Fuel Cells最新文献

{"title":"Mesoporous NiCo2O4 /Ti3C2Tx MXene Nanohybrids as Bifunctional Electrocatalysts for Enhanced Hydrogen and Oxygen Evolution Reactions","authors":"Keerthana Rajaram,&nbsp;Balaji Parasuraman,&nbsp;Logesh Pachamuthu,&nbsp;Ganapathi Bharathi,&nbsp;Pazhanivel Thangavelu","doi":"10.1002/fuce.70166","DOIUrl":"https://doi.org/10.1002/fuce.70166","url":null,"abstract":"<div>\u0000 \u0000 <p>Two-dimensional metal carbide-based MXenes, particularly Ti₃C₂T<i>_x</i>, have garnered significant attention as potential alternatives to noble metal-based electrocatalysts for water splitting reaction due to their high electrical conductivity, tunable surface chemistry, and large surface area. In this study, we report the Ti₃C₂T<i>_x</i>/NiCo₂O₄ nanohybrid catalyst for efficient overall alkaline water electrolysis. The hybrid architecture comprised of multi-layered Ti₃C₂T<i>_x</i> nanosheets uniformly decorated with NiCo₂O₄ nanoparticles, enables enhanced charge transport and maximizes electrochemical active surface area, thereby enhancing electrocatalytic performance for both HER and OER in alkaline media. Furthermore, the Ti₃C₂T<i>_x</i>/NiCo₂O₄ nanocomposite exhibits a low overpotential of 191 and 321 mV@10 mA cm⁻² for HER and OER, respectively. Corresponding Tafel slopes of 80 and 75 mV dec⁻¹ further indicate favourable reaction kinetics. In a two-electrode configuration, the composite achieves an overall water splitting cell voltage of 1.71 V at 10 mA/cm⁻², highlighting its bifunctional electrocatalytic capability. Moreover, the nanohybrid demonstrates excellent long-term stability, maintaining consistent performance over 12 h of continuous operation. This work provides a rational tailoring strategy for engineering cost-effective and efficient electrocatalyst for overall water splitting, leveraging the synergistic interactions between MXene nanosheets and spinel metal oxide nanostructures.</p>\u0000 </div>","PeriodicalId":12566,"journal":{"name":"Fuel Cells","volume":"26 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive Analytical Design of a Grid-Connected Fuel Cell Power Conversion System: From the PEMFC Stack to the Converter, Inverter, LCL Filter, and Control Parameters","authors":"Serhat Doğan,&nbsp;Bilal Gümüş,&nbsp;Ardashir Mohammadzadeh,&nbsp;Cem Haydaroğlu","doi":"10.1002/fuce.70172","DOIUrl":"https://doi.org/10.1002/fuce.70172","url":null,"abstract":"<div>\u0000 \u0000 <p>Grid-connected proton exchange membrane fuel cell (PEMFC) systems require the coordinated design of various power conversion and control stages. However, in most studies reported in the literature, either only a specific stage of the power conversion chain is addressed, or circuit and control parameters are provided directly without explaining how they are derived. This study introduces an integrated analytical design procedure in which all parameters of the power conversion chain are determined starting from the nominal power and operating voltage of the PEMFC stack. Based on a PEMFC stack with a nominal power of 50 kW, the parameters of the boost converter, DC-link, the inverter-side and grid-side inductors of the LCL filter, phase-locked loop (PLL), and associated controller parameters are specified. The DC-link voltage is rated at 1000 V, taking into account the inverter's modulation requirements. Accordingly, the inductor and capacitor values of the boost converter, the LCL filter components, the damping resistance, the control gains for the boost converter's cascade control loops, the inner grid current controller gains, and the PLL gains are derived in that order. The designed system is validated in the MATLAB/Simulink environment using a grid-following inverter connected to a 380 V, 50 Hz grid. Simulation results demonstrate that the DC-link voltage is sustained at the desired value, the active and reactive powers transferred to the grid track different reference values, and the inverter operates in synchronization with the grid. When switching frequency components are taken into account, the total harmonic distortion values for current and voltage are measured at 3.30% and 0.32%, respectively. Overall, the findings indicate that the proposed procedure provides a repeatable approach for determining appropriate power ratings and control parameters for grid-connected PEMFC systems.</p></div>","PeriodicalId":12566,"journal":{"name":"Fuel Cells","volume":"26 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Target Stacked LSTM Framework for Dynamic Setpoint Optimization and Hydrogen Consumption Reduction in PEM Fuel Cells","authors":"Hasan Onur Ataç,&nbsp;Mehmet Güçyetmez","doi":"10.1002/fuce.70174","DOIUrl":"https://doi.org/10.1002/fuce.70174","url":null,"abstract":"<div>\u0000 \u0000 <p>Proton exchange membrane fuel cells (PEMFCs) are considered one of the most promising clean energy technologies; however, improving operational efficiency while minimizing hydrogen consumption remains a critical challenge for their large-scale deployment. This study proposes an artificial intelligence-driven predictive control framework based on a multi-target stacked long short-term memory (LSTM) network for simultaneous prediction of stack voltage and hydrogen flow rate, followed by dynamic operating setpoint optimization. The model was trained in 26 930 min of experimental PEMFC operating data, enriched through temporal feature engineering, thereby increasing the input dimensionality from 4 to 16 features. The proposed architecture achieved coefficients of determination (<i>R</i>²) of 0.7931 for stack voltage and 0.7135 for hydrogen flow rate, with low prediction errors (MAPE of 0.68% and 1.25%, respectively). A two-dimensional optimization strategy identified the recommended operating condition at 71°C and 320 kPa, achieving an 87.0% voltage constraint satisfaction rate while reducing hydrogen consumption by 0.25% under a universal setpoint (0.41% under per-sample optimization). One-dimensional optimization yielded statistically significant hydrogen savings of 0.063 LPM (0.41%), verified using Wilcoxon signed-rank testing and bootstrap confidence intervals (CIs). Monte Carlo dropout analysis produced mean predictive uncertainties below 0.40%, while the average inference latency of 41.4 ms confirmed real-time applicability. Furthermore, the optimized operating strategy can reduce annual carbon emissions by up to 0.90 tCO₂ per year for a 50-unit fleet (0.018 tCO₂ per unit) for grey hydrogen systems, demonstrating the proposed framework's potential for intelligent, low-carbon PEMFC operation.</p>\u0000 </div>","PeriodicalId":12566,"journal":{"name":"Fuel Cells","volume":"26 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reduced-Order Parameter Identification of a Polymer Electrolyte Membrane Fuel Cell Using a DE-Enhanced BO Variant for PEMFC Fitting","authors":"Badreddine Kanouni,&nbsp;Abdelbaset Laib,&nbsp;Ahmed Elsanabary,&nbsp;Mokhtar Aly,&nbsp;Mohammad A. Abido","doi":"10.1002/fuce.70164","DOIUrl":"https://doi.org/10.1002/fuce.70164","url":null,"abstract":"<div>\u0000 \u0000 <p>Polymer electrolyte membrane fuel cells (PEMFCs) are a promising technology for sustainable energy systems because of their high efficiency, low emissions, and quiet operation. Accurate parameter identification is essential for reliable modeling and performance optimization of PEMFC systems under varying operating conditions. This study proposes the mutated bonobo optimizer (Mu-BO), a hybrid optimization method that incorporates a Differential Evolution (DE) mutation operator into the bonobo optimizer (BO). Mu-BO is used to identify the parameters of semi-empirical PEMFC models while improving search robustness and reducing sensitivity to parameter settings. The method is evaluated using datasets from three PEMFC systems: a 250 W cell, an SR-12 500 W stack, and a NedStack PS6 6 kW stack. Its performance is compared with contemporary state-of-the-art optimization methods and the original BO algorithm. The results show that Mu-BO achieves higher accuracy, faster convergence, and more consistent performance across all datasets. A sensitivity analysis investigates the effect of parameter variations on the sum of squared errors (SSE), providing further insight into model performance. In addition, a reduced-order PEMFC model is developed to improve computational efficiency, reducing computation time by 12.17%, 15.32%, and 29.01% for the 250 W, SR-12, and NedStack PS6 systems, respectively, without compromising accuracy.</p>\u0000 </div>","PeriodicalId":12566,"journal":{"name":"Fuel Cells","volume":"26 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Explainable Fault Diagnosis Method for PEMFCs Using a CEO-EMSTGAT Model","authors":"Rui Quan,&nbsp;Weiming Zeng,&nbsp;Xin Guan","doi":"10.1002/fuce.70173","DOIUrl":"https://doi.org/10.1002/fuce.70173","url":null,"abstract":"<div>\u0000 \u0000 <p>To enhance the safety and reliability of proton exchange membrane fuel cells (PEMFCs), this study evaluated an application-specific engineering framework that integrated an enhanced multi-sequence spatial-temporal graph attention network (EMSTGAT), the hybrid QAAdam optimizer, and chaotic evolution optimization (CEO) for hyperparameter search. EMSTGAT combined dilated one-dimensional convolution and a bidirectional gated recurrent unit (BiGRU) for temporal representation with k-nearest neighbor (KNN) graph construction and multi-head graph attention for adaptive inter-variable modeling. CEO was used to select model and training hyperparameters, and was treated as a tuning component rather than an architectural innovation. Under the 80% training-data setting, the integrated model achieves an accuracy of 0.9873, significantly outperforms comparative models such as SVM (0.9170), BiGRU (0.9210), XGBoost (0.9222), RF (0.9257), Transformer (0.9555), and iTransformer (0.9675). Ablation and noise-robustness evaluations quantify the observed gains of the integrated configuration, and SHAP analysis identifies T_Stack_inlet, U_totV, and PW as the most influential features in the present dataset.</p>\u0000 </div>","PeriodicalId":12566,"journal":{"name":"Fuel Cells","volume":"26 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A D3QN-Driven Deep Reinforcement Learning Energy Management Strategy for Fuel Cell Vehicles Combined With Transformer Speed Prediction","authors":"Rui Quan,&nbsp;Xin Guan,&nbsp;Weiming Zeng,&nbsp;Jialei Xu","doi":"10.1002/fuce.70170","DOIUrl":"https://doi.org/10.1002/fuce.70170","url":null,"abstract":"<div>\u0000 \u0000 <p>To reduce hydrogen consumption and alleviate powertrain system degradation in fuel cell hybrid electric vehicles (FCHEVs) under complex driving cycles, this study proposes a deep reinforcement learning (DRL)-based energy management strategy (EMS) integrating a lightweight Transformer model with a dueling double deep Q-network (D3QN). A multi-objective operating cost function is established by comprehensively considering equivalent hydrogen usage, fuel cell (FC) durability loss, lithium-ion battery health degradation, and state of charge (SOC) balancing performance. The lightweight Transformer model predicts short-term vehicle speed, and the D3QN algorithm dynamically allocates power between the FC and lithium-ion batteries. Results under the China light-duty vehicle test cycle (CLTC) and Worldwide Harmonized Light Vehicles Test Procedure (WLTP) show the Transformer-based speed predictor outperforms bidirectional long short-term memory (BiLSTM) and Markov models, cutting root mean square error (RMSE) and mean absolute error (MAE) by up to 30.70% and 23.07%. On CLTC, the total operating cost of the Transformer-D3QN EMS decreases by 0.62%–23.0% versus DQN, DDPG, D3QN, and BiLSTM-D3QN strategies; under WLTP, cost falls 2.00%–7.91%, close to the theoretical optimum solved by dynamic programming (DP). The proposed DRL-EMS with speed prediction effectively improves the economy, durability, and operational robustness of FCHEVs.</p>\u0000 </div>","PeriodicalId":12566,"journal":{"name":"Fuel Cells","volume":"26 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-Constrained Reinforcement Learning for Energy Management in Fuel-Cell Hybrid UAVs","authors":"Yi Li,&nbsp;Jianxin Pan,&nbsp;Huachen Du,&nbsp;Xinghe Fan,&nbsp;Yufei Wang,&nbsp;Xiaoxing Zhang,&nbsp;Zhaohu Fan","doi":"10.1002/fuce.70171","DOIUrl":"https://doi.org/10.1002/fuce.70171","url":null,"abstract":"<div>\u0000 \u0000 <p>Fuel-cell hybrid UAVs require energy management strategies to maintain efficiency during rapid load transients. Existing methods often neglect the fuel cell's limited dynamic response, leading to frequent and abrupt power adjustments. To address this, we propose a physics-constrained energy management strategy based on the soft actor–critic (SAC) algorithm. A minimum fuel-cell power holding time and a maximum allowable power change are embedded into the control environment to ensure that the generated fuel-cell power commands satisfy the prescribed supervisory constraints. The proposed strategy is evaluated under multiple flight scenarios and compared with fuzzy logic (FL), equivalent consumption minimization strategy (ECMS), and constrained dynamic programming (DP). The results show that SAC achieves a hydrogen consumption of 5.4679 g, only 0.32% higher than DP, while maintaining a final SOC of 59.67%. Its equivalent hydrogen consumption is 5.4811 g, which is 0.79% and 1.32% lower than those of ECMS and FL, respectively. Moreover, SAC achieves a fuel-cell power STD of 91.38 W, representing reductions of 41.82% and 47.05% compared with ECMS and FL, respectively, while the RMS of the fuel-cell power change rate is 25.29 W·s⁻¹. These results demonstrate that the proposed strategy provides well-balanced performance in terms of hydrogen consumption, battery SOC regulation, and fuel-cell power regulation.</p>\u0000 </div>","PeriodicalId":12566,"journal":{"name":"Fuel Cells","volume":"26 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrochemical and Impedance Insights Into MEA-Based Ammonia Electrolysis Cells","authors":"Cheng Liu,&nbsp;Yanchen Lai,&nbsp;Bowen Wang,&nbsp;Liang Hao,&nbsp;Lei Xing,&nbsp;Hui Luo,&nbsp;Rong Wang,&nbsp;Jiacheng Shang,&nbsp;Xinmin Ruan,&nbsp;Kui Jiao","doi":"10.1002/fuce.70165","DOIUrl":"https://doi.org/10.1002/fuce.70165","url":null,"abstract":"<div>\u0000 \u0000 <p>Ammonia, owing to its dual characteristics of high-efficiency hydrogen storage and safe storage and transportation, has gradually become a research hotspot and a focus of attention in the energy sector as an ideal carrier for cross-regional hydrogen transportation. The theoretical thermodynamic potential of ammonia electrolysis is only 0.06 V, which is much lower than that of water electrolysis for hydrogen production (1.23 V). Therefore, ammonia electrolysis for hydrogen production is expected to become an important hydrogen production technology in the future. In this study, by varying operating conditions such as operating temperature, ammonia solution concentration, and cathode KOH concentration, the effects of these conditions on the electrochemical performance of a membrane electrode assembly-based ammonia electrolysis cell (AEC) were investigated in detail. Meanwhile, we employed the method of fitting an electrochemical impedance spectroscopy equivalent circuit model to quantify the various components of impedance of the AEC at different operating conditions, including ohmic impedance, anode activation impedance, cathode activation impedance, and mass transport impedance. Additionally, the operational stability of the AEC was analyzed through short-term stability tests and microscopic characterization methods. Experimental results show that relatively optimal performance is exhibited when the operating temperature is 90°C, the ammonia solution concentration is 3 M, and the cathode KOH concentration is 1 M, with a peak current density reaching 598 mA cm⁻². Performance decay is mainly ascribed to intermediate adsorption, catalyst agglomeration, and valence state changes of Pt and Ir. This study provides an experimental basis for optimizing the operating conditions of MEA-based AEC and enhancing their stability.</p>\u0000 </div>","PeriodicalId":12566,"journal":{"name":"Fuel Cells","volume":"26 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Along-the-Channel Model of Anion Exchange Membrane Fuel Cells With CO2-Free Cathode Gas Stream","authors":"Samuele Cappelli,&nbsp;Thomas Stadlbauer,&nbsp;Patrick Pertl,&nbsp;Thomas Schubert,&nbsp;Anton Markus Reiter,&nbsp;Christoph Pötsch","doi":"10.1002/fuce.70158","DOIUrl":"https://doi.org/10.1002/fuce.70158","url":null,"abstract":"<div>\u0000 \u0000 <p>In this work, an analytical along-the-channel model for anion exchange membrane fuel cell (AEMFC) is used to improve the understanding of the cell inner behavior in operations with CO₂-free cathode gas stream. The multi-physics approach includes mass and charge transport, electrochemical reactions, heat transfer, and two-phase water transport. The gas flow channel (GFC) is discretized in a series of 0D segments, modeled as single electrical circuits connected in parallel. This approach balances computational efficiency with an electrochemical representation of the different overvoltages as determined by the local operating conditions and by the membrane electrode assembly (MEA) properties. Internal current losses, activation losses, and concentration losses are modeled as voltage sources in each segment, whereas both MEA and interfacial contact resistance lead to the additional ohmic resistance. Altogether, this model highlights the challenge related with the water management within AEMFC, and it provides a tool to assess the effect of MEA properties on cell performances.</p>\u0000 </div>","PeriodicalId":12566,"journal":{"name":"Fuel Cells","volume":"26 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abnormal Performance Recovery During Long-Term −30°C Cold Start Cycles: Mechanism and Engineering Strategies for a Metallic Bipolar Plate Proton Exchange Membrane Fuel Cell Stack","authors":"Yu Wang,&nbsp;Hongyou Bian,&nbsp;Jia He","doi":"10.1002/fuce.70162","DOIUrl":"https://doi.org/10.1002/fuce.70162","url":null,"abstract":"<div>\u0000 \u0000 <p>Driven by global carbon neutrality initiatives, proton exchange membrane fuel cells (PEMFCs) have emerged as a core zero-emission power technology for transportation. However, their large-scale application in cold regions is severely constrained by low-temperature cold start capability, particularly the mandatory requirement of unassisted cold start at −30°C in road vehicle fuel cell regulations. A critical and previously unreported phenomenon—abnormal performance recovery during long-term freeze–thaw cycles—has been identified in high-power metallic bipolar plate (BPP) PEMFC stacks, which constitutes the core innovation of this work. Existing studies mostly focus on single cells or small-scale stacks, with limited insights into the long-term cold start cycle characteristics of high-power metallic BPP stacks at −30°C, and the mechanism behind such abnormal performance recovery during freeze–thaw cycles remains poorly understood. In this work, a homemade 60 kW PEMFC stack with 154 cells and metallic BPPs was subjected to 40 repeated −30°C unassisted cold start cycles, combined with steady-state polarization curve tests, multi-condition electrochemical impedance spectroscopy (EIS), and ex situ microstructural characterizations. The results show that the metallic BPP stack achieved −30°C unassisted cold start within 153 s, exhibiting overwhelming advantages in low-temperature adaptability. Most notably, the stack performance presented a distinct two-stage non-monotonic evolution that is unprecedented in existing research: continuous attenuation in the first 30 cycles (1.6% voltage degradation at 2.0 A cm⁻²), followed by full recovery to the initial level in Cycles 30–40, which was predominantly driven by a 30.6% reduction in concentration impedance. This abnormal performance recovery, the core innovation of this study, was verified to be a reversible effect derived from the dynamic balance between irreversible structural damage and reversible performance gains, including deep activation of the membrane electrode assembly (MEA), pore structure reconstruction of the catalyst layer (CL), and water management optimization within the stack. This reversible gain completely disappeared after accelerated calendar aging, accompanied by significant performance degradation with a 5.28% voltage decay at 2.0 A cm⁻². Severe irreversible damage occurred in the end cells due to the thermal edge effect, which became the core limitation of the stack's full-life cycle lifetime. This work, for the first time, systematically reveals the abnormal non-monotonic performance recovery phenomenon and its intrinsic mechanism in high-power metallic BPP stacks during long-term −30°C cold start cycles and proposes systematic engineering optimization strategies, providing critical theoretical and experimental support for the commercial application of automotive PEMFCs in cold regions.</p>\u0000 </div>","PeriodicalId":12566,"journal":{"name":"Fuel Cells","volume":"26 5","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149147001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}