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

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

Engineering and characterization of avian coronavirus mutants expressing reporter proteins from the replicase gene

Avian coronavirus, also known as infectious bronchitis virus (IBV), belongs to the genus Gammacoronavirus and is the causative agent of infectious bronchitis, a highly contagious respiratory disease in the poultry industry. In virology studies, reverse genetics systems based on BACs are extremely valuable because they allow us to manipulate viral genes. In our study, we assembled the complete genome of the IBV strain Beaudette-FUB into an artificial bacterial chromosome (BAC), producing an infectious BAC clone. From this constructed IBV BAC clone, we successfully rescued infectious viruses with identical growth characteristics to the parental viruses. To establish genetically stable EGFP viruses, we then inserted the EGFP ORF into 11 putative cleavage sites of 3CLpro. Of these, we identified three insertion sites located at the outermost 3’ end of the replicase gene– between the coding sequences of Nsp13 (helicase), Nsp14 (RNA exonuclease), Nsp15 (RNA endonuclease), and Nsp16 (RNA methyltransferase) could tolerate heterologous genes in the IBV genome. Additionally, we found that fluorescent proteins expressed by the replicase gene can be efficiently cleaved by the 3CLpro and released from the replicase polyprotein. Furthermore, we also determined the genetic stability of these three EGFP-replicase viruses. Among them, the engineered Nsp13-EGFP-Nsp14 virus still exhibited high stability in DF-1 cells after 20 serial passages. The colocalization results showed that EGFP, together with dsRNA or RdRp, accumulated in the well-defined foci at the early stage of infection. When the infection progressed, EGFP proteins were produced and distributed throughout the cytoplasm. Our studies have shown that the replicase-EGFP viruses could be used to study viral replication and transcription, to screen antiviral drugs on a large scale, to develop multivalent vaccines, and even that the potential positions could be applied to other coronaviruses

Adaptive Multi-objective Optimization for Energy Efficient Interference Coordination in Multi-Cell Networks

In this paper, we investigate the distributed power allocation for multi-cell OFDMA networks taking both energy efficiency and inter-cell interference (ICI) mitigation into account. A performance metric termed as throughput contribution is exploited to measure how ICI is effectively coordinated. To achieve a distributed power allocation scheme for each base station (BS), the throughput contribution of each BS to the network is first given based on a pricing mechanism. Different from existing works, a biobjective problem is formulated based on multi-objective optimization theory, which aims at maximizing the throughput contribution of the BS to the network and minimizing its total power consumption at the same time. Using the method of Pascoletti and Serafini scalarization, the relationship between the varying parameters and minimal solutions is revealed. Furthermore, to exploit the relationship an algorithm is proposed based on which all the solutions on the boundary of the efficient set can be achieved by adaptively adjusting the involved parameters. With the obtained solution set, the decision maker has more choices on power allocation schemes in terms of both energy consumption and throughput. Finally, the performance of the algorithm is assessed by the simulation results.Comment: 29 page