State of Charge Estimation of Power Lithium Battery Based on Extended Kalman Filter

被引：0
|
作者：
Feng, Huizong [1 ]
Qin, Liangyan [1 ]
Xu, Yang [1 ]
机构：
[1] Chongqing Univ Posts & Telecommun, Sch Automat, Chongqing, Peoples R China
来源：
2019 CHINESE AUTOMATION CONGRESS (CAC2019) | 2019年
关键词：
SOC; Equivalent circuit model; EKF algorithm; MANAGEMENT-SYSTEMS; PACKS;
D O I：
10.1109/cac48633.2019.8996745
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The chemical reaction process of the vehicle power lithium battery is complicated, and the battery has typical time-varying and nonlinear characteristics. High precision SOC (State Of Charge) estimation algorithm takes a long time and is computationally intensive. In practical use, the ampere-hour integral method is applied more, but it is easy to cause time accumulation error. In this paper, considering the simplification and accuracy of the algorithm, the second-order RC equivalent circuit model is built according to the battery test data, and the SOC estimation of the battery is completed by EKF (Extended Kalman Filtering) algorithm. The simulation results show that the estimation accuracy can be kept within 3%.
引用
收藏
页码：518 / 523
页数：6
相关论文
共 50 条
  • [31] Improved State-of-Charge and Voltage estimation of a Lithium-ion battery based on Adaptive Extended Kalman Filter
    Velivela, Naga Prudhvi
    Guha, Arijit
    2023 IEEE TRANSPORTATION ELECTRIFICATION CONFERENCE & EXPO, ITEC, 2023,
  • [32] State of charge estimation of lithium-ion battery based on double deep Q network and extended Kalman filter
    You, Guodong
    Wang, Xue
    Fang, Chengxin
    Zhang, Shang
    Hou, Xiaoxin
    2020 INTERNATIONAL CONFERENCE ON GREEN DEVELOPMENT AND ENVIRONMENTAL SCIENCE AND TECHNOLOGY, 2020, 615
  • [33] Comparing Particle Filter and Extended Kalman Filter for Battery State-Of-Charge Estimation
    Restaino, Rocco
    Zamboni, Walter
    38TH ANNUAL CONFERENCE ON IEEE INDUSTRIAL ELECTRONICS SOCIETY (IECON 2012), 2012, : 4018 - 4023
  • [34] Estimation of State of Charge of Battery Based on Extended Kalman Filtering
    Chen Huangjie
    Ma Yan
    Zhao Haiyan
    2013 32ND CHINESE CONTROL CONFERENCE (CCC), 2013, : 7632 - 7635
  • [35] State estimation of lithium polymer battery based on Kalman filter
    Jiabo Li
    Min Ye
    Kangping Gao
    Shengjie Jiao
    Xinxin Xu
    Ionics, 2021, 27 : 3909 - 3918
  • [36] State estimation of lithium polymer battery based on Kalman filter
    Li, Jiabo
    Ye, Min
    Gao, Kangping
    Jiao, Shengjie
    Xu, Xinxin
    IONICS, 2021, 27 (09) : 3909 - 3918
  • [37] Estimation of Battery State of Charge for UAV Using Adaptive Extended Kalman Filter
    Uhm, Taewon
    Jo, Kyoungyong
    Kim, Seungkeun
    JOURNAL OF THE KOREAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES, 2023, 51 (04) : 243 - 251
  • [38] Robustness Evaluation of Extended and Unscented Kalman Filter for Battery State of Charge Estimation
    Huang, Chao
    Wang, Zhenhua
    Zhao, Zihan
    Wang, Long
    Lai, Chun Sing
    Wang, Dong
    IEEE ACCESS, 2018, 6 : 27617 - 27628
  • [39] Research on battery state of charge estimation based on variable window adaptive extended Kalman filter
    He, Zhigang
    Zhang, Xianggang
    Fu, Xurui
    Pan, Chaofeng
    Jin, Yingjie
    INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE, 2024, 19 (01):
  • [40] Battery State of Charge Estimation Based on a Combined Model of Extended Kalman Filter and Neural Networks
    Chen, Zhihang
    Qiu, Shiqi
    Masrur, M. Abul
    Murphey, Yi Lu
    2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2011, : 2156 - 2163
12345