摘要
电池健康状态(SOH)精准的预估有利于实时监测单体电池的健康信息,为自身的故障诊断提供可靠保障,提高电池组的整体寿命和动力性能。选用遗传与蚁群的混合算法(GAAA)对Elman神经网络进行改进,并利用Matlab仿真平台进行实验,在Advisor2002汽车仿真软件上搭建整车模型,获取样本数据,实验大大提高了预测的精度与速度。
Accurate forecast of battery state of health （SOH） contributes to real-time monitoring of mono- mer battery health information, and provides reliable guarantee for its fault diagnosis, so the overall life of the battery pack and dynamic performance will be improved. The Elman neural network was improved by using the mixed algorithm of genetic and ant-colony algorithm, and by using the Matlab simulation plat- form, through the experiment on Advisor 2002 vehicle simulation software, the vehicle model was built, the sample data were obtained, the precision and speed of prediction were greatly improved by the experi- ment.
作者
刘婉晴
LIU Wan-qing(College of Electrical Engineering, North China University of Science and Technology, Tangshan Hebei 063009, Chin)
出处
《华北理工大学学报（自然科学版）》
CAS
2017年第1期91-95,共5页
Journal of North China University of Science and Technology：Natural Science Edition
基金
河北联合大学横向科研项目(61203343)
关键词
ELMAN神经网络
遗传蚁群算法
Advisor2002软件
battery SOH
Elman neural network
genetic and ant-colony algorithm
Advisor 2002 vehicle simulation software
