Design and evaluation of adaptive neural fuzzy-based pressure control for PEM fuel cell system

Phan, Van Du; Trinh, Hoai-An; Ahn, Kyoung Kwan

doi:10.1016/j.egyr.2022.09.036

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…However, the model adaptive control can adapt to the change in system parameters and update the model parameters according to the control period to realize the accurate control of gas supply pressure [78]. Trinh et al [79] adopted a super-twisted sliding mode control (STSMC) based on an adaptive neuro-fuzzy inference system (ANFIS). As shown in Figure 9, the error, the integral of the error, and the derivative of the error are the inputs of the ANFIS.…”

Section: Pressure Control Of Adsmentioning

confidence: 99%

Research Progress on Gas Supply System of Proton Exchange Membrane Fuel Cells

Cheng,

Wu,

Mou

et al. 2024

Processes

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Proton exchange membrane fuel cells (PEMFCs) are attracting attention for their green, energy-saving, and high-efficiency advantages, becoming one of the future development trends of renewable energy utilization. However, there are still deficiencies in the gas supply system control strategy that plays a crucial role in PEMFCs, which limits the rapid development and application of PEMFCs. This paper provides a comprehensive and in-depth review of the PEMFC air delivery system (ADS) and hydrogen delivery system (HDS) operations. For the ADS, the advantages and disadvantages of the oxygen excess ratio (OER), oxygen pressure, and their decoupling control strategies are systematically described by the following three aspects: single control, hybrid control, and intelligent algorithm control. Additionally, the optimization strategies of the flow field or flow channel for oxygen supply speeds and distribution uniformity are compared and analyzed. For the HDS, a systematic review of hydrogen recirculation control strategies, purge strategies, and hydrogen flow control strategies is conducted. These strategies contribute a lot to improving hydrogen utilization rates. Furthermore, hydrogen supply pressure is summarized from the aspects of hybrid control and intelligent algorithm control. It is hoped to provide guidance or a reference for research on the HDS as well as the ADS control strategy and optimization strategy.

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Section: Pressure Control Of Adsmentioning

confidence: 99%

Research Progress on Gas Supply System of Proton Exchange Membrane Fuel Cells

Cheng,

Wu,

Mou

et al. 2024

Processes

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“…In [18], a functional order fuzzy PID controller was developed for a fuel cell, utilizing a neural network optimization approach to update the PID parameters [18]. Authors in [19] concentrate on super-twisting sliding mode and adaptive ANFIS procedure to increase stack life of the PEMFC. Ref.…”

Section: Introductionmentioning

confidence: 99%

Design of a Fuzzy Adaptive Voltage Controller for a Nonlinear Polymer Electrolyte Membrane Fuel Cell with an Unknown Dynamical System

Ghasemi,

Sedighi,

Ghasemi

et al. 2023

Sustainability

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This paper presents a fuzzy adaptive controller (FAC) for improving the efficiency and stability of fuel cells, assuming that the nonlinear dynamic model of the system is unknown. In polymer electrolyte membrane fuel cells, the output voltage should be controlled within a given interval. In contrast to prior studies that focused on designing controllers for known dynamical models of PEM fuel cells, the suggested approach addresses the real-world case of a PEM fuel cell with unknown dynamics. An intelligent technique is identified in the suggested strategy to approximate the state-space model of fuel cells to manage unknown functions. On an unknown model of fuel cells, traditional adaptive and fuzzy adaptive controllers are both implemented and compared. The main advantages of the proposed methodology are (1) stability of the closed-loop system using Lyapunov, (2) robustness against external disturbances, (3) application of the FAC to a PEM fuel cell, (4) convergence of the tracking error to 0, and (5) overcoming both unknown dynamics and uncertainty in the system. The most important and valuable advantages of the proposed system are its robustness, tracking error convergence, and Lyapunov stability. This manuscript aims to illustrate the responsiveness and fluency of the proposed procedure using a mathematical formulation of a multi-quadrotor system. As a result, the FAC is more efficient than the traditional one. To validate the controller performance, both the adaptive and fuzzy adaptive controllers are applied to a numerical model of a fuel cell and then compared.

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