健康状况
估计员
频域
计算机科学
稳健性(进化)
控制理论(社会学)
统计
电池(电)
工程类
数学
人工智能
物理
化学
功率(物理)
生物化学
控制(管理)
量子力学
计算机视觉
基因
作者
Zhou Yong,Guangzhong Dong,Qianqian Tan,Xueyuan Han,Chunlin Chen,Jingwen Wei
出处
期刊:Energy
[Elsevier]
日期:2022-09-23
卷期号:262: 125514-125514
被引量:33
标识
DOI:10.1016/j.energy.2022.125514
摘要
Due to lithium-ion batteries’ complex behaviors, accurate estimation of state-of-health is still a critical challenge in battery systems’ prognosis and health management. Most existing efforts in battery health prognosis focus on feature engineering using low-frequency sampled time-domain response. These efforts may not completely reflect the battery health status in automotive applications due to information missing in the high or medium frequency range. This paper proposes a data-driven state-of-health estimation method using high and medium frequency range impedance spectroscopy data. First, battery health indicators are extracted from electrochemical impedance spectroscopy data. It is found that the Nyquist diagram shows semicircle characteristics at high and medium frequency ranges. The center and radius of this circle show high dependence on battery health. Then, a recurrent Gaussian process regression with a one-step delay feedback loop is designed to provide a smooth and accurate battery state-of-health estimate. Finally, the proposed health indicators and state-of-health estimators are validated using experimental data on different cells. The results demonstrate the high accuracy and robustness of the proposed health indicators and state-of-health estimator, suggesting a 1.12% estimation error. This study shows the prospect of health prognosis using robust geometric impedance spectrum indicators in energy storage systems. • Geometric impedance spectrum features are extracted for battery health estimation. • A recurrent Gaussian process regression is employed for smooth health estimation. • Health indicators are evaluated by Spearman correlation and Pearson coefficients. • The proposed methods are validated by extensive experimental datasets.
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