期刊
ENERGIES
卷 14, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/en14092526
关键词
artificial neural network (ANN); multi-frequency electrical impedance spectroscopy (EIS); lithium-ion (Li-ion) battery; static state of charge (SOC); potentiostats; galvanostats
资金
- Information and Communications Research Laboratories, Division for Biomedical & Industrial IC Technology, Green Electronics Design & Application Dept, Industrial Technology Research Institute (ITRI)
- Ministry of Science and Technology Taiwan [MOST 108-2218-E-011-025_]
The paper proposes an ANN-based multi-frequency EIS technique to estimate the static SOC of Li-ion batteries, with experimental results showing that the number of neurons in the hidden layer significantly affects the model performance, achieving the highest accuracy with 35 neurons.
An artificial neural network (ANN) based multi-frequency electrical impedance spectroscopy (EIS) technique is proposed to estimate the static state of charge (SOC) of lithium-ion (Li-ion) battery in this paper. The proposed ANN-based multi-frequency EIS technique firstly collects the data of AC independence and their corresponding static SOC. With battery discharging current and multi-frequency EIS results, an ANN model is built and trained to estimate SOC. The measurement data is obtained using the potentiostats/galvanostats device, and the ANN is trained using the neural network toolbox in MATLAB. According to the experimental results, the performance of the proposed ANN model is dependent on the number of neurons in the hidden layer. The proposed method is validated with a set of random discharging processes. The high accuracy of SOC estimation is able to be achieved with the average error reduced to 1.92% when the number of neurons in the hidden layer is 35. Therefore, the proposed ANN-based multi-frequency EIS technique can be utilized to measure the static SOC of random discharge of Li-ion batteries.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据