Applied Energy最新文献_第3页

Toward low-data and real-time PEMFC diagnostic: Multi-sine stimulation and hybrid ECM-informed neural network

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-22 DOI: 10.1016/j.apenergy.2025.125959

Zhongyong Liu , Hao Sun , Lifeng Xu , Lei Mao , Zhiyong Hu , Jingguo Li

Proton exchange membrane fuel cell (PEMFC) faults often occur abruptly, during which the multi electrochemical processes within the cell exhibit distinct changes in behavior. Consequently, real-time monitoring of multi-electrochemical processes (MEP) information is crucial for diagnosing PEMFC faults. However, existing methods struggle to balance real-time performance, interpretability, and low training data requirements, significantly limiting their reliability and feasibility for PEMFC fault diagnosis in practical scenarios. To address these issues, a novel hybrid ECM-Informed Neural Network (V-ECM) is proposed to efficiently characterize the states of the PEMFC internal electrochemical reaction, which effectively integrates real-time performance with interpretability while reducing dependence on large training datasets. The key innovations include: (1) Real-time capability—Designing a multi-sine excitation signal using the Distribution of Relaxation Time (DRT), which significantly accelerates signal acquisition speed of the electrochemistry-related response voltage. (2) High interpretability and low data dependency—Integrating physical constraints of electrochemical processes from a mechanism-based equivalent circuit model (ECM) into the loss function, to guide feature learning in the deep neural network, which contribute to improving interpretability and reducing reliance on training data. Compared to existing state-of-the-art PEMFC fault diagnosis methods, the proposed method offers high-precision, high-efficiency fault diagnosis, promising a viable solution for real-time PEMFC fault diagnosis in practical applications.

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引用次数: 0

Electrolytic hydrogen in a large-scale decarbonized grid with energy reservoirs: An assessment of carbon intensity and integrity

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-22 DOI: 10.1016/j.apenergy.2025.125938

Carlos E. Driemeier , Giovana C. Tonon , Mateus F. Chagas , Gabriel P. Petrielli , Daniele S. Henzler , Luísa C.M. Gomes , Bruno E. Limeira , Thayse A.D. Hernandes , Edvaldo R. Morais

Electrolytic hydrogen (e-H₂) is under scrutiny worldwide to become a primary vector for decarbonization. Production of e-H₂ in Brazil is a unique case study because of the singularities of the electricity grid, which has a continental scale, is highly decarbonized (93 % renewables in 2023), and incorporates substantial energy storage (210 TWh) and dispatch flexibility (circa 50 GW) in hydro reservoirs. In this distinctive context, this study evaluates the carbon intensity (quantified through a cradle-to-gate life cycle assessment) and the requirements to ensure the carbon integrity of grid-connected e-H₂ production. The study gathers inventories of solar and wind energy systems and alkaline electrolyzers. It also presents georeferenced modeling of carbon emission factors for solar and wind energy, along with hourly simulations of grid-connected e-H₂ production. Carbon intensities within 2.9–4.0 kgCO₂eq kgH₂⁻¹ are calculated with solar energy and as low as 1.0 kgCO₂eq kgH₂⁻¹ with wind energy. Energy sourcing from the best wind sites leads to the lowest carbon intensities, even if adding the impacts of long-distance (2000 km) transmission. Simulation of e-H₂ production with wind energy assisted by energy storage in hydro reservoirs shows that electrolysis at a high capacity factor (≈90 %) is possible without impacting grid emissions and reservoir functionality. This result demonstrates that the requirement of hourly matching between additional energy generation and consumption is unsound for e-H₂ production in a grid rich in renewables. Instead, the temporality of electrolysis must consider the permissible temporal unmatching enabled by the grid-based energy storage and the complementarity between the legacy and the additional renewable sources.

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引用次数: 0

Metallic heterostructure enables high performance in low temperature ceramic fuel cells

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-21 DOI: 10.1016/j.apenergy.2025.125969

Wenjuan Zhao , Enyi Hu , Jun Wang , Bin Lin , Guoqing Wang , Faze Wang , Bin Zhu , Peter Lund , Muhammad Imran Asghar

Heterostructure fuel cells offer substantial advantages, including low-temperature operation and improved ionic conductivity. However, their underlying mechanisms and industrial development remain insufficient to meet essential scientific requirements and the need for rigorous adaptability testing. In this study, we present a metallic heterostructure CeO₂/LiCoO₂ as a high-performance fuel cell electrolyte, combining density functional theory (DFT) calculations with experimental validation. The CeO₂/LiCoO₂ heterostructure is synthesized via a simple solid-state reaction. DFT analysis confirms the successful formation of the CeO₂/LiCoO₂ heterostructure facilitated by the interaction of p-type CeO₂ and n-type LiCoO₂, with hybridized O-2p and Co-3d orbitals crossing the Fermi level. The electrochemical experiments reveal that the CeO₂/LiCoO₂ metallic heterostructure fuel cell achieves a remarkable power density of 863 mW·cm⁻² and an enhanced ionic conductivity of 0.56 S·cm⁻¹ at 500 °C, underscoring its superior performance. Furthermore, the CeO₂/LiCoO₂ metallic heterostructure effectively suppress the reduction of Ce⁴⁺/Ce³⁺, significantly enhancing operational stability. This work advances the understanding of metallic heterostructure fuel cells, demonstrating their potential in achieving superior ionic conductivity for practical applications.

{"title":"Metallic heterostructure enables high performance in low temperature ceramic fuel cells","authors":"Wenjuan Zhao , Enyi Hu , Jun Wang , Bin Lin , Guoqing Wang , Faze Wang , Bin Zhu , Peter Lund , Muhammad Imran Asghar","doi":"10.1016/j.apenergy.2025.125969","DOIUrl":"10.1016/j.apenergy.2025.125969","url":null,"abstract":"<div><div>Heterostructure fuel cells offer substantial advantages, including low-temperature operation and improved ionic conductivity. However, their underlying mechanisms and industrial development remain insufficient to meet essential scientific requirements and the need for rigorous adaptability testing. In this study, we present a metallic heterostructure CeO<sub>2</sub>/LiCoO<sub>2</sub> as a high-performance fuel cell electrolyte, combining density functional theory (DFT) calculations with experimental validation. The CeO<sub>2</sub>/LiCoO<sub>2</sub> heterostructure is synthesized via a simple solid-state reaction. DFT analysis confirms the successful formation of the CeO<sub>2</sub>/LiCoO<sub>2</sub> heterostructure facilitated by the interaction of <em>p</em>-type CeO<sub>2</sub> and <em>n</em>-type LiCoO<sub>2</sub>, with hybridized O-2<em>p</em> and Co-3<em>d</em> orbitals crossing the Fermi level. The electrochemical experiments reveal that the CeO<sub>2</sub>/LiCoO<sub>2</sub> metallic heterostructure fuel cell achieves a remarkable power density of 863 mW·cm<sup>−2</sup> and an enhanced ionic conductivity of 0.56 S·cm<sup>−1</sup> at 500 °C, underscoring its superior performance. Furthermore, the CeO<sub>2</sub>/LiCoO<sub>2</sub> metallic heterostructure effectively suppress the reduction of Ce<sup>4+</sup>/Ce<sup>3+</sup>, significantly enhancing operational stability. This work advances the understanding of metallic heterostructure fuel cells, demonstrating their potential in achieving superior ionic conductivity for practical applications.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"391 ","pages":"Article 125969"},"PeriodicalIF":10.1,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel probabilistic carbon price prediction model: Integrating the transformer framework with mixed-frequency modeling at different quartiles 新型概率碳价格预测模型：将变压器框架与不同四分位数的混合频率建模相结合

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-21 DOI: 10.1016/j.apenergy.2025.125951

Mingyang Ji , Juntao Du , Pei Du , Tong Niu , Jianzhou Wang

Most of the previous carbon price forecasting studies focus on point prediction based on same-frequency data, which ignores the large amount of prediction information provided by mixed-frequency data, while point prediction fails to quantify the uncertainty of carbon price fluctuations. Therefore, to fill this research gap, and improve the accuracy of carbon price prediction, this study proposes a novel hybrid forecasting model by integrating quantile regression, deep learning, and mixed-frequency modeling. Firstly, this study introduces twenty-three variables from energy commodities, market indexes, macroeconomic indicators, and environmental indicators, and then the feature selection method is applied for data dimensionality reduction to obtain the input factors. Subsequently, this study innovatively integrates mixed-frequency data sampling regression (MIDAS) and quantile regression (QR) into the Transformer architecture to construct a hybrid forecasting model, i.e., the QRTransformer-MIDAS model, and achieves point and interval prediction of low-frequency carbon price using high-frequency input factors. Meanwhile, the probabilistic prediction is implemented using kernel density estimation (KDE). In the comparison experiments, the proposed hybrid forecasting model realize mean absolute percentage errors (MAPE) of 1.46 % and 1.33 % for point predictions in Shanghai and Hubei carbon price datasets, respectively, moreover, with 95 % confidence intervals, the coverage width criterion (CWC) achieves 0.35 and 0.38, respectively, which outperforms the benchmark models. These experimental results confirm the practicality and robustness of the hybrid forecasting model proposed in this study.

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引用次数: 0

Fuel cell health state estimation based on a novel dynamic degradation model under non-fixed dynamic vehicle working conditions 基于非固定动态车辆工况下新型动态退化模型的燃料电池健康状态估计

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-21 DOI: 10.1016/j.apenergy.2025.125955

Jianwei Li , Weitao Zou , Hongwen He , Chenyu Zhang , Shuang Zhai , Xinming Wan , Zhanxing Mao

Proton exchange membrane fuel cells (PEMFCs) hold significant promise for vehicle applications due to their low carbon emissions and high efficiency. Accurate assessment of the state of health (SOH) of fuel cells is crucial for extending system life and minimizing overall costs. The SOH of a fuel cell is typically defined by the voltage decay under constant current. However, evaluating the health of fuel cells under dynamic vehicle conditions is challenging, as it is difficult to obtain the voltage decay pattern under constant current in such settings. Existing research has focused primarily on SOH estimation under steady-state or fixed-cycle conditions, yielding relatively good results, but there is a lack of studies on SOH evaluation under dynamic conditions. To address this gap, this paper presents a durability experiment conducted on a 120 kW automotive fuel cell system under non-fixed cycle dynamic conditions. Focusing on ohmic polarization decay as the key degradation index, we integrated an equivalent circuit model with a steady-state empirical model to establish a nonlinear fuel cell degradation model suitable for dynamic conditions. Using unscented Kalman filtering (UKF), the polarization curves are reconstructed at different stages of decay to evaluate the fuel cell’s health status. The feasibility and accuracy of the proposed method were verified through experimental data.

{"title":"Fuel cell health state estimation based on a novel dynamic degradation model under non-fixed dynamic vehicle working conditions","authors":"Jianwei Li , Weitao Zou , Hongwen He , Chenyu Zhang , Shuang Zhai , Xinming Wan , Zhanxing Mao","doi":"10.1016/j.apenergy.2025.125955","DOIUrl":"10.1016/j.apenergy.2025.125955","url":null,"abstract":"<div><div>Proton exchange membrane fuel cells (PEMFCs) hold significant promise for vehicle applications due to their low carbon emissions and high efficiency. Accurate assessment of the state of health (SOH) of fuel cells is crucial for extending system life and minimizing overall costs. The SOH of a fuel cell is typically defined by the voltage decay under constant current. However, evaluating the health of fuel cells under dynamic vehicle conditions is challenging, as it is difficult to obtain the voltage decay pattern under constant current in such settings. Existing research has focused primarily on SOH estimation under steady-state or fixed-cycle conditions, yielding relatively good results, but there is a lack of studies on SOH evaluation under dynamic conditions. To address this gap, this paper presents a durability experiment conducted on a 120 kW automotive fuel cell system under non-fixed cycle dynamic conditions. Focusing on ohmic polarization decay as the key degradation index, we integrated an equivalent circuit model with a steady-state empirical model to establish a nonlinear fuel cell degradation model suitable for dynamic conditions. Using unscented Kalman filtering (UKF), the polarization curves are reconstructed at different stages of decay to evaluate the fuel cell’s health status. The feasibility and accuracy of the proposed method were verified through experimental data.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"391 ","pages":"Article 125955"},"PeriodicalIF":10.1,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Market-based wind power investments under financial frictions

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-21 DOI: 10.1016/j.apenergy.2025.125425

Emilie Rosenlund Soysal

As support mechanisms aimed at promoting investment in renewable energy are phased out, producers must consider electricity market risk. Market price exposure affects economic feasibility of investment by making producers’ revenue stream vulnerable to the merit-order effect and by increasing revenue risk, which leads to higher costs of capital. The cost of capital is detrimental to the profitability of capital-intensive renewable energy sources, such as wind and solar power. To analyse the connection between electricity market exposure, the merit-order effect and the financing costs of wind power, this paper models the cost of capital of wind power investments in West-Denmark based on simulated return distributions. The return distributions are generated using a novel price forecasting model, an Adaptive Network-based Fuzzy Inference System, that predicts hourly prices from the residual load, natural gas price, and carbon price. Although it is a purely data-driven model, it reproduces the merit-order effect. The results emphasise the importance of recognising the endogenous changes in financing costs for accurate assessments of the profitability of wind power projects and the design of effective policies for incentivising renewable energy investments. The findings suggest that a higher carbon price can improve revenue distributions and lower financing costs, but its effectiveness diminishes at high levels of installed wind capacity.

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引用次数: 0

Policy-relevance of a model inter-comparison: Switzerland in the European energy transition

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-21 DOI: 10.1016/j.apenergy.2025.125906

Ambra Van Liedekerke , Blazhe Gjorgiev , Jonas Savelsberg , Xin Wen , Jérøme Dujardin , Ali Darudi , Jan-Philipp Sasse , Evelina Trutnevyte , Michael Lehning , Giovanni Sansavini

The energy transition is reshaping electricity systems, bringing new challenges, and emphasizing the need for strategic planning. Energy policies play a crucial role in guiding this transition. However, assessing their impacts often requires robust modeling involving multiple models and going beyond a single country’s scope, analyzing international interactions. In this study, we examine three Swiss energy policies, analyzing their impacts on both the national energy system and the cross-border electricity flows. We use a model inter-comparison approach with four electricity system models to explore scenarios involving Swiss renewable generation targets, the Swiss market integration, and the Swiss winter import limitations, in the context of various European electricity developments. The results indicate that a renewable generation target leads to a reduction in net imports and electricity prices. Additionally, reduced market integration impacts both Swiss and European energy transitions by limiting trade benefits, underutilizing Variable Renewable Energy Sources (VRES), and increasing electricity supply costs. Lastly, we observe that limiting Swiss winter imports adversely affects electricity trading, driving up both supply costs and electricity prices.

{"title":"Policy-relevance of a model inter-comparison: Switzerland in the European energy transition","authors":"Ambra Van Liedekerke , Blazhe Gjorgiev , Jonas Savelsberg , Xin Wen , Jérøme Dujardin , Ali Darudi , Jan-Philipp Sasse , Evelina Trutnevyte , Michael Lehning , Giovanni Sansavini","doi":"10.1016/j.apenergy.2025.125906","DOIUrl":"10.1016/j.apenergy.2025.125906","url":null,"abstract":"<div><div>The energy transition is reshaping electricity systems, bringing new challenges, and emphasizing the need for strategic planning. Energy policies play a crucial role in guiding this transition. However, assessing their impacts often requires robust modeling involving multiple models and going beyond a single country’s scope, analyzing international interactions. In this study, we examine three Swiss energy policies, analyzing their impacts on both the national energy system and the cross-border electricity flows. We use a model inter-comparison approach with four electricity system models to explore scenarios involving Swiss renewable generation targets, the Swiss market integration, and the Swiss winter import limitations, in the context of various European electricity developments. The results indicate that a renewable generation target leads to a reduction in net imports and electricity prices. Additionally, reduced market integration impacts both Swiss and European energy transitions by limiting trade benefits, underutilizing Variable Renewable Energy Sources (VRES), and increasing electricity supply costs. Lastly, we observe that limiting Swiss winter imports adversely affects electricity trading, driving up both supply costs and electricity prices.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"391 ","pages":"Article 125906"},"PeriodicalIF":10.1,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Combining physics-based and data-driven modeling for building energy systems

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-21 DOI: 10.1016/j.apenergy.2025.125853

Leandro Von Krannichfeldt , Kristina Orehounig , Olga Fink

Building energy modeling plays a vital role in optimizing the operation of building energy systems by providing accurate predictions of the building’s real-world conditions. In this context, various techniques have been explored, ranging from traditional physics-based models to data-driven models. Recently, researchers are combining physics-based and data-driven models into hybrid approaches. This includes using the physics-based model output as additional data-driven input, learning the residual between physics-based model and real data, learning a surrogate of the physics-based model, or fine-tuning a surrogate model with real data. However, a comprehensive comparison of the inherent advantages of these hybrid approaches is still missing. The primary objective of this work is to evaluate four predominant hybrid approaches in building energy modeling through a real-world case study, with focus on indoor thermodynamics. To achieve this, we devise three scenarios reflecting common levels of building documentation and sensor availability, assess their performance, and analyze their explainability using hierarchical Shapley values. The real-world study reveals three notable findings. First, greater building documentation and sensor availability lead to higher prediction accuracy for hybrid approaches. Second, the performance of hybrid approaches depends on the type of building room, but the residual approach using a Feedforward Neural Network as data-driven sub-model performs best on average across all rooms. This hybrid approach also demonstrates a superior ability to leverage the simulation from the physics-based sub-model. Third, hierarchical Shapley values prove to be an effective tool for explaining and improving hybrid models while accounting for input correlations.

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引用次数: 0

On the dual-resource overnight charging problem of battery electric buses

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-21 DOI: 10.1016/j.apenergy.2025.125924

Zhixin Wang , Feifeng Zheng , Sadeque Hamdan , Oualid Jouini

Battery electric buses (BEBs) enhance urban sustainability but face challenges with costly charging and resource waste due to overlooked staffing and scheduling inefficiencies. This study incorporates dispatcher resources into the overnight charging scheduling of BEBs, addressing the dual-resource synergy problem involving both charging piles and dispatchers. By doing so, it fills the existing research gap concerning the role of charging dispatchers in the charging scheduling process. It also accounts for battery degradation costs and the nonlinearity of charging times. Initially, we employed a single-stage mixed-integer linear programming model, which we later developed into a two-stage model for more efficient management of large-scale public transport systems. Our numerical tests confirm the enhanced computational efficiency of the two-stage model. A case study of bus networks of varying sizes, based on the Shanghai TELD charging depot, is conducted. The results demonstrate that the dual-resource joint charging schedule effectively reduces the total operating cost by 10.08 %–12.29 % and helps optimize the allocation of charging resources within the depot. The outcomes of the case study offer valuable insights to managers, underscoring the importance of scientific battery SOC management, judicious equilibrium of charging pile resources, and optimal formulation of charging resource allocation strategies. Future research could explore the model’s scalability to larger and more complex BEB networks, considering diverse regional infrastructure configurations.

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引用次数: 0

Integrating hourly quasi-input-output into multi-city power system planning considering spatio-temporal emission variations

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2025-04-19 DOI: 10.1016/j.apenergy.2025.125950

Hui Han , Shuifa Lin , Weijiao Li , Jiaming Li , Jianyi Lin , Rui Jing

Decarbonisation increases the complexity of the spatio-temporal dynamics in CO₂ emission distributions within the power system. Existing power system planning studies have not thoroughly explored the spatio-temporal allocation of emission responsibility, consequently leading to inefficiencies for certain stakeholders. This research innovatively integrates the hourly quasi-input-output (QIO) approach into the multi-city capacity expansion planning (CEP) model, thus enabling an allocation of carbon emissions with high spatio-temporal resolutions. Using Anhui Province, China, with its 16 cities as a case study, the model is validated and reveals that the traditional annual emission accounting approach is projected to either underestimate or overestimate CO₂ emissions in 2040, as hourly emission factors could vary sixfold due to day-night variations in photovoltaic power output. Emission factors tend to be underestimated in cities dominated by renewables, while coal-dominant cities face the opposite issue due to the substantial transmission of high-embodied-emission electricity to other cities at nighttime. To mitigate the unfair allocation of emission burdens among cities, renewable-dominant cities need to take greater responsibility for managing CO₂ emissions in coal-dominant cities, particularly in future power systems with higher renewable energy penetration.

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引用次数: 0