Nonlinear Internal Model Control of Pem Fuel Cell

Bedi, Pallavi; Methekar, Ravi; Patwardhan, Sachin C.; Prasad, Vinay; Gudi, Ravindra D.

doi:10.3182/20070606-3-mx-2915.00135

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Table 1 gives a brief summary of the discussed PEMFC models. [94][95][96][97][98][99][100], which fit to none of the categories. Table 2 summarizes the advantages and disadvantages of these methods.…”

Section: Fig 2 Pemfc Models Categoriesmentioning

confidence: 99%

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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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Section: Fig 2 Pemfc Models Categoriesmentioning

confidence: 99%

“…In [92], the Volterra and Wiener model methods are utilized to obtain a linear PEMFC model for vehicular applications. In [93], the nonlinear black box time series model of [94] and the proposed PEMFC control approach of [95] are combined to follow the optimum operating points of the fuel cell.…”

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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“…The output power of the fuel cell is controlled by power conditioning devices such as DC/DC converters or in the case of fuel cells with low output power, used for experimental purposes, by an electronic load. The power output can be regulated by switching rules, adaptive control, an adaptive peak seeking controller, nonlinear internal model control, or model predictive control (MPC). , The problem of preventing air starvation is extensively studied, and many control configurations have been suggested. Safe operation is guaranteed by proper air management, an objective which can be accomplished by a feedback scheme, a reference governor, a static feedforward structure, , adaptive control algorithms, , or MPC schemes. , With regard to thermal management, various contributions have been reported that address the temperature objective using control schemes, such as proportional–integral (PI) controllers, , a variable structure control with a rapid-convergent sliding mode controller, a fuzzy-based approach with a genetic algorithm and multiparametric MPC controllers …”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Empowering the Performance of Advanced NMPC by Multiparametric Programming—An Application to a PEM Fuel Cell System

Ziogou

Pistikopoulos

Georgiadis

et al. 2013

Ind. Eng. Chem. Res.

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Fuel cell (FC) systems are part of a prominent key enabling technology for achieving efficient and carbon-free electricity generation and, as such, their optimum operation is of great importance. This work presents the combination of two advanced model predictive control (MPC) methodologies to guarantee the optimal operation of a polymer electrolyte membrane (PEM) fuel cell system. More specifically, at the core of the proposed framework is a nonlinear model predictive control (NMPC) formulation that solves online a nonlinear programming (NLP) problem using a simultaneous direct transcription optimization method. The performance of the NLP solver is enhanced by a warm-start initialization and a search space reduction (SSR) technique. A piecewise affine (PWA) approximation of the variable's feasible space is used to define the boundaries of the search space computed offline, using a multiparametric quadratic programming (mpQP) method. The proposed unified framework is developed and deployed online to an industrial automation system. The response of the multivariable nonlinear controller is assessed through a set of experimental studies, illustrating that the control objectives are achieved and the fuel cell system operates in a stable environment, regardless of the varying operating conditions.

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