State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network

Abstract 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.