Stochastic Methods for Parameter Estimation of Multiphysics Models of Fuel Cells

Alotto, Piergiorgio

doi:10.1109/tmag.2013.2283889

Cited by 11 publications

(12 citation statements)

References 10 publications

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“…The voltage round-trip efficiency (43) varies with the working points on v(j) and with the operating conditions (T, p, a w ). Fig.…”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, this cell could only operate in FC-mode, so that only FC-mode experimental points could be obtained. The parameters identification has been carried out by means of a stochastic optimization procedure [43] that is capable of automatically identifying the model parameters which allow the model performance to match the experimental data in a proper number of iterations avoiding to fall into local minima. Typically, the search space for each parameter ranges from one-tenth of an initial reference value taken from the literature to a tenfold higher value, i.e.…”

Section: Numerical Implementationmentioning

confidence: 99%

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Modeling the performance of hydrogen–oxygen unitized regenerative proton exchange membrane fuel cells for energy storage

Guarnieri

Alotto

Moro

2015

Journal of Power Sources

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“…The voltage round-trip efficiency (43) varies with the working points on v(j) and with the operating conditions (T, p, a w ). Fig.…”

Section: Resultsmentioning

confidence: 99%

Section: Numerical Implementationmentioning

confidence: 99%

Modeling the performance of hydrogen–oxygen unitized regenerative proton exchange membrane fuel cells for energy storage

Guarnieri

Alotto

Moro

2015

Journal of Power Sources

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“…A more ambitious challenge consists in identifying several physical parameters of the materials of a PEMFC by means of an optimization approach. An algorithm of this kind, built over an early investigation on the capability of stochastic methods to deal with FCs [52], uses a detailed multiphysical performance model that employs such parameters and takes into account some physical control quantities [53]. A straightforward use of a stochastic optimizer with a large number of unknowns and a weakly constrained nonlinear problem can result in duplicity.…”

Section: Identificationmentioning

confidence: 99%

Distributed and Lumped Parameter Models for Fuel Cells

Guarnieri¹,

Alotto²,

Moro³

2020

Thermodynamics and Energy Engineering

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The chapter presents a review of modeling techniques for three types of fuel cells that are gaining industrial importance, namely, polymer electrolyte membrane (PEMFC), direct methanol (DMFC), and solid oxide (SOFC) fuel cells (FCs). The models presented are both multidimensional, suitable for investigating distributions, gradients, and inhomogeneities inside the cells, and zero-dimensional, which allows for fast analyses of overall performance and can be easily interfaced with or embedded in other numerical tools, for example, for studying the interaction with static converters needed to control the electric power flow. Thermal dependence is considered in all models. Some special numerical approaches are presented, which allow facing specific problems. An example is the Proper Generalized Decomposition (PDG) that allows overcoming the challenges arising from the extreme aspect ratio of the thin electrolyte separating anode and cathode. The use of numerical modeling as part of identification techniques, particularly by means of stochastic optimization approaches, for extracting the material parameters from multiple in situ measurements is also discussed and examples are given. Merits and demerits of the different models are discussed.

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“…It should be noted that in [45] ten parameters of a new semi-empirical model, which is based on [43] with an additional cathode inlet pressure actor, are estimated by an AC-POA, but only its common parameters with other manuscripts is reported in Table 4. The other manuscripts, which are based on optimization algorithms, have worked on the models with more dynamic properties [67][68][69][70][71][72][73]. A summary of the methods employed in these papers is given in Table 5.…”

Section: Fig 2 Pemfc Models Categoriesmentioning

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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Stochastic Methods for Parameter Estimation of Multiphysics Models of Fuel Cells

Cited by 11 publications

References 10 publications

Modeling the performance of hydrogen–oxygen unitized regenerative proton exchange membrane fuel cells for energy storage

Modeling the performance of hydrogen–oxygen unitized regenerative proton exchange membrane fuel cells for energy storage

Distributed and Lumped Parameter Models for Fuel Cells

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

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