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This study proposes two artificial intelligence-based methods for maximum power point tracking in fuel cells, using imperialist competitive algorithm-trained neural networks and adaptive neuro-fuzzy inference systems. The methods aim to improve power generation efficiency and power supply quality in proton exchange membrane fuel cells. Simulation results show the proposed methods outperform conventional fuzzy methods.

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What they did

System
MFC

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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.

Key findings

  • The proposed methods can effectively track the maximum power point in fuel cells under rapidly changing operating conditions
  • Imperialist competitive algorithm-trained neural networks and adaptive neuro-fuzzy inference systems can accurately determine the fuel cell voltage corresponding to the maximum power point
  • The proposed methods demonstrate better performance compared to conventional fuzzy methods in terms of power generation efficiency and power supply quality

Keywords

Control theory (sociology)Maximum power principleDuty cycleMaximum power point trackingProton exchange membrane fuel cellController (irrigation)

Identifiers

DOI
10.1063/5.0144806
Journal
AIP Advances
Year
2023