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

Authors

Chaolong Zhang, 1. School of Physics and Electronic Engineering, Anqing Normal University, Anqing 246011, China; ;2. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China;
Yigang He, 2. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China;
Lifen Yuan, 2. School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China;

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.

Recommended Citation

Zhang, Chaolong; He, Yigang; and Yuan, Lifen (2019) "Approach for Lithium-ion Battery RUL Prognostics Based on CPSO-RVM," Journal of System Simulation: Vol. 30: Iss. 5, Article 40.
DOI: 10.16182/j.issn1004731x.joss.201805040
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss5/40

First Page

1935

Revised Date

2017-07-25

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201805040

Last Page

1940

CLC

TM933.4

Recommended Citation

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.

DOI

10.16182/j.issn1004731x.joss.201805040