Adaptive Nonlinear Parameter Estimation for a Proton Exchange Membrane Fuel Cell

Abstract

Parameter estimation is vital for modeling and control of fuel cell systems. However, the nonlinear parameterization is an intrinsic characteristic in the fuel cell models such that classical parameter estimation schemes developed for linearly parameterized systems cannot be applied. In this paper, an alternative framework of adaptive parameter estimation is designed to address the real-time parameter estimation for fuel cell systems. The parameter estimation can be divided into two cascaded components. First, the dynamics with the unknown parameters are estimated by a new unknown system dynamics estimator (USDE). Inspired by an invariant manifold, this USDE is designed by applying simple filter operations such that the information of the state derivative is not required. Secondly, an adaptive law driven by the function approximation error is proposed for recovering unknown model parameters. Exponential convergence of the estimated parameters to the true values can be proved under the monotonicity condition. Finally, experimental results on a practical proton exchange membrane fuel cell system are given to verify the effectiveness of the proposed schemes.

Parameter Estimation of Two Classes of Nonlinear Systems with Non-separable Nonlinear Parameterizations

“…The interested reader is refered to (Xing et al, 2022) where a detailed review of the literature on parameter estimation of fuel cell systems is reported.…”

“…Two often encountered cases are cos(θ i •h i (u, y)) or e θi•hi (u,y) . In particular, the last example appears in many physical processes including Arrenhius laws (Silberberg, 2006), biochemical reactors (Dochain, 2003), friction models (Armstrong-Hélouvry et al, 1994), windmill systems (Bobtsov et al, 2022b), fuel cell systems (Xing et al, 2022), photovoltaic arrays (Bobtsov et al, 2022a) and models of elastic moments (Schauer et al, 2005;Sharma et al, 2012;Yang and de Queiroz, 2018). This paper is devoted to the development of a systematic methodology for the parameter identification of systems containing this kind of exponential terms.…”

Real-time parameter estimation of polymer electrolyte membrane fuel cell in absence of excitation

“…It should be remarked that the knowledge of these parameters is crucial for deploying model-based PEMFC algorithms. For this reason, there is a necessity of exploiting real-time parameter estimation algorithms to compute online the unknown model parameters from easily measurable data [13][14][15][16][17][18].…”

Parameters identification for the nonlinear systems via target system-based method with intermittent adjustment

“…Nonlinear systems are widely studied in the fields of natural and industrial engineering technology, such as negative resistance oscillators in electric circuits [11], artificial neural networks simulating biological structures [18] and so on. The phenomena of chaos, bifurcation and strange attractors of nonlinear systems are increasingly of interest to scholars, making their applications in the fields of biology [32], chemistry [9,26], meteorology [21], economics [23], physics [34] and engineering technology [20] more widespread as well.…”

“…However, models established for specific problems often have some unknown parameters, so it is crucial to identify the unknown parameters in the models with the help of experimental data. Many methods existing in current researches for parameters identification are based on the stability theory of dynamical systems, such as the synchronization method based on the LaSalle's principle [16,10,28], Lyapunov stability method [28], adaptive method [26,35,27] and many other methods [6,22,24]. In order to realize the identification of unknown parameters of a system, synchronization will be an intermediate step generally.…”