Elena Breaz

Dynamic Phenomena Coupling Analysis and Modeling of Proton Exchange Membrane Fuel Cells

Dynamic variable coupling analysis is an important method to properly design a control structure for complex multivariable and multi-physical dynamic systems, such as fuel cells. The fuel cell is an electrochemical energy conversion device which includes different inter-coupled dynamic phenomena in electrical, fluidic, and thermal domains. In order to achieve optimized fuel cell performance, different operation variables, such as fuel cell temperature, inlet air flow rate, hydrogen pressure, and membrane water content, need to be properly controlled. In this paper, variable coupling analyses of fuel cell dynamic behaviors are presented and discussed based on a proton exchange membrane fuel cell (PEMFC) dynamic model, which considers in particular the transient phenomena in both fluidic and thermal domain. The analyses of dynamic phenomena step responses are conducted using the relative gain array for various control input variables. Quantitative analyses of coupling effects in different physical domains are shown and discussed. The analysis results can be used to optimize the controller design for fuel cell system.

A Modified Relevance Vector Machine for PEM Fuel-Cell Stack Aging Prediction

Proton exchange membrane fuel cells (PEMFCs) are considered as a potential candidate in the green-energy applications in the near future. Comparing with other energy options, the PEMFCs need only hydrogen and air during operation. Meanwhile, as a by-product during operation, water is produced. This energy-conversion process is 100% eco-friendly and completely unharmful to the environment. However, PEMFCs are vulnerable to the impurities of hydrogen or fluctuation of operational condition, which could cause the degradation of output performance over time during operation. Thus, the prediction of the performance degradation is critical to the PEMFC system. In this work, a novel PEMFC performance-forecasting model based on a modified relevance vector machine (RVM) has been proposed, followed by a comparison with the approach of classic support vector machine (SVM). First, the theoretical formulation of RVM is briefly introduced, then the implementation steps of RVM using the experimental aging data sets of PEMFC stack output voltage are presented. By considering the specific feature of aging data-prediction problem, an innovative modified RVM formulation is proposed. The results of proposed modified RVM method are analyzed and compared to the results of SVM. The results have demonstrated that the modified RVM can achieve better performance of prediction than SVM, especially in the cases with relatively small training data sets. This novel method based on modified RVM approach has been demonstrated to show its effectiveness on forecasting the performance degradation of PEMFCs.

A modified relevance vector machine for PEM fuel cell stack aging prediction

Proton Exchange Membrane Fuel Cells (PEMFCs) are considered as a potential candidate in the green energy applications in the near future. The fuel cells show multiple advantages compared to conventional energy sources. They need only hydrogen and air during operation, meanwhile, produce only water which is 100% environmental friendly. However, PEMFCs are vulnerable to the impurities of hydrogen or fluctuation of operational condition etc., which can lead to the degradation of output performance over time when operating. The prediction of the performance degradation is quite important for the PEMFC system management. In this work, a novel prediction method based on a modified Relevance Vector Machine (RVM) is proposed, followed by a comparison with classic Support Vector Machine (SVM) approach. Firstly, the mathematical theory of RVM is explained, then the implementation of RVM using the experimental aging data sets of PEMFC stack output voltage is discussed. By considering the specific feature of aging data prediction problem, an innovative modified RVM formulation is proposed. The results from proposed RVM method are analyzed and compared with the results getting from SVM. The results have demonstrated that, the RVM can achieve better performance than the SVM, especially in the cases with relatively small initial experimental data sets. This novel method based on modified RVM approach has been demonstrated to be a good candidate to predict the degradation of output performance of PEMFCs.

Mathematical modeling of proton exchange membrane fuel cell with integrated humidifier for mobile applications

Fuel cells are considered as one of the principal candidates to take part of the worldwide future clean and renewable energy solution. This paper presents a mathematical model of a proton exchange membrane fuel cell (PEMFC) with its integrated humidifier, in which the dynamic behaviors are considered for mobile applications. The model will be integrated in a hybrid system and it was developed on the mass balance and electrochemical principles. The inlet air is humidified by a membrane-based passive humidifier which presents the advantage of not adding parasitic power loss in the system. The presented model is useful for co-simulation with electrical or hybrid vehicle model for power components conditioning purpose.

Parasitics-Assisted Soft-Switching and Secondary Modulated Snubberless Clamping Current-Fed Bidirectional Voltage Doubler for Fuel Cell Vehicles

A current-fed full-bridge bidirectional voltage doubler with secondary-assisted device voltage clamping and zero-current commutation (ZCC) is proposed for fuel cell vehicles (FCVs). The proposed topology is suitable for interfacing energy storage and/or fuel cell stack with a dc bus in FCVs. A voltage doubler on the secondary side is selected to enhance the gain by 2

Degradation prediction of PEM fuel cell stack based on multi-physical aging model with particle filter approach

\times

Prediction of PEMFC stack aging based on Relevance Vector Machine

, reduce transformer size, and efficiently reduce the low-frequency dc current harmonics. Parasitics-based zero-voltage switching (ZVS), secondary-based zero-current switching of low-voltage-side devices, and ZVS of secondary devices are achieved. The proposed secondary modulation technique naturally clamps the voltage across the primary-side devices with ZCC, thus eliminating the necessity for traditional active-clamp or passive snubbers. Switching losses are reduced, owing to the soft-switching of all semiconductor devices. Steady-state analysis and design are studied and explained. The experimental results of a 1-kW proof-of-concept hardware prototype are shown to demonstrate the performance and confirm the proposed claims.

Degradation Prediction of PEM Fuel Cell Stack Based on Multiphysical Aging Model With Particle Filter Approach

In this paper, a novel prediction approach for proton exchange membrane fuel cell (PEMFC) performance degradation is proposed based on a multi-physical aging model with particle filter approach. The proposed multi-physical aging model uses aging coefficients to describe fuel cell different physical aging phenomena over time, including membrane conductivity losses, reduction of reactants mass transfer and reaction activity losses. In order to accurately model the activation loss, the implicit Butler-Volmer equation is used. The initial values of the aging parameters are tuned by fitting the fuel cell polarization curve at the beginning of life. Based on the initialized aging model, the first step of prediction approach is to estimate all the aging parameters using Bayesian Monte Carlo-based Particle Filter (PF) during the learning phase of experimental aging test. The suitable fitting curve function is then selected to satisfy the degradation behavior of each trained aging parameter, and further provide the extrapolated values of aging parameters in the validation phase. By applying these extrapolated aging parameters into aging model, the prediction result of fuel cell output voltage in the validation phase can be obtained. The results demonstrate that the proposed approach have good prediction performance for fuel cell degradation. In addition, each obtained aging parameters provides an insight into the different degree of physical aging process over time during the fuel cell operating, which is important to understand degradation mechanisms.

Dynamic variable coupling analysis and modeling of proton exchange membrane fuel cells for water and thermal management

Proton Exchange Membrane Fuel Cell (PEMFC) systems have been proved to be promising energy sources representing other conventional energy sources. However the life span is limited to some factors like intolerance of impurities or oscillation of working conditions, which can lead to output voltage ageing over operation. The prediction of output voltage drop trends is one of the major tasks of PEMFC system management. In this work, a prediction method of Relevance Vector Machine (RVM) is proposed, which can either give good accuracy and a confidential interval. Firstly the mathematical theory is explained thoroughly, and then the RVM is implemented to predict two voltage dropping trends based on two degradation data of a PEMFC. Finally the results are discussed and the effectiveness is evaluated. The RVM is proved to be a good candidate to predict the degradation trends of PEMFC.

Parasitics assisted soft-switching and naturally commutated current-fed bidirectional push-pull voltage doubler

In this paper, a novel degradation prediction model for proton-exchange-membrane fuel cell (PEMFC) performance is proposed based on a multiphysical aging model with particle filter (PF) and extrapolation approach. The proposed multiphysical aging model considers major internal physical aging phenomena of fuel cells, including fuel cell ohmic losses, reaction activity losses, and reactants mass transfer losses. Furthermore, in order to obtain accurate values of electrochemical activation losses under a variable load profile, a bisection solver is presented to solve the implicit Butler–Volmer equation. The proposed aging model is initialized at first by fitting the PEMFC polarization curve at the beginning of lifetime. During the prediction process, the aging dataset is then divided into two parts, learning and prediction phases. The PF framework is used to study the degradation characteristics and update the aging parameters during the learning phase. The suitable fitting curve functions are then selected to satisfy the degradation trends of trained aging parameters, and used to further extrapolate the future values of aging parameters in the prediction phase. By using these extrapolated aging parameters, the prediction results are thus obtained from the proposed aging model. Three experimental validations with different aging testing profiles have been performed. The results demonstrate the robustness and advantages of the proposed prediction method.