A Novel Online SOC Estimation Method for the Power Lithium Battery Pack Based on the Unscented Kalman Filter

被引：0
|
作者：
Wang, Shun-Li [1 ]
Shang, Li-Ping [1 ]
Li, Zhan-Feng [1 ]
Xie, Wei [2 ]
Yuan, Hui-Fang [1 ]
机构：
[1] Southwest Univ Sci & Technol, Mianyang 621010, Peoples R China
[2] Sichuan Huatai Elect Co Ltd, Suining 629000, Peoples R China
来源：
INTERNATIONAL CONFERENCE ON ENERGY DEVELOPMENT AND ENVIRONMENTAL PROTECTION (EDEP 2017) | 2017年 / 168卷
关键词：
lithium battery pack; SOC estimation; unscented Kalman filter; equivalent circuit model; state of balance; CHARGE ESTIMATION; STATE; TRACKING; ALGORITHM;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
The SOC (State Of Charge) estimation is a core aspect for the associated BMS (Battery Management System) equipment of the lithium battery pack, in which the KF (Kalman Filter)-based methods have been extensively used but suffers from the linearization accuracy drawbacks. A novel UKF (Unscented Kalman Filter) estimation method battery model is proposed for the SOC estimation of the lithium battery pack, in which the linearization treatment is not required and fewer Sigma data points are used, reducing the computational requirement of the SOC estimation. The UKF method improves the SOC covariance properties for the lithium battery pack, the estimation performance of which has been validated by the experimental results. The proposed SOC estimation method has a RMSE (Root Mean Square Error) value of 1.42%, playing an important role in the popularization and application of the lithium battery pack.
引用
收藏
页码：98 / 105
页数：8
相关论文
共 50 条
  • [1] Lithium Battery SOC Estimation Based on Improved Unscented Kalman Filter
    Hu, Jieyu
    Wu, Songrong
    Wang, YiYang
    Lu, Fan
    Liu, Dong
    [J]. PROCEEDINGS OF THE 15TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2020), 2020, : 511 - 515
  • [2] Estimation of Lithium Battery SOC Based on Fuzzy Unscented Kalman Filter Algorithm
    Zhang, Xiaozhou
    Zhang, Ruiping
    [J]. 2021 IEEE IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA (IEEE I&CPS ASIA 2021), 2021, : 200 - 204
  • [3] The lithium battery SOC estimation on square root unscented Kalman filter
    Liu, Qinghe
    Yu, Quanqing
    [J]. ENERGY REPORTS, 2022, 8 : 286 - 294
  • [4] Lithium battery SOC estimation based on whale optimization algorithm and unscented Kalman filter
    Wu, Zhongqiang
    Wang, Guoyong
    Xie, Zongkui
    He, Yilin
    Lu, Xueqin
    [J]. JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY, 2020, 12 (06)
  • [5] Estimation algorithm research for lithium battery SOC in electric vehicles based on adaptive unscented Kalman filter
    Bo Li
    Shaoyi Bei
    [J]. Neural Computing and Applications, 2019, 31 : 8171 - 8183
  • [6] Estimation algorithm research for lithium battery SOC in electric vehicles based on adaptive unscented Kalman filter
    Li, Bo
    Bei, Shaoyi
    [J]. NEURAL COMPUTING & APPLICATIONS, 2019, 31 (12): : 8171 - 8183
  • [7] Adaptive Strong Tracking Unscented Kalman Filter Based SOC Estimation for Lithium-ion Battery
    Liu, Miao
    Cui, Naxin
    Liu, Shulin
    Wang, Chunyu
    Zhang, Chenghui
    Gong, Sizhao
    [J]. 2017 CHINESE AUTOMATION CONGRESS (CAC), 2017, : 1437 - 1441
  • [8] SOC Estimation of Li-Ion Battery Based on Unscented Kalman Filter
    Li, Zheng
    Chen, Qiushuo
    Yue, Feihong
    Zhang, Yan
    [J]. 2018 21ST INTERNATIONAL CONFERENCE ON ELECTRICAL MACHINES AND SYSTEMS (ICEMS), 2018, : 2177 - 2182
  • [9] Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter
    Wang, Hao
    Zheng, Yanping
    Yu, Yang
    [J]. MATHEMATICS, 2021, 9 (15)
  • [10] Unscented Kalman Filter-Based Battery SOC Estimation and Peak Power Prediction Method for Power Distribution of Hybrid Electric Vehicles
    Wang, Weida
    Wang, Xiantao
    Xiang, Changle
    Wei, Chao
    Zhao, Yulong
    [J]. IEEE ACCESS, 2018, 6 : 35957 - 35965
12345