Objective:
Lithium-ion batteries are important components used in electric automobiles (EVs), fuel cell EVs
and other hybrid EVs. Therefore, it is greatly important to discover its remaining useful life (RUL).
Methods:
In this paper, a battery RUL prediction approach using multiple kernel extreme learning machine (MKELM) is
presented. The MKELM’s kernel keeps diversified by consisting multiple kernel functions including Gaussian kernel
function, Polynomial kernel function and Sigmoid kernel function, and every kernel function’s weight and parameter are
optimized through differential evolution (DE) algorithm.
Results :
Battery capacity data measured from NASA Ames Prognostics Center are used to demonstrate the prediction
procedure of the proposed approach, and the MKELM is compared with other commonly used prediction methods in
terms of absolute error, relative accuracy and mean square error.
Conclusion:
The prediction results prove that the MKELM approach can accurately predict the battery RUL.
Furthermore, a compare experiment is executed to validate that the MKELM method is better than other prediction
methods in terms of prediction accuracy.
State-of-health estimation and remaining useful life prediction of lithium-ion batteries based on extreme learning machine
