Energy Conversion and Management-X最新文献

{"title":"Design and Performance Analysis of a Hybrid Solar-Wind Tree System with IoT based Real-Time Monitoring in Bangladesh","authors":"Arman Jahan Eva,&nbsp;Al Amin,&nbsp;Md Nasir Uddin,&nbsp;Tamim Ahmed,&nbsp;Abu Anas Nasim,&nbsp;Md Sahadat Hossain Sani,&nbsp;Md Shamsul Arefin","doi":"10.1016/j.ecmx.2026.101566","DOIUrl":"10.1016/j.ecmx.2026.101566","url":null,"abstract":"<div><div>The global transition toward renewable energy has intensified interest in hybrid energy solutions that combine solar and wind power to enhance sustainability and reliability. Several studies have examined such integrated systems, however, most existing designs remain expensive, visually unsuitable for public spaces, and lack real-time monitoring or optimization capabilities, particularly in developing nations such as Bangladesh. Despite Bangladesh’s favorable solar irradiance and moderate wind potential, rural and urban public areas lack an efficient, decorative, and smart energy infrastructure. To address this gap, we propose a novel IoT-enabled hybrid solar wind energy tree tailored for public and rural settings in Bangladesh. Unlike conventional solutions, our proposed design integrates both energy sources into a visually appealing and self-sustaining structure capable of live monitoring and remote management. The system uses low-cost hardware (Arduino Nano, ESP8266, INA226 sensors) and simulation tools (COMSOL Multiphysics for structural integrity, HOMER Pro for cost analysis and MATLAB Simulink for performance modeling) using MPPT. A prototype was implemented for economic evaluation under site-specific conditions in Rajbari, Bangladesh (23°40.8′N, 89°31.3′E) demonstrating a Levelized Cost of Energy (LCOE) $0.11/kWh to $0.18/kWh for a single unit, which is approximately 50% lower than a standalone solar system. The community-scale configuration achieved a daily energy generation of up to 165 kWh under optimal conditions with an LCOE of $0.2997/kWh. This integrated solution offers a scalable, cost-effective, and environmentally friendly pathway to decentralized energy access in developing regions.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101566"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Techno-economic feasibility of hybrid renewable systems and green hydrogen production in special economic zones (SEZs)","authors":"Abdul Rafay ,&nbsp;Asadullah ,&nbsp;Muhammad Zubair Iftikhar ,&nbsp;Syed Ali Abbas Kazmi ,&nbsp;Dong Ryeol Shin ,&nbsp;Muhammad Waseem","doi":"10.1016/j.ecmx.2026.101635","DOIUrl":"10.1016/j.ecmx.2026.101635","url":null,"abstract":"<div><div>Pakistan’s fossil-heavy power mix drives cost volatility and rising emissions. We assess hybrid renewable energy systems (HRES) for five Special Economic Zones (SEZs), Allama Iqbal Industrial City (AIIC), Bostan, Dhabeji, Mohmand Marble City (MMC), and Rashakai, using RETScreen with policy-aware finance, export-meter accounting, and a combined-margin grid baseline (0.56 tCO₂ MWh⁻¹). The preferred portfolio (∼0.53 GW of PV, wind, and canal-drop hydro) exports ≈1.30 TWh yr⁻¹ at ∼$42–49 MWh⁻¹ LCOE while avoiding ≈728 ktCO₂ yr⁻¹. Site comparisons show Dhabeji delivers the largest energy export (∼413 GWh yr⁻¹) and highest abatement (∼231 ktCO₂ yr⁻¹); Bostan and MMC achieve the fastest paybacks (both 10.3 years); AIIC provides a scalable 200-MW anchor (payback 11.6 years); and Rashakai contributes ∼ 261 GWh yr⁻¹ with 10.6-year payback. Power-to-X analysis of modeled surplus (central case: 10% surplus, PEM 70% efficiency; 47.6 kWh kg⁻¹ SEC) yields ≈2.73 kt H₂ yr⁻¹ at ∼$6.10–6.43 kg⁻¹ LCOH. Separately accounting carbon revenues, electricity-displacement and green-hydrogen credits together provide ≈$3.39 M yr⁻¹ at $5 tCO₂⁻¹ (net of a 10% MRV haircut), scaling proportionally with price. Overall, the portfolio offers a replicable pathway for SEZ decarbonization, prioritizing Dhabeji for grid impact, Bostan/MMC for rapid cash recovery, and AIIC as a bankable anchor.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101635"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solar-integrated blue hydrogen production with optimized post-combustion carbon capture: A techno-economic and exergoeconomic assessment","authors":"Farzin Hosseinifard ,&nbsp;Mohsen Salimi ,&nbsp;Milad Hosseinpour ,&nbsp;Majid Amidpour","doi":"10.1016/j.ecmx.2026.101528","DOIUrl":"10.1016/j.ecmx.2026.101528","url":null,"abstract":"<div><div>Hydrogen production plays a key role in the energy transition. However, conventional methods of hydrogen produc’tion, such as steam methane reforming (SMR), are associated with high emissions. To address this issue, carbon capture utilization and storage (CCUS) can be used to convert grey hydrogen into blue hydrogen. However, this process is often inefficient due to its high energy consumption and challenges related to post-combustion carbon capture in conventional configurations, as well as its dependence on fossil fuels. In this research, to enhance the sustainability of blue hydrogen, renewable energy sources, such as solar energy (including photovoltaic system and parabolic trough), are used to power optimized carbon capture plants. Aspen HYSYS v11 and Thermoflex are employed to simulate the production of low-carbon blue hydrogen. By optimizing a standard post-combustion carbon capture configuration and integrating it with a solar plant, a 79% reduction in energy penalties is achieved. This optimization leads to an estimated reduction of approximately 310 tonnes of CO₂ per day for the blue hydrogen plant, which has a total production capacity of 214.2 tonnes per day. Feasibility, exergy, and exergoeconomic analyses reveal the following efficiency metrics: exergy efficiency for SMR, PCC (Post-combustion Carbon Capture), and the solar plant is 95.5%, 82.3%, and 15%, respectively, while exergoeconomic efficiency is 30%, 20.8%, and 28.45%. The levelized cost of hydrogen (LCOH) was compared across different technologies, showing that grey hydrogen costs approximately $1.53 per kg. Incorporating carbon capture technology increases the cost to $2.01 per kg while enhancing sustainability. However, optimizing the carbon capture process and integrating solar energy can reduce the cost to $1.74 per kg.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101528"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic effects of ZnO in Cu-based catalysts for CO2 reduction: mechanistic insights into enhanced C2 product formation","authors":"Masoud Safari Yazd ,&nbsp;Mohammadreza Omidkhah ,&nbsp;Mahmoud Moharrami ,&nbsp;Farshid Sobhani Bazghaleh ,&nbsp;Hamidreza Rahmani ,&nbsp;Azam Akbari","doi":"10.1016/j.ecmx.2026.101647","DOIUrl":"10.1016/j.ecmx.2026.101647","url":null,"abstract":"<div><div>The electrochemical reduction of CO₂ (CO₂RR) to multi-carbon products such as ethanol (C₂H₅OH) and ethylene (C₂H₄) is a promising strategy for mitigating CO₂ emissions and producing valuable chemicals. In this study, we investigate the role of ZnO in enhancing the performance of Cu-based catalysts for CO₂RR. Using both experimental and theoretical approaches, we demonstrate that ZnO incorporation significantly improves the catalytic efficiency of Cu by modifying its electronic structure, stabilizing key intermediates, and facilitating C–C coupling. DFT calculations show that ZnO stabilizes intermediates such as *CO and *HCOH, promoting their hydrogenation and enhancing C₂ product formation. The presence of oxygen vacancies (OVs) on the Cu-ZnO interface is found to facilitate proton-coupled electron transfer (PCET) and H-spillover, leading to improved catalytic performance. XPS and UV–Vis DRS analyses confirm that ZnO modifies the Cu surface, increasing the Cu⁰/Cu⁺ species and narrowing the band gap, which enhances charge transfer and intermediate stabilization. The CZ catalyst exhibits significantly higher Faradaic efficiency for C₂ products compared to the Cu catalyst, as confirmed by experimental data. These findings highlight the importance of defect engineering in the design of more efficient catalysts for CO₂ reduction. This study provides valuable insights into optimizing Cu-based catalysts for sustainable CO₂ utilization and C₂ product formation.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101647"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A simulation-based assessment of Community-Based Energy Trading for circular energy sharing in low-income communities","authors":"T. Michael-Ahile ,&nbsp;J.A. Samuels ,&nbsp;M.J. Booysen","doi":"10.1016/j.ecmx.2026.101665","DOIUrl":"10.1016/j.ecmx.2026.101665","url":null,"abstract":"<div><div>The growing adoption of decentralised renewable energy, particularly solar photovoltaic (PV) systems, presents opportunities to advance low-carbon development and circular economy objectives. However, unequal policy frameworks, infrastructure availability, and socio-economic conditions continue to limit equitable outcomes, especially in under-resourced communities. This study presents a simulation-based proof-of-concept evaluation of a Community-Based Energy Trading (CBET) framework designed to operationalise energy circularity within low-income communities through local energy redistribution. The proposed CBET model centres on a public school equipped with solar PV generation acting as a single prosumer that redistributes surplus electricity to nearby households using rule-based energy allocation rather than optimisation-based control. Using empirical electricity demand data for one school and five households in Cape Town, South Africa, two system configurations are simulated: CBET without household battery storage and CBET with battery-enabled households. Performance is evaluated in terms of local renewable energy utilisation, household electricity cost reductions, and peak-period grid demand. Results indicate that CBET increases local solar energy reuse efficiency to approximately 90% and reduces household reliance on grid electricity by up to 16% when battery storage is included. In addition, peak-hour demand is reduced by 13%, contributing to improved grid stability, energy equity, waste minimisation, and community-level resilience. These findings demonstrate that meaningful techno-economic benefits can be achieved through simplified community-level energy sharing arrangements without complex market mechanisms. However, the results are contingent on the assumed demand profiles and the single case configuration analysed. The study positions CBET as a feasible proof-of-concept for community-scale circular energy sharing systems operating under local capacity and policy constraints in the Global South.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101665"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the critical battery electrochemical parameters across different phases of a single discharge process using a transformer framework","authors":"Chi-Jyun Ko,&nbsp;Cheng-Hsi Tien,&nbsp;Kuo-Ching Chen,&nbsp;Chih-Hung Chen","doi":"10.1016/j.ecmx.2026.101629","DOIUrl":"10.1016/j.ecmx.2026.101629","url":null,"abstract":"<div><div>Accurate characterization of electrochemical parameters is critical for interpreting the time- and frequency-domain responses of energy conversion devices and for enabling precise, efficient, and controllable management. In the context of lithium-ion batteries, this study introduces a deep learning (DL) framework using electrochemically derived simulation data to predict discharge curves and to distinguish the dominant internal electrochemical parameters for each of the three constant-current stages. Extracting attention weights from the transformer encoder helps identify the most influential parameters at each stage. Our study reveals subtle variations in the key electrochemical parameters that control discharge across different time-evolution stages. During the early stage of discharge, the behavior is mainly governed by positive-electrode parameters, such as the volume fraction of active material and the maximum concentration in the positive electrode. As the discharge progresses, however, negative-electrode parameters—particularly, the volume fraction of active material in the negative electrode, become increasingly influential. These outcomes are further verified through two additional operations: Sobol-based global sensitivity analysis and Shapley additive explanations. This DL framework reproduces the time-dependent battery behavior during a single discharge while elucidating the relationship between electrochemical parameters and battery response, thereby enabling efficient parameter assessment or identification and rational voltage-window selection for battery applications.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101629"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A quantum inspired machine learning agent for optimal unified control of battery energy storage systems in renewable microgrids","authors":"Arun Kumar Rawat,&nbsp;Subhash Chandra,&nbsp;Vinay Kumar Deolia","doi":"10.1016/j.ecmx.2026.101862","DOIUrl":"10.1016/j.ecmx.2026.101862","url":null,"abstract":"<div><div>This paper introduces quantum entangled reservoir computing (QERC), a unified glass box control paradigm for battery energy storage systems (BESS) that replaces opaque black box learning with a transparent, physics inspired framework. Unlike randomly initialized neural networks or conventional Echo state networks, QERC employs a fixed complex valued reservoir constructed using unitary entanglement matrices, ensuring inherent Lyapunov stability and energy conserving dynamic behaviour analogous to quantum chaotic systems. Implemented in MATLAB/Simulink on a heavily modified IEEE 14-bus renewable microgrid, a rigorous benchmark selected to validate topological robustness under severe solar, wind intermittency and non-linear dynamic load changes, the proposed architecture simultaneously addresses five coupled objectives, voltage regulation, frequency support, state of charge (SOC) estimation, harmonic mitigation, and battery loss minimization, within a single coherent computational substrate, thereby avoiding fragmented cascaded control loops. By leveraging high bandwidth internal dynamics and deterministic 10 μs computational latency, QERC operates as an embedded virtual active power filter (V-APF), reducing grid total harmonic distortion (THD) from above 5% to below 3.5% under dynamic loading conditions. A regularized single shot ridge regression training strategy penalizes high frequency control jitter, suppressing battery micro-cycling and yielding approximately 35.9% reduction in battery operational losses. Performance evaluation demonstrates voltage regulation within ± 0.028p.u, frequency containment within ± 0.14 Hz, and highly accurate SOC estimation with 0.21% RMSE, by consistently outperforming a comprehensive suite of state of art baselines, spanning predictive (MPC), heuristic (FA-PSO), classical (VSG), and deep learning (RL) paradigms. These results validate the broad applicability of QERC. These results establish QERC as interpretable and computationally efficient control framework that bridges high performance AI methods with the stringent stability and real-time requirements of modern power microgrids.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101862"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147858629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Techno-economic feasibility and performance evaluation of replacing diglycolamine (DGA) with diethanolamine (DEA) in a dual-pressure gas sweetening unit: A comprehensive field case study","authors":"Mahdi Garousi ,&nbsp;Marzie Farjami ,&nbsp;Hasan Ebrahimi","doi":"10.1016/j.ecmx.2026.101813","DOIUrl":"10.1016/j.ecmx.2026.101813","url":null,"abstract":"<div><div>The removal of hydrogen sulfide (H₂S) from refinery gas streams is essential for operational safety, environmental compliance, and equipment integrity. This study provides a comprehensive technical and economic evaluation of replacing diglycolamine (DGA) with diethanolamine (DEA) in a dual-pressure gas sweetening unit at the Tehran Oil Refining Company. Through a carefully managed solvent transition and continuous monitoring, DEA demonstrated superior performance. The use of DEA achieved a significant reduction in outlet H₂S concentration, particularly in the low-pressure absorber, and simultaneously reduced steam consumption in the stripper reboiler by 9.6%. A key finding was the complete elimination of the energy-intensive reclaimer unit, which resulted in 100% energy savings for that reclaimer unit compared to its operation with DGA. Economically, the switch to DEA yielded approximately USD 152,000 in annual solvent cost savings and contributed to total annual savings exceeding USD 225,000. Additionally, DEA’s reliable domestic availability in Iran mitigates supply chain risks, establishing DEA as a technically superior, economically advantageous, and strategically resilient alternative to DGA for refinery gas sweetening applications.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101813"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance analysis of a novel battery thermal management system integrating thermoelectric and dielectric immersion cooling in EVs","authors":"Md Ahnaf Adit,&nbsp;Samiul Hasan,&nbsp;Nirendra Nath Mustafi","doi":"10.1016/j.ecmx.2026.101550","DOIUrl":"10.1016/j.ecmx.2026.101550","url":null,"abstract":"<div><div>Effective thermal management of lithium-ion (Li-ion) batteries in electric vehicles (EVs) is essential for ensuring safety, extending battery life, and maintaining performance under varying operating conditions. This study presents a novel battery thermal management system (BTMS) that integrates thermoelectric cooling with dielectric immersion cooling, and evaluates its performance through both simulation and experimentation. A relatively new 26650 LiFePO₄ battery model, characterized by high capacity and high discharge capability was selected due to its elevated heat generation. The proposed BTMS was first analyzed numerically using computational fluid dynamics (CFD) to assess temperature distribution and cooling effectiveness. Subsequent experimental testing was performed with a physical battery cell simulator, and the measured data were compared with CFD predictions. In all cases, the experiments yielded slightly higher temperature values than those predicted by simulation. At the maximum coolant flow rate of 1.96 L/min, the BTMS reduced the temperature rise of the battery cell simulator by 28.78 %, 41.52 %, and 46.54 % at discharge rates of 5.8 C, 7.7 C, and 9.6 C, respectively, compared to operation without any BTMS. Under the highest discharge rate (9.6 C), where heat generation was greatest, temperature reductions of 9.71 K, 12.57 K, and 16.57 K were achieved over 375 s for coolant flow rates of 0.58 L/min, 1.08 L/min, and 1.96 L/min, respectively. Overall, the developed BTMS proved highly effective in controlling the temperature of the Li-ion battery cell simulator. The findings offer valuable guidance for designing and implementing thermoelectric–dielectric immersion cooling technologies, particularly for high-performance EV applications.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101550"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning for fuel cell remaining useful life prediction: A review","authors":"Zaid Allal ,&nbsp;Hassan N. Noura ,&nbsp;Flavien Vernier ,&nbsp;Ola Salman ,&nbsp;Khaled Chahine","doi":"10.1016/j.ecmx.2026.101597","DOIUrl":"10.1016/j.ecmx.2026.101597","url":null,"abstract":"<div><div>Accurate prediction of the Remaining Useful Life (RUL) of fuel cell (FC) systems is essential to ensure operational reliability, optimize maintenance strategies, and extend system lifetime in safety-critical hydrogen applications. As FC degradation is governed by complex, nonlinear, and stochastic mechanisms, machine learning (ML) has emerged as a powerful paradigm for data-driven prognostics. This paper presents a structured and comprehensive review of recent ML-based approaches for FC RUL estimation, encompassing supervised, unsupervised, and hybrid methodologies, including regression techniques, support vector machines, ensemble models, neural networks, and advanced deep learning architectures. Despite notable progress, our analysis reveals persistent limitations in the current literature, particularly the widespread neglect of underlying electrochemical and physical degradation laws, as well as the scarcity and ambiguity of explicit RUL and End-of-Life (EoL) labels in publicly available datasets. These challenges significantly constrain model generalization, interpretability, and real-world applicability. To address these gaps, we conduct a comparative analysis of more than 20 recent state-of-the-art studies and propose a unified and generalizable RUL estimation pipeline. This framework integrates data acquisition, preprocessing, feature engineering, model design, and validation, while explicitly accounting for physical consistency and operational constraints. In addition, the paper formulates practical, multi-level recommendations, including first-order guidelines for data modeling and learning strategies, second-order recommendations targeting validation protocols and real-world deployment, and the systematic integration of uncertainty quantification (UQ) techniques to enhance robustness, interpretability, and trustworthiness. By consolidating methodological insights, emerging paradigms, and deployment-oriented considerations, this review provides a comprehensive reference and a forward-looking roadmap for the development of reliable, physics-consistent, and scalable RUL prognostic frameworks for fuel cell systems.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101597"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}