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

Adaptive estimation of time-varying parameters with application to roto-magnet plant

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper presents an alternative adaptive parameter estimation framework for nonlinear systems with time-varying parameters. Unlike existing techniques that rely on the polynomial approximation of time-varying parameters, the proposed method can directly estimate the unknown time-varying parameters. Moreover, this paper proposes several new adaptive laws driven by the derived information of parameter estimation errors, which achieve faster convergence rate than conventional gradient descent algorithms. In particular, the exponential error convergence can be rigorously proved under the well-recognized persistent excitation condition. The robustness of the developed adaptive estimation schemes against bounded disturbances is also studied. Comparative simulation results reveal that the proposed approaches can achieve better estimation performance than several other estimation algorithms. Finally, the proposed parameter estimation methods are verified by conducting experiments based on a roto-magnet plant.Peer ReviewedPostprint (author's final draft

Real-time adaptive parameter estimation for a polymer electrolyte membrane fuel cell

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIn this paper, we propose real-time adaptive parameter estimation methods for a polymer electrolyte membrane fuel cell (PEMFC) to facilitate the modeling and the subsequent control synthesis. Specifically, the electrochemical model of this fuel cell is in a nonlinearly parametric formulation. Hence, most of existing parameter estimation techniques for PEMFC mainly rely on the optimization approaches, requiring heavy computational costs or even offline implementation. In comparison to those methods, real-time adaptive parameter estimation methods for nonlinearly parametric system are developed in this paper. First, the nonlinearly parametric function is linearized by using the Taylor series expansion. Then, adaptive parameter estimation methods are proposed for estimating the constant or time-varying parameters of PEMFC. Different from the well-recognized adaptive parameter estimation methods, the proposed adaptive laws are driven by the extracted estimation errors, so that exponential convergence of the parameter estimation error can be guaranteed, without using any predictors or observers. Finally, practical experiments in a H-100 PEMFC system are conducted, which illustrate satisfactory performances of the presented parameter estimation methods under different operation scenariosPeer ReviewedPostprint (author's final draft

Composite PID control with unknown dynamics estimator for rotomagnet plant

Although PID control has been widely used in practical engineering, its ability to reject external disturbance and to handle severe nonlinearities should be further enhanced. In this paper, we present a simple robust unknown dynamics estimation, which can be easily incorporated into PID control to achieve satisfactory control performance for a rotomagnet plant subject to period disturbance. The use of this estimator together with PID control leads to a feedforward like composite control framework. Unlike other estimators, only low-pass filter operations on the input and output and simple algebraic operations are needed to construct our estimator, while exponential convergence can be guaranteed. Numerical simulations are given to show the validity of the proposed estimator and composite PID control.Postprint (published version

Adaptive nonlinear parameter estimation for a proton exchange membrane fuel cell

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksParameter 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.Peer ReviewedPostprint (author's final draft

Predictive value of radiomics-based machine learning for the disease-free survival in breast cancer: a systematic review and meta-analysis

PurposeThis study summarized the previously-published studies regarding the use of radiomics-based predictive models for the identification of breast cancer-associated prognostic factors, which can help clinical decision-making and follow-up strategy.Materials and methodsThis study has been pre-registered on PROSPERO. PubMed, Embase, Cochrane Library, and Web of Science were searched, from inception to April 23, 2022, for studies that used radiomics for prognostic prediction of breast cancer patients. Then the search was updated on July 18, 2023. Quality assessment was conducted using the Radiomics Quality Score, and meta-analysis was performed using R software.ResultsA total of 975 articles were retrieved, and 13 studies were included, involving 5014 participants and 35 prognostic models. Among the models, 20 models were radiomics-based and the other 15 were based on clinical or pathological information. The primary outcome was Disease-free Survival (DFS). The retrieved studies were screened using LASSO, and Cox Regression was applied for modeling. The mean RQS was 18. The c-index of radiomics-based models for DFS prediction was 0.763 (95%CI 0.718-0.810) in the training set and 0.702 (95%CI 0.637-0.774) in the validation set. The c-index of combination models was 0.807 (95%CI0.736-0.885) in the training set and 0.840 (95%CI 0.794-0.888) in the validation set. There was no significant change in the c-index of DFS at 1, 2, 3, and over 5 years of follow-up.ConclusionThis study has proved that radiomics-based prognostic models are of great predictive performance for the prognosis of breast cancer patients. combination model shows significantly enhanced predictive performance.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022332392

Modeling and Control for a Fuel Cell System

Tesis doctoral presentada para lograr el título de Doctor por la Universidad Politécnica de Cataluña.--2021-07-01Les piles de combustible són dispositius electroquímics que permeten transformar l'hidrogen i l'oxigen en energia elèctrica i calor. La degradació durant l'operació pot reduir la vida útil de les piles de combustible, que és el principal obstacle per a una utilització comercial. Per tant, es requereixen models fiables i estratègies de control adequades per entendre el seu comportament i garantir el seu funcionament. Aquesta tesi doctoral té com a objectiu desenvolupar un model orientat al control, proposar mètodes d'estimació de paràmetres desconeguts i dissenyar estratègies de control adequades per als sistemes de piles de combustible.En primer lloc, es proposa un model orientat al control d'un sistema de pila de combustible i es realitza la seva anàlisi. A continuació, es proposa una estratègia d'ajust paramètric basat en optimització per eixams de partícules combinada amb un mètode de descens per gradient clàssic. El mètode es valida utilitzant dades experimentals provinents de piles de combustible reals. Posteriorment, es formula una anàlisi sistemàtica dels punts d'equilibri. Els resultats de l'anàlisi mostren la regió de màxima eficiència on es pot obtenir la màxima potència elèctrica en estat estacionari per cada temperatura. A més, la teoria de Lyapunov s'utilitza per caracteritzar l'estabilitat local dels punts d'equilibri. A més, es fa una comparació entre el model inicial no lineal i el model linealitzat. Finalment, s'analitza la resposta en freqüència del model linealitzat, que proporciona informació clau sobre el disseny del sistema de control per tal d’operar de manera eficient el sistema de piles de combustible.A més, es presenten mètodes d'estimació de paràmetres adaptatius per a un sistema de pila de combustible. Val a dir que la majoria de tècniques adaptatives d'estimació de paràmetres són aplicables a sistemes amb paràmetres que intervenen linealment. A més, l'estimació de paràmetres que varien en el temps continua sent un problema obert. No obstant això, la parametrització no lineal i els paràmetres que varien en el temps són característiques intrínsecs en els models de piles de combustible. Per tant, es consideren els sistemes parametritzats linealment i no linealment. També s'estudien mètodes d’estimació de paràmetres adaptatius amb paràmetres variables i constants. En concret, les propietats de la convexitat i la monotonicitat s'utilitzen per primera vegada per separat per tal de linealitzar les funcions paramètriques no lineals. A continuació, les lleis adaptatives es dissenyen utilitzant els errors d'estimació extrets de manera que es pot garantir la convergència exponencial de l'error d'estimació de paràmetres.Finalment, es donen resultats experimentals sobre un sistema pràctic de piles de combustible per verificar l'eficàcia dels esquemes proposats.A més, hi ha incertesa en el modelat, pertorbacions externes i soroll durant els processos de modelatge i experimentació. A causa d’aquestes dinàmiques desconegudes, les estratègies de control convencionals poden no aconseguir els resultats esperats. Per abordar aquest problema, es proposa un controlador compost proporcional-integral (PI) amb un estimador de dinàmica del sistema desconegut per a un sistema de pila de combustible. Per ser específics, l'estratègia de control es desenvolupa reduint la temperatura de l'aire d'entrada mitjançant l'augment del flux massiu d'aire per tal d’eliminar l'excés de calor. A més, es proposa un estimador de dinàmica del sistema desconegut per tal de compensar l'efecte de la dinàmica desconeguda. La construcció de l'estimador es dissenya a través de la recerca d'una varietat invariant que impliqui la relació entre variables conegudes i la varietat desconeguda. A més, l'estimador proposat es combina fàcilment amb l'estratègia de control de PI proposada i garanteix la convergència exponencial dels errors estimats. Finalment, els resultats experimentals il·lustren l’eficàcia de l'estratègia de control proposada

Signal Analysis of Fretting Damages on Electrial Connector Systems

Electrical connectors are widely utilized for signal communications in automotive electronic systems whose performance is related to the reliability of the entire system. Electrical connectors are frequently affected by the engine vibration, resulting in fretting damages on electrical connector. In this thesis, the main propose is to find a signal analysis method to predict the fretting damages on fuel pump connector induced by engine vibration. The data of the fuel pump connector is studied from a vibration test of the four-cylinder engine and the dominating frequencies are used in the fretting test to verify the analysis method. The fretting damage is identified through visual inspection by microscope. The model of the connector is built in COMSOL to explain the fretting on the contact surfaces. The results present the signal analysis method can be directly used to predict the risk of fretting damages during the engine vibration. Some significant frequencies are pointed out as guidelines for future tests and optimization