Energy Conversion and Management-X最新文献_第7页

Policy pathways for clean energy and climate mitigation: insights from long-term scenario modelling 清洁能源和减缓气候变化的政策途径：来自长期情景建模的见解

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-22 DOI: 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.

巴基斯坦的能源部门一直依赖进口化石燃料，这些燃料仍然昂贵，造成了空气污染，并增加了温室气体（GHG）排放。在本研究中，使用低排放分析平台（LEAP）模型比较了2021年至2050年的三种电力供应情景，包括照常营业（BAU）情景、替代能源和可再生能源政策（AREP 2019）情景和更高目标的可持续途径（SP）情景。对这些情景进行比较，以评估可再生能源政策和干预措施在确保能源供应安全方面的能力，并在可持续发展目标（特别是关于清洁能源的可持续发展目标7和关于气候行动的可持续发展目标13）的背景下减缓气候变化。建模结果估计，到2050年，巴基斯坦的电力需求将在1489太瓦时左右，而按BAU计算，温室气体排放量将从每年1亿吨二氧化碳-e（2025年）增加到每年564.7亿吨二氧化碳-e。相反，SP方案，相比之下，假设更快地转向可再生能源，将限制2050年的排放量约为3400万吨二氧化碳-e，比BAU减少90%以上。此外，SP方案与巴基斯坦IGCEP计划的成本基准一致。然而，要达到这一水平，需要对电网基础设施进行重大升级，包括巴基斯坦正在开发的先进输电和智能配电系统。这些发现凸显了巴基斯坦加快向可再生能源发展的迫切需要。通过更好的政策和投资来利用该国大量未使用的可再生资源，对于改善能源安全和保护环境免受气候变化的影响至关重要。

{"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}

引用次数: 0

Machine learning-assisted innovative charging strategy for e-mobility in rural communities operated by redundant energy on solar PV mini-grids 基于太阳能光伏微电网冗余能源的农村电动交通机器学习辅助创新充电策略

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-22 DOI: 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.

利用太阳能进行近乎零排放的绿色交通，对于解决交通部门现代能源获取、温室气体排放和全球变暖的挑战具有全球重要性。在全球南方和大多数离网地区，太阳能光伏微型电网正在被用来提供能源。然而，在日照高峰时段，这些微型电网系统有多余的能量，可以用于进一步的盈利活动。电动交通是一个关键的用例，可以纳入微型电网的运行，以最大限度地减少冗余能源，提高系统性能，并提高微型电网的盈利能力。在本研究中，提出了一种基于机器学习（ML）的控制系统模型，该模型结合了用于电动三轮车充电的物联网（IoT），以优化微型电网对绿色交通能源的使用。在一个案例研究中，我们对人工神经网络、极端梯度增强、k近邻和随机森林这三个ML模型进行了训练，以预测高效电动汽车（EV）充电所需的冗余能量。结果表明，3个小区平均日冗余电量分别为56.98 ~ 119.86 kWh、74.39 ~ 311.87 kWh和57.03 ~ 274.66 kWh。通过对ML模型的验证，所有模型都能成功预测冗余能量。

{"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}

引用次数: 0

Economic dispatch optimization of a metal hydride storage system for supplying heat and electricity in a residential application 住宅供热用电金属氢化物储能系统的经济调度优化

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-21 DOI: 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}

引用次数: 0

AI-driven optimization approaches of metal–organic frameworks for enhanced methane delivery 人工智能驱动的金属有机框架优化方法增强甲烷输送

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-21 DOI: 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 (

R^{2}

) 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.

甲烷是天然气的主要成分，与其他以石油为基础的燃料相比，它排放的二氧化碳较少，但在有效储存和运输方面面临挑战。先进的吸附材料提供了一种安全且具有成本效益的解决方案，金属有机框架（mof）正在成为汽车天然气储存和输送的有前途的候选者。该研究采用人工智能驱动优化（AiDO）来确定提高甲烷吸收率的最佳参数，同时改善重量和体积输送。我们使用实验数据开发并验证了三种机器学习模型：极端梯度增强（XGBoost）、Kolmogorov-Arnold网络（KAN）和卷积神经网络（CNN）。所有模型均表现出较强的预测性能，其中XGBoost取得了优异的结果，均方根误差（RMSE）为0.0103，决定系数（R2）为0.9722。当集成到优化框架中时，XGBoost模型确定了甲烷输送的最佳条件，预测室温重力输送量为724.14 cm3/g，体积输送量为602.21 cm3/cm3，范围为65 - 5 bar。灵敏度分析验证了AiDO方法的稳健性，强调了其有效降低成本和提高多孔mof性能的潜力。

{"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}

引用次数: 0

Innovative syngas-biodiesel blends: a step towards cleaner and greener engine technology 创新的合成气-生物柴油混合物：迈向更清洁、更环保的发动机技术的一步

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-20 DOI: 10.1016/j.ecmx.2026.101603

Manikandan Ezhumalai , Mohan Govindasamy , Ratchagaraja Dhairiyasamy , Deekshant Varshney , Subhav Singh

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_x 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_x emissions and further optimize Biodiesel–syngas blending ratios.

对可持续和更清洁的化石燃料替代品的需求日益增长，加强了对生物柴油和内燃机气体燃料的研究。然而，大多数现有的研究都集中在单个生物柴油原料或柴油-合成气组合上，对富含合成气的混合生物柴油的协同效应了解有限。本研究的目的是在双燃料压缩点火发动机上，评估朱莉花和松木油甲酯生物柴油混合燃料与富氢合成气的性能、燃烧和排放特性。在Kirloskar SV1发动机上进行了不同负载和合成气流量的实验，并使用响应面法（RSM）和方差分析（ANOVA）对性能指标进行了分析。结果显示，J60 + P40混合20 L/min合成气实现了31.2%的制动热效率，比纯生物柴油提高了12%，同时减少了8%的制动特定燃料消耗和25%的烟雾不透明度。CO和HC的排放量分别下降了18%和22%，但由于燃烧温度的升高，NOx的排放量略微增加了5%。这些发现表明，合成气富集提高了燃烧效率，支持了清洁能源的利用。未来的研究应侧重于整合废气再循环（EGR）或催化后处理，以减少NOx排放，并进一步优化生物柴油-合成气混合比例。

{"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}

引用次数: 0

Machine learning for fuel cell remaining useful life prediction: A review 燃料电池剩余使用寿命预测的机器学习研究进展

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-20 DOI: 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.

准确预测燃料电池（FC）系统的剩余使用寿命（RUL）对于确保运行可靠性、优化维护策略和延长安全关键氢应用系统的使用寿命至关重要。由于FC退化受复杂、非线性和随机机制的控制，机器学习（ML）已成为数据驱动预测的强大范例。本文对最近基于ml的FC RUL估计方法进行了结构化和全面的回顾，包括有监督、无监督和混合方法，包括回归技术、支持向量机、集成模型、神经网络和高级深度学习架构。尽管取得了显著进展，但我们的分析揭示了当前文献中持续存在的局限性，特别是普遍忽视了潜在的电化学和物理降解规律，以及公开可用数据集中明确的RUL和寿命终止（EoL）标签的稀缺性和模糊性。这些挑战极大地限制了模型的泛化、可解释性和现实世界的适用性。为了解决这些差距，我们对20多项最新的研究进行了比较分析，并提出了一个统一的、可推广的RUL估计管道。该框架集成了数据采集、预处理、特征工程、模型设计和验证，同时明确地考虑了物理一致性和操作约束。此外，本文还提出了实用的、多层次的建议，包括针对数据建模和学习策略的一阶指南，针对验证协议和现实世界部署的二阶建议，以及不确定性量化（UQ）技术的系统集成，以增强鲁棒性、可解释性和可信度。通过整合方法学见解、新兴范例和面向部署的考虑，本综述为燃料电池系统可靠、物理一致和可扩展的RUL预测框架的开发提供了全面的参考和前瞻性路线图。

{"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}

引用次数: 0

Comparative performance evaluation of implicit and explicit models of photovoltaic modules integrated to power converters 集成在电源变流器上的光伏组件的隐式和显式模型的性能比较评价

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-20 DOI: 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.

光伏（PV）系统的优化和效率对于最大化其能源输出和确保可持续能源解决方案至关重要。光伏系统的性能在很大程度上取决于选择符合应用特定要求的电源转换器。因此，使用功率转换器对光伏系统进行准确建模对于预测系统行为、促进优化和制定有效的控制策略至关重要。这项工作提出了与各种非理想功率转换器（如升压，buck-boost和反激转换器）集成的隐式和显式光伏模型的发展，以提供现实的比较性能分析。在不同的辐照水平和负载条件下，分析了光伏系统的特性，包括集成了各种电源转换器拓扑的光伏模块，以评估隐式和显式建模方法对系统整体性能的影响。基于计算时间和迭代次数，比较了不同转换器拓扑下各种建模方法的性能。利用实验数据对集成升压变换器的PV模型进行了验证。结果表明，Lambert W函数通过显著减少计算时间和最小化误差变化，比传统的迭代方法（如fzero和fsolve）提供了显着的优势。本研究通过确定各种电源转换器拓扑结构的最有效建模和计算方法，有助于优化光伏系统性能，实现更快，更准确的仿真，提高设计和运行效率。

{"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}

引用次数: 0

Enhancing transient response in AC microgrids: A multi-objective optimization approach for improved active power management 增强交流微电网暂态响应：改进有功功率管理的多目标优化方法

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-20 DOI: 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.

许多住宅和当地消费者已经接受了单相逆变器用于自我消费和能源交易。然而，它们的采用对微电网（mg）内电能的有效管理提出了挑战，特别是关于上升时间和超调等瞬态响应、适当的有功功率控制或不同运行条件下的最佳性能。传统的逆变器控制器虽然易于编程，但往往面临目标冲突，其中一个参数的改善会降低另一个参数。这种限制使非线性系统的控制变得复杂，有可能造成高能瞬变，从而损坏组件并缩短功率半导体的使用寿命，从而导致昂贵的维护费用。本研究提出了一种鲁棒策略，着重于主要控制，使用高阶滑模控制器（SMC）和由多目标优化（MOO）方法调谐的PI滑动面来解决这些问题。有功功率的控制在与主电网同步的DQ框架下以锁相环的方式进行。我们的方法旨在同时改善有功功率的上升时间和超调量。MOO技术，如多目标遗传算法（MOGA），多目标差分进化（MODE）和多目标自适应模拟退火（MOASA），在我们基于pscad的研究中显示了重要的前景，该研究使用了MG实验室工业逆变器的数据。结果与粒子群优化（PSO）技术进行了比较。绝对误差积分（IAE）、平方误差积分（ISE）、时间和绝对误差积分（ITAE）、时间和平方误差积分（ITSE）等性能指标表明，SMC-2+MOO优于PSO等传统方法，为MG效率管理提供了优越的解决方案。

{"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}

引用次数: 0

Reinforcement learning in pulse energy converters, process knowledge learned to transfer perspective framework 强化学习在脉冲能量转换器，过程知识学习转移的视角框架

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-19 DOI: 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.

本文以脉冲能量转换器（PEC）为例，提出了基于任务的能量传递控制强化学习（RL）方法框架。重点是RL设计的评估方面，反映在制定所学知识和转移安全系统应用的能力。设计人员对过程知识的认识对控制系统的设计至关重要，这始于对过程的必要初始知识的定义，并随着RL知识的转化而继续，包括存储和内部或外部的可转移性。可转移的知识是归纳知识，而不是关于高级RL公式的超参数的知识，但它确实包含了这些信息。不是一种模拟确定性的方法，而是一种模拟确定性不确定性的方法。混合系统建模为启发式和RL公式的实现提供了想象的确定性基础。定义、假设、评论和定理等形式的温和数学表达有助于支持从已经继承的知识开始的可转移知识公式的想法。重点是过程知识的归纳获取和对学习算法的认识论内涵的认识，以及对本体的明确转化。马尔可夫决策过程（MDP）是一个清晰的数学工具和建模框架，它将过程状态的数学空间与我们的概率空间和基于启发式的实时决策相结合。

{"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}

引用次数: 0

Performance analysis of a novel battery thermal management system integrating thermoelectric and dielectric immersion cooling in EVs 一种新型电池热管理系统集成热电和介质浸没冷却的电动汽车性能分析

IF 7.6 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2026-01-19 DOI: 10.1016/j.ecmx.2026.101550

Md Ahnaf Adit, Samiul Hasan, Nirendra Nath Mustafi

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.

电动汽车中锂离子电池的有效热管理对于确保安全性、延长电池寿命以及在不同操作条件下保持性能至关重要。本文提出了一种将热电冷却与介质浸没冷却相结合的新型电池热管理系统，并通过仿真和实验对其性能进行了评价。相对较新的26650 LiFePO4电池型号，由于其发热量增加，具有高容量和高放电能力的特点。首先使用计算流体动力学（CFD）对所提出的BTMS进行数值分析，以评估温度分布和冷却效果。随后在物理电池模拟器上进行了实验测试，并将测量数据与CFD预测结果进行了比较。在所有情况下，实验得出的温度值都略高于模拟预测的温度值。在最大冷却液流量为1.96 L/min时，与不添加BTMS时相比，BTMS在放电率为5.8 C、7.7 C和9.6 C时分别使电池模拟器的温升降低了28.78%、41.52%和46.54%。在最高流量（9.6℃）下，当冷却剂流量为0.58 L/min、1.08 L/min和1.96 L/min时，在375 s内温度分别降低了9.71 K、12.57 K和16.57 K。总体而言，所开发的BTMS在锂离子电池模拟器的温度控制方面非常有效。研究结果为热电介质浸没冷却技术的设计和实现提供了有价值的指导，特别是在高性能电动汽车应用中。

{"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}

引用次数: 0