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Parameter sensitivity analysis of a reduced-order electrochemical-thermal model for heat generation rate of lithium-ion batteries

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  • Song, Minseok
  • Choe, Song-Yul

Abstract

A reduced-order electrochemical-thermal model is capable of accurately estimating heat generation rate of lithium-ion batteries with high accuracy. However, as the number of parameters drastically increases, an identification of the parameters becomes essential but challenging. In this paper, a sensitivity analysis of its physical parameters is conducted to study the influence of the parameters on the heat generation rate for the first time. First, 23 potential parameters are selected, where the ranges of the parameters are assumed based on the literature. The parameters are then varied, and the corresponding heat generation rate is calculated to quantify their effects. The analysis has shown various tendencies of the sensitivity of the parameters, and their behavior is explained from further analysis. Based on their sensitivities, the parameters are clustered into three groups dependent upon dominant ranges of state of charge, where insensitive parameters are excluded. Finally, the parameters in each group are identified in their dominant ranges of the state of charge separately based on a three-stage identification procedure. For the identification, the genetic algorithm is used, where the objective function is to minimize the model error for the battery temperature. The results of the identification have shown that the parameters are converged within an optimal solution. The model with the identified parameters is further validated against the experimental data using a pouch type large format lithium-manganese-cobalt-oxide/ Graphite cell at constant charge and discharge profiles over the full range of the state of charge and driving cycles, which has shown a good agreement.

Suggested Citation

  • Song, Minseok & Choe, Song-Yul, 2022. "Parameter sensitivity analysis of a reduced-order electrochemical-thermal model for heat generation rate of lithium-ion batteries," Applied Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s0306261921012320
    DOI: 10.1016/j.apenergy.2021.117920
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    References listed on IDEAS

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    1. Bi, Yalan & Choe, Song-Yul, 2020. "An adaptive sigma-point Kalman filter with state equality constraints for online state-of-charge estimation of a Li(NiMnCo)O2/Carbon battery using a reduced-order electrochemical model," Applied Energy, Elsevier, vol. 258(C).
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    5. Jaguemont, J. & Boulon, L. & Dubé, Y., 2016. "A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures," Applied Energy, Elsevier, vol. 164(C), pages 99-114.
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    2. Li, Dan & Li, Yijun & Wang, Chaoqun & Chen, Min & Wu, Qi, 2023. "Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks," Applied Energy, Elsevier, vol. 331(C).

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