荷电状态
开路电压
估计
计算机科学
国家(计算机科学)
健康状况
锂(药物)
锂离子电池
离子
控制理论(社会学)
电压
电力系统
可靠性工程
断层(地质)
汽车工程
作者
Xiaolei Bian,Zhongbao Wei,Weihan Li,Josep Pou,Dirk Uwe Sauer,Longcheng Liu
出处
期刊:IEEE Transactions on Power Electronics
[Institute of Electrical and Electronics Engineers]
日期:2022-02-01
卷期号:: 1-1
被引量:9
标识
DOI:10.1109/tpel.2021.3104723
摘要
The state of health (SOH) is a vital parameter enabling the reliability and life diagnostic of lithium-ion batteries. A novel fusion-based SOH estimator is proposed in this study, which combines an open circuit voltage (OCV) model and the incremental capacity analysis. Specifically, a novel OCV model is developed to extract the OCV curve and the associated features-of-interest (FOIs) from the measured terminal voltage during constant-current charge. With the determined OCV model, the disturbance-free incremental capacity (IC) curves can be derived, which enables the extraction of a set of IC morphological FOIs. The extracted model FOI and IC morphological FOIs are further fused for SOH estimation through an artificial neural network. Long-term degradation data obtained from different battery chemistries are used for validation. Results suggest that the proposed fusion-based method manifests itself with high estimation accuracy and high robustness.
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