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

Abstract

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 LiFePO 4 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.

Advances in performance degradation mechanism and safety assessment of LiFePO4 for energy storage

“…Model-based approaches provide detailed insights into the complex electrochemical processes occurring within a battery [62][63][64][65]. Researchers like Jung [66,67] have developed LiFePO 4 cycle life models based on the Arrhenius equation, explaining the temperature's influence based on its spatial position. Their research examines the model's dependencies on various temperature types, including ambient, external, internal, and overall average temperatures.…”

An improved adaptive spherical unscented Kalman filtering method for the accurate state‐of‐charge estimation of lithium‐ion batteries

“…Therefore, real‐time and accurate estimation of the SOC will provide significant help to battery management 4–6 . However, due to the complex internal structure of the lithium‐ion battery itself, the SOC of the lithium‐ion battery is affected by various complex factors such as self‐discharge current, self‐discharge effect, internal temperature, external ambient temperature, and battery aging, which makes it difficult to accurately estimate out the value of SOC 7,8 . In order to accurately estimate the SOC of lithium‐ion batteries, relevant scholars have conducted a lot of research and proposed many methods for SOC estimation.…”