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Due to the non-renewable nature and pollution associated with fossil fuels, there is widespread research into alternative energy sources. As a novel energy device, a proton exchange membrane fuel cell (PEMFC) is considered a promising candidate for transportation due to its advantages, including zero carbon emissions, low noise, and high energy density. However, the commercialization of fuel cells faces a significant challenge related to aging and performance degradation during operation. In order to comprehensively address the issue of fuel cell aging and performance decline, this paper provides a detailed review of aging mechanisms and influencing factors from the perspectives of both the PEMFC system and the stack. On this basis, this paper offers targeted solutions to degradation issues stemming from various aging factors and presents research on aging prediction methods to proactively mitigate aging-related problems. Furthermore, to enhance prediction accuracy, this paper categorizes and analyzes the degradation index and accuracy evaluation criteria commonly employed in the existing fuel cell aging research. The results indicate that specific factors leading to aging-related failures are often addressed via targeted solving methods, corresponding to specific degradation indexes. The significance of this study lies in the following aspects: (1) investigating the aging factors in fuel cells and elucidating the multiple aging mechanisms occurring within fuel cells; (2) proposing preventive measures, solutions, and aging prediction methods tailored to address fuel cell aging issues comprehensively, thereby mitigating potential harm; and (3) summarizing the degradation index and accuracy evaluation standards for aging prediction, offering new perspectives for resolving fuel cell aging problems. [ABSTRACT FROM AUTHOR]

This paper proposes a novel error correction grey prediction model for degradation prediction of renewable energy storages. The proposed approach uses an error correction factor ψ to eliminate the inherent error of the original grey model (GM), and at the same time retain the original simplicity and fast prototyping. In addition, due to the uncertainty and complexity of failure mechanisms, a trigonometric residual modification is considered in order to well-describe the influence of operating conditions or cyclic fluctuation on the renewable energy storages. Two experimental case studies, including lithium-ion battery and fuel cell aging tests, are performed to validate the performance of the proposed method. In particular, the accuracy of the proposed method is investigated for different prediction horizon lengths, in order to further demonstrate its effectiveness and robustness. It is worth mentioning that the proposed method can ensure the accuracy of the remaining useful life estimation in the case of long-term forecasting, and thus, the maintenance management and corrective action of renewable energy storages can be scheduled earlier, leading to more effective cost minimization and risk mitigation. [ABSTRACT FROM AUTHOR]

The polarization characteristic (V I curve) describes the basic working performance of proton exchange membrane (PEM) fuel cell. The variations of internal parameters including physical and empirical ones have great impact on the polarization characteristic. This paper investigates the parametric variations effect on the fuel cell voltage during the full range current. Some important parameters in the electrochemical domain and fluidic domain are selected and their influences on the mass transfer and electrochemical reaction are studied. The analytic results show that the positive- and negative-deviations of the selected parameters have very different effect on the cell voltage. Multi-parameter sensitivity analysis (MPSA) based on Monte-Carlo method is used to identify the sensitivity index of each parameter. The study results could provide a guidance for the parameter identification during the fuel cell modeling or manufacturing. [ABSTRACT FROM AUTHOR]

Data-driven methods have been widely applied to fault diagnosis and aging predictions to assist fuel cell Prognostic and Health Management (PHM) system, in order to achieve early maintenance management and corrective measures for fuel cell systems. This paper proposes a novel fuel cell aging prediction method considering the applicability of data and algorithm. This method first adopts empirical mode decomposition (EMD) to split the aging data into several intrinsic mode functions (IMFs), and each IMF represents a different characteristic. Then the sample entropy (SE) is used as the quantitative criterion for complexity threshold. Furthermore, the nonlinear autoregressive neural network (NARNN) and the Long Short-Term Memory (LSTM) recurrent neural network are combined to ensure the applicability of data and algorithm. The results show that EMD can split the various data types of the aging data and weaken or even eliminate the excessive mutation phenomenon that occurs at the beginning of each experimental fuel cell. In addition, the targeted selection of data-driven methods can ensure the applicability of the data and algorithm. Finally, by comparing different prediction methods, the proposed method shows higher accuracy in the prediction of each experimental dataset, and good generality for different fuel cell types. • The aging data is adaptively split into IMFs reflecting different characteristics. • Sample entropy is used as the complexity threshold quantitative criterion for IMFs. • The NARNN and LSTM are combined to ensure the applicability of data and algorithm. • Four datasets are used to validate the advantages of the proposed method. • The proposed method has generality for different fuel cell types aging predictions. [ABSTRACT FROM AUTHOR]

• PDE is used to describe the coupling effects of multi-physical domains in fuel cell. • The PDE-TMET framework achieves rapid prototyping and online implementation. • SSOR increases convergence probability and thus improve calculation efficiency. • The calculation time can be significantly shortened from hours even days to seconds. • MAPE of different physical distribution characteristics can remain within 10%. Fuel cell model provides state observation and quantitative analysis for Hardware-In-the-Loop (HIL) test platform. For a high-dimensional multi-physics model, the observed distribution of physical properties is meaningful for fuel cell stack optimization, manufacturing process and model-based control development. However, traditional modeling approach is difficult to consider both the calculation accuracy and efficiency. To solve this problem, this paper proposes a high-dimensional multi-physics real-time modeling approach for fuel cells based on partial differential equations (PDEs)-tridiagonal matrix equation transformation (TMET) and symmetric successive overrelaxation (SSOR) algorithm. In the proposed method, a generalized framework of TMET and a novel staggered grid technique is developed. In this case, both the convection and diffusion phenomena can be accurately described. For different fuel cells or parameters change, supplementary model modification or development is not required, only the global variables need to be re-assigned. Moreover, SSOR with bi-directional iteration mechanism is used to increase the probability of computational convergence and thus improve the calculation efficiency. Compared with commercial COMSOL model, by using the proposed modeling approach, the mean absolute percentage error (MAPE) of the simulated polarization output characteristics is 1.77%, the MAPE of different distribution characteristics can remain within 10%, and the calculation time can be shortened from hours even days to seconds. [ABSTRACT FROM AUTHOR]