TY - JOUR A2 - Aliyev, Rafik AU - Zhang, Chaolong AU - He, Yigang AU - Yuan, Lifeng AU - Xiang, Sheng AU - Wang, Jinping PY - 2015 DA - 2015/08/30 TI - Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM SP - 918305 VL - 2015 AB - 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. SN - 1687-5265 UR - https://doi.org/10.1155/2015/918305 DO - 10.1155/2015/918305 JF - Computational Intelligence and Neuroscience PB - Hindawi Publishing Corporation KW - ER -