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机译:埃尔曼神经网络在振动应力作用下锂离子电池的间接RUL预测
Hebei Univ Technol, State Key Lab Reliabil & Intelligence Elect Equip, Tianjin 300130, Peoples R China|Hebei Univ Technol, Sch Elect Engn, Key Lab Electromagnet Field & Elect Apparat Relia, Tianjin 300130, Peoples R China;
Hebei Univ Technol, Sch Elect Engn, Key Lab Electromagnet Field & Elect Apparat Relia, Tianjin 300130, Peoples R China;
Hebei Univ Technol, Sch Elect Engn, Key Lab Electromagnet Field & Elect Apparat Relia, Tianjin 300130, Peoples R China;
Hebei Univ Technol, Sch Elect Engn, Key Lab Electromagnet Field & Elect Apparat Relia, Tianjin 300130, Peoples R China;
Hebei Univ Technol, Control Sci & Engn Coll, Tianjin 300130, Peoples R China;
Lithium-ion battery; Vibration stress; Remaining useful life (RUL); Health indicator (HI); Long short-term memory (LSTM); Elman neural network;
机译:基于长时记忆和Elman神经网络混合模型的锂离子电池剩余使用寿命预测
机译:SOH基于高斯工艺回归与间接健康指标的高斯进程回归的SOH和RUL预测
机译:基于粒子滤波和径向基函数神经网络的锂离子电池自适应预测
机译:利用温度变化率的锂离子电池RUL预后方法
机译:基于改进的戴维南电路模型的神经网络锂离子电池充电状态估计
机译:使用人工神经网络剩余的使用寿命(RUL)生产线设备的预测
机译:基于自回归移动平均模型和ELMAN神经网络融合的锂离子电池健康估算状态
机译:自适应递归神经网络用于剩余锂离子电池寿命预测。