Energy Conversion and Management-X最新文献

{"title":"SOFC polarization curve normalization and reduced order model generation for rapid and accurate performance prediction","authors":"Trevor J. Kramer,&nbsp;David Schafer,&nbsp;Griffin Layhew,&nbsp;Daniel Cannon,&nbsp;Sam Chumney,&nbsp;Rory Roberts","doi":"10.1016/j.ecmx.2026.101622","DOIUrl":"10.1016/j.ecmx.2026.101622","url":null,"abstract":"<div><div>The need for rapid and accurate performance estimations for solid oxide fuel cells (SOFCs) under wide ranges of operating conditions grows as more SOFC hybrid power plants gain traction as possible players in the future power generation landscape. Typical one-dimensional, steady-state SOFC modeling requires numerically solving differential equations which can impose added difficulties to lower fidelity, higher level power generation system models. The handling of the SOFC polarization behavior and how it changes due to variation in operating conditions can be captured through multiple normalization techniques. It was found from a literature survey that the general polarization behavior of SOFCs remains relatively constant, and independent of specific measured performance and testing conditions. Polarization curve normalization utilizing peak power conditions can be implemented seamlessly with SOFC reduced order modeling performance predictions. The relative changes in peak power due to variation in operating conditions can be captured with regression based reduced order models allowing for an infinite number of SOFC performances to be represented through the normalized reduced order SOFC model discussed in this work.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101622"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080325","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-assisted innovative charging strategy for e-mobility in rural communities operated by redundant energy on solar PV mini-grids","authors":"Gidphil Mensah ,&nbsp;Richard Opoku ,&nbsp;Francis Davis ,&nbsp;George Yaw Obeng ,&nbsp;Oliver Kornyo ,&nbsp;Daniel Marfo ,&nbsp;Michael Addai ,&nbsp;Jesse Damptey ,&nbsp;Samuel Dodobatia Wetajega","doi":"10.1016/j.ecmx.2026.101591","DOIUrl":"10.1016/j.ecmx.2026.101591","url":null,"abstract":"<div><div>Green transportation using solar energy with nearly zero emissions is of global importance to address the challenges of modern energy access for the transport sector, greenhouse gas emissions and global warming. In the Global South and in most off-grid areas, solar PV mini-grids are being used to provide energy access. However, there is redundant energy from these mini-grid systems during peak sunshine hours, which could be used for further profitable activities. E-mobility is a key use case that could be incorporated into the operation of mini-grids to minimise redundant energy, improve system performance, and increase mini-grid profitability. In this study, a model of a Machine Learning (ML)-based control system incorporating Internet of Things (IoT) for e-tricycle charging is proposed to optimise the use of energy from mini-grids for green transportation. In a case study, three ML models, namely Artificial Neural Network, Extreme Gradient Boosting, K-Nearest Neighbour and Random Forest, were trained on data acquired from three mini-grids to predict redundant energy for efficient electric vehicle (EV) charging. The results revealed that on average, the three communities had redundant energy in the ranges of 56.98–119.86 kWh, 74.39–311.87 kWh, and 57.03–274.66 kWh per day. Having validated the ML models, all the models could predict redundant energy successfully.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101591"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039631","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":"Navigating towards efuel: A scientometric insight into the application of membrane reactors","authors":"Rahbaar Yeassin ,&nbsp;Prangon Chowdhury ,&nbsp;Prithibi Das ,&nbsp;Ephraim Bonah Agyekum ,&nbsp;Omar Farrok ,&nbsp;Pankaj Kumar","doi":"10.1016/j.ecmx.2026.101545","DOIUrl":"10.1016/j.ecmx.2026.101545","url":null,"abstract":"<div><div>The urgent need to decarbonize high-emission sectors has driven the development of Power-to-X technologies, which convert renewable electricity into electrofuels (efuels). Despite their potential, efuel production faces challenges such as high energy demand and low conversion efficiency. Membrane reactors, which integrate reaction and separation, offer a promising solution by improving yields and reducing energy requirements. This review presents a scientometric analysis of membrane reactors for efuel production using the Scopus database from 2003 to 2024. Analyzing 30 publications, six thematic clusters were identified using VOSviewer and Bibliometrix. Keyword co-occurrence and factorial analyses highlight main research themes and emerging areas, revealing gaps in reactor configuration optimization. Influential studies show that membrane reactors can enhance CO₂ conversion and methane yield compared to conventional systems, though challenges remain in membrane selectivity, economic viability, and long-term durability under real feedstock conditions. Additional issues include scalable module manufacturing and the lack of harmonized techno-economic, life cycle, and performance metrics. Sector-specific analysis identifies positive dynamics, such as compatibility with existing infrastructure, improved energy security, and supportive policies, as well as negative dynamics, including high production costs, resource competition, technological uncertainties, and new safety and regulatory requirements. By mapping research progress, this study provides insights to guide the advancement of membrane reactors and support sustainable efuel production and decarbonization goals.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101545"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039760","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 simplified multi-physics approach for bifacial photovoltaic modules: Theory and validation of peculiar module layout","authors":"Emanuele Ogliari,&nbsp;Alberto Dolara,&nbsp;Domenico Mazzeo,&nbsp;Luca Lazzari,&nbsp;Sonia Leva","doi":"10.1016/j.ecmx.2025.101499","DOIUrl":"10.1016/j.ecmx.2025.101499","url":null,"abstract":"<div><div>This work aims to develop and integrate three sub-models into a simplified multi-physics tool for simulating bifacial PV (bPV) devices. While similar tools exist, they often rely on complex modeling. In contrast, this study investigates a simpler approach that achieves comparable accuracy. The proposed models are also experimentally validated under a specific case study: a Vertical Bifacial PV (VBPV) installation. This setup is relatively novel and provides valuable insights into the feasibility of VBPV systems for agricultural and space-constrained applications, highlighting the strong dependence between environmental conditions and PV module performance.</div><div>For the optical model, a 2D View Factor method is implemented, demonstrating high sensitivity to the module’s surroundings. Results show that this simplified approach can achieve errors below 5%. The electrical modeling is the core of this study. Two parameter estimation methods are applied: a traditional experimental data-fitting approach and a data-driven stochastic method based on Particle Filtering. The latter is an innovative technique for this type of estimation. Three different electrical models for bPV are numerically solved and compared, showing good accuracy, with errors below 4%. Notably, a newly proposed circuit model outperforms the other two. A simplified 0-D lumped thermal model is developed and validated to complete the multi-physics framework, showing deviations of up to 7% in temperature estimation.</div><div>The integration of the best-performing electrical model with the optical and thermal sub-models results in a comprehensive tool capable of estimating power and energy with errors of 5% and 2%, respectively. These findings demonstrate that a simplified approach could support the estimation of PV performance based on field measurements and weather data for VBPV installations.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101499"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190001","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":"Development of geothermal power generation system using geothermal fluids under harsh conditions","authors":"Norihiro Fukuda ,&nbsp;Yasuhiro Fujimitsu","doi":"10.1016/j.ecmx.2026.101654","DOIUrl":"10.1016/j.ecmx.2026.101654","url":null,"abstract":"<div><div>High-enthalpy geothermal fluids contain significant amounts of impurities, corrosive gases, and non-condensable gases (NCGs). In addition, steam is superheated and contains no liquid water, which can cause problems such as corrosion and scaling if introduced directly into power generation facilities. This study builds upon the proven conventional method of introducing scrubbed steam into a turbine, while also proposing the recovery of heat from the scrubber drain as flash steam. Three types of power generation cycles are analyzed quantitatively: a direct expansion system, a stand-alone Organic Rankine Cycle (ORC), and a hybrid system combining the two. The analysis includes calculations of power output and heat recovery for each configuration to better evaluate the quality of the recovered energy, based on exergy analysis.</div><div>Results show that, in addition to the conventional approach, recovering flash steam from the scrubber drain can regain approximately half of the desuperheating losses caused by steam scrubbing. Under geothermal fluid conditions of 13.8 MPa, 450 °C, and 100 t/h of superheated steam, this corresponds to a gross power output of 24.1 MW, compared with 21.2 MW when the drain heat is not recovered.</div><div>Furthermore, while an ORC alone is not well suited to high-enthalpy geothermal sources due to the small latent heat of low-boiling-point working fluids and the high exhaust temperature of the ORC turbine, combining a direct expansion turbine with an ORC enables efficient cascading use of heat. This hybrid approach eliminates the need for dedicated gas extraction systems, making it particularly advantageous under high-NCG conditions and robust against variations in gas concentration, achieving a gross power output of 22.1 MW for the hybrid configuration under the considered geothermal fluid conditions.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101654"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190007","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":"Energy analysis of large cruise ships case study of thermal and electric demands and supply during different scenarios","authors":"Marouane Barbri ,&nbsp;Max Zimmermann ,&nbsp;Felix Dahms ,&nbsp;Karsten Müller","doi":"10.1016/j.ecmx.2026.101651","DOIUrl":"10.1016/j.ecmx.2026.101651","url":null,"abstract":"<div><div>Cruise ships, with their intricate energy infrastructure, exhibit a level of complexity comparable to that of urban energy systems. In context, large cruise vessels rank among the most energy-intensive mobile infrastructures. During sea operation, conventional cruise ships typically consume on the order of 140–150 t fuel per day, with the largest vessels reaching approximately 250 t per day. Even when alongside, they maintain substantial auxiliary loads, often requiring electrical power in the megawatt range. Against the backdrop of international shipping emissions of roughly 1,076 Mt CO₂e in 2018 which counts to around 3% of global anthropogenic emissions, improving cruise-ship energy efficiency is therefore relevant both to meeting sector-wide decarbonisation objectives and to mitigating local air-emission burdens in port cities <span><span>[1]</span></span>.</div><div>This study presents a detailed assessment of the energy demands of a 300-metre-long cruise ship with a capacity of approximately 4,300 passengers during a seven-day voyage. The analysis considers three distinct operational modes: sailing at sea, manoeuvring (e.g., harbour entry), and port stays. Fuel input is systematically traced and divided into thermal and electrical energy pathways, enabling a mode-specific evaluation of energy flows and system efficiency. The results reveal that up to 57% of thermal energy is rejected during sea operations, with overall system efficiency ranging from 52% (sea mode) to 67% (harbour mode). The feasibility of utilising surplus steam for battery charging is demonstrated, offering approximately 9 MWh of electrical storage over the course of one week to support zero-emission port operations.</div><div>Furthermore, the integration of an Organic Rankine Cycle (ORC) was investigated. While technically feasible, its relatively low efficiency (approx. 7%) and system complexity present challenges for retrofitting existing ships. In contrast, slow steaming was found to reduce fuel consumption by 9% and thermal dumping by 15–17%, representing a practical and readily deployable strategy for improving energy efficiency and reducing emissions.</div><div>These findings provide new insights into the operational energy performance of cruise vessels and offer a robust foundation for data-informed optimisation strategies to support the maritime sector’s transition towards low-emission and energy-efficient operation.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101651"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189897","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":"Policy pathways for clean energy and climate mitigation: insights from long-term scenario modelling","authors":"Rohan Kumar ,&nbsp;Mohsin Pervez ,&nbsp;Ammara Kanwal ,&nbsp;Majid Ali ,&nbsp;Muhammad Asim ,&nbsp;Nadia Shahzad ,&nbsp;Adnan Tariq","doi":"10.1016/j.ecmx.2026.101595","DOIUrl":"10.1016/j.ecmx.2026.101595","url":null,"abstract":"<div><div>The energy sector in Pakistan continuously relying on imported fossil fuels, which remain costly, contribute to air pollution, and increase greenhouse gas (GHG) emissions. In this study, the Low Emission Analysis Platform (LEAP) model is used to compare three electricity supply scenarios between 2021 and 2050, including a Business-as-Usual (BAU) scenario, the Alternative and Renewable Energy Policy (AREP 2019) scenario, and a higher target Sustainable Pathway (SP) scenario. The scenarios are compared to evaluate the capabilities of renewable energy policies and interventions in ensuring that energy supply is secured, and climate change is mitigated in the context of Sustainable Development Goals (especially SDG 7 on clean energy and SDG 13 on climate action). The modelling outcomes estimate that by 2050, the electricity demand in Pakistan will be around 1489 TWh, whereas the GHG emissions will increase from 100 MtCO₂-e(2025) to 564.7 MtCO₂-e annually under BAU. Conversely, the SP scenario, by contrast, where a faster switch to renewables is assumed, would limit 2050 emissions to approximately 34 MtCO₂-e, with more than 90% reduction over BAU. Moreover, SP scenario is consistent with cost benchmarks of Pakistan’s IGCEP plan. However, achieving this level assumes significant grid infrastructure upgrades, including advanced transmission and smart distribution systems, which are under ongoing development in Pakistan. These findings highlight Pakistan’s urgent need to speed up the move toward renewable energy. Using the country’s large, unused renewable resources through better policies and investments is essential for improving energy security and protecting the environment from climate change.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101595"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189898","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":"Evaluation and performance prediction of a hybrid solar-based cycle based on trough collector and PCM storage using artificial intelligence","authors":"Sara Borhani,&nbsp;Peyman Pourmoghadam,&nbsp;Nastaran Zirak,&nbsp;Alibakhsh Kasaeian","doi":"10.1016/j.ecmx.2026.101532","DOIUrl":"10.1016/j.ecmx.2026.101532","url":null,"abstract":"<div><div>It is essential to develop a trustworthy and meticulous output power forecasting method to certify solar multigeneration systems stability, credibility, and power dispatchability. Therefore, this study focuses on improving the conventional forecasting tools using an evolutionary algorithm PSO. At first, a dataset is provided by simulating the proposed hybrid system in TRNSYS software. Then, intelligent forecasting approaches like adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP) neural networks, are modeled using MATLAB software. The MLP and ANFIS networks are optimized via the PSO algorithm during the training process with specific inputs and targets. The evaluated input parameters consist of solar radiation, dry ambient temperature, and wet bulb. The total efficiency of the proposed system is determined as the target variable of all intelligent networks. Sensitivity analysis estimated the optimal dataset division as 60  % for ANN and 70 % for ANFIS. PSO optimization reduced prediction errors by 99.9 %. The ANN-PSO model had the best accuracy (MSE: 0.026 train, 0.025 test), while ANN achieved the highest correlation (R = 0.893 train, 0.873 test). The results demonstrate that the PSO algorithm works as intended for optimizing the forecasting tools and the comparison results indicate that the ANN-PSO method outperforms the other developed methods.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101532"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189901","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":"Comparative optimization and exergy analysis of solar–LNG integrated Rankine cycles among different hot tank outlet temperatures","authors":"Han Zhang,&nbsp;Huiming Mao","doi":"10.1016/j.ecmx.2026.101609","DOIUrl":"10.1016/j.ecmx.2026.101609","url":null,"abstract":"<div><div>With solar energy playing an increasingly crucial role in the worldwide shift toward renewable resources, a comparative two-objective optimization is performed on a two-tank solar field integrated with an organic Rankine cycle (ORC) and liquefied natural gas under three hot tank outlet temperatures of 200 <span><math><msup><mrow></mrow><mrow><mo>∘</mo></mrow></msup></math></span>C, 250 <span><math><msup><mrow></mrow><mrow><mo>∘</mo></mrow></msup></math></span>C, and 300 <span><math><msup><mrow></mrow><mrow><mo>∘</mo></mrow></msup></math></span>C. Optimization objectives include maximizing the system efficiency and minimizing the electricity production cost (EPC). The comprehensive optimization includes 8 variables, 11 working fluids, and 16 structures, with the results analyzed using the thermodynamic weight. Detailed analysis is further performed on two representative scenarios: the Equal Scenario and the Thermodynamic Scenario. The findings show that the R-ORC is preferred when thermodynamic considerations are the primary focus, whereas the B-ORC is more beneficial when the economic aspect is the main priority. At a hot tank outlet temperature of 300 <span><math><msup><mrow></mrow><mrow><mo>∘</mo></mrow></msup></math></span>C, the Equal Scenario attains 97.81% of the Thermodynamic Scenario‘s system efficiency while reducing EPC by as much as 9.35%. This result demonstrates that a slight sacrifice in thermodynamic performance could yield notable economic improvements. The condenser exhibits the highest exergy loss fraction among all components.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101609"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080072","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":"Optimal long-term planning of a green hydrogen production system under alternative technological options","authors":"Dana Alghool ,&nbsp;Mohamed Haouari ,&nbsp;Paolo Trucco","doi":"10.1016/j.ecmx.2026.101549","DOIUrl":"10.1016/j.ecmx.2026.101549","url":null,"abstract":"<div><div>Green hydrogen (H<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>) is a key pillar of sustainable energy strategies but remains more expensive than fossil alternatives. This study develops a mixed integer linear programming model to optimize long-term green H<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> production, maximizing economic returns under varying technologies, water sources, and demand scenarios. The system integrates electrolyzers, photovoltaic (PV) panels, PV-thermal (PV-T) collectors, and two water sources: treated sewage effluent (TSE) and industrial produced (IP) water. Revenues from oxygen by-products and surplus electricity are included. A key novelty is comparing PV-T collectors with PV panels. Using Qatar as the reference context for 2025–2050, results show that 99% of H<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> is produced using PV-T collectors due to higher efficiency, while PV panels mainly supply electricity to the grid. TSE is the preferred water source, and oxygen sales are the largest revenue stream. The Levelized Cost of Hydrogen (LCOH) falls by 2.8% under medium demand but rises by 18.3% under high demand. A carbon tax shifts water use from TSE to IP, with little change in system configuration. Sensitivity analysis identifies oxygen prices and PV-T fixed costs as key LCOH drivers. Scenario analysis incorporates demand growth, carbon taxation, and technology improvements. Three combined scenarios are evaluated with assigned probabilities. The medium-demand scenario yields the lowest expected LCOH, offering a resilient pathway that balances scalability and cost efficiency for large-scale green H<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> deployment in Qatar.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101549"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145996500","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}