储能科学与技术 ›› 2024, Vol. 13 ›› Issue (4): 1142-1153.doi: 10.19799/j.cnki.2095-4239.2023.0889
• 电池智能制造、在线监测与原位分析专刊 • 上一篇 下一篇
常小兵1,2(), 侯宗尚1,2, 刘连起1,2, 王光1,2, 谢家乐1,2()
收稿日期:
2023-12-07
修回日期:
2023-12-25
出版日期:
2024-04-26
发布日期:
2024-04-22
通讯作者:
谢家乐
E-mail:220222216031@ncepu.edu.cn;tellerxie@ncepu.edu.cn
作者简介:
常小兵(1998—),男,硕士,研究方向为电池热管理,E-mail:220222216031@ncepu.edu.cn;
基金资助:
Xiaobing CHANG1,2(), Zongshang HOU1,2, Lianqi LIU1,2, Guang WANG1,2, Jiale XIE1,2()
Received:
2023-12-07
Revised:
2023-12-25
Online:
2024-04-26
Published:
2024-04-22
Contact:
Jiale XIE
E-mail:220222216031@ncepu.edu.cn;tellerxie@ncepu.edu.cn
摘要:
准确估计电池的荷电状态(SOC)和内部温度可以提高电池的性能和安全性。其中,电池模型的准确性和估计算法的适用性是关键。为了解决这两个问题,本文建立了圆柱形锂离子电池的多参数电热耦合模型。模型考虑电池SOC与温度变化之间的耦合关系,并且利用改进的熵热系数实验获得电池运行中产生的可逆热与不可逆热,通过可变遗忘因子最小二乘算法(VFFRLS)进行参数辨识,并对比独立的电模型与热模型的SOC与内部温度估计结果,验证了多参数电热耦合模型的准确性,结果证明所提模型相比较于单独的电热模型,估计精度提高了70%以上。最后,设计了一种基于奇异值分解的卡尔曼滤波(SVD-AUKF)算法来同时在线估计SOC和内部温度,并在改进的动态测试(DST)工况下对所提方法进行实验验证。结果表明:所提方法相较于扩展卡尔曼滤波(EKF)与无迹卡尔曼滤波(UKF)算法,能实现更高精度的SOC和温度估计,SOC与内部温度的平均误差分别是5%和0.2 ℃。
中图分类号:
常小兵, 侯宗尚, 刘连起, 王光, 谢家乐. 基于电热耦合效应的锂电池荷电状态与温度状态联合估计[J]. 储能科学与技术, 2024, 13(4): 1142-1153.
Xiaobing CHANG, Zongshang HOU, Lianqi LIU, Guang WANG, Jiale XIE. Joint estimation of the state of charge and temperature of lithium batteries based on the electric thermal coupling effect[J]. Energy Storage Science and Technology, 2024, 13(4): 1142-1153.
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