[1]宋 磊,陆春光,刘 琳,等.基于修正安时积分法的磷酸铁锂电池荷电状态估计[J].郑州大学学报(工学版),2023,44(06):84-90.[doi:10.13705/j.issn.1671-6833.2023.06.003]
　SONG Lei,LU Chunguang,LIU Lin,et al.State of Charge Estimation of LiFePO4 Battery Based on Modified Amper-hour Integral Method[J].Journal of Zhengzhou University (Engineering Science),2023,44(06):84-90.[doi:10.13705/j.issn.1671-6833.2023.06.003]
点击复制

基于修正安时积分法的磷酸铁锂电池荷电状态估计()
分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
44
期数:
2023年06期
页码:
84-90
栏目:
出版日期:
2023-12-25

文章信息/Info

Title:
State of Charge Estimation of LiFePO4 Battery Based on Modified Amper-hour Integral Method
作者:
宋 磊1 ;  陆春光1 ;  刘 琳2 ;  刘世芳3; 4 ;  王要强3; 4
1. 国网浙江省电力有限公司,浙江 杭州 310014;2. 国网浙江杭州市萧山区供电有限公司,浙江 杭州 311200;3. 郑 州大学 电气与信息工程学院,河南 郑州 450001;4. 郑州大学 河南省电力电子与电力系统工程技术研究中心,河南 郑州 450001
Author(s):
SONG Lei1;  LU Chunguang1;  LIU Lin2;  LIU Shifang3; 4;  WANG Yaoqiang3; 4
1. State Grid Zhejiang Electric Power Co. , Ltd. , Hangzhou 310014, China; 2. State Grid Zhejiang Hangzhou Xiaoshan District Power Supply Co. , Ltd. , Hangzhou 311200, China; 3. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 4. Henan Engineering Research Center of Power Electronics and Energy Systems, Zhengzhou University, Zhengzhou 450001, China
关键词:
移动储能系统;  磷酸铁锂电池;  安时积分法;  自适应无迹卡尔曼滤波
Keywords:
mobile energy storage system;  LiFePO4 battery;  amper-hour integral method;  adaptive unscented Kalman filter
分类号:
TM912
DOI:
10.13705/j.issn.1671-6833.2023.06.003
文献标志码:
A
摘要:
鉴于磷酸铁锂电池特性曲线存在电压平台区,以及电压、电流存在测量误差,导致估计其荷电状态十分困 难。 为了提高磷酸铁锂电池在电压平台区荷电状态估计的准确性,提出了一种基于安时积分法的改进卡尔曼滤波 算法。 首先,分别采用安时积分法和 AUKF 算法估计磷酸铁锂电池 SOC。 其次,计算 2 种算法估计值的增量,利用 2 种估计算法的特性,通过对比增量关系以确定最优估计值,对 AUKF 算法的估计结果进行修正。 最后,所提方法 的有效性在磷酸铁锂电池多种运行工况下得到了验证。 实验结果表明:所提方法在存在电压偏差的情况下,能保 持 SOC 估计误差小于 0. 02,实现了 SOC 的准确估计。
Abstract:
It was difficult to estimate the state of charge of LiFePO4 battery due to a large voltage platform area and voltage and current measurement errors. In order to improve the accuracy of estimation of the state of charge of lithium iron phosphate batteries in voltage platform area, an improved Kalman filter algorithm based on amper-hour integral method was proposed. Firstly, amper-hour integration method and AUKF algorithm were used to estimate SOC of lithium iron phosphate batteries. Secondly, the increment of the estimated value of the two algorithms was calculated. The characteristics of the two estimation algorithms were used to determine the optimal estimated value by comparing the increment relationship, and the estimated result of the AUKF algorithm was corrected. Finally, the effectiveness of the proposed method had been verified in various operating conditions of lithium iron phosphate batteries. Experimental results showed that the proposed method could keep the SOC estimation error less than 0.02 under the condition of voltage deviation, and achieve accurate SOC estimation.

参考文献/References:

[1] 王少华. 电动汽车动力锂电池模型参数辨识和状态估 计方法研究[D] . 长春: 吉林大学,2016. 

WANG S H. Research on parameter identification and state estimation method of power lithium battery model for electric vehicle[D] . Changchun: Jilin University,2016. 
[2] SHRIVASTAVA P, SOON T K, BIN IDRIS M Y I, et al. Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries [ J] . Renewable and Sustainable Energy Reviews, 2019, 113: 109233. 
[3] LIN X F, KIM Y, MOHAN S, et al. Modeling and estimation for advanced battery management[ J] . Annual Review of Control, Robotics, and Autonomous Systems, 2019, 2: 393-426. 
[4] DONG G Z, WEI J W, ZHANG C B, et al. Online state of charge estimation and open circuit voltage hysteresis modeling of LiFePO4 battery using invariant imbedding method[ J] . Applied Energy, 2016, 162: 163-171.
 [5] HU X S, LI S B, PENG H E. A comparative study of equivalent circuit models for Li-ion batteries[ J] . Journal of Power Sources, 2012, 198: 359-367. 
[6] LI W H, FAN Y, RINGBECK F, et al. Electrochemical model-based state estimation for lithium-ion batteries with adaptive unscented Kalman filter [ J] . Journal of Power Sources, 2020, 476: 228534.
 [7] RINGBECK F, GARBADE M, SAUER D U. Uncertainty-aware state estimation for electrochemical model-based fast charging control of lithium-ion batteries[ J] . Journal of Power Sources, 2020, 470: 228221. 
[8] 刘湘东, 刘承志, 杨梓杰, 等. 基于无迹卡尔曼滤波 的全钒液流电池状态估计[ J] . 中国电机工程学报, 2018, 38(6) : 1769-1777, 16. 
LIU X D, LIU C Z, YANG Z J, et al. States estimation of vanadium redox flow battery based on unscented Kalman filter[ J] . Proceedings of the CSEE, 2018, 38( 6) : 1769-1777, 16. 
[9] 续远. 基于安时积分法与开路电压法估 测 电 池 SOC [ J] . 新型工业化, 2022, 12(1) :123-124, 127. 
XU Y. Estimation of battery SOC based on ampere-hour integration method and open circuit voltage method [ J] . The Journal of New Industrialization, 2022, 12(1) :123- 124, 127. 
[10] LIN C, TANG A H, XING J L. Evaluation of electrochemical models based battery state-of-charge estimation approaches for electric vehicles [ J ] . Applied Energy, 2017, 207: 394-404.
 [11] MOURA S J, CHATURVEDI N A, KRSTIC ’ M. Adaptive partial differential equation observer for battery state-ofcharge / state-of-health estimation via an electrochemical model[ J] . Journal of Dynamic Systems, Measurement, and Control, 2014, 136(1) : 011015.
 [12] STURM J, ENNIFAR H, ERHARD S V, et al. State estimation of lithium-ion cells using a physicochemical model based extended Kalman filter [ J ] . Applied Energy, 2018, 223: 103-123. 
[13] TANIM T R, RAHN C D, WANG C Y. State of charge estimation of a lithium ion cell based on a temperature dependent and electrolyte enhanced single particle model [ J] . Energy, 2015, 80: 731-739.
 [14] PLETT G L. Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs [ J]. Journal of Power Sources, 2006, 161(2): 1356-1368.
 [15] WANG W H, MU J Y. State of charge estimation for lithium-ion battery in electric vehicle based on Kalman filter considering model error [ J ] . IEEE Access, 2019, 7: 29223-29235. 
[16] TRAN N T, VILATHGAMUWA M, LI Y, et al. State of charge estimation of lithium ion batteries using an extended single particle model and sigma-point Kalman filter [C]∥2017 IEEE Southern Power Electronics Conference ( SPEC) . Piscataway: IEEE, 2018: 1-6.
 [17] SHU X, LI G, SHEN J W, et al. An adaptive fusion estimation algorithm for state of charge of lithium-ion batteries considering wide operating temperature and degradation[ J] . Journal of Power Sources, 2020, 462: 228132.
 [18] LI W H, CAO D C, JÖST D, et al. Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries[ J] . Applied Energy, 2020, 269: 115104.
 [19] RAMADESIGAN V, CHEN K J, BURNS N A, et al. Parameter estimation and capacity fade analysis of lithium-ion batteries using reformulated models [ J] . Journal of the Electrochemical Society, 2011, 158(9) : A1048.
 [20] STETZEL K D, ALDRICH L L, TRIMBOLI M S, et al. Electrochemical state and internal variables estimation using a reduced-order physics-based model of a lithium-ion cell and an extended Kalman filter[ J] . Journal of Power Sources, 2015, 278: 490-505.

更新日期/Last Update: 2023-10-22