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Framework for measurement of battery state-of-health (resistance) integrating overpotential effects and entropy changes using energy equilibrium

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  • Singh, Karanjot
  • Tjahjowidodo, Tegoeh
  • Boulon, Loïc
  • Feroskhan, Mir

Abstract

The internal resistance of a battery represents the losses due to heat generation during energy conversion. The state-of-health is used to quantify the increase (degradation) of resistance with usage. However, the current state-of-health analysis merges the total internal resistance into one component. Consequently, the underlying cause of resistance degradation is not understood leading to incorrect estimate of battery health. Therefore, this paper presents a comprehensive framework based on energy equilibrium for the categorization and health analysis of total internal resistance. It is divided into 2 components: one based on irreversible overpotential (includes polarization) effects and a new second resistance component originated from reversible entropy changes. For LiFePO4 cells used in this work, it is observed that the contribution of entropy changes (hitherto unrecognized) to the overall losses increases from 4−10% to more than 40% as state-of-charge reduces. State-of-health of each component is obtained by the determination of its associated degradation factor to quantify the underlying mechanism of resistance degradation. In conclusion, the increase in irreversible resistance is primarily attributed to the permanent loss of active material. Correspondingly, the reversible resistance increase is associated to the formation of concentration gradients in the electrodes due to past load profile and ambient conditions.

Suggested Citation

  • Singh, Karanjot & Tjahjowidodo, Tegoeh & Boulon, Loïc & Feroskhan, Mir, 2022. "Framework for measurement of battery state-of-health (resistance) integrating overpotential effects and entropy changes using energy equilibrium," Energy, Elsevier, vol. 239(PA).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pa:s0360544221021903
    DOI: 10.1016/j.energy.2021.121942
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    1. Wen, Shuang & Lin, Ni & Huang, Shengxu & Wang, Zhenpo & Zhang, Zhaosheng, 2023. "Lithium battery health state assessment based on vehicle-to-grid (V2G) real-world data and natural gradient boosting model," Energy, Elsevier, vol. 284(C).

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