State-of-Charge Estimation of the Lithium-Ion Battery Using Neural Network Based on an Improved Thevenin Circuit Model

被引：0
|
作者：
Zhang, Haoliang [1 ]
Na, Woonki [1 ]
Kim, Jonghoon [2 ]
机构：
[1] Calif State Univ Fresno, Dept Elect & Comp Engn, Fresno, CA 93740 USA
[2] Chungnam Natl Univ, Dept Elect Engn, Daejon, South Korea
来源：
2018 IEEE TRANSPORTATION AND ELECTRIFICATION CONFERENCE AND EXPO (ITEC) | 2018年
关键词：
NEURAL NETWORK; SOC; LITHIUM-ION BATTERY; KALMAN FILTER; VOLTAGE;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper focuses on real-time estimation of State of Charge (SOC) in Lithium-Ion battery. Because of the highly complex electrochemical reaction inside the battery the conventional first order battery model is not accurate and cannot respond to the battery's conditions correctly because of the simplicity of the model. So, the neural network (NN) is selected to estimate the SOC dynamically due to its strong nonlinear fitting ability. The NN strategy also was used to implement the parameter identification for the battery model.
引用
收藏
页码：342 / 346
页数：5
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