Pub 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":"2026-01-22","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}
Pub Date : 2026-01-22DOI: 10.1016/j.ecmx.2026.101595
Rohan Kumar , Mohsin Pervez , Ammara Kanwal , Majid Ali , Muhammad Asim , Nadia Shahzad , Adnan Tariq
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.
{"title":"Policy pathways for clean energy and climate mitigation: insights from long-term scenario modelling","authors":"Rohan Kumar , Mohsin Pervez , Ammara Kanwal , Majid Ali , Muhammad Asim , Nadia Shahzad , 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 MtCO<sub>2</sub>-e(2025) to 564.7 MtCO<sub>2</sub>-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 MtCO<sub>2</sub>-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":"2026-01-22","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}
Pub Date : 2026-01-21DOI: 10.1016/j.ecmx.2026.101579
Carlos Muñoz , Nies Reininghaus , Julián Puszkiel , Astrid Pistoor , Michael Kroener , Alexander Dyck , Martin Vehse , Thomas Klassen , Julian Jepsen
To achieve affordable, clean energy, incorporating renewable energy into existing energy systems is the key. One challenge is the fluctuating nature of renewable resources, which can be asynchronous with energy demands. Hydrogen storage, particularly metal hydride storage, is a favorable solution for balancing supply and demand. In particular, metal hydride storage, compared with pressurized or liquefied hydrogen storage, is a favorable technology choice due to its storage energy density (50-100 kg H˙2/m3) and its low operating temperature and pressure. This paper presents a simulation-based framework to investigate the optimal design and operation of a coupled Electrolyzer-Fuel Cell-Metal Hydride system (SET-Unit) for minimizing operational and capital expenses in a residential application. The results show that integrating heat pumps with a metal-hydride storage system and photovoltaics can achieve 83% energy self-sufficiency and a 7.1-year payback period. Combining SET-Unit, gas boilers, and photovoltaics can result in 28% energy self-sufficiency, annual savings of over 2221 EUR, and a payback period of 7.4 years. The SET-Unit, combined with renewable energy sources such as photovoltaics, and the in-market available gas boilers or heat pumps, shows benefits in efficiency, annual energy cost reduction, and a relatively short payback period for the household. Using the low end of published values for capital expenses, economic feasibility can be achieved.
要获得负担得起的清洁能源，将可再生能源纳入现有能源系统是关键。其中一个挑战是可再生资源的波动性，它可能与能源需求不同步。氢的储存，特别是金属氢化物的储存，是平衡供需的一个很好的解决方案。特别是，与加压或液化氢储存相比，金属氢化物储存由于其储存能量密度（50-100 kg H˙2/m3）和较低的工作温度和压力，是一种较好的技术选择。本文提出了一个基于仿真的框架来研究耦合电解槽-燃料电池-金属氢化物系统（SET-Unit）的优化设计和运行，以最大限度地减少住宅应用中的运营和资本支出。结果表明，将热泵与金属氢化物存储系统和光伏相结合，可以实现83%的能源自给自足，投资回收期为7.1年。将SET-Unit、燃气锅炉和光伏相结合，可以实现28%的能源自给自足，每年节省超过2221欧元，投资回收期为7.4年。SET-Unit与可再生能源（如光伏）和市场上可用的燃气锅炉或热泵相结合，在效率、年度能源成本降低和家庭投资回收期相对较短等方面显示出优势。使用公布的资本支出值的低端，可以实现经济可行性。
{"title":"Economic dispatch optimization of a metal hydride storage system for supplying heat and electricity in a residential application","authors":"Carlos Muñoz , Nies Reininghaus , Julián Puszkiel , Astrid Pistoor , Michael Kroener , Alexander Dyck , Martin Vehse , Thomas Klassen , Julian Jepsen","doi":"10.1016/j.ecmx.2026.101579","DOIUrl":"10.1016/j.ecmx.2026.101579","url":null,"abstract":"<div><div>To achieve affordable, clean energy, incorporating renewable energy into existing energy systems is the key. One challenge is the fluctuating nature of renewable resources, which can be asynchronous with energy demands. Hydrogen storage, particularly metal hydride storage, is a favorable solution for balancing supply and demand. In particular, metal hydride storage, compared with pressurized or liquefied hydrogen storage, is a favorable technology choice due to its storage energy density (50-100 kg H˙2/m<sup>3</sup>) and its low operating temperature and pressure. This paper presents a simulation-based framework to investigate the optimal design and operation of a coupled Electrolyzer-Fuel Cell-Metal Hydride system (SET-Unit) for minimizing operational and capital expenses in a residential application. The results show that integrating heat pumps with a metal-hydride storage system and photovoltaics can achieve 83% energy self-sufficiency and a 7.1-year payback period. Combining SET-Unit, gas boilers, and photovoltaics can result in 28% energy self-sufficiency, annual savings of over 2221 EUR, and a payback period of 7.4 years. The SET-Unit, combined with renewable energy sources such as photovoltaics, and the in-market available gas boilers or heat pumps, shows benefits in efficiency, annual energy cost reduction, and a relatively short payback period for the household. Using the low end of published values for capital expenses, economic feasibility can be achieved.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101579"},"PeriodicalIF":7.6,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080378","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 : 2026-01-21DOI: 10.1016/j.ecmx.2026.101605
Helder R.O. Rocha , Sara Abou Dargham , Jimmy Romanos , Wesley Costa , Roy Roukos , Jair A.L. Silva , Heinrich Wörtche
Methane, the primary component of natural gas, emits less carbon dioxide than other petroleum-based fuels but faces challenges in efficient storage and transportation. Advanced adsorption materials provide a safe and cost-effective solution, with metal–organic frameworks (MOFs) emerging as promising candidates for natural gas storage and delivery in vehicles. This research employed AI-Driven Optimization (AiDO) to identify optimal parameters for enhancing methane uptake while simultaneously improving both gravimetric and volumetric delivery. We developed and validated three machine learning models: eXtreme Gradient Boosting (XGBoost), Kolmogorov–Arnold Network (KAN), and Convolutional Neural Network (CNN), using experimental data. All models demonstrated strong predictive performance, with XGBoost achieving outstanding results, including a Root Mean Squared Error (RMSE) of 0.0103 and a coefficient of determination () of 0.9722. When integrated into an optimization framework, the XGBoost model identified optimal conditions for methane delivery, predicting a room temperature gravimetric delivery of 724.14 cm3/g, and a volumetric delivery of 602.21 cm3/cm3 from 65 to 5 bar. Sensitivity analysis validated the robustness of the AiDO methodology, highlighting its potential to effectively reduce costs and enhance the performance of porous MOFs.
{"title":"AI-driven optimization approaches of metal–organic frameworks for enhanced methane delivery","authors":"Helder R.O. Rocha , Sara Abou Dargham , Jimmy Romanos , Wesley Costa , Roy Roukos , Jair A.L. Silva , Heinrich Wörtche","doi":"10.1016/j.ecmx.2026.101605","DOIUrl":"10.1016/j.ecmx.2026.101605","url":null,"abstract":"<div><div>Methane, the primary component of natural gas, emits less carbon dioxide than other petroleum-based fuels but faces challenges in efficient storage and transportation. Advanced adsorption materials provide a safe and cost-effective solution, with metal–organic frameworks (MOFs) emerging as promising candidates for natural gas storage and delivery in vehicles. This research employed AI-Driven Optimization (AiDO) to identify optimal parameters for enhancing methane uptake while simultaneously improving both gravimetric and volumetric delivery. We developed and validated three machine learning models: eXtreme Gradient Boosting (XGBoost), Kolmogorov–Arnold Network (KAN), and Convolutional Neural Network (CNN), using experimental data. All models demonstrated strong predictive performance, with XGBoost achieving outstanding results, including a Root Mean Squared Error (RMSE) of 0.0103 and a coefficient of determination (<span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>) of 0.9722. When integrated into an optimization framework, the XGBoost model identified optimal conditions for methane delivery, predicting a room temperature gravimetric delivery of 724.14 cm<sup>3</sup>/g, and a volumetric delivery of 602.21 cm<sup>3</sup>/cm<sup>3</sup> from 65 to 5 bar. Sensitivity analysis validated the robustness of the AiDO methodology, highlighting its potential to effectively reduce costs and enhance the performance of porous MOFs.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101605"},"PeriodicalIF":7.6,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039789","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 increasing demand for sustainable and cleaner alternatives to fossil fuels has intensified research on Biodiesel and gaseous fuels for internal combustion engines. However, most existing studies focus on individual biodiesel feedstocks or diesel–syngas combinations, leaving limited understanding of the synergistic effects of blended biodiesels enriched with Syngas. This study aims to evaluate the performance, combustion, and emission characteristics of Juliflora and Pine Oil Methyl Ester Biodiesel blends integrated with hydrogen-rich Syngas in a dual-fuel compression ignition engine. Experiments were conducted on a Kirloskar SV1 engine at varying loads and syngas flow rates, and performance metrics were analyzed using Response Surface Methodology (RSM) and ANOVA. Results revealed that the J60 + P40 blend with 20 L/min syngas achieved a brake thermal efficiency of 31.2%, a 12% improvement over neat Biodiesel, while reducing brake-specific fuel consumption by 8% and smoke opacity by 25%. CO and HC emissions decreased by 18% and 22%, respectively, though NOx increased marginally by 5% due to elevated combustion temperatures. These findings demonstrate that syngas enrichment enhances combustion efficiency and supports the utilization of cleaner energy. Future research should focus on integrating exhaust gas recirculation (EGR) or catalytic after-treatment to mitigate NOx emissions and further optimize Biodiesel–syngas blending ratios.
{"title":"Innovative syngas-biodiesel blends: a step towards cleaner and greener engine technology","authors":"Manikandan Ezhumalai , Mohan Govindasamy , Ratchagaraja Dhairiyasamy , Deekshant Varshney , Subhav Singh","doi":"10.1016/j.ecmx.2026.101603","DOIUrl":"10.1016/j.ecmx.2026.101603","url":null,"abstract":"<div><div>The increasing demand for sustainable and cleaner alternatives to fossil fuels has intensified research on Biodiesel and gaseous fuels for internal combustion engines. However, most existing studies focus on individual biodiesel feedstocks or diesel–syngas combinations, leaving limited understanding of the synergistic effects of blended biodiesels enriched with Syngas. This study aims to evaluate the performance, combustion, and emission characteristics of Juliflora and Pine Oil Methyl Ester Biodiesel blends integrated with hydrogen-rich Syngas in a dual-fuel compression ignition engine. Experiments were conducted on a Kirloskar SV1 engine at varying loads and syngas flow rates, and performance metrics were analyzed using Response Surface Methodology (RSM) and ANOVA. Results revealed that the J60 + P40 blend with 20 L/min syngas achieved a brake thermal efficiency of 31.2%, a 12% improvement over neat Biodiesel, while reducing brake-specific fuel consumption by 8% and smoke opacity by 25%. CO and HC emissions decreased by 18% and 22%, respectively, though NO<sub>x</sub> increased marginally by 5% due to elevated combustion temperatures. These findings demonstrate that syngas enrichment enhances combustion efficiency and supports the utilization of cleaner energy. Future research should focus on integrating exhaust gas recirculation (EGR) or catalytic after-treatment to mitigate NO<sub>x</sub> emissions and further optimize Biodiesel–syngas blending ratios.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101603"},"PeriodicalIF":7.6,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039765","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 : 2026-01-20DOI: 10.1016/j.ecmx.2026.101597
Zaid Allal , Hassan N. Noura , Flavien Vernier , Ola Salman , Khaled Chahine
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.
{"title":"Machine learning for fuel cell remaining useful life prediction: A review","authors":"Zaid Allal , Hassan N. Noura , Flavien Vernier , Ola Salman , 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":"2026-01-20","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}
Pub Date : 2026-01-20DOI: 10.1016/j.ecmx.2026.101601
Reshma V.P. , Anjan N. Padmasali , Arjun M.
The optimization and efficiency of photovoltaic (PV) systems are crucial for maximizing their energy output and ensuring sustainable energy solutions. The performance of a PV system is critically dependent on the selection of a power converter that aligns with the application’s specific requirements. Therefore, accurately modeling the PV system with the power converter is essential for predicting system behavior, facilitating optimization, and developing effective control strategies. This work presents the development of implicit and explicit PV models integrated with various non-ideal power converters, such as boost, buck-boost, and flyback converters, to provide a realistic comparative performance analysis. The characteristics of the PV system, incorporating PV modules integrated with various power converter topologies, are analyzed under varying irradiance levels and load conditions to assess the impact of implicit and explicit modeling approaches on overall system performance. The performance of various modeling approaches is compared across different converter topologies based on computation time and iteration count. The PV model integrated with a boost converter is validated using experimental data. The results demonstrate that the Lambert W function offers a notable advantage over conventional iterative methods, such as fzero and fsolve, by significantly reducing computation time and minimizing error variation. This research contributes to optimizing PV system performance by identifying the most efficient modeling and computational methods for various power converter topologies, enabling faster and more accurate simulations and enhancing design and operational efficiency.
{"title":"Comparative performance evaluation of implicit and explicit models of photovoltaic modules integrated to power converters","authors":"Reshma V.P. , Anjan N. Padmasali , Arjun M.","doi":"10.1016/j.ecmx.2026.101601","DOIUrl":"10.1016/j.ecmx.2026.101601","url":null,"abstract":"<div><div>The optimization and efficiency of photovoltaic (PV) systems are crucial for maximizing their energy output and ensuring sustainable energy solutions. The performance of a PV system is critically dependent on the selection of a power converter that aligns with the application’s specific requirements. Therefore, accurately modeling the PV system with the power converter is essential for predicting system behavior, facilitating optimization, and developing effective control strategies. This work presents the development of implicit and explicit PV models integrated with various non-ideal power converters, such as boost, buck-boost, and flyback converters, to provide a realistic comparative performance analysis. The characteristics of the PV system, incorporating PV modules integrated with various power converter topologies, are analyzed under varying irradiance levels and load conditions to assess the impact of implicit and explicit modeling approaches on overall system performance. The performance of various modeling approaches is compared across different converter topologies based on computation time and iteration count. The PV model integrated with a boost converter is validated using experimental data. The results demonstrate that the Lambert W function offers a notable advantage over conventional iterative methods, such as fzero and fsolve, by significantly reducing computation time and minimizing error variation. This research contributes to optimizing PV system performance by identifying the most efficient modeling and computational methods for various power converter topologies, enabling faster and more accurate simulations and enhancing design and operational efficiency.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101601"},"PeriodicalIF":7.6,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080238","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 : 2026-01-20DOI: 10.1016/j.ecmx.2026.101602
Oscar Gonzales-Zurita , Mario González-Rodríguez , Jean-Michel Clairand , Guillermo Escrivá-Escrivá
Many residential and local consumers have embraced single-phase inverters for self-consumption and energy trading. However, their adoption challenges the efficient management of electrical energy within microgrids (MGs), particularly regarding transient responses like rise time and overshoot, an appropriate active power control, or an optimum performance under different operating conditions. Conventional inverter controllers, while easy to program, often face conflicting objectives, where improving one parameter degrades another. This limitation complicates the control of nonlinear systems, risking high-energy transients that can damage components and reduce the lifespan of power semiconductors, leading to costly maintenance.
This study proposes a robust strategy focused on primary control using a higher-order sliding mode controller (SMC) with a PI sliding surface tuned by multi-objective optimization (MOO) methods to address these issues. The control of active power is performed under the DQ frame synchronized to the main grid under a PLL method. Our approach aims to improve both the rise time and the overshoot of active power simultaneously. MOO techniques such as Multi-Objective Genetic Algorithm (MOGA), Multi-Objective Differential Evolution (MODE), and Multi-Objective Adaptive Simulated Annealing (MOASA) have shown significant promise in our PSCAD-based research using data from an industrial inverter in an MG laboratory.
The results were compared with particle swarm optimization (PSO) techniques. Performance indices like integral of absolute error (IAE), integral of square error (ISE), integral of time and absolute error (ITAE), and integral of time and square error (ITSE) demonstrated that SMC-2+MOO outperforms traditional methods like PSO, offering a superior solution for managing MG efficiency.
{"title":"Enhancing transient response in AC microgrids: A multi-objective optimization approach for improved active power management","authors":"Oscar Gonzales-Zurita , Mario González-Rodríguez , Jean-Michel Clairand , Guillermo Escrivá-Escrivá","doi":"10.1016/j.ecmx.2026.101602","DOIUrl":"10.1016/j.ecmx.2026.101602","url":null,"abstract":"<div><div>Many residential and local consumers have embraced single-phase inverters for self-consumption and energy trading. However, their adoption challenges the efficient management of electrical energy within microgrids (MGs), particularly regarding transient responses like rise time and overshoot, an appropriate active power control, or an optimum performance under different operating conditions. Conventional inverter controllers, while easy to program, often face conflicting objectives, where improving one parameter degrades another. This limitation complicates the control of nonlinear systems, risking high-energy transients that can damage components and reduce the lifespan of power semiconductors, leading to costly maintenance.</div><div>This study proposes a robust strategy focused on primary control using a higher-order sliding mode controller (SMC) with a PI sliding surface tuned by multi-objective optimization (MOO) methods to address these issues. The control of active power is performed under the DQ frame synchronized to the main grid under a PLL method. Our approach aims to improve both the rise time and the overshoot of active power simultaneously. MOO techniques such as Multi-Objective Genetic Algorithm (MOGA), Multi-Objective Differential Evolution (MODE), and Multi-Objective Adaptive Simulated Annealing (MOASA) have shown significant promise in our PSCAD-based research using data from an industrial inverter in an MG laboratory.</div><div>The results were compared with particle swarm optimization (PSO) techniques. Performance indices like integral of absolute error (IAE), integral of square error (ISE), integral of time and absolute error (ITAE), and integral of time and square error (ITSE) demonstrated that SMC-2+MOO outperforms traditional methods like PSO, offering a superior solution for managing MG efficiency.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101602"},"PeriodicalIF":7.6,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039763","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 : 2026-01-19DOI: 10.1016/j.ecmx.2026.101599
Robert Baždarić, Jasmin Ćelić
The article presents the task-based methodological framework of reinforcement learning (RL) for the control of energy transfer using the example of pulse energy converters (PEC). The focus is on the evaluation aspects of RL design as reflected in the formulation of knowledge learned and the ability to transfer for safe system application. The designer’s awareness of the process knowledge that is critical to the design of the control system begins with the definition of the necessary initial knowledge about the process and continues with the transformation of the RL knowledge, including storage and internal or external transferability. The transferable knowledge is inductive knowledge and not knowledge about the hyperparameters of the higher-level RL formulation, but it does contain this information. Not as a method for modelling deterministic certainty, but for modelling deterministic uncertainty. Modelling hybrid systems provides the imaginative deterministic foundations for the implementation of heuristics and RL formulations. The mild mathematical expressions in the form of definitions, assumptions, remarks and theorems serve to support the idea of transferable knowledge formulations that start from already inherited knowledge. The emphasis is on the inductive acquisition of process knowledge and the awareness of the epistemic connotation of the learning algorithm to ontics with the clear transformation. Markov Decision Processes (MDP) is a clear mathematical tool and modelling framework that merges the mathematical spaces of process states with our probability spaces and heuristics-based decision making in real time.
{"title":"Reinforcement learning in pulse energy converters, process knowledge learned to transfer perspective framework","authors":"Robert Baždarić, Jasmin Ćelić","doi":"10.1016/j.ecmx.2026.101599","DOIUrl":"10.1016/j.ecmx.2026.101599","url":null,"abstract":"<div><div>The article presents the task-based methodological framework of reinforcement learning (RL) for the control of energy transfer using the example of pulse energy converters (PEC). The focus is on the evaluation aspects of RL design as reflected in the formulation of knowledge learned and the ability to transfer for safe system application. The designer’s awareness of the process knowledge that is critical to the design of the control system begins with the definition of the necessary initial knowledge about the process and continues with the transformation of the RL knowledge, including storage and internal or external transferability. The transferable knowledge is inductive knowledge and not knowledge about the hyperparameters of the higher-level RL formulation, but it does contain this information. Not as a method for modelling deterministic certainty, but for modelling deterministic uncertainty. Modelling hybrid systems provides the imaginative deterministic foundations for the implementation of heuristics and RL formulations. The mild mathematical expressions in the form of definitions, assumptions, remarks and theorems serve to support the idea of transferable knowledge formulations that start from already inherited knowledge. The emphasis is on the inductive acquisition of process knowledge and the awareness of the epistemic connotation of the learning algorithm to ontics with the clear transformation. Markov Decision Processes (MDP) is a clear mathematical tool and modelling framework that merges the mathematical spaces of process states with our probability spaces and heuristics-based decision making in real time.</div></div>","PeriodicalId":37131,"journal":{"name":"Energy Conversion and Management-X","volume":"30 ","pages":"Article 101599"},"PeriodicalIF":7.6,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039790","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}
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.
{"title":"Performance analysis of a novel battery thermal management system integrating thermoelectric and dielectric immersion cooling in EVs","authors":"Md Ahnaf Adit, Samiul Hasan, 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 LiFePO<sub>4</sub> 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":"2026-01-19","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}
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