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.

为了改善健康状态（SOH）估计和剩余使用寿命（RUL）预测，提出了多个电池共享的预后框架。设计了一个变种的长短期记忆（LSTM）神经网络（NN），称为AST-LSTM NN，以保证所提出框架的性能。首先，输入和忘记门通过固定连接耦合，这导致同时确定旧信息和新数据。其次，进行新输入和历史单元状态的元素乘积，以筛选出更多有益的信息。第三，将来自“恒定误差圆盘传送带”（CEC）的窥孔连接添加到输出门中，以屏蔽不需要的误差信号。AST-LSTM NN具有多对一和一对一的映射结构，分别经过训练有素，可以预测SOH和RUL。