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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2015
- Source ID
- 10.1155/2015/918305
Entities
People
- Chaolong Zhang
- Jinping Wang
- Lifeng Yuan
- Sheng Xiang
- Yigang He
Organizations
- Anqing Normal University
- Hefei University of Technology