健康状况
稳健性(进化)
锂离子电池
电池(电)
计算机科学
鉴定(生物学)
可靠性(半导体)
人工神经网络
可靠性工程
工程类
人工智能
化学
功率(物理)
生物化学
物理
植物
量子力学
生物
基因
作者
Yongliang Shi,Yujun Hu,X. Y. Zhai
出处
期刊:Journal of physics
[IOP Publishing]
日期:2023-10-01
卷期号:2615 (1): 012016-012016
标识
DOI:10.1088/1742-6596/2615/1/012016
摘要
Abstract Accurately assessing the State of Health (SOH) of batteries and conducting knee point detection is of utmost importance in prolonging their lifespan. However, the generalization ability and robustness of individual methods are limited. To address this, a novel lithium-ion battery SOH estimation model is proposed, incorporating the Bald Eagle Search (BES) optimization algorithm in conjunction with the Gate Recurrent Unit (GRU) neural network. The selection of highly correlated health indicators (HI) with battery SOH is performed using the Pearson correlation coefficient. The BES algorithm is employed to optimize the selection of parameters for GRU networks. The model reliability is verified using the MIT-Stanford lithium-ion battery dataset. The results illustrate that the proposed BES-GRU model effectively predicts the SOH of batteries and identifies knee points, effectively improving the estimation accuracy.
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