NAVIGATE

Table of Contents

STATISTICS

Viewed1030

Downloads968

State of Charge Estimation of LiFePO4 Battery Based on Modified Amper-hour Integral Method
[HTML]   PDF下载 (968)
[1]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]
Copy
Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44 Number of periods: 2023 06 Page number: 84-90 Column: Public date: 2023-12-25
Title:
State of Charge Estimation of LiFePO4 Battery Based on Modified Amper-hour Integral Method
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
Keywords:
mobile energy storage system;  LiFePO4 battery;  amper-hour integral method;  adaptive unscented Kalman filter
CLC:
TM912
DOI:
10.13705/j.issn.1671-6833.2023.06.003
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.
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.
Similar References:
Memo

-

Last Update: 2023-10-22
Copyright © 2023 Editorial Board of Journal of Zhengzhou University (Engineering Science)