Combined prediction method of lithium-ion battery life based on CEEMDAN–LSTM

As a new generation of new energy battery, lithium-ion battery is widely used in various fields, including electronic products, electric vehicles, and power supply, due to its advantages of high energy density, light weight, long cycle life, small self-discharge, no memory effect, and no pollution. With the wide application of lithium-ion battery, numerous research on its performance has been done, including its health assessment as one of the hot spots. Repeated charging and discharging of a lithium-ion battery that was run under full charge state results to internal irreversible chemical changes leading to a fall in the maximum available capacity. Specifically, a decline to 70%–80% of the rated capacity results in lithium-ion battery failure. Battery failure may lead to electrical equipment damage, resulting in safety accidents. Therefore, it is of great significance to predict the remaining usable life of lithium-ion battery for improving system reliability. In this paper, a combination prediction model for lithium-ion batteries with multimode decomposition was presented based on the long and short-term memory (LSTM) prediction model to learn about small changes in its degradation process. A complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm was used to divide the capacity into main degradation trend and some local degradation trend. Long Short-Term Memory Neural Network (LSTMNN) algorithm was then introduced to perform the capacity prediction of decomposed degradation data. Finally, some prediction results were integrated effectively. The maximum mean absolute percentage error (MAPE) of the proposed CEEMDAN–LSTM lithium-ion battery combination prediction model does not exceed 1.5%. The average relative error is less than 3%, which is better than the other prediction model.