基于分数阶联合卡尔曼滤波的磷酸铁锂电池简化阻抗谱模型参数在线估计

Abstract
Figure/Table
References
Related Citation (14)

Download: PDF (24039 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Battery modeling and online battery model parameter estimation are the key technologies of EV battery management system. Based on the battery simplified electrochemical impedance spectroscopy which contains a fractional component, this paper establishes the state transition and systematic observation equations for the nonlinear system of LiFePO₄ secondary battery. Then, the diffusion polarization voltage and model parameters are estimated online with the fractional joint Kalman filter (FJKF). The experimental results show that, this model can reflect the dynamic characteristics very well, and FJKF parameter estimation algorithm can maintain good accuracy. Meanwhile, the method is suitable for a variety of load conditions. The model parameters obtained by this algorithm have good stability.

Key words： LiFePO4 battery, fractional battery model, electrochemical impedance spectroscopy, fractional joint Kalman filter

Received: 17 October 2014 Published: 03 January 2017

PACS:

TM912

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Li Xiaoyu
	Zhu Chunbo
	Wei Guo
	Lu Rengui

Cite this article:

Li Xiaoyu,Zhu Chunbo,Wei Guo等. Online Parameter Estimation of a Simplified Impedance Spectroscopy Model Based on the Fractional Joint Kalman Filter for LiFe PO₄ Battery[J]. Transactions of China Electrotechnical Society, 2016, 31(24): 141-149.

URL:

https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2016/V31/I24/141

[1] 马玲玲, 杨军, 付聪, 等. 电动汽车充放电对电网影响研究综述[J]. 电力系统保护与控制, 2013, 41(3): 140-148.
Ma Lingling, Yang Jun, Fu Cong, et al. Review on impact of electric car charging and discharging on power grid[J]. Power System Protection and Control, 2013, 41(3): 140-148.
[2] 高明煜, 何志伟, 徐杰. 基于采样点卡尔曼滤波的动力电池SOC估计[J]. 电工技术学报, 2011, 26(11): 161-167.
Gao Mingyu, He Zhiwei, Xu Jie. Sigma point Kalman filter based SOC estimation for power supply battery[J]. Transactions of China Electrotechnical Society, 2011, 26(11): 161-167.
[3] 于海芳, 逯仁贵, 朱春波, 等. 基于安时法的镍氢电池SOC估计误差校正[J]. 电工技术学报, 2012, 27(6): 12-18.
Yu Haifang, Lu Rengui, Zhu Chunbo, et al. State of charge estimation calibration for Ni-MH battery based on ampere-hour method[J]. Transactions of China Electrotechnical Society, 2012, 27(6): 12-18.
[4] 刘艳莉, 戴胜, 程泽, 等. 基于有限差分扩展卡尔曼滤波的锂离子电池SOC估计[J]. 电工技术学报,2014, 29(1): 221-228.
Liu Yanli, Dai Sheng, Cheng Ze, et al. Estimation of state of charge of lithium-ion battery based on finite difference extended Kalman filter[J]. Transactions of China Electrotechnical Society, 2014, 29(1): 221-228.
[5] Xiong R, Sun F, He H, et al. A data-driven adaptive state of charge and power capability joint estimator of lithium-ion polymer battery used in electric vehicles[J]. Energy, 2013, 63(12): 295-308.
[6] Andre D, Appel C, Soczka-Guth T, et al. Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries[J]. Journal of Power Sources, 2013, 224(2): 20-27.
[7] 马泽宇, 姜久春, 张维戈, 等. 锂离子动力电池热老化的路径依赖性研究[J]. 电工技术学报, 2014, 29(5): 221-227.
Ma Zeyu, Jiang Jiuchun, Zhang Weige, et al. Research on path dependence of large format LiMn 2 O 4 battery degradation in thermal aging[J]. Transactions of China Electrotechnical Society, 2014, 29(5): 221-227.
[8] 陈大分, 姜久春, 王占国, 等. 动力锂离子电池分布参数等效电路模型研究[J]. 电工技术学报, 2013, 28(7): 169-176.
Chen Dafen, Jiang Jiuchun, Wang Zhanguo, et al. Research on distribution parameters equivalent circuit model of power lithium-ion batteries[J]. Transactions of China Electrotechnical Society, 2013, 28(7): 169-176.
[9] 李国杰, 唐志伟, 聂宏展, 等. 钒液流储能电池建模及其平抑风电波动研究[J]. 电力系统保护与控制, 2010, 38(22): 115-120.
Li Guojie, Tang Zhiwei, Nie Hongzhan, et al. Modelling and controlling of vanadium redox flow battery to smooth wind power fluctuations[J]. Power System Protection and Control, 2010, 38(22): 115- 120.
[10] 高金辉, 唐静, 贾利锋. 太阳能电池参数求解新算法[J]. 电力系统保护与控制, 2012, 40(9): 133-136.
Gao Jinhui, Tang Jing, Jia Lifeng. A novel parameter extraction method for solar cells[J]. Power System Protection and Control, 2012, 40(9): 133-136.
[11] 娄素华, 易林, 吴耀武, 等. 基于可变寿命模型的电池储能容量优化配置[J]. 电工技术学报, 2015, 30(4): 265-271.
Lou Suhua, Yi Lin, Wu Yaowu, et al. Optimizing deployment of battery energy storage based on lifetime predication[J]. Transactions of China Electrotechnical Society, 2015, 30(4): 265-271.
[12] Zhang L, Wang L, Hinds G, et al. Multi-objective optimization of lithium-ion battery model using genetic algorithm approach[J]. Journal of Power Sources, 2014, 270(12): 367-378.
[13] Yoon S, Hwang I, Lee C, et al. Power capability analysis in lithium ion batteries using electrochemical impedance spectroscopy[J]. Journal of Electro- analytical Chemistry, 2011, 655(1): 32-38.
[14] Xu J, Mi C, Cao B, 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(1): 277-284.
[15] Fleischer C, Waag W, Heyn H, et al. On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models part 1[J]. Journal of Power Sources, 2014, 260(8): 276-291.
[16] Fleischer C, Waag W, Heyn H, et al. On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models part 2[J]. Journal of Power Sources, 2014, 262(9): 457-482.
[17] Waag W, Fleischer C, Sauer D. Adaptive on-line prediction of the available power of lithium-ion batteries[J]. Journal of Power Sources, 2013, 242(11): 548-559.
[18] Sierociuk D, Dzielinski A. Fractional Kalman filter algorithm for the states, parameters and order of fractional system estimation[J]. International Journal of Applied Mathematics & Computer Science, 2006, 16(1): 129-140.
[19] Sabatier J, Aoun M, Oustaloup A, et al. Fractional system identification for lead acid battery state of charge estimation[J]. Singal Processing, 2006, 86(10): 2647-2657.
[20] Pei L, Zhu C, Wang T, et al. Online peak power prediction based on a parameter and state estimation for lithium-ion batteries in electric vehicles[J]. Energy, 2014, 66(3): 766-778.