IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v193y2017icp507-514.html
   My bibliography  Save this article

Dynamic modeling of a microbial fuel cell considering anodic electron flow and electrical charge storage

Author

Listed:
  • Park, Jae-Do
  • Roane, Timberley M.
  • Ren, Zhiyong Jason
  • Alaraj, Muhannad

Abstract

To describe the anodic electron flow and electric charge storage behavior of an MFC system from an electrical perspective, a dynamic model based on a novel electrical equivalent circuit is developed. Conventional equivalent circuits typically have series impedances to model the system from the standpoint of terminal quantities: output voltage and current. However, the conventional approaches do not properly explain internal anodic electron flow and double-layer charge storage characteristics of MFCs. The proposed model uses an equivalent capacitance in parallel and series resistances to accurately model and characterize the anodic electron flow, electrical charge storage, and the dynamic characteristics of both output voltage and current. Two straightforward test methods are proposed to determine the equivalent circuit parameters. Experimental results showed the validity of proposed MFC model.

Suggested Citation

  • Park, Jae-Do & Roane, Timberley M. & Ren, Zhiyong Jason & Alaraj, Muhannad, 2017. "Dynamic modeling of a microbial fuel cell considering anodic electron flow and electrical charge storage," Applied Energy, Elsevier, vol. 193(C), pages 507-514.
    • Handle: RePEc:eee:appene:v:193:y:2017:i:c:p:507-514
    DOI: 10.1016/j.apenergy.2017.02.055
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261917301927
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2017.02.055?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mingant, R. & Bernard, J. & Sauvant-Moynot, V., 2016. "Novel state-of-health diagnostic method for Li-ion battery in service," Applied Energy, Elsevier, vol. 183(C), pages 390-398.
    2. Chen, Zeyu & Xiong, Rui & Tian, Jinpeng & Shang, Xiong & Lu, Jiahuan, 2016. "Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles," Applied Energy, Elsevier, vol. 184(C), pages 365-374.
    3. Pandey, Prashant & Shinde, Vikas N. & Deopurkar, Rajendra L. & Kale, Sharad P. & Patil, Sunil A. & Pant, Deepak, 2016. "Recent advances in the use of different substrates in microbial fuel cells toward wastewater treatment and simultaneous energy recovery," Applied Energy, Elsevier, vol. 168(C), pages 706-723.
    4. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
    5. Tanaka, T. & Ito, S. & Muramatsu, M. & Yamada, T. & Kamiko, H. & Kakimoto, N. & Inui, Y., 2015. "Accurate and versatile simulation of transient voltage profile of lithium-ion secondary battery employing internal equivalent electric circuit," Applied Energy, Elsevier, vol. 143(C), pages 200-210.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Chin-Tsan & Huang, Yan-Sian & Sangeetha, Thangavel & Yan, Wei-Mon, 2018. "Assessment of recirculation batch mode operation in bufferless Bio-cathode microbial Fuel Cells (MFCs)," Applied Energy, Elsevier, vol. 209(C), pages 120-126.

    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.
    1. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    2. Xiong, Rui & Tian, Jinpeng & Mu, Hao & Wang, Chun, 2017. "A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries," Applied Energy, Elsevier, vol. 207(C), pages 372-383.
    3. Jong-Hyun Lee & In-Soo Lee, 2021. "Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result," Energies, MDPI, vol. 14(15), pages 1-16, July.
    4. Zhao, Yang & Liu, Peng & Wang, Zhenpo & Zhang, Lei & Hong, Jichao, 2017. "Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods," Applied Energy, Elsevier, vol. 207(C), pages 354-362.
    5. Shehzar Shahzad Sheikh & Mahnoor Anjum & Muhammad Abdullah Khan & Syed Ali Hassan & Hassan Abdullah Khalid & Adel Gastli & Lazhar Ben-Brahim, 2020. "A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach," Energies, MDPI, vol. 13(14), pages 1-16, July.
    6. Tongrui Zhang & Ran Li & Yongqin Zhou, 2023. "Battery Fault Diagnosis Method Based on Online Least Squares Support Vector Machine," Energies, MDPI, vol. 16(21), pages 1-17, October.
    7. Yang, Duo & Wang, Yujie & Pan, Rui & Chen, Ruiyang & Chen, Zonghai, 2018. "State-of-health estimation for the lithium-ion battery based on support vector regression," Applied Energy, Elsevier, vol. 227(C), pages 273-283.
    8. Wenxian Duan & Chuanxue Song & Silun Peng & Feng Xiao & Yulong Shao & Shixin Song, 2020. "An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery," Energies, MDPI, vol. 13(23), pages 1-19, December.
    9. Mehmet C. Yagci & Thomas Feldmann & Elmar Bollin & Michael Schmidt & Wolfgang G. Bessler, 2022. "Aging Characteristics of Stationary Lithium-Ion Battery Systems with Serial and Parallel Cell Configurations," Energies, MDPI, vol. 15(11), pages 1-19, May.
    10. Guoqing Jin & Lan Li & Yidan Xu & Minghui Hu & Chunyun Fu & Datong Qin, 2020. "Comparison of SOC Estimation between the Integer-Order Model and Fractional-Order Model Under Different Operating Conditions," Energies, MDPI, vol. 13(7), pages 1-17, April.
    11. Hu, Lin & Hu, Xiaosong & Che, Yunhong & Feng, Fei & Lin, Xianke & Zhang, Zhiyong, 2020. "Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering," Applied Energy, Elsevier, vol. 262(C).
    12. Berecibar, M. & Gandiaga, I. & Villarreal, I. & Omar, N. & Van Mierlo, J. & Van den Bossche, P., 2016. "Critical review of state of health estimation methods of Li-ion batteries for real applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 572-587.
    13. Rousseau, Raphaël & Etcheverry, Luc & Roubaud, Emma & Basséguy, Régine & Délia, Marie-Line & Bergel, Alain, 2020. "Microbial electrolysis cell (MEC): Strengths, weaknesses and research needs from electrochemical engineering standpoint," Applied Energy, Elsevier, vol. 257(C).
    14. Tang, Raymond Chong Ong & Jang, Jer-Huan & Lan, Tzu-Hsuan & Wu, Jung-Chen & Yan, Wei-Mon & Sangeetha, Thangavel & Wang, Chin-Tsan & Ong, Hwai Chyuan & Ong, Zhi Chao, 2020. "Review on design factors of microbial fuel cells using Buckingham's Pi Theorem," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    15. He, Hongwen & Xiong, Rui & Peng, Jiankun, 2016. "Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform," Applied Energy, Elsevier, vol. 162(C), pages 1410-1418.
    16. Xu, Yuan-wu & Wu, Xiao-long & Zhong, Xiao-bo & Zhao, Dong-qi & Sorrentino, Marco & Jiang, Jianhua & Jiang, Chang & Fu, Xiaowei & Li, Xi, 2021. "Mechanism model-based and data-driven approach for the diagnosis of solid oxide fuel cell stack leakage," Applied Energy, Elsevier, vol. 286(C).
    17. Ozkurt, Celil & Camci, Fatih & Atamuradov, Vepa & Odorry, Christopher, 2016. "Integration of sampling based battery state of health estimation method in electric vehicles," Applied Energy, Elsevier, vol. 175(C), pages 356-367.
    18. Yang, Fangfang & Li, Weihua & Li, Chuan & Miao, Qiang, 2019. "State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network," Energy, Elsevier, vol. 175(C), pages 66-75.
    19. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2014. "A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries," Applied Energy, Elsevier, vol. 135(C), pages 81-87.
    20. Yu, Quanqing & Dai, Lei & Xiong, Rui & Chen, Zeyu & Zhang, Xin & Shen, Weixiang, 2022. "Current sensor fault diagnosis method based on an improved equivalent circuit battery model," Applied Energy, Elsevier, vol. 310(C).

    Corrections

    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:193:y:2017:i:c:p:507-514. 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.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.