恒流
健康状况
常量(计算机编程)
电池(电)
瞬态(计算机编程)
卷积神经网络
电压
人工神经网络
估计员
人工智能
电气工程
计算机科学
物理
工程类
功率(物理)
程序设计语言
操作系统
统计
量子力学
数学
作者
Haokai Ruan,Zhongbao Wei,Wentao Shang,Xuechao Wang,Haibo He
出处
期刊:Applied Energy
[Elsevier]
日期:2023-04-01
卷期号:336: 120751-120751
被引量:21
标识
DOI:10.1016/j.apenergy.2023.120751
摘要
State of health (SOH) estimation is essential to the health diagnostic of lithium-ion battery. The data-driven approach with charging feature extraction is promising for online SOH estimation and has been widely explored over years. However, their deployment can be barriered by the lack of complete charging data in real-world applications. Motivated by this, this paper proposes an artificial intelligence-based SOH estimator using the transient phase between constant current (CC) and constant voltage (CV) charging, which is easily obtained in real-world charging scenarios. Specifically, a convolutional neural network (CNN) model is proposed to explain the relationship between the charging data and the SOH. Following this endeavor, the transfer learning is exploited for model mitigation and SOH estimation on different battery types, relying on much reduced amount of data for efficient CNN model re-training. The validation experiments are conducted based on the aging data obtained on LiNiCoAlO2 (NCA) and LiCoO2 (LCO) cells. Results suggest that the proposed method realizes accurate SOH estimation requiring only a short segment from the CC-CV transient phase, so that can meet a broad range of real-world charging scenarios. Moreover, the efficient model transfer promises expected performance with different battery types. The short version of the paper was presented at ICAE2021, Nov 29 - Dec 5, 2021. This paper is a substantial extension of the short version of the conference paper.
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