学习迁移
健康状况
电池(电)
估计
锂(药物)
锂离子电池
国家(计算机科学)
计算机科学
传输(计算)
人工智能
心理学
工程类
物理
热力学
系统工程
算法
功率(物理)
精神科
并行计算
作者
Wei Liu,Shu‐Qin Gao,Wendi Yan
出处
期刊:Journal of electrochemical energy conversion and storage
[ASME International]
日期:2024-02-05
卷期号:: 1-34
摘要
Abstract Rapid and accurate estimation of the state of health of lithium-ion batteries is of great significance. This paper aims to address two issues faced when applying deep learning methods to estimate the health status of lithium-ion batteries: high data quality requirements and poor model generalizability. And this paper proposes a comparison-transfer learning approach with cyclic synchronization to estimate the state of health of lithium-ion batteries. Firstly, a cyclic synchronization method based on the Bezier curve fitting algorithm is introduced to synchronize the data obtained at different charge-discharge cycles of the lithium-ion battery, facilitating input to the model. Secondly, a comparison-transfer network using Pearson correlation coefficient is proposed to transfer knowledge from the source dataset to predict the target dataset under different environmental temperatures. By training a pre-trained model on the source dataset and utilizing the correlation coefficient to analyze the similarity between the source and target datasets, the accumulated knowledge in the source dataset can be effectively utilized to enhance prediction performance on the target dataset. In the experiments, the proposed method is validated using the lithium-ion battery aging public datasets. The experimental results demonstrate that the proposed approach achieves superior prediction performance in the case of small sample sizes, exhibiting higher accuracy and stability compared to traditional deep learning methods.
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