4.7 Article

Cycle-life prediction model of lithium iron phosphate-based lithium-ion battery module

Journal

INTERNATIONAL JOURNAL OF ENERGY RESEARCH
Volume 45, Issue 11, Pages 16489-16496

Publisher

WILEY
DOI: 10.1002/er.6895

Keywords

Arrhenius equation; capacity fade; cycle life model; Lithium‐ ion battery module; temperature effect

Funding

  1. Korea Electric Power Corporation [R18XA04]
  2. National Research Foundation of Korea [NRF-2017R1D1A1B04033503]

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The temperature difference between internal and external temperatures in a battery module is significant, making it essential to study the module temperature when developing a battery cycle life model. According to experimental data, the total average temperature-based model exhibits the lowest average percentage error.
The aging rate of Li-ion batteries depends on temperature and working conditions and should be studied to ensure an efficient supply and storage of energy. In a battery module, the thermal energy released by the exothermic reaction occurring within each cell is transferred to its adjacent cells, thus leading to a higher internal temperature than that of a single cell. Therefore, there exists a considerable difference between the internal and external temperatures of the module. Thus, it is essential to study the battery module temperature when developing its cycle life (capacity fade) model. In this study, an accelerated cycle life experiment is conducted on an 8-cell LiFePO4 battery. Eight thermocouples were placed internally and externally at selected points to measure the internal and external temperatures within the battery module. This model is developed based on the Arrhenius equation, which explains the effect of temperature according to its spatial position. The models are developed according to the ambient, external, internal, and total average temperatures generated in the battery module, which are verified with the collected experimental results. The results indicate that the total average temperature-based model exhibits the lowest average percentage error when compared with the experimental data.

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