Accelerated Aging Analysis on Cycle Life of LiFePO₄/Graphite Batteries Based on Different Rates

Abstract

In this study, different charge/discharge rates (1/3C, 1C, 1.5C, 1.8C, 2.0C and 2.5C) are used to accelerate the aging of commercial LiFePO4/graphite cells. The capacity attenuation mechanism is investigated by disassembling the aged full cells and analyzing the morphologies and chemical composition of electrode surfaces, the single electrode potentials, and so forth, when the capacity retention of full cell decays to 95 %, 90 %, 85 %, and 80 %. Through the tests, the capacity fade of the full cell is mainly ascribed to the irreversible loss of active lithium. However, when the test rate is not lower than 2.0C, the dominant mechanism for consumption of active lithium is changed to lithium deposition on the anode during the charging process instead of the generation of SEI film. As a consequence, the capacity decay rate at 2.0C or 2.5C is accelerated to a large degree. The capacity loss with cycling time is fitted by Arrhenius equations. The pre‐exponential factor at a given rate (Arate) increases linearly with the rising test rate when it is not more than 1.8C, and this corresponds to the results of disassembly analysis. Therefore, an unknown Arate in a reasonable range can be extrapolated by the data of other Arate values at relatively high rates. In addition, the calculated Arate after 10 % capacity loss can be used to replace the fitted value by the whole aging data. This provides a rapid and practical approach to estimate the lifetime of the full cell.

Li-ion Battery Reliability – A Case Study of the Apple iPhone®

“…High C-rates cause larger strain gradients and cracks in the electrode particles and SEI layer [57], [58], resulting in higher battery degradation. Figure 6 presents an example of the nonlinear degradation effects of C-rates on a Li-ion battery.…”

“…In addition, as the C-rate increases, the battery temperature due to ohmic heating increases, which in turn accelerates battery degradation [14]. The elevated temperature promotes the decomposition of electrolyte, leading to the more rapid consumption of active lithium, and reduction in cathode performance due to the generation of a surface layer and structure degradation [54], [57]. As a result, the decrease of capacity and the increase of resistance of Li-ion batteries are accelerated when cycling under high temperature.…”

Uncertainty-aware and explainable machine learning for early prediction of battery degradation trajectory

“…The cycling charge rate affects the degradation of LiFePO4/ graphite cells [34][35][36] and is always recorded in cycling experiments; therefore, we include the maximum, minimum, and mean charging rate as features to account for cycling conditions. The discharge rates might also be considered, but we do not use them because all cells in the dataset are discharged at the same rate.…”

Uncertainty-aware and explainable machine learning for early prediction of battery degradation

“…The cycling charge rate affects the degradation of LiFePO4/graphite cells [27][28][29] and is always recorded in cycling experiments; therefore, we include the maximum, minimum and mean charging rate as features to account for the cycling conditions. The discharge rates might also be considered, but we do not use them because all cells in the dataset are discharged at the same rate 8 .…”