Electricity Quantity Prediction Model of Power Battery based on PSO-BP Neural Network

被引：0
|
作者：
He, Zhao [1 ]
Wen, Junfeng [2 ]
Lin, Qionglian [1 ]
机构：
[1] Guangzhou Inst Energy Testing, Guangdong Key Lab Battery Safety, Guangzhou 511447, Guangdong, Peoples R China
[2] ZKSkynet Guangdong Technol Co LTD, Guangzhou 510075, Peoples R China
来源：
2022 41ST CHINESE CONTROL CONFERENCE (CCC) | 2022年
关键词：
Power battery; Electricity quantity; Particle swarm optimization; BP algorithm; Predictive model; 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：
Power battery is the core equipment of electric vehicles. The role of power battery is to achieve power supply. Real-time monitoring of battery voltage level is the key to ensure stable operation of the battery. Predicting the power battery electricity quantity during the power charge in advance can improve the operating efficiency of the system. The paper establishes a power battery electricity quantity prediction method based on particle swarm optimization (PSO) BP algorithm. First, important parameter data are collected and the influence of the associated data is analyzed on the power battery electricity quantity. Then, the particle swarm optimization BP algorithm is proposed by using the training set data to predict and model the power battery electricity quantity. Finally, the test set data is used to conduct a comprehensive simulation on the established power battery electricity quantity prediction model. The results show that the model prediction data can fit well with the measured data, indicating the effectiveness of the power battery electricity quantity prediction model in this paper.
引用
收藏
页码：1428 / 1433
页数：6
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