Abstract: The lithium-ion batteries often suffer from sudden and occasional capacity regeneration due to the complex and discontinued working conditions. Thus, the capacity degradation data shows the nonlinear and nonstationary trend, which makes it difficult to achieve high prediction accuracy. Therefore, a remaining useful life(RUL) prediction method of lithium-ion batteries based on variational mode decomposition (VMD) and Gaussian process regression(GPR) is proposed to treat this problem. Firstly, VMD is employed to decompose the capacity degradation data and to extract the global degradation, local regeneration, and random fluctuation components. Then, different GPR prediction models are built for each component by choosing suitable kernel functions. Lastly, the predicted components are superimposed to obtain the capacity prediction result and the predicted RUL. The proposed method is validated through a case study using NASA dataset. Results show that the proposed method outperforms the GPR models without VMD decomposing.

Key words: lithium-ion battery, capacity, variational mode decomposition, Gaussian process regression, remaining useful life prediction