The SOC Estimation and Simulation of Power Battery Based on Self-recurrent Wavelet Neural Network

被引:0
|
作者
Gao, Ai-yun [1 ,2 ]
Zhang, Feng-li [1 ,2 ]
Fu, Zhu-mu [3 ]
Zhang, Zhi-chao [1 ,2 ]
Li, Hao-di [1 ,2 ]
机构
[1] Henan Univ Sci & Technol, Sch Vehicle & Transportat Engn, Luoyang 471023, Peoples R China
[2] Henan Univ Sci & Technol, Sch Informat Engn, Luoyang 471023, Peoples R China
[3] Henan Key Lab Robot & Intelligent Syst, Luoyang 471023, Peoples R China
来源
2017 CHINESE AUTOMATION CONGRESS (CAC) | 2017年
基金
中国国家自然科学基金;
关键词
self-recurrent wavelet neural network; state of charge (SOC); estimation error; STATE;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Improving the accuracy of power battery state of charge (SOC) estimation is an important basis for formulating and optimizing the control strategy of hybrid electric vehicle. Aiming at the problem of the traditional SOC large estimation error, a SOC estimation method based on self -recurrent wavelet neural network (SRWNN) is proposed. Firstly, the equivalent model of power battery and its SOC estimation model are established. Then, The SOC estimation algorithm based on SRWNN is designed in detail. At last, The SOC estimation method based on SRWNN is compared with other SOC estimation methods front SOC estimation error and vehicle performances in the Matlab simulation environment. The simulation results show that its SOC estimation accuracy is highest and vehicle performances by using SRWNN is best.
引用
收藏
页码:4247 / 4252
页数:6
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