Battery (electricity)

Quantum mechanics

Algorithm

Engineering

State of health

Lithium (medication)

Reliability engineering

Neuroscience

Lithium-ion battery

Working memory

Short-term memory

Cognition

Systems engineering

Psychiatry

Power (physics)

Computer science

Estimation

Thermodynamics

Physics

Psychology

Long short term memory

Artificial intelligence

Term (time)

Artificial neural network

Recurrent neural network

State (computer science)

Journal of Power Sources

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.

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