Hierarchical Estimation Model of State-of-Charge and State-of-Health for Power Batteries Considering Current Rate

被引：58
作者：
Xu, Peihang [1 ]
Hu, Xiaoyi [2 ]
Liu, Benlong [2 ]
Ouyang, Tiancheng [3 ]
Chen, Nan [4 ]
机构：
[1] Guangxi Univ, Sch Mech Engn, Dept Vehicle Engn, Nanning 530004, Peoples R China
[2] Guangxi Univ, Sch Mech Engn, Nanning 530004, Peoples R China
[3] Guangxi Univ, Sch Mech Engn, Dept Energy & Power Engn, Nanning 530004, Peoples R China
[4] Southeast Univ, Sch Mech Engn, Dept Vehicle Engn, Nanjing 211189, Peoples R China
来源：
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | 2022年 / 18卷 / 09期
基金：
中国国家自然科学基金;
关键词：
State of charge; Informatics; Mathematical models; Voltage; Mechanical engineering; Batteries; Temperature; Battery management system (BMS); electric vehicle (EV); extended Kalman filter; state-of-charge (SOC); state-of-health (SOH); ALGORITHM; CAPACITY; PACKS;
D O I：
10.1109/TII.2021.3131725
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
For power batteries used in the electric vehicle, accurate state-of-charge (SOC) and state-of-health (SOH) are important. Meanwhile, the current rate in actual working conditions often varies dramatically. However, the influence of current rates on voltage and battery states estimation has been neglected for a long time. To solve this problem, in this article, a hierarchical estimation model considering the current rate is proposed. The fractional-order model is used in the battery modeling, the data-driven method is used to identify parameters, and a multiscale dual extended Kalman filter (DEKF) is used in estimation of battery states. For SOC estimation, the proposed method improves the accuracy by 35.8% and 36.5% compared with traditional DEKF under two conditions. For SOH estimation, the proposed method improves the accuracy by 34.8% and 43.1% under two current conditions.
引用
收藏
页码：6150 / 6159
页数：10
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