This review discusses the progress of research on sulfonated poly(ether ether ketone) (SPEEK) and its composite membranes in proton exchange membrane fuel cells (PEMFCs). SPEEK is a promising material for replacing traditional perfluorosulfonic acid membranes due to its excellent thermal stability, mechanical property, and tunable proton conductivity. By adjusting the degree of sulfonation (DS) of SPEEK, the hydrophilicity and proton conductivity of the membrane can be controlled, while also balancing its mechanical, thermal, and chemical stability. Researchers have developed various composite membranes by combining SPEEK with a range of organic and inorganic materials, such as polybenzimidazole (PBI), fluoropolymers, and silica, to enhance the mechanical, chemical, and thermal stability of the membranes, while reducing fuel permeability and improving the overall performance of the fuel cell. Despite the significant potential of SPEEK and its composite membranes in PEMFCs, there are still challenges and room for improvement, including proton conductivity, chemical stability, cost-effectiveness, and environmental impact assessments.

Accurate and reliable mathematical modeling is essential for the optimal control and performance analysis of polymer electrolyte membrane fuel cell (PEMFC) systems, which are mainly implemented based on accurate parameter estimation. In this paper, a multi-strategy tuna swarm optimization (MS-TSO) is proposed to estimate the parameters of PEMFC voltage models and compare them with other optimizers such as differential evolution, the whale optimization approach, the salp swarm algorithm, particle swarm optimization, Harris hawk optimization and the slime mould algorithm. In the optimizing routine, the unidentified factors of the PEMFCs are used as the decision variables, which are optimized to minimize the sum of square errors between the estimated and measured data. The optimizers are examined based on three PEMFC datasets including BCS500W, NedStackPS6 and harizon500W as well as a set of experimental data which are measured using the Greenlight G20 platform with a 25 cm² single cell at 353 K. It is confirmed that MS-TSO gives better performance in terms of convergence speed and accuracy than the competing algorithms. Furthermore, the results achieved by MS-TSO are compared with other reported approaches in the literature. The advantages of MS-TSO in ascertaining the optimum factors of various PEMFCs have been comprehensively demonstrated.

Proton exchange membrane fuel cells have attracted widespread attention due to their cleanliness and high energy density, but the performance degradation during operation greatly limits their commercialization. Therefore, the reliable degradation prediction of fuel cell performance is of great significance. The recovery phenomenon of the reversible voltage loss that occurs during the operation of fuel cells has posed great difficulties for model training and prediction. Moreover, the models may easily and erroneously learn the combined trends in the recovery of reversible voltage loss and performance degradation. To address this issue, this paper employs the Transformer model to predict the performance degradation of fuel cells. By utilizing the unique self-attention structure and masking mechanism of the Transformer model, the signal for the recovery of the reversible voltage loss is adopted as the input for the model to avoid interference from information before voltage recovery on subsequent predictions. Experimental results show that the model has the highest prediction accuracy at various prediction starting points. Meanwhile, it can predict the accelerated performance degradation of fuel cells, which has positive implications for health management.

A novel combined approach is proposed in this study by integrating electrochemical impedance spectroscopy (EIS) with a gated recurrent unit neural network for the state of health (SOH) estimation and degradation quantification of proton exchange membrane fuel cells (PEMFCs). This approach firstly overcomes the limitations of traditional voltage-based health indicators by fully leveraging the sensitivity of EIS to PEMFC degradation, while enhancing practical applicability through data-driven modeling. A high-fidelity EIS dataset was acquired under dynamic automotive load cycles through a 1488-hour durability test, covering a wide range of current densities (0.3-1.6 A/cm2). The developed EIS-GRU model demonstrated high prediction accuracy, with root mean square error below 0.02 when at least 70% of the data was adopted for training. In addition, the coupling relationship between EIS and the SOH of the PEMFC is analyzed by employing the distribution of relaxation times method, and a decoupling EIS model is developed to facilitate a more accurate quantitative assessment of SOH and degradation losses at distinct relaxation times during the degradation process. By integrating the physical interpretability of electrochemical analysis with the predictive capability of data-driven modeling, the proposed approach provides a new paradigm for accurate and explainable PEMFC health prognostics.