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.
journal_name
Comput Intell Neuroscijournal_title
Computational intelligence and neuroscienceauthors
Zhang C,He Y,Yuan L,Xiang S,Wang Jdoi
10.1155/2015/918305subject
Has Abstractpub_date
2015-01-01 00:00:00pages
918305eissn
1687-5265issn
1687-5273journal_volume
2015pub_type
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