Abstract
In order to study the degradation mechanism of lithium-ion batteries subjected to vibration aging in actual use and also to achieve capacity estimation and prediction, the following work has been done: First, the road spectra of two commonly seen domestic roads in China are collected in the field and modeled on a six degrees-of-freedom motion platform as the vibration working conditions of the batteries. Second, aging cycle experiments were conducted on batteries with different placement directions (X-axis direction, Y-axis direction, and Z-axis direction) under two vibration conditions, and the effects of experimental conditions on the decline results were analyzed; third, quantification of battery decline patterns to analyze the main causes of battery capacity decline; and then, through further analysis of the two vibration conditions on the lithium battery by in-situ and ex-situ methods as its internal mechanisms. Finally, the quantified results were input into the generative adversarial networks and long-term and short-term memory network prediction model to predict the capacity, and the errors of 20 predictions are as follows: the average values are 2.8561% for Group X, 2.7997% for Group Y, 3.0182% for Group Z, and 2.9478% for Group N, which meet the requirements of battery management system estimation. This paper provides a basis for the study of aging mechanism and capacity estimation of lithium-ion batteries under vibration aging conditions, which helps manufacturers to package batteries more rationally to extend battery life and develop battery management system (BMS)-related strategies.
