Predicting the degradation behaviors is challenging and essential for prognostics and health management for proton exchange membrane fuel cells (PEMFCs). However, existing methods based on data-driven or model-based methods can face the problem of significant performance inconsistencies in different prediction stages. We investigate the cause and attribute it to the ignorance of the voltage recovery phenomena of PEMFCs observed during the frequent start-stop processes during practical applications. A novel prognostic method is proposed to provide a more comprehensive analysis of PEMFC aging that integrates data-driven and model-based methods. Specifically, a physics-based aging model considering voltage recovery (PA-VR) is first reported as a model-based method to enhance the prediction effect at voltage mutation points. Then, the moving window method with iterative function is used to combine the data-driven method with the PA-VR model, which realizes the online update of model parameters. Finally, the weightings on individual approaches are dynamically determined at different stages throughout the PEMFC lifecycle. The proposed hybrid method achieves an effective improvement in prediction performance by combining the overall degradation trend predicted by the PA-VR model and the local dynamic characteristics predicted by the data-driven method.

To ensure effective heat recovery of thermoelectric generators, a cooling system is necessary to maintain the working temperature difference of the thermoelectric couples, which decreases continuously due to thermal diffusion. In order to evaluate and improve the thermoelectric performance of a concentric annular thermoelectric generator under various cooling methods, a comprehensive numerical model of the thermo-fluid-electric multi-physics field for an annular thermoelectric generator with a concentric annular heat exchanger was developed using the finite-element method. The effects of four cooling methods and different exhaust parameters on the thermoelectric performance were investigated. The results show that, in comparison to the cocurrent cooling pattern, the countercurrent cooling pattern effectively reduces temperature distribution non-uniformity and hence increases the maximum output power; however, it requires more thermoelectric semiconductor materials. Furthermore, when using the cocurrent air-cooling method, high exhaust temperatures may result in lower output power; high exhaust mass flow rates result in high exhaust resistance and reduce system net power. The maximum net power output P_net = 432.42 W was obtained using the countercurrent water-cooling, corresponding to an optimal thermoelectric semiconductor volume of 9.06 × 10⁻⁴ m³; when compared to cocurrent water-cooling, the maximum net power increased by 8.9%, but the optimal thermoelectric semiconductor volume increased by 21.4%.