Investigation of the Storage Life of Lithium-ion Battery Based on the Metabolism GM(1, 1)-Neural Network

LI Ju-chen; HU Yu-li; HAO Ze-hua; ZHANG ZI-zheng; ZHENG Yi

doi:10.11993/j.issn.2096-3920.2022.02.014

Volume 30 Issue 2

Apr 2022

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Article Navigation > Journal of Unmanned Undersea Systems > 2022 > 30(2): 231-236

LI Ju-chen, HU Yu-li, HAO Ze-hua, ZHANG ZI-zheng, ZHENG Yi. Investigation of the Storage Life of Lithium-ion Battery Based on the Metabolism GM(1, 1)-Neural Network[J]. Journal of Unmanned Undersea Systems, 2022, 30(2): 231-236. doi: 10.11993/j.issn.2096-3920.2022.02.014

Citation:

LI Ju-chen, HU Yu-li, HAO Ze-hua, ZHANG ZI-zheng, ZHENG Yi. Investigation of the Storage Life of Lithium-ion Battery Based on the Metabolism GM(1, 1)-Neural Network[J]. Journal of Unmanned Undersea Systems, 2022, 30(2): 231-236. doi: 10.11993/j.issn.2096-3920.2022.02.014

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Investigation of the Storage Life of Lithium-ion Battery Based on the Metabolism GM(1, 1)-Neural Network

doi: 10.11993/j.issn.2096-3920.2022.02.014

School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China

Received Date: 2021-03-24
Accepted Date: 2021-11-30
Rev Recd Date: 2021-06-18

Abstract

Abstract

Active torpedoes are in storage most of the time, and lithium-ion batteries are the main power sources. Therefore, it is important to study the storage life of lithium-ion batteries. In this study, a 18650 lithium cobalt oxide battery was used as the research object. Through the accelerated life test, the battery capacity and internal resistance change curves with time under different stress conditions are obtained, and the storage condition that is helpful to alleviate the attenuation of battery life is determined as 25℃ and 30% state of charge(SOC). This study integrates the advantages of the gray prediction method and neural network, and uses the metabolic GM(1, 1)-neural network method to predict the capacity of lithium-ion batteries. The combined prediction model was verified to be better than the gray prediction model and metabolic GM(1, 1). The prediction model has a higher accuracy and is more suitable for predicting the storage life of lithium-ion batteries under different stress conditions. This further verifies the storage conditions that are conducive to alleviating the attenuation of battery life.
- lithium-ion battery,
- accelerated life test,
- grey neural network,
- storage life

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References(12)

References

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