Predicting the lifespan of lithium-ion batteries has been a never-ending challenge for engineers and industry specialists. One common method for estimating battery life is the 'accelerated cyclic aging test', in which stress factors such as current rates, Depth of Discharge (DOD), and temperatures are intensified to speed up the aging process. These tests, however, rely on a model-based relationship between the accelerated conditions and real-life applications, presenting certain limitations.
Understanding these limitations becomes particularly critical when considering a phenomenon known as 'apparent aging', the temporary loss in capacity during the active cycling phase that can be significantly recovered during the resting phase of the battery.
A remarkable study conducted by Morales Torricos, Endisch, and Lewerenz (2023) [1] has significantly contributed to our understanding of lithium-ion cell lifespan, focusing on the apparent aging during accelerated cycling aging tests of cylindrical silicon-containing Li-ion cells. Their insights shed light on lithium distribution inhomogeneity and its effect on capacity and resistance in the aging process.
During the cycling phase of the accelerated cyclic aging test, lithium-ion batteries demonstrate varying degrees of apparent aging. This is significantly influenced by factors such as the average State of Charge (SOC), DOD, and the intensity of charge/discharge currents. The higher the DOD and current rates, the stronger the apparent aging. Moreover, batteries cycled at an average SOC of 20% and 70% experience stronger apparent aging compared to those cycled at 50% SOC.
A detailed analysis of this phenomenon points towards the cycling-induced inhomogenization of the lithium distribution throughout the anode area. The higher-pressure gradients during cycling aggravate this inhomogeneity, leading to apparent aging. Subsequently, during the resting phase, rehomogenization of lithium distribution is hypothesized to occur, facilitating the recovery of the lost capacity.
Incorporating the dynamics of lithium distribution into lifespan prediction models is crucial for achieving accuracy. The conventional models based on accelerated cyclic aging tests tend to overestimate the battery's degradation due to the apparent aging observed during the active cycling phase. Consequently, these models must consider the potential for recovery of this apparent aging due to the rehomogenization of lithium distribution during the resting phases.
Achieving a comprehensive understanding of lithium distribution dynamics during accelerated cyclic aging tests is key to improving the accuracy of lifespan estimations for lithium-ion batteries.