A novel U-Net based data-driven vanadium redox flow battery modelling approach
•The proposed approach employs a data-trained U-Net to accurately describe the nonlinear dynamics of VRB electrical variables, and the technique does not require any empirical parameter adjustment. Unlike most of the existing methods, the influence of varying input flow rate is considered, which fun...
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| Published in | Electrochimica acta Vol. 444; p. 141998 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier Ltd
10.03.2023
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| Abstract | •The proposed approach employs a data-trained U-Net to accurately describe the nonlinear dynamics of VRB electrical variables, and the technique does not require any empirical parameter adjustment. Unlike most of the existing methods, the influence of varying input flow rate is considered, which fundamentally improves model accuracy.•It is theoretically model parameter independent and can be utilized directly by electrical engineers with no electrochemical background.•U-Net is well-known for its high computational efficiency in training. More importantly, the trained U-Net is mathematically simple, allowing it to be easily incorporated into numerical simulation models of power system.
This research proposes a highly accurate data-driven vanadium redox flow battery (VRB) modelling approach for power engineering applications. The proposed approach addresses the common problem of excessive model dependency in the existing electrochemical principle or equivalent circuit based VRB modelling methods. Furthermore, an experimentally trained U-Net is applied to directly learn the behavioral relationship between VRB current, flow rate, state-of-charge, and voltage with excellent accuracy, avoiding the usage of model parameters that are subject to variations. Once trained, the U-Net based neural network becomes mathematically very simple and thus, can be easily implemented in simulation studies. This contributes to substantially simplify the analysis of electrical systems with VRB. The validity of the proposed approach is verified experimentally. |
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| AbstractList | •The proposed approach employs a data-trained U-Net to accurately describe the nonlinear dynamics of VRB electrical variables, and the technique does not require any empirical parameter adjustment. Unlike most of the existing methods, the influence of varying input flow rate is considered, which fundamentally improves model accuracy.•It is theoretically model parameter independent and can be utilized directly by electrical engineers with no electrochemical background.•U-Net is well-known for its high computational efficiency in training. More importantly, the trained U-Net is mathematically simple, allowing it to be easily incorporated into numerical simulation models of power system.
This research proposes a highly accurate data-driven vanadium redox flow battery (VRB) modelling approach for power engineering applications. The proposed approach addresses the common problem of excessive model dependency in the existing electrochemical principle or equivalent circuit based VRB modelling methods. Furthermore, an experimentally trained U-Net is applied to directly learn the behavioral relationship between VRB current, flow rate, state-of-charge, and voltage with excellent accuracy, avoiding the usage of model parameters that are subject to variations. Once trained, the U-Net based neural network becomes mathematically very simple and thus, can be easily implemented in simulation studies. This contributes to substantially simplify the analysis of electrical systems with VRB. The validity of the proposed approach is verified experimentally. |
| ArticleNumber | 141998 |
| Author | Zhang, Xinan Iu, Herbert Fernando, Tyrone Li, Yifeng Li, Ran Xiong, Binyu Zhang, Shaofeng |
| Author_xml | – sequence: 1 givenname: Ran surname: Li fullname: Li, Ran organization: Department of Electrical, Electronic and Computer Engineering, University of Western Australia, Perth 6009, Australia – sequence: 2 givenname: Binyu surname: Xiong fullname: Xiong, Binyu organization: School of Automation, Wuhan University of Technology, Wuhan, Hubei 430070, China – sequence: 3 givenname: Shaofeng surname: Zhang fullname: Zhang, Shaofeng organization: School of Automation, Wuhan University of Technology, Wuhan, Hubei 430070, China – sequence: 4 givenname: Xinan surname: Zhang fullname: Zhang, Xinan email: xinan.zhang@uwa.edu.au organization: Department of Electrical, Electronic and Computer Engineering, University of Western Australia, Perth 6009, Australia – sequence: 5 givenname: Yifeng orcidid: 0000-0002-4991-8643 surname: Li fullname: Li, Yifeng organization: Voith Group, Germany – sequence: 6 givenname: Herbert orcidid: 0000-0002-0687-4038 surname: Iu fullname: Iu, Herbert organization: Department of Electrical, Electronic and Computer Engineering, University of Western Australia, Perth 6009, Australia – sequence: 7 givenname: Tyrone surname: Fernando fullname: Fernando, Tyrone organization: Department of Electrical, Electronic and Computer Engineering, University of Western Australia, Perth 6009, Australia |
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| Cites_doi | 10.1016/j.jpowsour.2021.230892 10.2352/J.ImagingSci.Technol.2020.64.2.020508 10.1016/j.rser.2015.09.098 10.1016/j.est.2022.104171 10.1016/j.jpowsour.2022.231668 10.1016/j.jpowsour.2016.09.123 10.1016/j.apenergy.2021.117177 10.1016/j.jpowsour.2021.230034 10.1109/TEC.2021.3061493 10.1016/j.apenergy.2015.08.028 10.1016/j.jpowsour.2015.04.169 10.1016/j.apenergy.2021.117962 10.1021/acssuschemeng.1c00233 10.1016/j.apenergy.2020.115530 10.1016/j.jpowsour.2019.227684 10.1016/j.jpowsour.2020.228375 10.1016/j.jpowsour.2022.231147 10.1016/j.ultras.2019.03.014 10.1016/j.jpowsour.2021.230087 |
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| References | Du (bib0017) 2020; 64 He, Stinis, Tartakovsky (bib0015) 2022; 528 Zhou (bib0005) 2015; 158 Chun, Kim, Han (bib0024) 2019; 52 Wei (bib0010) 2016; 332 Fasahat, Manthouri (bib0027) 2020; 469 Ra, Bhattacharjee (bib0013) 2022 Bao, Murugesan, Kamp, Shao, Yan, Wang (bib0007) 2020; 3 Choi, Kim, Kim, Choi (bib0012) 2020; 450 Cheng, Tenny, Pizzolato, Forner-Cuenca, Verda, Chiang, Behrou (bib0008) 2020; 279 Chen (bib0030) 2022; 521 Zhou (bib0023) 2018 Choi (bib0021) 2018 Yang, Faraji, Basu (bib0019) 2019; 96 Chou (bib0003) 2021; 9 Li (bib0028) 2021; 506 Howard, Yu, Wang, Tartakovsky (bib0014) 2022; 542 Lee (bib0025) 2021; 36 Xiong (bib0011) 2019; 7 Bao (bib0022) 2020 Zhang, Xing (bib0020) 2018 Ronneberger, Fischer, Brox (bib0016) 2015 Emmett, Roberts (bib0002) 2021; 506 Xi (bib0029) 2022; 305 Zhang (bib0009) 2015; 290 Shi (bib0004) 2022; 50 Hossain (bib0001) 2016; 60 Wan, Liang, Jiang, Sun, Djilali, Zhao (bib0006) 2021; 298 Gaál, G., B. Maga, and A. Lukács, Attention u-net based adversarial architectures for chest x-ray lung segmentation. arXiv preprint arXiv:2003.10304, 2020. Li, Tao (bib0026) 2020 Wan (10.1016/j.electacta.2023.141998_bib0006) 2021; 298 Hossain (10.1016/j.electacta.2023.141998_bib0001) 2016; 60 10.1016/j.electacta.2023.141998_bib0018 Emmett (10.1016/j.electacta.2023.141998_bib0002) 2021; 506 Zhou (10.1016/j.electacta.2023.141998_bib0005) 2015; 158 Ronneberger (10.1016/j.electacta.2023.141998_bib0016) 2015 Cheng (10.1016/j.electacta.2023.141998_bib0008) 2020; 279 Li (10.1016/j.electacta.2023.141998_bib0026) 2020 Choi (10.1016/j.electacta.2023.141998_bib0021) 2018 Ra (10.1016/j.electacta.2023.141998_bib0013) 2022 Bao (10.1016/j.electacta.2023.141998_bib0022) 2020 Choi (10.1016/j.electacta.2023.141998_bib0012) 2020; 450 Du (10.1016/j.electacta.2023.141998_bib0017) 2020; 64 Yang (10.1016/j.electacta.2023.141998_bib0019) 2019; 96 Howard (10.1016/j.electacta.2023.141998_bib0014) 2022; 542 Chun (10.1016/j.electacta.2023.141998_bib0024) 2019; 52 Bao (10.1016/j.electacta.2023.141998_bib0007) 2020; 3 Chen (10.1016/j.electacta.2023.141998_bib0030) 2022; 521 He (10.1016/j.electacta.2023.141998_bib0015) 2022; 528 Wei (10.1016/j.electacta.2023.141998_bib0010) 2016; 332 Xi (10.1016/j.electacta.2023.141998_bib0029) 2022; 305 Chou (10.1016/j.electacta.2023.141998_bib0003) 2021; 9 Shi (10.1016/j.electacta.2023.141998_bib0004) 2022; 50 Li (10.1016/j.electacta.2023.141998_bib0028) 2021; 506 Xiong (10.1016/j.electacta.2023.141998_bib0011) 2019; 7 Zhang (10.1016/j.electacta.2023.141998_bib0020) 2018 Zhou (10.1016/j.electacta.2023.141998_bib0023) 2018 Fasahat (10.1016/j.electacta.2023.141998_bib0027) 2020; 469 Zhang (10.1016/j.electacta.2023.141998_bib0009) 2015; 290 Lee (10.1016/j.electacta.2023.141998_bib0025) 2021; 36 |
| References_xml | – volume: 60 start-page: 1168 year: 2016 end-page: 1184 ident: bib0001 article-title: Role of smart grid in renewable energy: an overview publication-title: Renew. Sustain. Energy Rev. – volume: 96 start-page: 24 year: 2019 end-page: 33 ident: bib0019 article-title: Robust segmentation of arterial walls in intravascular ultrasound images using dual path U-Net publication-title: Ultrasonics – volume: 64 start-page: 1 year: 2020 end-page: 12 ident: bib0017 article-title: Medical image segmentation based on u-net: a review publication-title: J. Imaging Sci. Technol. – volume: 52 start-page: 129 year: 2019 end-page: 134 ident: bib0024 article-title: Parameter identification of an electrochemical lithium-ion battery model with convolutional neural network publication-title: Proceedings of the IFAC-PapersOnLine – volume: 521 year: 2022 ident: bib0030 article-title: State of health estimation for lithium-ion batteries based on temperature prediction and gated recurrent unit neural network publication-title: J. Power Sources – volume: 528 year: 2022 ident: bib0015 article-title: Physics-constrained deep neural network method for estimating parameters in a redox flow battery publication-title: J. Power Sources – volume: 469 year: 2020 ident: bib0027 article-title: State of charge estimation of lithium-ion batteries using hybrid autoencoder and long short term memory neural networks publication-title: J. Power Sources – year: 2018 ident: bib0020 article-title: CT artifact reduction via U-net CNN publication-title: Medical Imaging2018: Image Processing – volume: 36 start-page: 3108 year: 2021 end-page: 3117 ident: bib0025 article-title: Convolutional neural network-based false battery data detection and classification for battery energy storage systems publication-title: IEEE Trans. Energy Convers. – year: 2020 ident: bib0026 article-title: CNN and transfer learning based online SOH estimation for lithium-ion battery publication-title: Proceedings of the Chinese Control And Decision Conference (CCDC) – volume: 542 year: 2022 ident: bib0014 article-title: Physics-informed CoKriging model of a redox flow battery publication-title: J. Power Sources – volume: 9 start-page: 5377 year: 2021 end-page: 5387 ident: bib0003 article-title: Mathematical model to study vanadium ion crossover in an all-vanadium redox flow battery publication-title: ACS Sustain. Chem. Eng. – volume: 158 start-page: 157 year: 2015 end-page: 166 ident: bib0005 article-title: A vanadium redox flow battery model incorporating the effect of ion concentrations on ion mobility publication-title: Appl. Energy – volume: 290 start-page: 14 year: 2015 end-page: 24 ident: bib0009 article-title: A comprehensive equivalent circuit model of all-vanadium redox flow battery for power system analysis publication-title: J. Power Sources – year: 2020 ident: bib0022 article-title: Real image denoising based on multi-scale residual dense block and cascaded U-Net with block-connection publication-title: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops – volume: 298 year: 2021 ident: bib0006 article-title: A coupled machine learning and genetic algorithm approach to the design of porous electrodes for redox flow batteries publication-title: Appl. Energy – year: 2022 ident: bib0013 article-title: Prediction of vanadium redox flow battery storage system power loss under different operating conditions: machine learning based approach publication-title: Int. J. Energy Res. – year: 2015 ident: bib0016 article-title: U-net: convolutional networks for biomedical image segmentation publication-title: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention – volume: 506 year: 2021 ident: bib0002 article-title: Recent developments in alternative aqueous redox flow batteries for grid-scale energy storage publication-title: J. Power Sources – volume: 450 year: 2020 ident: bib0012 article-title: Multiple parameter identification using genetic algorithm in vanadium redox flow batteries publication-title: J. Power Sources – volume: 50 year: 2022 ident: bib0004 article-title: Dynamic modeling of long-term operations of vanadium/air redox flow battery with different membranes publication-title: J. Energy Storage – reference: Gaál, G., B. Maga, and A. Lukács, Attention u-net based adversarial architectures for chest x-ray lung segmentation. arXiv preprint arXiv:2003.10304, 2020. – year: 2018 ident: bib0021 article-title: Phase-aware speech enhancement with deep complex u-net publication-title: Proceedings of the International Conference on Learning Representations – start-page: 3 year: 2018 end-page: 11 ident: bib0023 article-title: Unet++: a nested u-net architecture for medical image segmentation publication-title: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support – volume: 305 year: 2022 ident: bib0029 article-title: Accurate and reliable state of charge estimation of lithium ion batteries using time-delayed recurrent neural networks through the identification of overexcited neurons publication-title: Appl. Energy – volume: 332 start-page: 389 year: 2016 end-page: 398 ident: bib0010 article-title: Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery publication-title: J. Power Sources – volume: 7 start-page: 162297 year: 2019 end-page: 162308 ident: bib0011 article-title: An enhanced equivalent circuit model of vanadium redox flow battery energy storage systems considering thermal effects publication-title: IEEE Access – volume: 3 year: 2020 ident: bib0007 article-title: Machine learning coupled multi-scale modeling for redox flow batteries publication-title: Adv. Theory Simul. – volume: 506 year: 2021 ident: bib0028 article-title: Physics-informed neural networks for electrode-level state estimation in lithium-ion batteries publication-title: J. Power Sources – volume: 279 year: 2020 ident: bib0008 article-title: Data-driven electrode parameter identification for vanadium redox flow batteries through experimental and numerical methods publication-title: Appl. Energy – year: 2022 ident: 10.1016/j.electacta.2023.141998_bib0013 article-title: Prediction of vanadium redox flow battery storage system power loss under different operating conditions: machine learning based approach publication-title: Int. J. Energy Res. – volume: 521 year: 2022 ident: 10.1016/j.electacta.2023.141998_bib0030 article-title: State of health estimation for lithium-ion batteries based on temperature prediction and gated recurrent unit neural network publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2021.230892 – ident: 10.1016/j.electacta.2023.141998_bib0018 – volume: 64 start-page: 1 year: 2020 ident: 10.1016/j.electacta.2023.141998_bib0017 article-title: Medical image segmentation based on u-net: a review publication-title: J. Imaging Sci. Technol. doi: 10.2352/J.ImagingSci.Technol.2020.64.2.020508 – volume: 60 start-page: 1168 year: 2016 ident: 10.1016/j.electacta.2023.141998_bib0001 article-title: Role of smart grid in renewable energy: an overview publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2015.09.098 – volume: 50 year: 2022 ident: 10.1016/j.electacta.2023.141998_bib0004 article-title: Dynamic modeling of long-term operations of vanadium/air redox flow battery with different membranes publication-title: J. Energy Storage doi: 10.1016/j.est.2022.104171 – volume: 542 year: 2022 ident: 10.1016/j.electacta.2023.141998_bib0014 article-title: Physics-informed CoKriging model of a redox flow battery publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2022.231668 – volume: 332 start-page: 389 year: 2016 ident: 10.1016/j.electacta.2023.141998_bib0010 article-title: Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2016.09.123 – volume: 298 year: 2021 ident: 10.1016/j.electacta.2023.141998_bib0006 article-title: A coupled machine learning and genetic algorithm approach to the design of porous electrodes for redox flow batteries publication-title: Appl. Energy doi: 10.1016/j.apenergy.2021.117177 – volume: 506 year: 2021 ident: 10.1016/j.electacta.2023.141998_bib0028 article-title: Physics-informed neural networks for electrode-level state estimation in lithium-ion batteries publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2021.230034 – volume: 36 start-page: 3108 issue: 4 year: 2021 ident: 10.1016/j.electacta.2023.141998_bib0025 article-title: Convolutional neural network-based false battery data detection and classification for battery energy storage systems publication-title: IEEE Trans. Energy Convers. doi: 10.1109/TEC.2021.3061493 – volume: 158 start-page: 157 year: 2015 ident: 10.1016/j.electacta.2023.141998_bib0005 article-title: A vanadium redox flow battery model incorporating the effect of ion concentrations on ion mobility publication-title: Appl. Energy doi: 10.1016/j.apenergy.2015.08.028 – volume: 290 start-page: 14 year: 2015 ident: 10.1016/j.electacta.2023.141998_bib0009 article-title: A comprehensive equivalent circuit model of all-vanadium redox flow battery for power system analysis publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2015.04.169 – volume: 305 year: 2022 ident: 10.1016/j.electacta.2023.141998_bib0029 article-title: Accurate and reliable state of charge estimation of lithium ion batteries using time-delayed recurrent neural networks through the identification of overexcited neurons publication-title: Appl. Energy doi: 10.1016/j.apenergy.2021.117962 – start-page: 3 year: 2018 ident: 10.1016/j.electacta.2023.141998_bib0023 article-title: Unet++: a nested u-net architecture for medical image segmentation – volume: 9 start-page: 5377 issue: 15 year: 2021 ident: 10.1016/j.electacta.2023.141998_bib0003 article-title: Mathematical model to study vanadium ion crossover in an all-vanadium redox flow battery publication-title: ACS Sustain. Chem. Eng. doi: 10.1021/acssuschemeng.1c00233 – volume: 3 issue: 2 year: 2020 ident: 10.1016/j.electacta.2023.141998_bib0007 article-title: Machine learning coupled multi-scale modeling for redox flow batteries publication-title: Adv. Theory Simul. – volume: 279 year: 2020 ident: 10.1016/j.electacta.2023.141998_bib0008 article-title: Data-driven electrode parameter identification for vanadium redox flow batteries through experimental and numerical methods publication-title: Appl. Energy doi: 10.1016/j.apenergy.2020.115530 – volume: 52 start-page: 129 year: 2019 ident: 10.1016/j.electacta.2023.141998_bib0024 article-title: Parameter identification of an electrochemical lithium-ion battery model with convolutional neural network – year: 2015 ident: 10.1016/j.electacta.2023.141998_bib0016 article-title: U-net: convolutional networks for biomedical image segmentation – volume: 450 year: 2020 ident: 10.1016/j.electacta.2023.141998_bib0012 article-title: Multiple parameter identification using genetic algorithm in vanadium redox flow batteries publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2019.227684 – volume: 469 year: 2020 ident: 10.1016/j.electacta.2023.141998_bib0027 article-title: State of charge estimation of lithium-ion batteries using hybrid autoencoder and long short term memory neural networks publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2020.228375 – year: 2018 ident: 10.1016/j.electacta.2023.141998_bib0021 article-title: Phase-aware speech enhancement with deep complex u-net – year: 2020 ident: 10.1016/j.electacta.2023.141998_bib0022 article-title: Real image denoising based on multi-scale residual dense block and cascaded U-Net with block-connection – volume: 528 year: 2022 ident: 10.1016/j.electacta.2023.141998_bib0015 article-title: Physics-constrained deep neural network method for estimating parameters in a redox flow battery publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2022.231147 – year: 2020 ident: 10.1016/j.electacta.2023.141998_bib0026 article-title: CNN and transfer learning based online SOH estimation for lithium-ion battery – volume: 7 start-page: 162297 year: 2019 ident: 10.1016/j.electacta.2023.141998_bib0011 article-title: An enhanced equivalent circuit model of vanadium redox flow battery energy storage systems considering thermal effects – year: 2018 ident: 10.1016/j.electacta.2023.141998_bib0020 article-title: CT artifact reduction via U-net CNN – volume: 96 start-page: 24 year: 2019 ident: 10.1016/j.electacta.2023.141998_bib0019 article-title: Robust segmentation of arterial walls in intravascular ultrasound images using dual path U-Net publication-title: Ultrasonics doi: 10.1016/j.ultras.2019.03.014 – volume: 506 year: 2021 ident: 10.1016/j.electacta.2023.141998_bib0002 article-title: Recent developments in alternative aqueous redox flow batteries for grid-scale energy storage publication-title: J. 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| Title | A novel U-Net based data-driven vanadium redox flow battery modelling approach |
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