电动汽车动力电池关键状态参数估计及管理
Estimation and Management of Key State Parameters of Electric Vehicle Power Battery
罗钿 1赵玛龙 1张文强 1张建伟 1安国伟1
作者信息
- 1. 兰州工业学院 汽车工程学院,甘肃 兰州 730050
- 折叠
摘要
通过分析电动汽车电池管理系统相关技术,提出了二阶等效电路模型并考虑了均衡电路的影响,建立了基于扩展卡尔曼滤波器(Extended Kalman Filter,EKF)的荷电状态估计算法规则,并将实验数据与真实数据进行了对比,验证了该模型SOC算法满足设计要求,同时对该设计现存的不足提出改进建议,并对今后的发展进行了预期.
Abstract
By analyzing the relevant technologies of electric vehicle battery management system,a second-order equivalent circuit model is proposed,and the influence of balanced circuits is taken into account to establish a state of charge estimation algorithm rule based on Extended Kalman Filter(EKF).Experimental data is com-pared with real data to verify that the SOC algorithm of the model meets the design requirements,and improve-ment suggestions are proposed for the existing shortcomings of the design,and expectations are made for future development.
关键词
电动汽车/电池管理系统/电池荷电状态(SOC)估算/卡尔曼滤波法Key words
electric vehicles/battery management system/estimation of battery state of charge(SOC)/Kalman filtering method引用本文复制引用
基金项目
甘肃省科技计划资助项目(23JRRA920)
2023年国家级大学生创新创业训练计划项目(DC202301-53)
出版年
2024
NETL
NSTL
万方数据