健康状况
电压
电池(电)
恒流
控制理论(社会学)
计算机科学
人工神经网络
过程(计算)
锂离子电池
耐久性
常量(计算机编程)
工程类
功率(物理)
人工智能
电气工程
物理
控制(管理)
量子力学
数据库
程序设计语言
操作系统
作者
Xin Lai,Yi Yao,Xiaopeng Tang,Yuejiu Zheng,Yuanqiang Zhou,Yao Sun,Furong Gao
出处
期刊:Energy
[Elsevier]
日期:2023-11-01
卷期号:282: 128971-128971
被引量:3
标识
DOI:10.1016/j.energy.2023.128971
摘要
The state of health (SOH) of batteries is an important but unmeasurable parameter closely related to battery safety and durability. However, most existing SOH estimation strategies rely on a specific load profile (e.g., constant current). To tackle this issue, we here report a method that first converts the dynamic voltage trajectories to the curves corresponding to the constant current profiles using a neural network model. Then, the aging characteristics are selected and the battery SOH is estimated accordingly from the converted voltage data using a Gaussian process regression model. Batteries with different aging degrees are tested with different working conditions to verify the proposed method. Numerically, the errors of the voltage reconstruction are bounded within 2 mV, while the SOH estimation errors under four dynamic working conditions remain within 2%. Our technical approach reduces the dependency of traditional SOH estimation methods on specific working conditions and shows strong potential for practical applications.
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