title: Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM.
abstract: Lithium-ion batteries are widely used in many electronic systems. Therefore, it is significantly important to estimate the lithium-ion battery's remaining useful life (RUL), yet very difficult. One important reason is that the measured battery capacity data are often subject to the different levels of noise pollution. In this paper, a novel battery capacity prognostics approach is presented to estimate the RUL of lithium-ion batteries. Wavelet denoising is performed with different thresholds in order to weaken the strong noise and remove the weak noise. Relevance vector machine (RVM) improved by differential evolution (DE) algorithm is utilized to estimate the battery RUL based on the denoised data. An experiment including battery 5 capacity prognostics case and battery 18 capacity prognostics case is conducted and validated that the proposed approach can predict the trend of battery capacity trajectory closely and estimate the battery RUL accurately.
authors.affiliation: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China ; School of Physics and Electronic Engineering, Anqing Normal University, Anqing 246011, China.
authors.forename: Chaolong
authors.initials: C
authors.lastname: Zhang
authors.affiliation: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
authors.forename: Yigang
authors.initials: Y
authors.lastname: He
authors.affiliation: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
authors.forename: Lifeng
authors.initials: L
authors.lastname: Yuan
authors.affiliation: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
authors.forename: Sheng
authors.initials: S
authors.lastname: Xiang
authors.affiliation: School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
authors.forename: Jinping
authors.initials: J
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D011211:Electric Power Supplies; D006801:Humans; D008094:Lithium / Q000737:chemistry*; D008962:Models, Theoretical; D060388:Support Vector Machine; D058067:Wavelet Analysis
chemical_list: D008094:Lithium