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The fuel cell with a ten-channel serpentine flow field has a low operating pressure drop, which is conducive to extended test operations and stable use. According to numerical results of the ten-channel serpentine flow field fuel cell, the multi-channel flow field usually has poor mass transmission under the ribs, and the lower pressure drop is not favorable for drainage from the outlet. In this paper, an optimized flow field is developed to address these two disadvantages of the ten-channel fuel cell. As per numerical simulation, the optimized flow field improves the gas distribution in the reaction area, increases the gas flow between the adjacent ribs, improves the performance of PEMFC, and enhances the drainage effect. The optimized flow field can enhance water pipe performance, increase fuel cell durability, and decelerate aging rates. According to further experimental tests, the performance of the optimized flow field fuel cell was better than that of the ten-channel serpentine flow field at high current density, and the reflux design requires sufficient gas flow to ensure the full play of the superior performance. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a novel robust prognostic approach that contains three phases for degradation prediction of proton exchange membrane fuel cell (PEMFC) performance and its remaining useful lifetime (RUL) estimation. In the first detrending phase, a physical aging model (PAM) is used to remove the non-stationary trend in the original fuel cell degradation data. In the second filtering phase, the order of autoregressive and moving average (ARMA) model is determined by autocorrelation function (ACF), partial ACF and Akaike information criterion. The linear component in the stationary time series is then filtered by the identified ARMA model. In the third prediction phase, the remaining nonlinear pattern is used to train the time delay neural network (TDNN), in order to provide the final prediction result. Since the proposed prognostic approach uses appropriate methods to analyze and preprocess the original degradation data (i.e., the PAM maintains stationary trend, and then the identified ARMA filters linear component), the remaining nonlinear pattern of stationary time series can thus guarantee a good convergence performance of TDNN. In order to experimentally demonstrate the robustness and prediction accuracy of the proposed approach, degradation tests are performed using two types of PEMFC stack. Highlights • Applicability of different prognostic approaches is considered in terms of data types. • PAM removes the non-stationary trend to obtain static time series. • The identified ARMA model filters the linear component of the stationary time series. • The remaining nonlinear component of time series is used to train TDNN. • The proposed method guarantees robustness due to proper data preprocessing. [ABSTRACT FROM AUTHOR]

This paper presents a novel two-dimensional real-time modeling approach for a proton exchange membrane fuel cell (PEMFC) based on a tridiagonal matrix algorithm (Thomas algorithm). The Thomas algorithm consists of a forward elimination and a backward substitution, its arithmetic complexity of computations being much lower than the Gaussian elimination. In order to use this advanced numerical solver, the differential equations of reactant gas convection and diffusion phenomena in serpentine channels are transformed into a tridiagonal equations system. In addition, a three-level bisection algorithm has been developed to solve spatial physical quantities distribution for electrochemical domain. The real-time computing methods developed in this paper are then implemented in C language for a fast execution time in a real-time processor. The proposed real-time model is experimentally validated using a 1.2 kW Ballard NEXA fuel cell system, and its practical feasibilities in advanced real-time control for PEMFC systems have been experimentally demonstrated in an RT-LAB real-time simulator. [ABSTRACT FROM PUBLISHER]

In this paper, an innovative robust prediction algorithm for performance degradation of proton exchange membrane fuel cell (PEMFC) is proposed based on a combination of model-based and data-driven prognostic method. A novel approach using the moving window method is applied, in order to 1) train the developed models; 2) update the weight factors of each method and 3) further fuse the predicted results iteratively. In the proposed approach, both model-based and data-driven methods are simultaneously used to achieve a better accuracy. During the prediction process, each dataset in the proposed moving window are divided into three sections respectively: training, evaluation and prediction. The training data are used first to identify the models parameters. The evaluation data are then used to measure the weight of each method, which represents the degree of confidence of each method in the actual state. Based on these dynamically adjusting weight factors, the prediction results from different methods are then fused using weighted average methodology to calculate the final prediction results. In order to verify the proposed method, three experimental validations with different aging testing profiles have been performed. The results demonstrate that the proposed hybrid prognostic approach can achieve a higher accuracy than conventional prediction methods. In addition, in order to find the satisfactory trade-off between the prediction accuracy and forecast time for optimizing on-line prognostic, the performance variation of proposed approach with different moving window length is further showed and discussed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]