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Analysis of the unsteady thermal response of a Li-ion battery pack to dynamic loads

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  • Saeed, Ali
  • Karimi, Nader
  • Paul, Manosh C.

Abstract

It is becoming increasingly apparent that wide application of electric vehicles (EVs) are subject to significant improvements in battery technology. Temperature sensitivity is a major issue adversely affecting battery performance and requiring a robust thermal control. Yet, this is challenged by the large variety of temporal scenarios though which heat is generated in a battery pack, demanding dynamic tools to predict the thermal evolution of batteries. Classical transfer functions provide a low-cost and effective predictive tool. However, they are limited to linear systems, while nonlinear predictive tools can become impractical for EV applications. Therefore, this study provides a methodology to assess the dynamics of battery cooling. This is achieved through conduction of high fidelity modelling of battery cooling exposed to different temporal disturbances on the internal heat generation. The results are then post-processed to evaluate the extent of linearity. A quantitative measure of non-linearity is further applied to clearly determine the degree of nonlinearity in the heat transfer response. It is shown that battery cooling system can be approximated as a linear dynamical system as long as the disturbances are of short duration and relatively low amplitude. Conversely, long and large amplitude temporal disturbances can render strongly nonlinear thermal responses.

Suggested Citation

  • Saeed, Ali & Karimi, Nader & Paul, Manosh C., 2021. "Analysis of the unsteady thermal response of a Li-ion battery pack to dynamic loads," Energy, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:energy:v:231:y:2021:i:c:s0360544221011956
    DOI: 10.1016/j.energy.2021.120947
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    References listed on IDEAS

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    1. Karimi, Nader, 2014. "Response of a conical, laminar premixed flame to low amplitude acoustic forcing – A comparison between experiment and kinematic theories," Energy, Elsevier, vol. 78(C), pages 490-500.
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    5. Satyam Panchal & Krishna Gudlanarva & Manh-Kien Tran & Roydon Fraser & Michael Fowler, 2020. "High Reynold’s Number Turbulent Model for Micro-Channel Cold Plate Using Reverse Engineering Approach for Water-Cooled Battery in Electric Vehicles," Energies, MDPI, vol. 13(7), pages 1-25, April.
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    Cited by:

    1. Li, Yang & Wang, Shunli & Chen, Lei & Qi, Chuangshi & Fernandez, Carlos, 2023. "Multiple layer kernel extreme learning machine modeling and eugenics genetic sparrow search algorithm for the state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 282(C).

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