An adaptive fuzzy logic controller (AFLC) for PEMFC fuel cell

Benchouia, Nedjem-Eddine; Derghal, A.; Mahmah, Bouziane; Madi, Belgacem; Khochemane, Lakhdar; Aoul, Elias Hadjadj

doi:10.1016/j.ijhydene.2015.05.189

Cited by 75 publications

(28 citation statements)

References 21 publications

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“…The intelligent, low-cost, and nonlinear controller is the fuzzy controller that is profit for very complex PEMFC system [15]. Problems in this research, the rule bases are not produced in a systematic method, and the stability is not proved.…”

Section: Introductionmentioning

confidence: 95%

Robust control design for air breathing proton exchange membrane fuel cell system via variable gain second‐order sliding mode

Mirrashid

Rakhtala

Ghanbari³

2018

Energy Science & Engineering

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The nonlinear and time‐dependent characteristic and unknown modeling uncertainty of proton exchange membrane fuel cell (PEMFC) such as complex electro‐chemical, thermal, and fluid mechanic phenomena make its controller design quite challenging. In this paper, a controller based on a super twisting algorithm (STA) with variable gains is proposed to control the air breathing system of PEMFC. The strategy includes regulating the oxygen excess ratio (λnormalO2) for preventing the stack oxygen starvation and maintaining optimum net power output in spite of external disturbances and model uncertainties. The proposed algorithm has the main advantages of the fixed gain STA, such as robustness against the disturbance and parametric uncertainties with the unknown boundary, chattering reduction, and finite time convergence. The Lyapunov analysis was proposed to assess the stability of the Variable Gain Super Twisting Algorithm (VGSTA). The results verified the effectiveness of the proposed controller with attaining robust regulation against uncertainties, disturbances, and noisy circumstance compared to fixed gain SOSM controllers.

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Section: Introductionmentioning

confidence: 95%

Robust control design for air breathing proton exchange membrane fuel cell system via variable gain second‐order sliding mode

Mirrashid

Rakhtala

Ghanbari³

2018

Energy Science & Engineering

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“…1 The chemical reaction at anode is: 2 2 → 4 + + 4 − (1) Electrons pass through the external electrical circuit to generate electric signal. In cathode side, the electrons and protons react with oxygen from air to produce water and heat as result of reaction Benchouia, et al, 2015, Beirami, et al, 2015, and Derbeli, et al, 2017 The chemical reaction at cathode is: 4 − + 4 + + 2 → 2 2…”

Section: Nonlinear Model Of Proton Exchange Membrane Fuel Cellsmentioning

confidence: 99%

A Neural Networks based Predictive Voltage-Tracking Controller Design for Proton Exchange Membrane Fuel Cell Model

Al-Araji¹,

Dhahad²,

Jaber³

2019

jcoeng

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In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based online auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking desired voltage and less energy consumption through investigating and comparing under random current variations with the minimum number of fitness evaluation less than 20 iterations.

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“…In [88], SVM is employed to model a PEMFC for real time and monitoring applications. Fuzzy logic control (FLC) principle is utilized in [89,90], in which an adaptive neuro-fuzzy inference system (ANFIS) is proposed for voltage modeling of PEMFC in high temperature condition and an adaptive FLC is used for adding the control of gas flow to a PEMFC model respectively. In [91], a black box approach is compared with a white box one and it is concluded that the black box model has higher accuracy.…”

Section: Black Box Based Identificationmentioning

confidence: 99%

Overview and benchmark analysis of fuel cell parameters estimation for energy management purposes

Kandidayeni

Macías

Amamou

et al. 2018

Journal of Power Sources

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Proton exchange membrane fuel cells (PEMFCs) have become the center of attention for energy conversion in many areas such as automotive industry, where they confront a high dynamic behavior resulting in their characteristics variation. In order to ensure appropriate modeling of PEMFCs, accurate parameters estimation is in demand. However, parameter estimation of PEMFC models is highly challenging due to their multivariate, nonlinear, and complex essence. This paper comprehensively reviews PEMFC models parameters estimation methods with a specific view to online identification algorithms, which are considered as the basis of global energy management strategy design, to estimate the linear and nonlinear parameters of a PEMFC model in real time. In this respect, different PEMFC models with different categories and purposes are discussed first. Subsequently, a thorough investigation of PEMFC parameter estimation methods in the literature is conducted in terms of applicability. Three potential algorithms for online applications, Recursive Least Square (RLS), Kalman filter, and extended Kalman filter (EKF), which has escaped the attention in previous works, have been then utilized to identify the parameters of two well-known semi-empirical models in the literature, Squadrito et. al and Amphlett et. al. Ultimately, the achieved results and future challenges are discussed.

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An adaptive fuzzy logic controller (AFLC) for PEMFC fuel cell

Cited by 75 publications

References 21 publications

Robust control design for air breathing proton exchange membrane fuel cell system via variable gain second‐order sliding mode

Robust control design for air breathing proton exchange membrane fuel cell system via variable gain second‐order sliding mode

A Neural Networks based Predictive Voltage-Tracking Controller Design for Proton Exchange Membrane Fuel Cell Model

Overview and benchmark analysis of fuel cell parameters estimation for energy management purposes

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