Abstract
This paper studies the accurate prediction problem of VRLA battery state of charge (SOC) and the remaining capability of the battery, after the comprehensive analysis of the various elements which affect the battery state of charge, we put forward a battery degradation test model based on GA - WNN, and carries on the verification test, in the meantime, we also make it contrast with other algorithms. The results of test show that the model of WNN Optimized by GA has shorter training time and high prediction accuracy, it can predict the battery remaining power more accurately.
Publisher
Trans Tech Publications, Ltd.
Reference7 articles.
1. Guo W X, Shu D. Study on the structure and property of lead tellurium alloy as the positive grid of leadacid batteries[J]. Journal of Alloys and Compounds, 2009, 475(1-2): 102-109.
2. Guo．W．X，Shu．D．Relations between internal resistance and capacity for batteries, Telecom Power Technology[J]. Telecom Power Technology, 2011, 01: 32-34.
3. Chang Li, GuoYang Luo. Application of kalman prediction algorithm combined with SVM in monitoring states of VRLA battery[J]. Journal of electrotechnics, 2011, 11: 168-174.
4. Gould C R, Bingham C M, Stone D A. New battery model and state-of-health determination through subspace parameter estimation and state-observer techniques[J]. IEEE Transactions on Vehicular Technology, 2009, 58(8): 3905-3916.
5. ]Jing Liang, Factors influencing service life of valve-regulated lead-acid batteries[J], the electric power of Guangdong, 2005, 02: 19-21.
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