Remaining useful life prediction of lithium-ion batteries based on wavelet denoising and transformer neural network - Hu, Wangyang; Zhao, Shaishai (Frontiers in Energy Research, 2022)
It is imperative to accurately predict the remaining useful life (RUL) of lithium-ion batteries to ensure the reliability and safety of related industries and facilities. For the noises in the measured aging data of lithium-ion batteries and the strong nonlinear characteristics of the aging process, this paper proposes a method for predicting lithium-ion batteries' RUL based on the wavelet threshold denoising and transformer model. To specify, firstly, the wavelet threshold denoising method is adopted to preprocess the measured data of discharging capacity of lithium-ion batteries to eliminate some noise signals. Secondly, based on the denoised data, the transformer model output's full connection layer is applied to replace the decoder layer for establishing the RUL prediction model of lithium-ion batteries. Finally, the discharging capacity of each charging-discharging cycle is predicted iteratively, then the RUL of lithium-ion batteries can be calculated eventually. Two groups of lithium-ion batteries' aging data from the Center for Advanced Life Cycle Engineering (CALCE) at the University of Maryland and the laboratory at Anqing Normal University (AQNU) are employed to individually verify the proposed method. The experimental results show that this method can overcome the impacts of data measurement noise, effectively predict the RUL of lithium-ion batteries, and present a sound generalization ability and high accuracy.