Physics

Pattern recognition (psychology)

Autoregressive integrated moving average

Mathematics

Statistics

Filter (signal processing)

Machine learning

Time series

Autoregressive model

Artificial intelligence

Power (physics)

Hilbert–Huang transform

Lithium-ion battery

Computer science

Battery (electricity)

Quantum mechanics

Computer vision

Artificial neural network

Grey relational analysis

Autoregressive–moving-average model

IEEE Access

This paper proposes a fusion model based on the autoregressive moving average (ARMA) model and Elman neural network (NN) to achieve accurate prediction for the state of health (SOH) of lithium-ion batteries. First, the voltage and capacity degradation variation of the battery are acquired through the battery lifecycle data, and the health factor related to the battery aging is selected according to the variation of the voltage profile. Second, the empirical mode decomposition (EMD) is employed to process the capacity degradation data and eliminate the phenomenon of tiny capacity recovery, and multiple data sequences, as well as the related residue, are extracted, then the grey relational analysis (GRA) between sub-sequences and health factor are discussed. Furthermore, the ARMA model and Elman NN model are respectively built by training the subsequent time series data and residue data. Finally, all the individual predictions are combined to generate the estimated SOH sequences. The experimental validation is performed to manifest that the addressed fusion method performs the SOH prediction with satisfactory accuracy, compared with the single ARMA method and Elman NN model.

State of Health Estimation for Lithium-ion Batteries Based on Fusion of Autoregressive Moving Average Model and Elman Neural Network