荷电状态
电荷(物理)
国家(计算机科学)
离子
计算机科学
材料科学
原子物理学
电池(电)
物理
热力学
算法
功率(物理)
量子力学
作者
Iman Babaeiyazdi,Afshin Rezaei-Zare,Shahab Shokrzadeh
出处
期刊:Energy
[Elsevier]
日期:2021-05-01
卷期号:223: 120116-120116
被引量:80
标识
DOI:10.1016/j.energy.2021.120116
摘要
Abstract Due to the significantly complex and nonlinear behavior of li-ion batteries, forecasting the state of charge (SOC) of the batteries is still a great challenge. Therefore, accurate SOC estimation is essential for the proper operation of batteries while the battery is monitored by the battery management system (BMS). To this end, this paper employs informative measurements of electrochemical impedance spectroscopy (EIS) in machine learning models (ML), i.e., linear regression model and Gaussian process regression (GPR), to accurately predict the SOC of li-ion batteries. First, a feature sensitivity analysis of the data is conducted to extract the most reliable features, i.e., the EIS impedances which are highly correlated with SOC, from EIS measurements. Then, the models are fed by the chosen features. The models are designed to train the input features and establish the mapping relationship between the selected features and the SOC. The results demonstrate that the error of the GPR model was found to be less than 3.8%. Considering onboard EIS measurements, this method can be practically embedded in the battery management system for accurate measurements of SOC of li-ion batteries and ensure the proper and efficient operation of battery-powered electric vehicles.
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