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A comparison and accuracy analysis of impedance-based temperature estimation methods for Li-ion batteries

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  • Beelen, H.P.G.J.
  • Raijmakers, L.H.J.
  • Donkers, M.C.F.
  • Notten, P.H.L.
  • Bergveld, H.J.

Abstract

In order to guarantee safe and proper use of Lithium-ion batteries during operation, an accurate estimate of the battery temperature is of paramount importance. Electrochemical Impedance Spectroscopy (EIS) can be used to estimate the battery temperature and several EIS-based temperature estimation methods have been proposed in the literature. In this paper, we argue that all existing EIS-based methods implicitly distinguish two steps: experiment design and parameter estimation. The former step consists of choosing the excitation frequency and the latter step consists of estimating the battery temperature based on the measured impedance resulting from the chosen excitation. By distinguishing these steps and by performing Monte-Carlo simulations, all existing methods are compared in terms of accuracy (i.e., mean-square error) of the temperature estimate. The results of the comparison show that, due to different choices in the two steps, significant differences in accuracy of the estimate exist. More importantly, by jointly selecting the parameters of the experiment-design and parameter-estimation step, a more-accurate temperature estimate can be obtained. In case of an unknown State-of-Charge, this novel method estimates the temperature with an average absolute bias of 0.4°C and an average standard deviation of 0.7°C using a single impedance measurement for the battery under consideration.

Suggested Citation

  • Beelen, H.P.G.J. & Raijmakers, L.H.J. & Donkers, M.C.F. & Notten, P.H.L. & Bergveld, H.J., 2016. "A comparison and accuracy analysis of impedance-based temperature estimation methods for Li-ion batteries," Applied Energy, Elsevier, vol. 175(C), pages 128-140.
    • Handle: RePEc:eee:appene:v:175:y:2016:i:c:p:128-140
    DOI: 10.1016/j.apenergy.2016.04.103
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    Cited by:

    1. Raijmakers, L.H.J. & Danilov, D.L. & Eichel, R.-A. & Notten, P.H.L., 2019. "A review on various temperature-indication methods for Li-ion batteries," Applied Energy, Elsevier, vol. 240(C), pages 918-945.
    2. Corey T. Love & Matthieu Dubarry & Tatyana Reshetenko & Arnaud Devie & Neil Spinner & Karen E. Swider-Lyons & Richard Rocheleau, 2018. "Lithium-Ion Cell Fault Detection by Single-Point Impedance Diagnostic and Degradation Mechanism Validation for Series-Wired Batteries Cycled at 0 °C," Energies, MDPI, vol. 11(4), pages 1-19, April.
    3. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    4. Haifeng Dai & Bo Jiang & Xuezhe Wei, 2018. "Impedance Characterization and Modeling of Lithium-Ion Batteries Considering the Internal Temperature Gradient," Energies, MDPI, vol. 11(1), pages 1-18, January.
    5. Huang, Peifeng & Ping, Ping & Li, Ke & Chen, Haodong & Wang, Qingsong & Wen, Jennifer & Sun, Jinhua, 2016. "Experimental and modeling analysis of thermal runaway propagation over the large format energy storage battery module with Li4Ti5O12 anode," Applied Energy, Elsevier, vol. 183(C), pages 659-673.

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