Affiliations: [a]
National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, China
| [b]
College of Engineering, Western New England University, Springfield, MA, USA
Correspondence:
[*]
Corresponding author. Jian Guo, College of Engineering, Western New England University, Springfield, MA 01119, USA. E-mail: [email protected].
Abstract: Lithium Ion batteries usually degrade to an unacceptable capacity level after hundreds or even thousands of charge and discharge cycles. The continuously observed capacity fade data over time and their internal structure can be informative for constructing capacity fade models. This paper applies a mean-covariance decomposition (MCD) modeling method using data within moving windows to analyze the capacity fade process. The proposed approach directly examines the variances and correlations in data of interest and reparameterize the correlation matrix in hyper-spherical coordinates using angle and trigonometric functions. To improve the interpretation of the prognostics model, the mean function is obtained based on physics of failure. Non-parametric methods are used to characterize the log variance and correlation through the number of cycles and time lags between capacity measurements, respectively. A numerical example is used to illustrate the superiority of the proposed method in prediction performance.
Keywords: Lithium ion battery, capacity fading, mean-covariance decomposition, correlation matrix reparameterization, physics of failure, non-parametric, moving window
