An Efficient Parameter Estimation Algorithm for Proton Exchange Membrane Fuel Cells

Abdel‐Basset, Mohamed; Mohamed, Reda; Chang, Victor

doi:10.3390/en14217115

Cited by 15 publications

(1 citation statement)

References 63 publications

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“…This problem is specifically attributable to the original GTO's insufficient exploitation ability. Moreover, equality between exploration and exploitation processes may degrade the algorithm's performance in instances where more exploration operators are required and vice versa [25]. Consequently, training the proposed WNN-based NARMA-L2 in this work resulted in insufficient precision.…”

Section: Modified Artificial Gorilla Troops Optimization Algorithm (M...mentioning

confidence: 95%

An Intelligent Feedforward Controller Utilizing a Modified Gorilla Troops Optimization for Nonlinear Systems

Mahmood¹,

Lufty²

2022

MMEP

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This paper presents an intelligent feedforward controller based on the feedback linearization approach to control nonlinear systems. In particular, the nonlinear autoregressive moving average (NARMA-L2) network is trained to reproduce the forward dynamics of the controlled system. Consequently, the trained NARMA-L2 network can be immediately integrated into the inverse feedforward control (IFC) structure. In order to improve the NARMA-L2 structure's ability to approximate nonlinear systems, the NARMA-L2 controller is comprised of two wavelet neural networks (WNNs). In addition, the RASP1 function was used as the mother wavelet function in the structure of the WNN rather than the more common Mexican Hat, Gaussian, and Morlet functions. To prevent the limitations of gradient descent (GD) methods, an artificial gorilla troops optimization (GTO) algorithm is used to determine the optimal settings for the NARMA-L2 inverse controller parameters. In particular, a modified version of the GTO algorithm, which is called the Modified GTO (MGTO) algorithm, is proposed in this work for training the NARMA-L2 inverse controller. This algorithm has demonstrated superior optimization outcomes in comparison to other methods. The effectiveness of the proposed control strategy is demonstrated using two nonlinear dynamical systems. Specifically, several evaluation tests are used to assess the effectiveness of the WNN-based NARMA-L2 in terms of control accuracy and robustness against external disturbances in each of the systems under consideration. These tests clearly demonstrated the effectiveness of the control system. Finally, a comparison study showed that the proposed WNN-based NARMA-L2 controller achieved better control results compared to the multilayer perceptron (MLP) and the radial basis function (RBF)-based NARMA-L2 controllers.

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Section: Modified Artificial Gorilla Troops Optimization Algorithm (M...mentioning

confidence: 95%

An Intelligent Feedforward Controller Utilizing a Modified Gorilla Troops Optimization for Nonlinear Systems

Mahmood¹,

Lufty²

2022

MMEP

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An improved chicken swarm optimization algorithm for extracting the optimal parameters of proton exchange membrane fuel cells

Ayvaz

2022

Intl J of Energy Research

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Summary In this study, an improved variant of chicken swarm optimization (CSO), named I‐CSO, is proposed to find the unknown parameters of the proton exchange membrane fuel cell (PEMFC) models. Although the basic CSO has a well‐established population hierarchy mechanism that gives it an important advantage over its competitors, it suffers from premature convergence and can be easily trapped into the local optima because of inadequate use of population information in the update rule of the rooster's position. In the proposed I‐CSO, this shortcoming is addressed by introducing a new learning strategy for the roosters, which play leadership roles in the foraging behavior of the chicken swarm, to improve the algorithm convergence capability. Moreover, an adaptive inertia weight is introduced to make the algorithm more stable by striking a better balance between the exploration and exploitation phase. The sum of absolute error between the actual and estimated voltage outputs of the stack is suggested as the objective function to perform the optimization. Besides the suggested one, two other objective functions are also used to evaluate the impact of objective function choice on the optimization results. The test of the method is performed on two commercial PEMFCs, which are BCS 500‐W Stack and NedStack PS6, and the results of I‐CSO are compared with those of other competitive algorithms published in the literature. The final results show that the use of the proposed I‐CSO with the suggested objective function demonstrates excellent performance in estimating the PEMFC model parameters with fewer errors.

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Control‐oriented estimation of the exchange current density in PEM fuel cells via stochastic filtering

Aguilar

Andrade‐Cetto

Husar

2022

Intl J of Energy Research

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Increasing efficiency and durability of fuel cells can be achieved through advanced model-based optimal control of its operating conditions, and the efficient online estimation of fuel cell parameters and internal states is fundamental for the implementation of such advanced controllers. The exchange current density is a driving parameter of performance for the catalyst layer of proton exchange membrane fuel cells (PEMFC). This study presents a control-oriented, stochastic filtering approach for online, continuous estimation of the exchange current density in low-temperature PEMFCs. The fuel cell is framed as a Markov model where the exchange current density is posed as the stochastic hidden state. The physics-based static equation of the exchange current density is converted into a state transition equation. This transition equation

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An Efficient Parameter Estimation Algorithm for Proton Exchange Membrane Fuel Cells

Cited by 15 publications

References 63 publications

An Intelligent Feedforward Controller Utilizing a Modified Gorilla Troops Optimization for Nonlinear Systems

An Intelligent Feedforward Controller Utilizing a Modified Gorilla Troops Optimization for Nonlinear Systems

An improved chicken swarm optimization algorithm for extracting the optimal parameters of proton exchange membrane fuel cells

Control‐oriented estimation of the exchange current density in PEM fuel cells via stochastic filtering

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