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Online state of health estimation of Li-ion polymer batteries using real time impedance measurements

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  • Mc Carthy, Kieran
  • Gullapalli, Hemtej
  • Kennedy, Tadhg

Abstract

State of health (SoH) monitoring is now an essential part of all advanced battery monitoring systems (BMS). Analysis of the health of the battery is essential for predicting the battery lifespan, ensuring efficient operation and protection against unsafe usage. In this paper, an accurate model for online capacity estimation of Li-ion batteries was developed using the linear relationship between the fade in capacity and the increase in impedance. Through the analysis of the effect of ageing on battery impedance characteristics, the relationship between the fade in battery capacity to the increase in battery impedance was used to estimate the current capacity of the battery. Firstly, analysis of the impedance at multiple frequencies was performed to ascertain which frequency’s displayed high correlation to the fade in battery capacity. Next, a calibration was performed for each battery to determine the capacity / impedance relationship. Finally, 6 Li-ion polymer batteries were tested over 300 cycles to validate the accuracy and reliability of the model. The capacity estimates indicated that the average RMSE (Ah) and percentage error (%) of estimates were very low at 0.0080 Ah and 0.241 %. All impedance measurements for the proposed method were performed online, indicating its suitability to be used in real-world applications.

Suggested Citation

  • Mc Carthy, Kieran & Gullapalli, Hemtej & Kennedy, Tadhg, 2022. "Online state of health estimation of Li-ion polymer batteries using real time impedance measurements," Applied Energy, Elsevier, vol. 307(C).
    • Handle: RePEc:eee:appene:v:307:y:2022:i:c:s030626192101477x
    DOI: 10.1016/j.apenergy.2021.118210
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    Cited by:

    1. Semeraro, Concetta & Caggiano, Mariateresa & Olabi, Abdul-Ghani & Dassisti, Michele, 2022. "Battery monitoring and prognostics optimization techniques: Challenges and opportunities," Energy, Elsevier, vol. 255(C).
    2. Dezhi Li & Dongfang Yang & Liwei Li & Licheng Wang & Kai Wang, 2022. "Electrochemical Impedance Spectroscopy Based on the State of Health Estimation for Lithium-Ion Batteries," Energies, MDPI, vol. 15(18), pages 1-26, September.
    3. Capkova, Dominika & Knap, Vaclav & Fedorkova, Andrea Strakova & Stroe, Daniel-Ioan, 2023. "Investigation of the temperature and DOD effect on the performance-degradation behavior of lithium–sulfur pouch cells during calendar aging," Applied Energy, Elsevier, vol. 332(C).
    4. Ming Zhang & Yanshuo Liu & Dezhi Li & Xiaoli Cui & Licheng Wang & Liwei Li & Kai Wang, 2023. "Electrochemical Impedance Spectroscopy: A New Chapter in the Fast and Accurate Estimation of the State of Health for Lithium-Ion Batteries," Energies, MDPI, vol. 16(4), pages 1-16, February.
    5. Harper, Gavin D.J. & Kendrick, Emma & Anderson, Paul A. & Mrozik, Wojciech & Christensen, Paul & Lambert, Simon & Greenwood, David & Das, Prodip K. & Ahmeid, Mohamed & Milojevic, Zoran & Du, Wenjia & , 2023. "Roadmap for a sustainable circular economy in lithium-ion and future battery technologies," LSE Research Online Documents on Economics 118420, London School of Economics and Political Science, LSE Library.

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