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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 1051SOC Estimation for Li-Ion Battery Using SVM Based...

SOC Estimation for Li-Ion Battery Using SVM Based on Particle Swarm Optimization

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Abstract:

State of charge (SOC) is very important parameter for monitoring the battery charge and discharge operation and estimating the drive distance of electric vehicle. Especially, with the cycle number increasing, the precision estimation of SOC for battery management system is still not well resolved. Therefore, in this study, aim at accurate sampling of voltage, current and temperature signals based on LTC6803-3 chip, the paper proposed a support vector machine (SVM) optimized by particle swarm optimization (PSO) to improve SOC estimation accuracy. The results demonstrate that the proposed PSO-SVM model has good forecasting performance.

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Periodical:

Advanced Materials Research (Volume 1051)

Pages:

1004-1008

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1051.1004

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Online since:

October 2014

Authors:

Gui Fang Guo*, Lin Shui, Xiao Lan Wu, Bing Gang Cao

Keywords:

Particle Swarm Optimization (PSO), SOC of Battery, Support Vector Machine Estimation

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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