计算机科学
背景(考古学)
卷积神经网络
人工神经网络
嵌入
健康状况
电池(电)
人工智能
机器学习
卷积(计算机科学)
功率(物理)
古生物学
物理
量子力学
生物
作者
Zhengyi Bao,Jiahao Nie,Huipin Lin,Jiahao Jiang,Zhiwei He,Mingyu Gao
出处
期刊:Energy
[Elsevier BV]
日期:2023-07-03
卷期号:282: 128306-128306
被引量:17
标识
DOI:10.1016/j.energy.2023.128306
摘要
Accurate estimation of the state of health (SOH) of lithium-ion batteries holds significant importance in guaranteeing the stable and secure functioning of electric vehicles. However, existing neural network-based methods suffer from limitations in capturing long-term serial relationships and extracting degenerate features. In light of these challenges, we propose a novel sequence-free framework for performing the SOH estimation task. Technically, a global-local context embedding module is proposed to learn both global- and local-range information context by two convolutional streams with different depths. With this module, a discriminatory feature learning can be guided. By integrating it into the convolution neural network, a novel time series prediction network, called improved convolution neural network (ICNN) is presented, which can effectively establish the mapping relationship between battery charging/discharging curves and battery SOH. Through rigorous experimentation on the CACLE dataset and NASA dataset, we demonstrate the efficacy of our proposed method, achieving mean absolute errors (MAEs) of 0.54% and 1.20% respectively. Our findings highlight the superiority of the proposed method compared to commonly used neural network methods in the domain of battery SOH estimation.
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