Fractional Model and State-of-Charge of Lithium Battery

doi:10.7652/xjtuxb201707018

您当前的位置：

首页 >

文章列表页 >

Fractional Model and State-of-Charge of Lithium Battery

更新时间：2025-07-09

- Fractional Model and State-of-Charge of Lithium Battery
- Vol. 51, Issue 7, Pages: 124-129(2017)
- 作者机构：
  
  西安交通大学机械工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201707018
  CLC： TM912.8
- Online First：10 July 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
Fractional Model and State-of-Charge of Lithium Battery[J]. 2017, 51(7): 124-129.
DOI：

Fractional Model and State-of-Charge of Lithium Battery[J]. 2017, 51(7): 124-129. DOI： 10.7652/xjtuxb201707018.

摘要

针对锂电池荷电状态估计不准确的问题

在对不同荷电状态的锂电池电化学阻抗谱进行了分析的基础上

利用分数阶建模思想建立了分数阶阻抗模型

并设计出一种分数阶卡尔曼滤波器

同时利用混合动力脉冲能力实验对建立的分数阶模型进行了参数辨识

从而实现了锂电池荷电状态的估算。实验及仿真结果表明:所设计的分数阶阻抗模型与分数阶卡尔曼滤波器能准确地描述锂电池的特性

使得荷电状态估算精度得以提高; 在城市道路循环工况下

锂电池的电压追踪误差可以稳定在0.05 V之内

在初始荷电状态未知的条件下

电池的荷电状态估计误差可以稳定在±1%。

Abstract

Focusing on the inaccuracy of state-of-charge estimation of lithium battery

the electrochemical impedance spectroscopy of different state-of-charge stages of lithium battery is analyzed

and a fractional impedance model and a fractional Kalman filter are then proposed following the fractional modeling idea. The parameters of the model are identified in the hybrid pulse power characteristic(HPPC)test

and the state-of-charge estimation of lithium battery is accomplished by fractional Kalman filter. The result shows that the working voltage error is below 0.05 V and the estimation of state-of-charge error is below ±1%

thus the model is accurate enough to describe the characteristic of lithium battery and the fractional Kalman filter improves the accuracy of state-of-charge estimation of lithium battery.

关键词

Keywords

references

王丹, 续丹, 曹秉刚. 电动汽车关键技术发展综述 [J]. 中国工程科学, 2013, 15(1): 68-72.

WANG Dan, XU Dan, CAO Binggang. Overview on key technique of electric vehicle [J]. Engineering Sc-iences, 2013, 15(1): 68-72.

WAAG W, FLEISCHER C, SAUER D U. Adaptive online prediction of the available power of lithium-ion batteries [J]. Journal of Power Sources, 2013, 242: 548-559.

LU Languang, HAN Xuebing. A review on the key issues for lithium-ion battery management in electric vehicles [J]. Journal of Power Sources, 2013, 226: 272-288.

HU Y, YURKOVICH S, GUEZENNEC Y, et al. Electrothermal battery model identification for automotive applications [J]. Journal of Power Sources, 2011, 196(1): 449-457.

欧阳佳佳, 毛良平, 张军明. 基于SOC-OCV曲线的卡尔曼滤波法SOC估计 [J]. 电力电子技术, 2016, 50(3): 98-100.

OUYANG Jiajia, MAO Liangping, ZHANG Junming. SOC estimation with Kalman filtering based on SOC-OCV curve [J]. Power Electronics, 2016, 50(3): 98-100.

HE Hongwen, XIONG Rui, GUO Hongqiang. Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles [J]. Applied Energy, 2012, 89(1): 413-420.

CHIANG Y, SEAN W, KE Jiacheng. Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electric vehicles [J]. Journal of Power Sources, 2011, 196(8): 3921-3932.

TSANG K M, SUN L, CHAN W L. Identification and modelling of lithium ion battery [J]. Energy Conversion and Management, 2010, 51(1): 2857-2862.

FLORES-TLACUAHUAC A. Optimization of fractional order dynamic chemical processing systems [J]. Industrial Engineering Chemistry Research, 2014, 53(13): 5110-5127.

RODRIGUES S, MUNICHANDRAIAH N, SHUKLA A K. A review of state-of-charge indication of batteries by means of A. C. impedance measurements [J]. Journal of Power Sources, 2000, 87(1): 12-20.

袁翔, 张毅. 动力锂电池阻抗特性的分析与验证 [J]. 汽车工程学报, 2014, 4(6): 447-454.

YUAN Xiang, ZHANG Yi. Analysis and verification of the power lithium battery impedance characteristics [J]. Chinese Journal of Automation Engineering, 2014, 4(6): 447-457.

VERBRUGGE M, TATE E. Adaptive state of charge algorithm for nickel metal hydride batteries including hysteresis phenomena [J]. Journal of Power Sources, 2004, 126(1): 236-249.

张文华. 磷酸铁锂电池循环过程中电化学交流阻抗研究 [J]. 电源技术, 2015, 39(1): 54-57.

ZHANG Wenhua. Electrochemical impedance analysis of LiFePO4/C batteries in cycling progress [J]. Chinese Journal of Power Sources, 2015, 39(1): 54-57.

杨晴霞. 一种锂电池充电状态的分数阶阻抗模型 [J]. 西安交通大学学报, 2015, 49(8): 128-132.

YANG Qingxia. A fractional impedance model for charge state estimation of lithium battery [J]. Journal of Xi'an Jiaotong University, 2015, 49(8): 128-132.[15] 庄全超, 徐守冬. 锂离子电池的电化学阻抗谱分析 [J]. 化学进展, 2010, 22(6): 1044-1057.

ZHUANG Quanchao, XU Shoudong. Diagnosis of electrochemical impedance spectroscopy in lithium ion batteries [J]. Progress in Chemistry, 2010, 22(6): 1044-1057.

THOMAS M G S R, BRUCE P G, GOODENOUGH J B. AC impedance analysis of polycrystalline insertion electrodes: application to Li1-x CoO2 [J]. Journal of the Electrochemical, 1985, 132: 1521-1528.

PEI L, WANG T, LU R, et al. Development of a volt-age relaxation model for rapid open circuit voltage prediction in lithium-ion batteries [J]. Journal of Power Sources, 2014, 253: 412-418.

LEITHD J, LEITHEAD W E. Survey of gain-scheduling analysis and design [J]. International Journal of Control, 2000, 73(11): 1001-1025.

ROSCA B, KESSELS J, BERGVELD H J, et al. Online parameter, state-of-charge and aging estimation of Li-ion batteries [C]∥2012 IEEE Vehicle Power and Propulsion Conference. Piscataway, NJ, USA: IEEE, 2012: 1122-1127.

XU Jun, MIC C, CAO Binggang, et al. A new method to estimate the state of charge of lithium-ion batteries based on the battery impedance model [J]. Journal of Power Sources, 2013, 233: 277-284.

严珍,王斌,徐俊,等.双参数组合优化的复合电源模式切换控制策略.2016,50(11):129-135.[doi:10.7652/xjtuxb2016 11020]

王斌,徐俊,曹秉刚,等.升压型电池-超级电容复合电源的自适应滑模控制.2016,50(10):36-41.[doi:10.7652/xjtuxb 201610006]

许广灿,徐俊,李士盈,等.电动汽车振动能量回收悬架及其特性优化.2016,50(8):90-95.[doi:10.7652/xjtuxb201608 015]

杨晴霞,曹秉刚,徐俊,等.一种估计锂电池充电状态的分数阶阻抗模型.2015,49(8):128-132.[doi:10.7652/xjtuxb 201508021]

王斌,徐俊,曹秉刚,等.电动汽车的多模式复合电源能量管理自适应优化.2015,49(12):130-136.[doi:10.7652/xjtuxb 201512021]

王斌,徐俊,曹秉刚,等.采用模拟退火算法的电动汽车复合电源能量管理系统优化.2015,49(8):90-96.[doi:10.7652/xjtuxb201508015]

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A Fractional Impedance Model for Charge State Estimation of Lithium Battery

A Cloud-Edge Collaborative State of Charge Estimation Method for Lithium-Ion Batteries in Mining Environments Integrating Deep Learning and Ampere-Hour Integration

An Estimation Method of Relative State-of-Health for Lithium-Ion Batteries Using Morlet Wavelet

Numerical Simulation of the Energy Conversion Process in Thermoacoustic Engines with Different Stack Materials

Integrated and Joint Design Optimization of Centrifugal Compressor Impeller and Casing Treatment at Multiple Working Points

Related Author

杨晴霞 1

曹秉刚 1

徐俊 1

李秀青 2

宁博 1

王斌 1

ZHANG Xiangfeng

CAO Fengjin

Related Institution

School of Intelligent Manufacturing and Modern Industry, Xinjiang University

State Key Laboratory of Safety Technology of Metal Mines, Changsha Institute of Mining Research Co. Ltd.

School of Mechanical and Automotive Engineering, Xiamen University of Technology

Shaanxi Provincial Key Laboratory of Intelligent Robots, Xi'an Jiaotong University

State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University

⁰