电池(电)
电池容量
均方误差
充电周期
电压
可靠性工程
弹道
计算机科学
锂离子电池
工作(物理)
降级(电信)
模拟
汽车工程
工程类
电气工程
汽车蓄电池
统计
电信
机械工程
功率(物理)
物理
数学
量子力学
天文
作者
Binghan Cui,Han Wang,Renlong Li,Lizhi Xiang,Huaian Zhao,Rang Xiao,Sai Li,Zheng Liu,Geping Yin,Xinqun Cheng,Yulin Ma,Hua Huo,Pengjian Zuo,Taolin Lu,Jun Xie,Chunyu Du
标识
DOI:10.1016/j.apenergy.2023.122080
摘要
Forecasting the battery performance accurately in the ultra-early stage can avoid safety incidents, analyze degradation patterns, and prolong battery cycle life, which is crucially essential for battery management. In this work, a mechanism and data-driven fusion model is developed to predict charging capacity and energy curves over the full life cycle of batteries in the case of only knowing the planned cycling protocol without any usage history. The proposed method can achieve accurate and robust prediction of three types of batteries under different working conditions and ambient temperatures with the root-mean-square error (RMSE) of 73.7, 100.9, and 45 mAh. The maximum charging capacity and energy trajectory can be extracted further. Moreover, the proposed method can also detect battery faults without setting a safety threshold in advance due to the inconsistency of the voltage and capacity evolutions of normal and faulty batteries.
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