Approach for Lithium-ion Battery RUL Prognostics Based on CPSO-RVM

doi:10.16182/j.issn1004731x.joss.201805040

Abstract

Abstract: On account of the measured battery state of health (SOH) data are often subject to different levels of noise pollution, a battery remaining useful life (RUL) prognostics approach is presented based on wavelet denoising and CPSO-RVM in the paper. Wavelet denoising is performed twice with different thresholds in order to weaken the strong noise and remove the weak noise. Relevance vector machine (RVM) improved by chaos particle swarm optimization (CPSO) algorithm is utilized to estimate the trend of battery SOH variation trajectory and predict the battery RUL based on the denoised data. RUL prognostic experiments using battery data provided by NASA are conducted and the effectiveness of the presented approach is validated.

Key words: lithium-ion battery, RUL, SOH, wavelet denoising, RVM, CPSO

CLC Number:

TM933.4

Zhang Chaolong, He Yigang, Yuan Lifen. Approach for Lithium-ion Battery RUL Prognostics Based on CPSO-RVM[J]. Journal of System Simulation, 2018, 30(5): 1935-1940.

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