储能科学与技术 ›› 2021, Vol. 10 ›› Issue (3): 1127-1136.doi: 10.19799/j.cnki.2095-4239.2021.0013
付诗意1,2(), 吕桃林1,2, 闵凡奇2,3,4, 罗伟林1,2,3, 罗承东1,2, 吴磊1,2, 解晶莹1,2()
收稿日期:
2021-01-11
修回日期:
2021-01-23
出版日期:
2021-05-05
发布日期:
2021-04-30
通讯作者:
解晶莹
E-mail:syFu1996@126.com;jyxie@hit.edu.cn
作者简介:
付诗意(1996—),男,硕士研究生,研究方向为锂离子电池状态预测诊断及管理、电池大数据分析等,E-mail:基金资助:
Shiyi FU1,2(), Taolin LYU1,2, Fanqi MIN2,3,4, Weilin LUO1,2,3, Chengdong LUO1,2, Lei WU1,2, Jingying XIE1,2()
Received:
2021-01-11
Revised:
2021-01-23
Online:
2021-05-05
Published:
2021-04-30
Contact:
Jingying XIE
E-mail:syFu1996@126.com;jyxie@hit.edu.cn
摘要:
综述了锂离子电池荷电状态(state of charge,SOC)估算方法的研究进展。作为电动汽车电池管理中的重要指标,SOC表征了电池在当前循环中剩余的电量。准确的SOC估算可有效地避免电池工作于过低电量等不良工况,保证电池始终运行在安全的状态中,从而有效提高电池使用的效率和延长使用寿命。介绍并比较了几种常用的SOC估算方法:安时积分法最为简单,但由于其是开环估算系统,无法对估计误差进行修正;开路电压法可以根据开路电压与SOC之间的对应关系实现查表式估算,然而由于需要长时间静置来获取稳定的电压值,不适用于在线估算;卡尔曼滤波族方法是前两种方法的结合,可依靠系统观测值的误差对状态估计值进行及时修正,搭配适合的电池模型可获得较高的估算精度且适用于在线估算;数据驱动的方法则需要长期性的历史数据进行数据库的建立。本文总结了每种SOC估算方法的优缺点以及改进的方案。基于以上分析,结合SOC估算算法在工程实际中应用的局限与面对的挑战,对锂离子电池SOC在线估算的发展做出了展望。
中图分类号:
付诗意, 吕桃林, 闵凡奇, 罗伟林, 罗承东, 吴磊, 解晶莹. 电动汽车用锂离子电池SOC估算方法综述[J]. 储能科学与技术, 2021, 10(3): 1127-1136.
Shiyi FU, Taolin LYU, Fanqi MIN, Weilin LUO, Chengdong LUO, Lei WU, Jingying XIE. Review of estimation methods on SOC of lithium-ion batteries in electric vehicles[J]. Energy Storage Science and Technology, 2021, 10(3): 1127-1136.
表1
EKF、UKF和CKF的递推过程"
对非线性系统 |
---|
EKF: 计算雅克比矩阵: 计算状态变量的一部预测及协方差矩阵: 计算卡尔曼增益: 计算观测值: 状态和协方差矩阵更新: UKF: 构造sigma点集与权值,对于n维状态向量,一共有2n+1个sigma点 |
计算sigma数据点集的一步预测值: 计算状态变量的一步预测及协方差矩阵: 计算新sigma点集: 利用新sigma点集代入观测方程: 计算观测估计值的均值: 计算自相关矩阵: 计算互相关矩阵: 计算卡尔曼增益: 状态和协方差矩阵更新: CKF: 构造容积点集,对于n维状态向量,一共有m=2n个容积点: 其中 传播容积点: 计算状态一步预测值: 计算状态预测方差矩阵: 计算方差矩阵平方根 构造新容积点集: 传播新容积点: 计算观测估计值: 计算自相关矩阵: 计算互相关矩阵: 计算卡尔曼增益: 状态和协方差矩阵更新: |
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