健康状况
人工神经网络
变压器
特征提取
计算机科学
工程类
人工智能
模式识别(心理学)
电压
电气工程
电池(电)
功率(物理)
量子力学
物理
作者
Kai Luo,Huiru Zheng,Zhicong Shi
标识
DOI:10.1016/j.jpowsour.2023.233139
摘要
Accurately estimating the state of health (SOH) of lithium-ion batteries (LIBs) can avoid safety accidents and economic losses, and it remains a big research challenge. In this paper, electrochemical impedance spectroscopy (EIS) is used as the feature for the SOH prediction. EIS contains rich information such as material properties and electrochemical reactions, which directly reflects the aging state of LIBs. In order to obtain valuable data for SOH estimation, we propose a new feature extraction method from the perspective of electrochemistry, and then apply the transformer-based neural network for SOH estimation. Through feature extraction, the mean absolute percentage error of the estimation is reduced to 1.63% in the whole life cycle, which is decreased by 70% compared to the original data before feature extraction.
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