短时记忆
人工神经网络
健康状况
计算机科学
电池(电)
连接(主束)
期限(时间)
均方误差
国家(计算机科学)
循环神经网络
人工智能
数据挖掘
算法
工程类
统计
数学
功率(物理)
量子力学
结构工程
物理
作者
Penghua Li,Zijian Zhang,Qingyu Xiong,Baocang Ding,Jie Hou,Luo De-chao,Yujun Rong,Shuaiyong Li
标识
DOI:10.1016/j.jpowsour.2020.228069
摘要
To improve state-of-health (SOH) estimation and remaining useful life (RUL) prediction, a prognostic framework shared by multiple batteries is proposed. A variant long-short-term memory (LSTM) neural network (NN), called AST-LSTM NN, is designed to guarantee the performance of proposed framework. Firstly, the input and forget gates are coupled by a fixed connection, which leads simultaneous determination of old information and new data. Secondly, the element-wise product of the new inputs and the historical cell states is conducted for screening out more beneficial information. Thirdly, a peephole connection from the “constant error carousel” (CEC) is added into the output gate to shield the unwanted error signals. AST-LSTM NNs, with mapping structures of many-to-one and one-to-one, are well-trained separately for the prediction of SOH and RUL. Compared with other data-driven methods, the experiments carried on NASA dataset demonstrate our method hits lower average root mean square, 0.0216, and conjunct error, 0.0831, for SOH and RUL, respectively.
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