Adaptive Tracking Constrained Controller Design for Solid Oxide Fuel Cells Based on a Wiener-Type Neural Network

Xia, Yan; Zou, Jianxiao; Yan, Wenxu; Li, Huayin

doi:10.3390/app8101758

Cited by 13 publications

(11 citation statements)

References 35 publications

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“…Those system models can either be derived in an equations-based form by representing phenomena such as heat conduction, heat convection, and exothermal reaction enthalpies [16]. Alternatively, data-driven options [5,6,29,30] are possible which identify the nonlinear dynamics by means of feedforward neural networks (approximating nonlinearities in the voltage-current characteristic as well as nonlinearities that can be traced back to Tafel's equation [1,25,31,32]). These neural network models are interfaced with linear dynamic elements to represent the thermal system behavior in terms of ordinary differential equations [33].…”

Section: Modeling Of the Electric Power Characteristic Of Solid Oxidementioning

confidence: 99%

“…For both formulations (30) and (31), the boundary of the admissible operating domain is represented by an infinitely large cost in (29). This infinite cost is attained on the boundary of the admissible operating domain, i.e., for those conditions at which the hydrogen mass flow is completely consumed by the production of the actual electric power P EL,k .…”

Section: Online Optimization Of the Electric Current And Hydrogen Masmentioning

confidence: 99%

“…If the system's optimization is initialized with an admissible operating point I k ,ṁ H 2 ,k satisfying the inequalities (30) and 31, it is guaranteed by the following optimization scheme that all subsequent operating points are also admissible.…”

Section: Online Optimization Of the Electric Current And Hydrogen Masmentioning

confidence: 99%

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Kalman Filter-Based Real-Time Implementable Optimization of the Fuel Efficiency of Solid Oxide Fuel Cells

Rauh

2021

Clean Technol.

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The electric power characteristic of solid oxide fuel cells (SOFCs) depends on numerous influencing factors. These are the mass flow of supplied hydrogen, the temperature distribution in the interior of the fuel cell stack, the temperatures of the supplied reaction media at the anode and cathode, and—most importantly—the electric current. Describing all of these dependencies by means of analytic system models is almost impossible. Therefore, it is reasonable to identify these dependencies by means of stochastic filter techniques. One possible option is the use of Kalman filters to find locally valid approximations of the power characteristics. These can then be employed for numerous online purposes of dynamically operated fuel cells such as maximum power point tracking or the maximization of the fuel efficiency. In the latter case, it has to be ensured that the fuel cell operation is restricted to the regime of Ohmic polarization. This aspect is crucial to avoid fuel starvation phenomena which may not only lead to an inefficient system operation but also to accelerated degradation. In this paper, a Kalman filter-based, real-time implementable optimization of the fuel efficiency is proposed for SOFCs which accounts for the aforementioned feasibility constraints. Essentially, the proposed strategy consists of two phases. First, the parameters of an approximation of the electric power characteristic are estimated. The measurable arguments of this function are the hydrogen mass flow and the electric stack current. In a second stage, these inputs are optimized so that a desired stack power is attained in an optimal way. Simulation results are presented which show the robustness of the proposed technique against inaccuracies in the a-priori knowledge about the power characteristics. For a numerical validation, three different models of the electric power characteristic are considered: (i) a static neural network input/output model, (ii) a first-order dynamic system representation and (iii) the combination of a static neural network model with a low-order fractional differential equation model representing transient phases during changes between different electric operating points.

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Section: Modeling Of the Electric Power Characteristic Of Solid Oxidementioning

confidence: 99%

Section: Online Optimization Of the Electric Current And Hydrogen Masmentioning

confidence: 99%

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Kalman Filter-Based Real-Time Implementable Optimization of the Fuel Efficiency of Solid Oxide Fuel Cells

Rauh

2021

Clean Technol.

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“…One class of such nonlinear modeling system is so-called block-oriented models that can be represented in various configurations where linear dynamic blocks and nonlinear static or dynamic subsystems are cascaded. e Hammerstein (H) model (static nonlinear block followed by a dynamic linear one) and the Wiener (W) system (a linear dynamic subsystem followed by a static nonlinear block) are the basic class of the cascaded systems which are widely used in many industrial practice engineering applications [14][15][16][17][18][19][20][21][22] and, therefore, the modeling approaches of such class of block-oriented models have received great attention for many years [23][24][25][26][27][28][29][30][31][32][33][34][35][36]. Hammerstein and Wiener systems are combined together to produce more complex subcategories, namely, Hammerstein-Wiener (HW) model (a linear block is cascaded between two nonlinear subsystems) and a Wiener-Hammerstein (WH) system (a nonlinear block is embedded between two linear blocks).…”

Section: Introductionmentioning

confidence: 99%

An Improved Method for Stochastic Nonlinear System’s Identification Using Fuzzy-Type Output-Error Autoregressive Hammerstein–Wiener Model Based on Gradient Algorithm, Multi-Innovation, and Data Filtering Techniques

et al. 2021

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This paper proposes an innovative identification approach of nonlinear stochastic systems using Hammerstein–Wiener (HW) model with output-error autoregressive (OEA) noise. Two fuzzy systems are suggested for the identification of the input and output nonlinear blocks of a proposed model from given input-output data measurements. In this work, the need for the commonly used assumptions including well-known structure of input and/or output nonlinearities and/or reversible nonlinear output is eliminated by replacing the intermediate variables and noise with their estimates. Four parametric estimation algorithms to identify the proposed fuzzy-type stochastic output-error autoregressive HW (FSOEAHW) model are derived based on backpropagation algorithm and multi-innovation and data filtering identification techniques. The proposed algorithms are improved backpropagation gradient (IBPG) algorithm, multi-innovation IBPG (MIIBPG) algorithm, a data filtering IBPG (FIBPG) algorithm, and a multi-innovation-based FIBPG (MIFIBPG) algorithm. The convergence of the parameter estimation algorithms is studied. The effectiveness of the proposed algorithms is shown by a given simulation example.

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“…Solid oxide fuel cells (SOFC) have received much attention over the last decade due to advantages, such as a high efficiency, quietness, multiple fuels, and the inexpensive catalyst nature [1][2][3][4]. One of the major challenges related to SOFCs is the short lifetime introduced by the high operating temperature.…”

Section: Introductionmentioning

confidence: 99%

A Theoretical Model for the Triple Phase Boundary of Solid Oxide Fuel Cell Electrospun Electrodes

et al. 2019

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Electrospinning is a new state-of-the-art technology for the preparation of electrodes for solid oxide fuel cells (SOFC). Electrodes fabricated by this method have been proven to have an experimentally superior performance compared with traditional electrodes. However, the lack of a theoretic model for electrospun electrodes limits the understanding of their benefits and the optimization of their design. Based on the microstructure of electrospun electrodes and the percolation threshold, a theoretical model of electrospun electrodes is proposed in this study. Electrospun electrodes are compared to fibers with surfaces that were coated with impregnated particles. This model captures the key geometric parameters and their interrelationship, which are required to derive explicit expressions of the key electrode parameters. Furthermore, the length of the triple phase boundary (TPB) of the electrospun electrode is calculated based on this model. Finally, the effects of particle radius, fiber radius, and impregnation loading are studied. The theory model of the electrospun electrode TPB proposed in this study contributes to the optimization design of SOFC electrospun electrode.

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Adaptive Tracking Constrained Controller Design for Solid Oxide Fuel Cells Based on a Wiener-Type Neural Network

Cited by 13 publications

References 35 publications

Kalman Filter-Based Real-Time Implementable Optimization of the Fuel Efficiency of Solid Oxide Fuel Cells

Kalman Filter-Based Real-Time Implementable Optimization of the Fuel Efficiency of Solid Oxide Fuel Cells

An Improved Method for Stochastic Nonlinear System’s Identification Using Fuzzy-Type Output-Error Autoregressive Hammerstein–Wiener Model Based on Gradient Algorithm, Multi-Innovation, and Data Filtering Techniques

A Theoretical Model for the Triple Phase Boundary of Solid Oxide Fuel Cell Electrospun Electrodes

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