Evaluating uncertainties in electrochemical impedance spectra of solid oxide fuel cells
Author
Abstract
Suggested Citation
DOI: 10.1016/j.apenergy.2021.117101
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Pasquier, Philippe & Marcotte, Denis, 2020. "Robust identification of volumetric heat capacity and analysis of thermal response tests by Bayesian inference with correlated residuals," Applied Energy, Elsevier, vol. 261(C).
- Gallo, Marco & Polverino, Pierpaolo & Mougin, Julie & Morel, Bertrand & Pianese, Cesare, 2020. "Coupling electrochemical impedance spectroscopy and model-based aging estimation for solid oxide fuel cell stacks lifetime prediction," Applied Energy, Elsevier, vol. 279(C).
- Choi, Wonjun & Menberg, Kathrin & Kikumoto, Hideki & Heo, Yeonsook & Choudhary, Ruchi & Ooka, Ryozo, 2018. "Bayesian inference of structural error in inverse models of thermal response tests," Applied Energy, Elsevier, vol. 228(C), pages 1473-1485.
- Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Jiang, Zhiqiang & Feng, Zhongkai & Zhou, Jianzhong, 2020. "Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model," Applied Energy, Elsevier, vol. 260(C).
- Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Wang, Chao & Yu, Xiang & Jiang, Zhiqiang & Zhou, Jianzhong, 2019. "Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
- Choi, Wonjun & Kikumoto, Hideki & Choudhary, Ruchi & Ooka, Ryozo, 2018. "Bayesian inference for thermal response test parameter estimation and uncertainty assessment," Applied Energy, Elsevier, vol. 209(C), pages 306-321.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Yang, Yang & Yu, Xiaoran & Zhu, Wenchao & Xie, Changjun & Zhao, Bo & Zhang, Leiqi & Shi, Ying & Huang, Liang & Zhang, Ruiming, 2023. "Degradation prediction of proton exchange membrane fuel cells with model uncertainty quantification," Renewable Energy, Elsevier, vol. 219(P2).
- Mohammad Alboghobeish & Andrea Monforti Ferrario & Davide Pumiglia & Massimiliano Della Pietra & Stephen J. McPhail & Sergii Pylypko & Domenico Borello, 2022. "Developing an Automated Tool for Quantitative Analysis of the Deconvoluted Electrochemical Impedance Response of a Solid Oxide Fuel Cell," Energies, MDPI, vol. 15(10), pages 1-22, May.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Zhang, Xueping & Han, Zongwei & Ji, Qiang & Zhang, Hongzhi & Li, Xiuming, 2021. "Thermal response tests for the identification of soil thermal parameters: A review," Renewable Energy, Elsevier, vol. 173(C), pages 1123-1135.
- Choi, Wonjun & Kikumoto, Hideki & Ooka, Ryozo, 2022. "Probabilistic uncertainty quantification of borehole thermal resistance in real-world scenarios," Energy, Elsevier, vol. 254(PC).
- Zhang, Xueping & Han, Zongwei & Li, Gui & Li, Xiuming, 2022. "Effect of temperature measurement error on parameters estimation accuracy for thermal response tests," Renewable Energy, Elsevier, vol. 185(C), pages 230-240.
- Fang, Ping & Fu, Wenlong & Wang, Kai & Xiong, Dongzhen & Zhang, Kai, 2022. "A compositive architecture coupling outlier correction, EWT, nonlinear Volterra multi-model fusion with multi-objective optimization for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 307(C).
- Jia, Jie & Lee, W.L. & Cheng, Yuanda, 2019. "Field demonstration of a first constant-temperature thermal response test with both heat injection and extraction for ground source heat pump systems," Applied Energy, Elsevier, vol. 249(C), pages 79-86.
- Pasquier, Philippe & Marcotte, Denis, 2020. "Robust identification of volumetric heat capacity and analysis of thermal response tests by Bayesian inference with correlated residuals," Applied Energy, Elsevier, vol. 261(C).
- Zhang, Xueping & Han, Zongwei & Meng, Xinwei & Li, Gui & Ji, Qiang & Li, Xiuming & Yang, Lingyan, 2021. "Study on high-precision identification method of ground thermal properties based on neural network model," Renewable Energy, Elsevier, vol. 163(C), pages 1838-1848.
- Shi, Jihao & Li, Junjie & Usmani, Asif Sohail & Zhu, Yuan & Chen, Guoming & Yang, Dongdong, 2021. "Probabilistic real-time deep-water natural gas hydrate dispersion modeling by using a novel hybrid deep learning approach," Energy, Elsevier, vol. 219(C).
- Liu, Guanjun & Wang, Yun & Qin, Hui & Shen, Keyan & Liu, Shuai & Shen, Qin & Qu, Yuhua & Zhou, Jianzhong, 2023. "Probabilistic spatiotemporal forecasting of wind speed based on multi-network deep ensembles method," Renewable Energy, Elsevier, vol. 209(C), pages 231-247.
- Yongqi Liu & Yuanyuan Li & Guibing Hou & Hui Qin, 2024. "Multi-Objective Short-Term Operation of Hydro–Wind–Photovoltaic–Thermal Hybrid System Considering Power Peak Shaving, the Economy and the Environment," Energies, MDPI, vol. 17(18), pages 1-24, September.
- Hou, D. & Hassan, I.G. & Wang, L., 2021. "Review on building energy model calibration by Bayesian inference," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
- Shi, Jihao & Zhang, Xinqi & Zhang, Haoran & Wang, Qiliang & Yan, Jinyue & Xiao, Linda, 2024. "Automated detection and diagnosis of leak fault considering volatility by graph deep probability learning," Applied Energy, Elsevier, vol. 361(C).
- Liu, Guanjun & Qin, Hui & Shen, Qin & Lyv, Hao & Qu, Yuhua & Fu, Jialong & Liu, Yongqi & Zhou, Jianzhong, 2021. "Probabilistic spatiotemporal solar irradiation forecasting using deep ensembles convolutional shared weight long short-term memory network," Applied Energy, Elsevier, vol. 300(C).
- Zhang, Jincheng & Zhao, Xiaowei, 2021. "Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning," Applied Energy, Elsevier, vol. 300(C).
- Liu, Xingdou & Zhang, Li & Wang, Jiangong & Zhou, Yue & Gan, Wei, 2023. "A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data," Renewable Energy, Elsevier, vol. 211(C), pages 948-963.
- Zhang, Changxing & Song, Wei & Liu, Yufeng & Kong, Xiangqiang & Wang, Qing, 2019. "Effect of vertical ground temperature distribution on parameter estimation of in-situ thermal response test with unstable heat rate," Renewable Energy, Elsevier, vol. 136(C), pages 264-274.
- Acikgoz, Hakan & Budak, Umit & Korkmaz, Deniz & Yildiz, Ceyhun, 2021. "WSFNet: An efficient wind speed forecasting model using channel attention-based densely connected convolutional neural network," Energy, Elsevier, vol. 233(C).
- Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
- Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong & Liu, Zhenkun, 2021. "Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection," Applied Energy, Elsevier, vol. 301(C).
- Wang, Jianzhou & Wang, Shuai & Zeng, Bo & Lu, Haiyan, 2022. "A novel ensemble probabilistic forecasting system for uncertainty in wind speed," Applied Energy, Elsevier, vol. 313(C).
More about this item
Keywords
Variational Bayes; Monte Carlo; Solid oxide fuel cells; Fractional-order systems;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:298:y:2021:i:c:s030626192100547x. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.