The rapid electrification of transportation demands intelligent coordination among heterogeneous energy subsystems within electric vehicles. This research establishes an analytics-driven management framework that unites photovoltaic generation, high-energy–density lithium-ion storage, and auxiliary fuel-cell support to achieve a balanced, sustainable, and economically viable propulsion system. Focusing on an urban case study in Xi’an, China, the model integrates real-time meteorological inputs and vehicle-operation data to dynamically regulate energy flows between PV modules and battery packs. A hybrid optimization layer couples techno-economic modeling with management-level decision analytics, allowing simultaneous assessment of power efficiency, operational scheduling, and lifecycle cost performance. Results show that the coordinated PV–battery strategy enhances driving range up to 61% while lowering equivalent energy cost and mitigating peak-load stress on urban charging infrastructure. Beyond the technical gains, the framework demonstrates how data-enabled decision mechanisms can inform managerial planning for fleet electrification and urban energy resilience. The study provides actionable insights for policymakers and industry practitioners seeking integrated strategies to strengthen the economic, environmental, and managerial dimensions of electric mobility, directly supporting the United Nations Sustainable Development Goal 7 on affordable and clean energy.
{"title":"Economic and management evaluation of vehicle-mounted photovoltaic–battery systems in electric vehicles under urban operating conditions","authors":"Junfeng Niu , Nesrine Gafsi , Pooya Ghodratallah , Rabeb Younes , Mohamed Shaban , Narinderjit Singh Sawaran Singh , Amina Hamdouni","doi":"10.1016/j.ecmx.2026.101628","DOIUrl":"10.1016/j.ecmx.2026.101628","url":null,"abstract":"<div><div>The rapid electrification of transportation demands intelligent coordination among heterogeneous energy subsystems within electric vehicles. This research establishes an analytics-driven management framework that unites photovoltaic generation, high-energy–density lithium-ion storage, and auxiliary fuel-cell support to achieve a balanced, sustainable, and economically viable propulsion system. Focusing on an urban case study in Xi’an, China, the model integrates real-time meteorological inputs and vehicle-operation data to dynamically regulate energy flows between PV modules and battery packs. A hybrid optimization layer couples techno-economic modeling with management-level decision analytics, allowing simultaneous assessment of power efficiency, operational scheduling, and lifecycle cost performance. Results show that the coordinated PV–battery strategy enhances driving range up to 61% while lowering equivalent energy cost and mitigating peak-load stress on urban charging infrastructure. Beyond the technical gains, the framework demonstrates how data-enabled decision mechanisms can inform managerial planning for fleet electrification and urban energy resilience. The study provides actionable insights for policymakers and industry practitioners seeking integrated strategies to strengthen the economic, environmental, and managerial dimensions of electric mobility, directly supporting the United Nations Sustainable Development Goal 7 on affordable and clean energy.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101628"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080319","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}
Pub Date : 2026-05-01Epub Date: 2026-01-24DOI: 10.1016/j.ecmx.2026.101627
Aditya Dinakar, D. Cenitta, R. Vijaya Arjunan, Venkatesh Bhandage, Krishnaraj Chadaga
Photovoltaic (PV) systems are responsible for the conversion of solar energy into electricity and with the rising usage of renewable energy, solar energy has emerged as one of the leading contributors. However, solar energy is dependent on various environmental conditions which raises the need for forecasting of the electricity produced. With the rise in the usage of machine learning (ML) there have been attempts to forecast the solar energy harvested by PV systems. In this study a robust framework is used to predict the current and voltage generated by a PV system. This study employs the use of feature selection using BorutaSHAP and Variance Inflation Factor (VIF) to train various ML models consisting of Linear Regression, tree-based models, TabNet and transformer-based models. These models were later interpreted using Explainable Artificial Intelligence (XAI) methods such as SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE), Local Interpretable Model-agnostic Explanations (LIME) and Diverse Counterfactual Explanations (DiCE). The best performing model was TabPFN, a transformer-based model and it achieved an R-squared of 0.998 and 0.934 for current and voltage respectively. This study shows a strong performing and interpretable framework to predict the current and voltage of a PV system.
{"title":"Explainable machine learning models for predicting current and voltage in photovoltaic systems","authors":"Aditya Dinakar, D. Cenitta, R. Vijaya Arjunan, Venkatesh Bhandage, Krishnaraj Chadaga","doi":"10.1016/j.ecmx.2026.101627","DOIUrl":"10.1016/j.ecmx.2026.101627","url":null,"abstract":"<div><div>Photovoltaic (PV) systems are responsible for the conversion of solar energy into electricity and with the rising usage of renewable energy, solar energy has emerged as one of the leading contributors. However, solar energy is dependent on various environmental conditions which raises the need for forecasting of the electricity produced. With the rise in the usage of machine learning (ML) there have been attempts to forecast the solar energy harvested by PV systems. In this study a robust framework is used to predict the current and voltage generated by a PV system. This study employs the use of feature selection using BorutaSHAP and Variance Inflation Factor (VIF) to train various ML models consisting of Linear Regression, tree-based models, TabNet and transformer-based models. These models were later interpreted using Explainable Artificial Intelligence (XAI) methods such as SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE), Local Interpretable Model-agnostic Explanations (LIME) and Diverse Counterfactual Explanations (DiCE). The best performing model was TabPFN, a transformer-based model and it achieved an R-squared of 0.998 and 0.934 for current and voltage respectively. This study shows a strong performing and interpretable framework to predict the current and voltage of a PV system.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101627"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080262","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}
Pub Date : 2026-05-01Epub Date: 2026-01-24DOI: 10.1016/j.ecmx.2026.101626
Ricardo González-Cárabes , Luis Bernardo López-Sosa , Janneth López-Mercado , José Guadalupe Rutiaga Quiñones , Francisco Javier Reynoso Marín , Luis Fernando Pintor-Ibarra , Luis Ángel Ascencio de la Cruz , Mario Morales Máximo , Arturo Aguilera Mandujano , Saúl Leonardo Hernández-Trujillo
This research presents an analysis of the energy potential of 5 agricultural crop residues in the state of Michoacán, Mexico, considering their possible use as solid biofuels. This study consists of five phases: (a) Identification of agricultural areas and collection of residues of each of the crops, Persea americana Mill. (avocado), Saccharum officinarum L. (sugarcane), Lens culinaris Medik. (lentil), Zea mays L. (corn) and Mangifera indica L (mango); (b) processing of the residues for characterization; (c) physicochemical characterization of the collected residues using characterization techniques such as CHONS, polymeric compound composition, FTIR, ash microanalysis and calorific value, in addition to the proximate analysis of the residues by obtaining the moisture, ash, volatiles and fixed carbon contents; (d) determination of the energy potential (TJ/year); (e) dissemination of results. The results of this research show values for the crops analyzed in terms of ash contents lower than 10%, percentages of volatile matter higher than 70%, while fixed carbon values were lower than 21%, elemental analysis showed results for carbon higher than 40%, lower than 7% for hydrogen, higher than 47% for oxygen and for nitrogen lower than 2%, in terms of polymeric compounds showed values higher than 12% for cellulose, values higher than 8% for hemicellulose, and regarding lignin, values above 5% were reported. The calorific value values were estimated between 15. MJ/kg and 19.8 MJ/kg, with energy potential values that could, in their minimum production, eventually satisfy the energy demand for cooking of 30% of the rural sector of the state.
{"title":"Exploring the energy potential of agricultural and agroindustrial residues in michoacán: characterization to determine the feasibility of solid biofuels","authors":"Ricardo González-Cárabes , Luis Bernardo López-Sosa , Janneth López-Mercado , José Guadalupe Rutiaga Quiñones , Francisco Javier Reynoso Marín , Luis Fernando Pintor-Ibarra , Luis Ángel Ascencio de la Cruz , Mario Morales Máximo , Arturo Aguilera Mandujano , Saúl Leonardo Hernández-Trujillo","doi":"10.1016/j.ecmx.2026.101626","DOIUrl":"10.1016/j.ecmx.2026.101626","url":null,"abstract":"<div><div>This research presents an analysis of the energy potential of 5 agricultural crop residues in the state of Michoacán, Mexico, considering their possible use as solid biofuels. This study consists of five phases: (a) Identification of agricultural areas and collection of residues of each of the crops, <em>Persea americana Mill.</em> (avocado)<em>, Saccharum officinarum</em> L<em>.</em> (sugarcane)<em>,</em> Lens culinaris <em>Medik.</em> (lentil)<em>, Zea mays</em> L<em>.</em> (corn) and <em>Mangifera indica</em> L (mango); (b) processing of the residues for characterization; (c) physicochemical characterization of the collected residues using characterization techniques such as CHONS, polymeric compound composition, FTIR, ash microanalysis and calorific value, in addition to the proximate analysis of the residues by obtaining the moisture, ash, volatiles and fixed carbon contents; (d) determination of the energy potential (TJ/year); (e) dissemination of results. The results of this research show values for the crops analyzed in terms of ash contents lower than 10%, percentages of volatile matter higher than 70%, while fixed carbon values were lower than 21%, elemental analysis showed results for carbon higher than 40%, lower than 7% for hydrogen, higher than 47% for oxygen and for nitrogen lower than 2%, in terms of polymeric compounds showed values higher than 12% for cellulose, values higher than 8% for hemicellulose, and regarding lignin, values above 5% were reported. The calorific value values were estimated between 15. MJ/kg and 19.8 MJ/kg, with energy potential values that could, in their minimum production, eventually satisfy the energy demand for cooking of 30% of the rural sector of the state.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101626"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080263","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}
Pub Date : 2026-05-01Epub Date: 2026-01-24DOI: 10.1016/j.ecmx.2026.101606
Ramin Mehdipour, Zahra Baniamerian, Seamus Garvey
Given the urgent need to transition from fossil fuels, this study investigates aqua-ammonia as an alternative to natural gas for space heating and local energy supply. The research evaluates the feasibility of transporting aqua-ammonia through existing natural gas pipelines, including the necessary adaptations. It compares the performance and economics of three alternative fuels—hydrogen, ammonia, and aqua-ammonia—with natural gas. Key quantitative findings are: for 15 wt% aqua-ammonia at typical urban pressures (0.2–13 bar) the pipeline energy transfer is 1.5–2.8 × that of natural gas. The required distribution network capacity for aqua-ammonia, depending on ammonia concentration, is 2.2–6.6 × smaller than comparable municipal water networks and can be 2–8 × smaller than current gas mains for the same delivered energy; ∼130 L of 15 wt% aqua-ammonia can meet the estimated daily heating energy of a typical UK household; and optimal aqua-ammonia concentrations for residential heating fall in the 10–15 wt% NH3 range (while 18–25% suits work/industrial applications). By contrast, hydrogen transport faces material and compression penalties (compressor energy can be ≈4 × that required for natural gas in comparable scenarios) and pure ammonia requires higher pressures (phase change issues above ≈8 bar). These quantitative results indicate that aqua-ammonia offers practical advantages in transportation efficiency and system design simplicity compared with gaseous alternatives that merit experimental follow-up.
{"title":"Aqua-ammonia; an alternative fuel to natural gas for space Heating: Fuel transmission and comparative analysis","authors":"Ramin Mehdipour, Zahra Baniamerian, Seamus Garvey","doi":"10.1016/j.ecmx.2026.101606","DOIUrl":"10.1016/j.ecmx.2026.101606","url":null,"abstract":"<div><div>Given the urgent need to transition from fossil fuels, this study investigates aqua-ammonia as an alternative to natural gas for space heating and local energy supply. The research evaluates the feasibility of transporting aqua-ammonia through existing natural gas pipelines, including the necessary adaptations. It compares the performance and economics of three alternative fuels—hydrogen, ammonia, and aqua-ammonia—with natural gas. Key quantitative findings are: for 15 wt% aqua-ammonia at typical urban pressures (0.2–13 bar) the pipeline energy transfer is 1.5–2.8 × that of natural gas. The required distribution network capacity for aqua-ammonia, depending on ammonia concentration, is 2.2–6.6 × smaller than comparable municipal water networks and can be 2–8 × smaller than current gas mains for the same delivered energy; ∼130 L of 15 wt% aqua-ammonia can meet the estimated daily heating energy of a typical UK household; and optimal aqua-ammonia concentrations for residential heating fall in the 10–15 wt% NH<sub>3</sub> range (while 18–25% suits work/industrial applications). By contrast, hydrogen transport faces material and compression penalties (compressor energy can be ≈4 × that required for natural gas in comparable scenarios) and pure ammonia requires higher pressures (phase change issues above ≈8 bar). These quantitative results indicate that aqua-ammonia offers practical advantages in transportation efficiency and system design simplicity compared with gaseous alternatives that merit experimental follow-up.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101606"},"PeriodicalIF":7.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080383","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}
Pub Date : 2026-05-01Epub Date: 2026-01-24DOI: 10.1016/j.ecmx.2026.101622
Trevor J. Kramer, David Schafer, Griffin Layhew, Daniel Cannon, Sam Chumney, Rory Roberts
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.
{"title":"SOFC polarization curve normalization and reduced order model generation for rapid and accurate performance prediction","authors":"Trevor J. Kramer, David Schafer, Griffin Layhew, Daniel Cannon, Sam Chumney, 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}
Pub Date : 2026-05-01Epub Date: 2026-01-22DOI: 10.1016/j.ecmx.2026.101591
Gidphil Mensah , Richard Opoku , Francis Davis , George Yaw Obeng , Oliver Kornyo , Daniel Marfo , Michael Addai , Jesse Damptey , Samuel Dodobatia Wetajega
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.
{"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 , Richard Opoku , Francis Davis , George Yaw Obeng , Oliver Kornyo , Daniel Marfo , Michael Addai , Jesse Damptey , 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}
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.
{"title":"Navigating towards efuel: A scientometric insight into the application of membrane reactors","authors":"Rahbaar Yeassin , Prangon Chowdhury , Prithibi Das , Ephraim Bonah Agyekum , Omar Farrok , 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 CO<sub>2</sub> 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}
Pub Date : 2026-05-01Epub Date: 2025-12-26DOI: 10.1016/j.ecmx.2025.101499
Emanuele Ogliari, Alberto Dolara, Domenico Mazzeo, Luca Lazzari, Sonia Leva
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.
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.
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.
{"title":"A simplified multi-physics approach for bifacial photovoltaic modules: Theory and validation of peculiar module layout","authors":"Emanuele Ogliari, Alberto Dolara, Domenico Mazzeo, Luca Lazzari, 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}
Pub Date : 2026-05-01Epub Date: 2026-02-04DOI: 10.1016/j.ecmx.2026.101654
Norihiro Fukuda , Yasuhiro Fujimitsu
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.
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.
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.
{"title":"Development of geothermal power generation system using geothermal fluids under harsh conditions","authors":"Norihiro Fukuda , 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}
Pub Date : 2026-05-01Epub Date: 2026-02-04DOI: 10.1016/j.ecmx.2026.101651
Marouane Barbri , Max Zimmermann , Felix Dahms , Karsten Müller
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 [1].
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.
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.
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.
{"title":"Energy analysis of large cruise ships case study of thermal and electric demands and supply during different scenarios","authors":"Marouane Barbri , Max Zimmermann , Felix Dahms , 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 CO<sub>2</sub>e 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}
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