A sample entropy based prognostics method for lithium-ion batteries using relevance vector machine. (October 2021)

• Record Type: Journal Article
• Authors: Jia, Shun; Ma, Bo; Guo, Wei; Li, Zhaojun Steven
• Is Part Of: Journal of manufacturing systems. Volume 61(2021)
• Page Start: 773
• Page End: 781
• Publication Date: 2021-10
• Subjects: Sample entropy -- Remaining useful life -- Prediction -- Relevance vector machine -- Linear weighting; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2021.03.019
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: Multiple sample entropy measures are used in Relevance Vector Machine for battery prognostics. Wavelet denoising is adopted to pre-process irregular data observations. A linear weighting method is used to adjust the contribution of individual input variable. Numerical examples show the advantages of the proposed modeling approach. Abstract: Over the increasing number of charging and discharging cycling processes of lithium-ion batteries, the aging and even failure of lithium-ion batteries may occur. If anomalies are not detected in time, lithium-ion batteries could cause major safety accidents. In this paper, a prognostics method integrating the sample entropies and relevance vector machine (RVM) is proposed to estimate the remaining useful life (RUL) of lithium-ion batteries. First, RUL prediction using multiple inputs, including the voltage sample entropy and the current sample entropy, are compared with prediction methods based on a single entropy input. The multiple entropy input method indicates better capability of describing the battery degradation process. In addition, the wavelet denoising method is used to pre-process the inputs to remove sudden and unusual changes in the battery capacity degradation data. A prediction model using the denoised entropy inputs is constructed through linearly weighting the entropy inputs in the RVM model. The weight for each input is assigned according to the individual contribution to the prediction accuracy. Experimental data from lithium-ion battery testing are applied to three prediction models with different entropy inputs. The results indicate that the proposed method has higher prediction accuracy than those in existing models only using a single sample entropy. The proposed method has potentials for the RUL estimation of industrial machinery in manufacturing.