New hybrid maximum power point tracking methods for fuel cell using artificial intelligent

Abstract

An efficient way to raise the proton exchange membrane fuel cell's (PEMFC's) power generation efficiency and power supply quality is to use maximum power point tracking (MPPT). Conventional MPPT approaches often have difficulty producing an effective control effect due to the PEMFC's inherent nonlinear characteristics. Another challenge for systems that track maximum power points is dealing with fast changes in operational conditions that affect FC's maximum power point (MPP). The main contribution of this study is the introduction of two artificial intelligence-based MPP control approaches for fuel cells operating under rapidly changing operating conditions. These methods are based on imperialist competitive algorithm-trained neural networks and adaptive neuro-fuzzy inference systems (ANFIS) (ICA NN). The proposed approaches determine the fuel cell voltage that corresponds to the maximum power point. Following that, a fuzzy logic controller is used to modify the duty cycle of a DC/DC boost converter for FC MPP tracking. The MATLAB environment is used to run simulations. The results of the proposed method are compared with those of the conventional fuzzy method. The results demonstrate that the suggested solutions function excellently in both normal operating conditions and quickly varying operating conditions. On the other hand, the suggested approaches can quickly locate and monitor the MPP of FC. Additionally, the suggested techniques increase the FC system's efficiency by absorbing more power.