LSTM-ARIMA Model with Multiscale Decomposition for Life Prediction of Lithium-ion Battery
The prediction of remaining useful life(RUL)of lithium-ion batteries is an important research direction in battery technology.Through accurate prediction of RUL,batteries can be better managed and maintained to extend their lifespan.To achieve accurate RUL prediction of lithium-ion batteries,a model which combines variational mode decomposi-tion(VMD)with long short-term memory(LSTM)and autoregressive integrated moving average(ARIMA)was proposed.Firstly,VMD algorithm was used to decompose the capacity data from the NASA lithium-ion battery dataset into multiple high-frequency and low-frequency components in order to reduce the noise interference in the capacity data.Then,with re-gard to the characteristics of each component,LSTM and ARIMA were used to establish separate sub-models to predict the high-frequency and low-frequency components,respectively.Finally,the predicted values of each sub-model were com-bined and reconstructed to obtain the RUL result of the lithium-ion battery.Experimental results showed that the VMD-LSTM-ARIMA prediction model had better RUL prediction capability compared with other prediction models.Furthermore,generalization experiments on the CALCE lithium-ion battery dataset showed that the model was applicable to different battery RUL prediction tasks.
lithium-ion batteryremaining life predictionvariational mode decompositionlong short-term memory neural networkARIMA
国家科技期刊平台
NSTL
万方数据
