{'fraction_non_alphanumeric': 0.02867383512544803, 'fraction_numerical': 0.013142174432497013, 'mean_word_length': 5.180811808118081, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Study on the Capacity Degradation Mechanism and Capacity Predication of Lithium-ion Battery under Different Vibration Conditions in Six Degrees-of-Freedom


 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-degree-of-freedom motion platform as the vibration working conditions of the batteries. Secondly, 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. Thirdly, quantification of battery decline patterns to analyze the main causes of battery capacity decline. 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 GAN-LSTM prediction model to predict the capacity, and the errors of 20 predictions are: 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 BMS-related strategies.