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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]

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]

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]

This paper presents a computationally efficient two-dimensional (2-D) steady-state model for fuel cell real-time control implementation. Both the fluid and electrochemical physical domains are considered in the proposed real-time model. The fuel cell under-rib convection is fully described by considering the geometry of serpentine channel. In addition, in order to solve the implicit activation voltage loss and further explore the computational performance, three numerical root-searching algorithms: bisection, secant, and Newton–Raphson methods are applied to the proposed implicit iterative solver and compared. The preferred secant method has been proven to effectively improve both the efficiency and robustness performance of the proposed real-time fuel cell model. Moreover, a computational fluid dynamic based COMSOL fuel cell model is used to validate the calculation accuracy. Furthermore, the practical feasibility of the presented real-time model has been verified using an RT-LAB simulator platform from Opal-RT. [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 online energy management control strategy is proposed based on a novel fractional-order extremum seeking (ES) method. The proposed method is an online adaptive optimization algorithm, which can be effectively used in the applications of fuel cell hybrid electric vehicles. Compared with the traditional integer-order ES method, the presented method uses Oustaloup approximation based fractional-order calculus in order to achieve faster convergence speed and higher robustness. A detailed mathematical analysis of the proposed method is presented to give a stability proof and shows how the fractional-order calculus improves the integer-order ES method. In order to support the stability analysis results and demonstrate the effectiveness and robustness of the proposed method, a hardware-in-the-loop test bench is developed to provide two experimental case studies. Experimental results show that, by using the presented fractional-order ES approach, the operation points of a proton exchange membrane fuel cell stack system can be effectively controlled in its maximum efficiency area. In addition, the fuel cell system durability can be improved. [ABSTRACT FROM PUBLISHER]

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. [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]

• Long duration dynamic load durability test is performed on a single PEMFC. • The attention mechanism is applied to combine with the RNN model (LSTM and GRU) to perform the prediction work. • Two datasets are processed to assess the efficiency of different prediction models. Currently, the larger-scaled commercialization of fuel cell technology is considerably impeded by the limited durability of fuel cells. Prognostics and health management (PHM) is one of the most widely researched technologies used to improve the durability of fuel cell devices. More recently, the combination of deep neural network approaches and PHM techniques shows a broad research prospect. Attention mechanisms can enhance their data processing ability, which helps to extract useful features more efficiently. Herein, we propose an attention-based Recurrent neural network (RNN) model to improve the prognostics of PHM, which enables a more accurate prediction of the output voltage degradation of proton exchange membrane fuel cell (PEMFC) based on the original long-term dynamic loading cycle durability test data. In particular, the prediction results with different prediction models, namely, long short-term memory (LSTM), gated recurrent unit (GRU), attention-based LSTM, and attention-based GRU are obtained and compared. For dynamic test data (dataset 1), the root mean square error results for the attention-based LSTM and GRU models are 0.016409 and 0.015518, respectively, whereas for the LSTM and GRU model the corresponding error results are 0.017637 and 0.018206, respectively. The same effects are demonstrated and proved for the pseudo–steady dataset (dataset 2). The attention-based RNN model achieves a high prediction accuracy, proving that it can help improve the prediction accuracy and may further help the implementation of PHM in the fuel cell system. [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]