Remaining useful life prediction method of lithium-ion batteries is based on variational modal decomposition and deep learning integrated approach

• Published in: Energy (Oxford) Vol. 282; p. 128984
• Main Authors: Wang, Shuai, Ma, Hongyan, Zhang, Yingda, Li, Shengyan, He, Wei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2023
• Subjects: Echo state network; Lithium-ion battery; Long-short-term memory; Remaining useful life; Variational modal decomposition; Lithium-ion battery; Variational modal decomposition; Long-short-term memory; Remaining useful life; Echo state network
• DOI: 10.1016/j.energy.2023.128984
• ISSN: 0360-5442

A common method based on variational modal decomposition (VMD) and an integrated depth model is proposed to address the problem that it is difficult to precisely anticipate the remaining useful life (RUL) of lithium-ion batteries (LIBs). Initially, VMD is employed to decompose the LIBs capacity data in multiple scales to obtain the signal's global degradation tendency and local random fluctuation components. Then, the global degradation trend and each fluctuation component are modeled using an echo state network (ESN) and a Bayesian optimized long short-term memory (LSTM) network, respectively. The final LIBs RUL prediction results are obtained by integrating the prediction outcomes. On three public LIBs datasets with distinct degradation characteristics, the performance of the proposed model is tested, and alternative prediction algorithms are compared. The results of the experiments show that the proposed model's maximum average absolute percentage error does not exceed 0.43%. The average relative error not more than 0.5%, indicating great accuracy and stability in prediction. •VMD algorithm parameters using automatic optimization.•The optimized VMD algorithm is used to separate the capacity decay curve.•An integrated depth prediction model is constructed.•Bayesian optimization of hyperparameters for deep learning algorithms.