Parameter estimation algorithm of H-100 PEM fuel cell

Best Oral Communication Award for Young Authors, atorgat pel comitè científic HYCELTEC 2019Polymer electrolyte membrane fuel cells (PEMFCs) have been recognized as one of the most promising eneygy conversion devices for commercial application due to their specific advantages, such as low operation temperature, zero pollutant emission, and high efficiency, etc. Since PEMFC is a highly nonlinear system and some parameters are related to the operation condition, most existing models are difficult to accurately predict the PEMFC characteristics. Thus, it is necessary to exploit parameter estimation methods for PEMFC to online determine the unknown model parameters by using easily measurable data to obtain concrete models. Most of the parameter estimations schemes for PEMFC have been designed based on intelligent optimization techniques. However, optimization methods cannot address the estimation problem online since they focus exclusively on offline searching procedure, which introduces heavy computational costs in the practical implementation and thus cannot be used in the real-time applications. Therefore, this paper aims to exploit real-time adaptive parameter estimation methods for a nonlinear parametric PEMFC system.Peer ReviewedAward-winningPostprint (author's final draft

Stability analysis of solid oxide fuel cell systems

Solid oxide fuel cells (SOFC), with entirely solid structure and high operating temperatures, have attracted research interest in recent years. Unlike other types of fuel cells, low electrode corrosion and low electrolyte looses are assumed due to its solid structure. Furthermore, the high operating temperatures enable SOFC to reach up to 50% to 65% efficiency with excellent impurity tolerance. However, there are several degradation mechanisms in SOFC, such as electrode delamination, electrolyte cracking, electrode poisoning, etc. Most of these degradations are related with the operation conditions, which can be optimized by appropriate control. Since most control algorithms are developed based on the mathematical models, it is important to obtain SOFC control-oriented models. Therefore, this paper aims to develop a SOFC control-oriented model, including the dynamics of inlet manifold, SOFC stack and outlet manifold. Moreover, equilibrium points are characterized and a stability around these equilibrium points analysis is performed. This information can provide guidelines for control strategies design.Postprint (published version

Maximal quantum Fisher information for general su(2) parametrization processes

Quantum Fisher information is a key concept in the field of quantum metrology, which aims to enhance the parameter accuracy by using quantum resources. In this paper, utilizing a representation of quantum Fisher information for a general unitary parametrization process, we study unitary parametrization processes governed by su(2) dynamics. We obtain the analytical expression for the Hermitian operator of the parametrization and the maximal quantum Fisher information. We find that the maximal quantum Fisher information over the parameter space consists of two parts, one is quadratic in the time and the other oscillates with the time. We apply our result to the estimation of a magnetic field and obtained the maximal quantum Fisher information. We further discuss a driving field with a time-dependent Hamiltonian and find the maximal quantum Fisher information of the driving frequency attains optimum when it is in resonance with the atomic frequency.Comment: 6 pages, 2 figures, published versio

Adaptive online parameter estimation algorithm of PEM fuel cells

Since most of fuel cell models are generally nonlinearly parameterized functions, existing modeling techniques rely on the optimization approaches and impose heavy computational costs. In this paper, an adaptive online parameter estimation approach for PEM fuel cells is developed in order to directly estimate unknown parameters. The general framework of this approach is that the electrochemical model is first reformulated using Taylor series expansion. Then, one recently proposed adaptive parameter estimation method is further tailored to estimate the unknown parameters. In this method, the adaptive law is directly driven by the parameter estimation errors without using any predictors or observers. Moreover, parameter estimation errors can be guaranteed to achieve exponential convergence. Besides, the online validation of regressor matrix invertibility are avoided such that computation costs can be effectively reduced. Finally, comparative simulation results demonstrate that the proposed approach can achieve better performance than least square algorithm for estimating unknown parameters of fuel cells.Postprint (published version

Robust adaptive control for robotic systems with guaranteed parameter estimation

In this paper, we propose a novel adaptive control scheme for robotic systems by incorporating the parameter error into the adaptive law. By carrying out filter operations, the robotic system is linearly parameterized without using the measurements of acceleration. Then a new adaptive algorithm is introduced to guarantee that the parameter error and control error exponentially converge to zero. In particular, we provide an intuitive method to verify the standard PE condition for the parameter estimation. The robustness against disturbances is also studied and comparisons to several adaptive laws are provided. Simulations with a realistic robot arm are presented to validate the improved performance.</p