SOC estimation algorithm of power lithium battery based on AFSA-BP neural network

被引：26

作者：

Wang, Qiuxia ^{[1
]}

Wu, Peizhou ^{[2
]}

Lian, Jialing ^{[1
]}

机构：

[1] Fujian Chuanzheng Commun Coll, Mech Engn Dept, Fuzhou, Fujian, Peoples R China

[2] South East Fujian Motor Corp Ltd, Purchasing Dept, Fuzhou, Fujian, Peoples R China

来源：

JOURNAL OF ENGINEERING-JOE | 2020年 / 2020卷 / 13期

关键词：

neural nets; secondary cells; backpropagation; power engineering computing; SOC estimation algorithm; power lithium battery; AFSA-BP neural network; accurate battery models; artificial fish swarm algorithm-back propagation neural network structure; optimising BP neural network; AFSA algorithm; AFSA-BP algorithm; iron phosphate power battery; voltage; 48; 0; V;

D O I：

10.1049/joe.2019.1214

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The non-linear characteristic of power lithium battery restricts the establishment of accurate battery models. To overcome this problem and estimate the battery state of charge (SOC) more accurately, the artificial fish swarm algorithm-back propagation (AFSA-BP) neural network structure was designed based on AFSA and BP neural network theory. According to the test parameters of power lithium battery, the related mathematical model was established. The flow charts of optimising BP neural network with AFSA algorithm and estimating SOC value by AFSA-BP algorithm are given. The specific implementation steps are elaborated. Using the 48 V, 50 Ah lithium iron phosphate (LiFePO4) power battery as experimental object, through the periodic charging and discharging experiments and software simulation, the correctness, validity and accuracy of the application of AFSA-BP neural network in estimating SOC value of the power lithium battery are verified.

引用

页码：535 / 539

页数：5

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