Next Energy最新文献

Power-to-methanol process assessment using enhanced kinetic models 利用增强型动力学模型对电力制甲醇过程进行评估

Next Energy

Pub Date : 2026-01-31 DOI: 10.1016/j.nxener.2026.100518

Vitória N. Silva Oliveira, Rafaelle Gomes Santiago, Moises Bastos-Neto, Célio L. Cavalcante Jr., F. Murilo T. Luna

In the energy transition scenario, Power-to-X processes play a crucial role by converting surplus electricity from renewable sources into fuels, chemicals, and other energy carriers. These technologies not only help to balance the supply and demand of energy but also promote decarbonization. In this study, the conversion of carbon dioxide and hydrogen into methanol (Power-to-Methanol) as a strategic solution to store and transport hydrogen was evaluated by modeling and simulation. The investigation addressed the rate laws governing the reactions in the hydrogenation of pollutant gases into methanol. Refitted and original Bussche-Froment (BF) and Graaf kinetic models were used to understand and identify the key factors in process efficiency for improving competitiveness compared to conventional processes. The sensitivity analysis revealed some similarities in both models; however, discrepancies in conversion predictions reached up to 49%, particularly at intermediate residence times, low temperatures, and high pressures. Selecting an appropriate residence time (below 0.1 h) proved critical to reducing divergences between models, providing actionable insight for reliable process design and optimization. These discrepancies between the models contribute to a broad theoretical optimal operating window for the process. Considering both models, the methanol production and CO₂ conversions were higher in temperatures between 200 and 250 °C. In this optimal temperature range, increasing the pressure contributed to higher methanol production. Increasing H₂/CO₂ ratio favored CO₂ conversion, achieving an average for both models of 44% with a ratio of 8:1. However, a ratio of 3:1 for the Graaf model and 2:1 for the BF model resulted in maximum methanol production. Finally, increasing the CO concentration raised the obtained methanol concentration but resulted in lower carbon dioxide conversion.

在能源转型情景中，Power-to-X过程将可再生能源的剩余电力转化为燃料、化学品和其他能源载体，发挥着至关重要的作用。这些技术不仅有助于平衡能源供需，而且还促进了脱碳。在本研究中，通过建模和仿真对二氧化碳和氢气转化为甲醇（Power-to-Methanol）作为储存和运输氢气的战略解决方案进行了评估。研究了污染气体加氢制甲醇反应的速率规律。采用改进的和原始的Bussche-Froment （BF）和Graaf动力学模型来了解和确定与传统工艺相比提高竞争力的工艺效率的关键因素。敏感性分析显示两种模型存在一定的相似性；然而，转换预测的差异高达49%，特别是在中间停留时间、低温和高压下。选择适当的停留时间（低于0.1 h）对于减少模型之间的差异至关重要，为可靠的过程设计和优化提供可操作的见解。这些模型之间的差异有助于为该过程提供一个广泛的理论最佳操作窗口。考虑到这两种模型，在200和250 °C之间的温度下，甲醇产量和二氧化碳转化率更高。在这个最佳温度范围内，增加压力有助于提高甲醇产量。H2/CO2比值的增加有利于CO2转化率，在比值为8:1的情况下，两种车型的平均转化率均达到44%。然而，Graaf模型的比例为3:1，BF模型的比例为2:1，结果是最大的甲醇产量。最后，增加CO浓度，得到的甲醇浓度升高，但导致二氧化碳转化率降低。

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引用次数: 0

Recent advances in fuel cell design and modeling: A comprehensive review 燃料电池设计与建模的最新进展：综述

Next Energy

Pub Date : 2026-01-29 DOI: 10.1016/j.nxener.2026.100517

Umang Bedi

Fuel cells are gaining popularity as sustainable alternatives to traditional energy sources due to their low environmental impact and high efficiency. Among these, direct alcohol fuel cells, proton exchange membrane fuel cells, and solid oxide fuel cells are highly promising. Fuel cells convert fuels, such as alcohols and hydrogen, into electricity with considerably higher efficiency than combustion engines, producing only water as the primary by-product. This clean energy pathway supports the United Nations Sustainable Development Goal (SDG) 7 (Affordable and Clean Energy) and advances SDG 13 (Climate Action) by reducing greenhouse gas emissions and dependency on fossil fuels. However, significant challenges remain before widespread implementation of fuel cells, including financial feasibility, long-term reliability, and scalability. Therefore, this review aims to address the existing gap in understanding how recent modeling and design advancements can overcome these limitations across different types of fuel cells. This review provides a comprehensive and current synthesis of recent fuel cell modeling and design, uniquely integrating bibliometric trends, experimental advances, and computational methods across all major types of fuel cells. Emphasis is placed on numerical optimization strategies, advancements in multi-physics simulations, sustainable material innovations, and emerging approaches such as artificial intelligence-assisted modeling and integrated multi-scale frameworks. The review offers a cross-disciplinary roadmap to improve the performance, durability, and commercial viability of next-generation fuel cell technologies.

燃料电池由于其低环境影响和高效率，作为传统能源的可持续替代品而越来越受欢迎。其中，直接酒精燃料电池、质子交换膜燃料电池和固体氧化物燃料电池是很有前途的。燃料电池以比内燃机高得多的效率将燃料（如酒精和氢）转化为电能，只产生水作为主要副产品。这一清洁能源途径支持联合国可持续发展目标7（负担得起的清洁能源），并通过减少温室气体排放和对化石燃料的依赖来推进可持续发展目标13（气候行动）。然而，在燃料电池的广泛应用之前，还存在一些重大挑战，包括经济可行性、长期可靠性和可扩展性。因此，本综述旨在解决现有的差距，了解最近的建模和设计进展如何克服不同类型燃料电池的这些限制。这篇综述提供了最近燃料电池建模和设计的全面和当前的综合，独特地整合了所有主要类型燃料电池的文献计量学趋势、实验进展和计算方法。重点放在数值优化策略、多物理场模拟的进展、可持续材料创新以及人工智能辅助建模和集成多尺度框架等新兴方法上。该综述提供了一个跨学科的路线图，以提高下一代燃料电池技术的性能、耐久性和商业可行性。

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引用次数: 0

Exploring the processing of nickel, manganese, and cobalt precursors for lithium-ion batteries in Morocco: Insights, challenges, and perspectives 探索摩洛哥锂离子电池镍、锰和钴前体的加工：见解、挑战和观点

Next Energy

Pub Date : 2026-01-29 DOI: 10.1016/j.nxener.2026.100516

Said Azerblou , Redouane Oubah , Hamza Ouachtouk , Elmostafa Tace

Sustainable energy transition, particularly via the use of electric vehicles (EVs), is a prominent solution to address the environmental challenges. A catalyst for this transformation is the ability to manufacture autonomous lithium-ion batteries (LIBs), a vital component for EVs. Leading countries are competing to ensure an independent and autonomous supply of raw materials necessary for this energy shift, especially for nickel manganese cobalt oxide (NMC) chemistry, a significant cathode for LIBs. Morocco, a North African country, has a large production capacity for NMC raw materials, including nickel, manganese, and cobalt, but these resources are mostly exported in their raw or intermediate forms without significant valorisation to meet the NMC cathode requirements. To mitigate this challenge, this article develops refining processes that valorise actual mineral resources to produce the battery-grade precursors necessary for NMC cathodes. A rigorous examination of earlier studies found that sulphate precursors are the main metal sources used to make NMC cathodes in both research and industry. Refining processes have been established to transform natural manganese ore into a high-purity manganese sulphate precursor. Regarding nickel and cobalt sulphate, transformational processes were developed by adapting existing facilities. These processes will enable Morocco to produce 600 metric tonnes of nickel sulphate, 9305 metric tonnes of cobalt sulphate, and 56,160 metric tonnes of manganese sulphate, which in turn allows the manufacture of almost 370,000 EVs. This work paves the way for Morocco to valorise its mineral resources and develop an integrated industrial ecosystem for the EVs supply chain.

可持续能源转型，特别是通过使用电动汽车（ev），是应对环境挑战的重要解决方案。这种转变的催化剂是制造自动锂离子电池（lib）的能力，这是电动汽车的重要组成部分。主要国家正在竞相确保这种能源转变所需的原材料的独立和自主供应，特别是镍锰钴氧化物（NMC）化学，这是锂离子电池的重要阴极。摩洛哥是一个北非国家，拥有大量的NMC原材料生产能力，包括镍、锰和钴，但这些资源大多以原料或中间形式出口，没有显着增值以满足NMC阴极的要求。为了缓解这一挑战，本文开发了一种精炼工艺，使实际矿产资源增值，以生产NMC阴极所需的电池级前体。对早期研究的严格检查发现，硫酸盐前体是研究和工业中用于制造NMC阴极的主要金属来源。建立了将天然锰矿石转化为高纯度硫酸锰前驱体的精炼工艺。对于镍和硫酸钴，通过改造现有设施开发了改造工艺。这些工艺将使摩洛哥能够生产600公吨硫酸镍、9305公吨硫酸钴和56160公吨硫酸锰，从而可以生产近37万辆电动汽车。这项工作为摩洛哥实现矿产资源增值和为电动汽车供应链开发综合工业生态系统铺平了道路。

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引用次数: 0

Integrated approach to additive manufacturing of multi-material components for ammonia decomposition reactors: A review 氨分解反应器多材料部件增材制造集成方法研究进展

Next Energy

Pub Date : 2026-01-23 DOI: 10.1016/j.nxener.2026.100515

Lennart Mesecke , Ina Meyer , Sascha Brechelt , Niclas Zerner , Marco-Nicolas Galati , Kiran Prabha , Christian Schröder , Volker Wesling , Stefan Kaierle , Henning Ahlers , Roland Lachmayer

Climate change necessitates the expansion of renewable energy systems, and sustainably produced hydrogen plays an important role in this expansion. For widespread use, there is a need for efficient hydrogen storage technologies. Ammonia facilitates the reversible storage of hydrogen, with the conversion occurring in catalytic reactors. This review proposes an integrated approach to enhance the efficiency of catalytic reactors through multi-material additive manufacturing (MMAM). It includes material development, process technology, and component design for both directed energy deposition and powder bed fusion MMAM processes. In this review, the current state of the literature in these areas is summarized, and the research needs are identified.

气候变化要求可再生能源系统的扩张，而可持续生产的氢气在这一扩张中发挥着重要作用。为了广泛使用，需要高效的储氢技术。氨有利于氢的可逆储存，在催化反应器中进行转化。本文提出了一种利用多材料增材制造（MMAM）提高催化反应器效率的综合方法。它包括材料开发，工艺技术，以及定向能量沉积和粉末床融合MMAM工艺的组件设计。在这篇综述中，综述了这些领域的文献现状，并确定了研究需求。

引用次数: 0

Predicting electric vehicle performance metrics using a convolution neural network-gated recurrent unit-attention based deep learning architecture 使用基于卷积神经网络的循环单元注意深度学习架构预测电动汽车性能指标

Next Energy

Pub Date : 2026-01-23 DOI: 10.1016/j.nxener.2026.100514

Shivi Sharma , Neetha S.S. , Pranav Arya , Chandra Prakash

The indicators of electric vehicle performance such as state of charge (SOC), remaining useful life (RUL), and charge demand need to be accurately forecasted to ensure maximum energy control and battery life. The models used are usually not able to capture the spatial and temporal correlation of battery data and be robust to the presence of noisy measurements. In this study, we model a sequential attention-based deep learning structure with convolutional neural networks, gated recurrent units, and an attention mechanism that can ultimately understand the local features, temporal relationships, and dynamic significance of various features in sequential battery data. The hybrid architecture of this model allows it to extract local spatial features, long-term sequential dependencies and dynamically find the importance of the critical time steps. We also develop a hybrid loss that is an accumulation of Huber loss and Mean Squared Error, which is much more resilient to outliers and at the same time has high prediction accuracy. It is experimentally proven that the proposed model has R2 values of 0.9575, 0.9558, and 0.9199 on SOC, RUL, and charge demand, respectively, which are better than the current single-architecture methods.

电动汽车性能指标如荷电状态（SOC）、剩余使用寿命（RUL）、充电需求等需要准确预测，以确保最大的能量控制和电池寿命。所使用的模型通常不能捕获电池数据的时空相关性，并且对噪声测量的存在具有鲁棒性。在本研究中，我们使用卷积神经网络、门控循环单元和注意机制建模了一个基于顺序注意的深度学习结构，该结构最终可以理解顺序电池数据中各种特征的局部特征、时间关系和动态意义。该模型的混合结构使其能够提取局部空间特征，长期顺序依赖关系并动态发现关键时间步长的重要性。我们还开发了一种混合损失，它是Huber损失和均方误差的积累，它对异常值的弹性更强，同时具有很高的预测精度。实验证明，该模型在SOC、RUL和充电需求上的R2值分别为0.9575、0.9558和0.9199，优于现有的单架构方法。

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引用次数: 0

Elevating PV model performance: Accurate and reliable parameter extraction of solar cell models with state-of-art metaheuristic algorithms 提高光伏模型性能：利用最先进的元启发式算法准确可靠地提取太阳能电池模型参数

Next Energy

Pub Date : 2026-01-20 DOI: 10.1016/j.nxener.2026.100513

Hüseyin Bakır

This study focuses on parameter identification of various solar cell (SC) and PV module configurations, including the single diode SC, double diode SC, STP6-120/36, STM6-40/36, and Photowatt-PWP201. In this direction, seven state-of-the-art metaheuristic algorithms, including dynamic fitness-distance balance-based LSHADE (dFDB-LSHADE), nonlinear marine predator algorithm (NMPA), hippopotamus optimization (HO), marine predators algorithm (MPA), walrus optimizer (WO), exponential distribution optimizer (EDO), and manta-ray foraging optimization (MRFO) are employed to extract the unknown model parameters based on voltage-current measurement data. The optimum configuration of the SC parameters is identified by minimizing the root mean square error (RMSE) between the simulated and measured cell currents. The effectiveness of the algorithms was tested through extensive experimentation, incorporating statistical analysis, convergence analysis, box plots, and model validation. The optimization findings show that the dFDB-LSHADE produced the lowest RMSE and the most accurate predictions for all SC models. The box plots and statistical metric results clearly demonstrate that dFDB-LSHADE is a robust and reliable method for the SC parameter identification problem.

本研究的重点是各种太阳能电池（SC）和光伏组件配置的参数辨识，包括单二极管SC、双二极管SC、STP6-120/36、STM6-40/36和Photowatt-PWP201。在此方向上，采用基于动态适应度-距离平衡的LSHADE （dFDB-LSHADE）、非线性海洋捕食者算法（NMPA）、河马优化算法（HO）、海洋捕食者算法（MPA）、海象优化器（WO）、指数分布优化器（EDO）和蝠鲼觅食优化器（MRFO）等7种最先进的元启发式算法提取基于电压电流测量数据的未知模型参数。通过最小化模拟和测量细胞电流之间的均方根误差（RMSE）来确定SC参数的最佳配置。通过大量的实验，包括统计分析、收敛分析、箱形图和模型验证，测试了算法的有效性。优化结果表明，dFDB-LSHADE对所有SC模型的RMSE最低，预测最准确。箱形图和统计度量结果清楚地表明，dFDB-LSHADE是一种鲁棒可靠的SC参数识别方法。

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引用次数: 0

Not all fugitives are bad: The case for using them to form low tortuosity - high porosity electrodes 并不是所有的逃犯都是坏的：用他们来形成低扭曲度-高孔隙率电极的情况

Next Energy

Pub Date : 2026-01-20 DOI: 10.1016/j.nxener.2026.100512

Gabriel M. Veith , Ethan D. Boeding , Rachel J. Korkosz , Khryslyn G. Araño , Yeyoung Ha , Chanaka Kumara , Cailin Duggan , Amanda L. Musgrove , Thomas Zac Ward , Robert L. Sacci , Beth L. Armstrong

This work focuses on the inclusion of an insoluble fugitive phase during slurry processing to form composite battery electrodes. The fugitive phases consist of natural derived products like alginic acid, sucrose, rice and potato starch, and carrageenans such as Irish Moss and synthetic pore-formers based on polymethyl methacrylate. The fugitive phases can be anaerobically thermally removed (350 °C) during binder crosslinking and electrode drying steps, resulting in electrodes with low tortuosities (approaching theoretical Bruggemann limits for spherical particles) and high porosities approaching 80%. The resulting ∼3 mg/cm² loaded electrodes suffer from poor electrical connectivity, lowering the effective material utilization, but represent an approach that could be utilized for the formation of solid-state batteries with infilling of materials into well-defined pores and optimized transport pathways.

这项工作的重点是在浆料加工过程中加入一种不溶性的逸散相，以形成复合电池电极。逸散相包括海藻酸、蔗糖、大米和土豆淀粉等天然衍生产品，以及卡拉胶（如爱尔兰苔藓）和基于聚甲基丙烯酸甲酯的合成成孔剂。在粘合剂交联和电极干燥过程中，可以厌氧热去除（350 °C）逸散相，从而使电极具有低弯曲度（接近球形颗粒的理论Bruggemann极限）和接近80%的高孔隙率。由此产生的~ 3 mg/cm2负载电极电连接性差，降低了有效材料利用率，但代表了一种可用于形成固态电池的方法，将材料填充到明确的孔隙中并优化运输途径。

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引用次数: 0

Standalone DC microgrids: Planning, operation and uncertainty management 独立直流微电网：规划、运行和不确定性管理

Next Energy

Pub Date : 2026-01-20 DOI: 10.1016/j.nxener.2026.100511

Hasith Jayasinghe , Kosala Gunawardane , Md. Alamgir Hossain , Ramon Zamora

Standalone power systems in remote areas have traditionally relied on continuously operating fossil fuel generators, leading to high operational costs, reduced efficiency, and substantial carbon emissions. Standalone direct current (DC) microgrids have emerged as a promising alternative due to their lower conversion losses, improved integration of renewable energy sources (RES), and enhanced compatibility with modern DC-native loads and storage technologies. Despite these advantages, the planning, operation, and uncertainty management of standalone DC microgrids remain technically challenging. Intermittent RES generation, stochastic load behaviour, lack of mature standards, and complex control requirements introduce significant design and operational challenges. While numerous studies have proposed methods to address issues in sizing, optimisation, control, energy management, and uncertainty management, a comprehensive and structured review that connects these aspects across the full lifecycle of DC microgrid development is still lacking. This article addresses this gap by providing a systematic review of the state-of-the-art in planning methodologies, operational strategies, and uncertainty management techniques for standalone DC microgrids. The review synthesises theoretical frameworks and practical implementations, critically evaluates existing approaches by identifying their strengths and limitations, and highlights the interdependencies among planning, real-time operation, and uncertainty mitigation. Finally, the article outlines key research challenges and future opportunities to support the reliable, cost-effective, and sustainable deployment of standalone DC microgrids. The novelty of this study lies in its integrated perspective spanning planning, operational control, and uncertainty management, offering valuable guidance for researchers, system designers, and practitioners.

传统上，偏远地区的独立电力系统依赖于持续运行的化石燃料发电机，导致运营成本高、效率低、碳排放量大。独立的直流（DC）微电网由于其较低的转换损耗、改进的可再生能源（RES）集成以及与现代直流本地负载和存储技术的增强兼容性而成为一种有前途的替代方案。尽管有这些优势，独立直流微电网的规划、运行和不确定性管理在技术上仍然具有挑战性。间歇性可再生能源的产生、随机负载行为、缺乏成熟的标准以及复杂的控制要求带来了重大的设计和操作挑战。虽然许多研究已经提出了解决规模、优化、控制、能源管理和不确定性管理等问题的方法，但在直流微电网发展的整个生命周期中将这些方面联系起来的全面和结构化的审查仍然缺乏。本文通过对独立直流微电网的规划方法、运营策略和不确定性管理技术进行系统回顾，解决了这一差距。该报告综合了理论框架和实际实施，通过确定现有方法的优势和局限性，批判性地评估了现有方法，并强调了规划、实时操作和减少不确定性之间的相互依赖关系。最后，文章概述了关键的研究挑战和未来的机遇，以支持可靠、经济、可持续的独立直流微电网部署。这项研究的新颖之处在于它的综合视角跨越计划、操作控制和不确定性管理，为研究者、系统设计者和实践者提供了有价值的指导。

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引用次数: 0

Performance optimization of perovskite solar cells using p-InGaN as a hole transport layer: A numerical comparison with spiro-OMeTAD and HTL-free designs 使用p-InGaN作为空穴传输层的钙钛矿太阳能电池性能优化：与spiro-OMeTAD和无html设计的数值比较

Next Energy

Pub Date : 2026-01-17 DOI: 10.1016/j.nxener.2025.100510

P.R. Jubu , B.J. Akeredolu , S.J. Ikwe , K.O. Ighodalo , O.S. Obaseki , Z.S. Mbalaha , S.K. Omotayo , Y. Yusof , M.Z. Pakhuruddin

Perovskite solar cells (PSCs) commonly utilize organic materials as a hole-transport layer (HTL) to enhance hole extraction to the back electrode, thereby boosting device performance. These organic HTL are chemically and thermally unstable, degrading when exposed to air and moisture. This work, for the first time, attempts to explore the possibility of replacing the conventional organic HTL with the thermally and chemically stable, high hole mobility, inorganic III-nitride p-InGaN material as an HTL in PSC. The p-InGaN HTL-based PSC achieved a power conversion efficiency (PCE) of 21.6%, which is reasonable when compared to the PCE of 32.9% and 34.5% delivered by the conventional organic Spiro-OMeTAD HTL and the HTL-free configurations. These can be attributed to the assisted hole extraction by the HTL and the direct contact of the MAPbI₃ perovskite absorber with the optimal higher work-function Pt back contact for the Spiro-OMeTAD HTL-based and HTL-free-based devices, respectively. We observed that variations in the back metal contact have a significant impact on the PCE of Spiro-OMeTAD HTL-based and HTL-free PSC, respectively. Although the p-InGaN and Spiro-OMeTAD HTL-based PSCs demonstrate equivalent values of PCE at all high temperatures, 400–700 K. The HTL-free cell shows higher thermal resilience compared to its HTL-based counterpart devices. Our work reveals that utilizing the p-InGaN HTL increases longevity due to material stability, whereas eliminating the HTL can deliver higher PCE and reduced costs.

钙钛矿太阳能电池（PSCs）通常利用有机材料作为空穴传输层（HTL）来增强后电极的空穴提取，从而提高器件性能。这些有机HTL在化学上和热上都不稳定，暴露在空气和湿气中会降解。这项工作首次尝试探索用热化学稳定、高空穴迁移率、无机iii -氮化p-InGaN材料取代传统有机HTL作为PSC中HTL的可能性。基于p-InGaN html的PSC实现了21.6%的功率转换效率（PCE），与传统有机Spiro-OMeTAD html和无html配置的PCE分别为32.9%和34.5%相比，这是合理的。这可归因于HTL的辅助孔提取，以及MAPbI3钙钛矿吸收剂与Spiro-OMeTAD基于HTL和无HTL的器件的最佳高功函数Pt背接触的直接接触。我们观察到，后金属接触的变化分别对Spiro-OMeTAD基于html和无html的PSC的PCE有显著影响。尽管p-InGaN和Spiro-OMeTAD基于html的psc在400-700 K的所有高温下都显示出相同的PCE值。与基于html的同类设备相比，无html电池显示出更高的热弹性。我们的研究表明，由于材料的稳定性，使用p-InGaN HTL可以延长使用寿命，而消除HTL可以提供更高的PCE并降低成本。

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引用次数: 0

Evidential multi-model CNN integration for visual fault detection in solar panels 基于证据的多模型CNN集成太阳能电池板视觉故障检测

Next Energy

Pub Date : 2026-01-14 DOI: 10.1016/j.nxener.2025.100509

Nadjia Khatir , Safia Nait-Bahloul

This study proposes an evidential fusion framework for classifying visual defects in solar panels using convolutional neural networks (CNNs) and Dempster-Shafer theory (DST). Three pretrained CNN models: ResNet50, MobileNetV2, and EfficientNetB0 are fine-tuned to detect multiple defect types, and their outputs are fused at the logit level using DST. Unlike conventional ensemble strategies such as majority voting, the proposed method explicitly accounts for uncertainty and conflict among predictions by assigning belief masses to sets of hypotheses. Experimental evaluations conducted on a multiclass solar panel dataset demonstrate that DST fusion consistently outperforms individual models and majority voting across all macro-averaged metrics, particularly in underrepresented or visually ambiguous classes such as Physical-Damage and Dusty. These findings underscore the potential of uncertainty-sensitive model fusion to enhance the robustness and interpretability of automated photovoltaic inspection systems.

本研究提出了一种基于卷积神经网络（cnn）和Dempster-Shafer理论（DST）的太阳能电池板视觉缺陷分类证据融合框架。三个预训练的CNN模型：ResNet50、MobileNetV2和EfficientNetB0被微调以检测多种缺陷类型，并且它们的输出使用DST在logit级别融合。与传统的集合策略（如多数投票）不同，该方法通过将信念质量分配给假设集，明确地解释了预测之间的不确定性和冲突。在多类别太阳能电池板数据集上进行的实验评估表明，DST融合在所有宏观平均指标上始终优于单个模型和多数投票，特别是在代表性不足或视觉模糊的类别（如物理损伤和Dusty）中。这些发现强调了不确定性敏感模型融合的潜力，以提高自动化光伏检测系统的鲁棒性和可解释性。

引用次数: 0