergence 25% quicker, as well as lower steady-state oscillations by about 30% and better tracking accuracy, which improved system efficiency by 8% when compared to traditional approaches. The research demonstrates that ANN-based MPPT achieves superior performance under dynamic fuel cell conditions, improving energy harvesting successes in renewable systems.