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电动汽车动力电池关键状态参数估计及管理

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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.
Estimation and Management of Key State Parameters of Electric Vehicle Power Battery
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.

electric vehiclesbattery management systemestimation of battery state of charge(SOC)Kalman filtering method

罗钿、赵玛龙、张文强、张建伟、安国伟

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兰州工业学院 汽车工程学院,甘肃 兰州 730050

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

甘肃省科技计划资助项目2023年国家级大学生创新创业训练计划项目

23JRRA920DC202301-53

2024

兰州工业学院学报
兰州工业学院

兰州工业学院学报

影响因子:0.205
ISSN:1009-2269
年,卷(期):2024.31(4)
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