基于自适应神经网络的电池寿命退化的预测  

Study on degenerate of lithium-ion battery based on fuzzy inference system

作　　者：史建平[1] 李蓓[1] 刘明芳[1] SHI Jian-ping;LI Bei;LIU Ming-fang(School of Electrical and Photoelectronic Engineering,Changzhou Institute of Technology,Changzhou Jiangsu 213032,China)

机构地区：[1]常州工学院电气与光电工程学院,江苏常州213032

出　　处：《电源技术》2018年第10期1488-1490,共3页Chinese Journal of Power Sources

摘　　要：为了提高对动力锂离子电池劣化程度的预测准确度,通过分析影响锂离子电池退化的内外部因素,确定退化的表征参数,建立了基于自适应神经网络的锂离子电池退化程度预测模型,用退化系数表征锂离子电池的退化程度。使用不同放电深度下的多组数据对模型进行训练、校验和仿真,验证了所建模型在锂离子电池退化程度预测方面的可靠性和适用性。In order to improve the prediction accuracy of degradation degree of power lithium ion battery,the degradation degree prediction model of lithium ion battery based on adaptive neural network was established by analyzing the internal and external factors influencing degradation of lithium ion battery and the characterization parameters of degradation were determined.The reliability and applicability of the model in predicting the degradation degree of lithium ion battery were verified by training,verifying and simulating the model with multiple sets of data at different discharge depths.

关 键 词：锂离子电池 退化 自适应神经网络 

分 类 号：TM912[电气工程—电力电子与电力传动]