An online identification method for equivalent model parameters of aging lithium-ion batteries

doi:10.19799/j.cnki.2095-4239.2020.0235

Abstract

Abstract:

The current challenges of modeling aging lithium-ion batteries include oversaturated model parameters and time-varying parameters, which cannot be evaluated with an online parameter identification of the model using the traditional least squares method with a fixed forgetting factor. This paper proposes a least squares method with a variable forgetting factor, which continuously updates the forgetting factor to better track the run-time utilization of battery aging characteristics. Using the first-order RC equivalent circuit model of the lithium battery as a model, a test platform was established for charge and discharge experiments, and the results were compared with the traditional least squares method with a fixed forgetting factor. The experimental results indicated that the proposed method can quickly converge and dynamically track battery aging. The average absolute error of the voltage parameters at the model terminal was found to be less than 25 mV. When the proposed method was run under a dynamic stress test with the typical working conditions of an energy storage system, the corresponding parameter identification accuracy was improved by 38.33%, indicating that the proposed method is highly accurate.

Key words: echelon use, Li-ion battery, decommissioned battery, equivalent circuit model, parameter identification, forgetting factor, least square method, SOC estimation

CLC Number:

TM 911

Banghua DU, Yu ZHANG, Tiezhou WU, Yanlin HE, Zilong LI. An online identification method for equivalent model parameters of aging lithium-ion batteries[J]. Energy Storage Science and Technology, 2021, 10(1): 342-348.

Figures/Tables 9

Fig.1

Fig.2

Fig.3

Fig.4

Table 1

Online identification process of variable forgetting factor least square method"

(1)参数初始化
$θ 0 = α 1, α 2, α 3 T P 0 = δ I 0$
(2)采集输入电流和输出电压，更新信息矩阵
$Φ k = U k, I 1, k, I 1, k - 1 T$
(3)预测输出电压
$U ? k = Φ k T θ ? k - 1$
(4)估计误差计算
$e k = U k - U ? k$
(5)可变遗忘因子计算
$μ k = m i n σ v σ ? q k ξ + σ ? e k - σ v, μ m a x$
(6)增益矩阵计算
$K k = P k - 1 Φ k T Φ k P k - 1 Φ k T + μ - 1$
(7)参数估计
$θ ? k = θ ? k - 1 + K k e k$
(8)更新方差矩阵
$P k = 1 μ I - K k Φ k P k - 1$
重复步骤(2)至(8)直至参数辨识结束

Table 1

Table 2

Fig.5

Fig.6

Fig.7

References 19

1	王开让, 白恺, 李娜, 等. 电动汽车动力电池梯次利用寿命预测方法研究[J]. 全球能源互联网, 2018(3): 375-382.
	WANG Kairang, BAI Kai, LI Na, et al. Research on life prediction for second-use of electric vehicle power battery[J]. Journal of Global Energy Interconnection, 2018(3): 375-382.
2	LIAO Qiangqiang, MU Miaomiao, ZHAO Shuqi, et al. Performance assessment and classification of retired lithium ion battery from electric vehicles for energy storage[J]. International Journal of Hydrogen Energy, 2017, 42(30): 18817-18823.
3	SCHUSTER S F, BRAND M J, CAMPESTRINI C, et al. Correlation between capacity and impedance of lithium-ion cells during calendar and cycle life[J]. Journal of Power Sources, 2016, 305: 191-199.
4	ZHANG Caiping, JIANG Jiuchun, ZHANG Weige, et al. A novel data-driven fast capacity estimation of spent electric vehicle lithium-ion batteries[J]. Energies, 2014, 7(12): 8076-8094.
5	JIANG Yan, JIANG Jiuchun, ZHANG Caiping, et al. Recognition of battery aging variations for LiFePO₄ batteries in 2nd use applications combining incremental capacity analysis and statistical approaches[J]. Journal of Power Sources, 2017, 360: 180-188.
6	BIZERAY A M, KIM J H, DUNCAN S R, et al. Identifiability and parameter estimation of the single particle lithium-ion battery model[J]. IEEE Transactions on Control Systems Technology, 2018, 27(5): 1862-1877.
7	LAI Xin, YI Wei, ZHENG Yuejiu, et al. An all-region state-of-charge estimator based on global particle swarm optimization and improved extended Kalman filter for lithium-ion batteries[J]. Electronics, 2018, 7(11): doi: 10.3390/electronics7110321.
8	TIAN Jinpeng, XIONG Rui, YU Quanqing. Fractional-order model-based incremental capacity analysis for degradation state recognition of lithium-ion batteries[J]. IEEE Transactions on Industrial Electronics, 2018, 66(2): 1576-1584.
9	张彩萍, 姜久春, 张维戈, 等. 梯次利用锂离子电池电化学阻抗模型及特性参数分析[J]. 电力系统自动化, 2013, 37(1): 54-58.
	ZHANG Caiping, JIANG Jiuchun, ZHANG Weige, et al. Characterization of electrochemical impedance equivalent model and parameters for Li-ion batteries echelon use[J]. Automation of Electric Power Systems, 2013, 37(1): 54-58.
10	WEI Zhongbao, ZOU Changfu, LENG Feng, et al. Online model identification and state-of-charge estimate for lithium-ion battery with a recursive total least squares-based observer[J]. IEEE Transactions on Industrial Electronics, 2017, 65(2): 1336-1346.
11	CHEN Zewang, YANG Liwen, ZHAO Xiaobing, et al. Online state of charge estimation of Li-ion battery based on an improved unscented Kalman filter approach[J]. Applied Mathematical Modelling, 2019, 70: 532-544.
12	MESBAHI T, KHENFRI F, RIZOUG N, et al. Dynamical modeling of Li-ion batteries for electric vehicle applications based on hybrid Particle Swarm-Nelder-Mead (PSO-NM) optimization algorithm[J]. Electric Power Systems Research, 2016, 131: 195-204.
13	YANG Hao, SUN Xianzhong, AN Yabin, et al. Online parameters identification and state of charge estimation for lithium-ion capacitor based on improved cubature Kalman filter[J]. Journal of Energy Storage, 2019, 24: doi:10.1002/er.6088.
14	BURGOS C, SÁEZ D, ORCHARD M E, et al. Fuzzy modelling for the state-of-charge estimation of lead-acid batteries[J]. Journal of Power Sources, 2015, 274: 355-366.
15	HU J N, HU J J, LIN H B, et al. State-of-charge estimation for battery management system using optimized support vector machine for regression[J]. Journal of Power Sources, 2014, 269: 682-693.
16	LAI Xin, ZHENG Yuejiu, SUN Tao. A comparative study of different equivalent circuit models for estimating state-of-charge of lithium-ion batteries[J]. Electrochimica Acta, 2018, 259: 566-577.
17	YU Quanqing, XIONG Rui, WANG Leyi, et al. A comparative study on open circuit voltage models for lithium-ion batteries[J]. Chinese Journal of Mechanical Engineering, 2018, 31(1): doi: 10.1186/S10033-018-0268-8.
18	费亚龙, 谢长君, 汤泽波, 等. 基于平方根无迹卡尔曼滤波的锂电池状态估计[J]. 中国电机工程学报, 2017, 37(15): 4514-4520.
	FEI Yalong, XIE Changjun, TANG Zebo, et al. State-of-charge estimation based on square root unscented Kalman filter algorithm for Li-ion batteries[J]. Proceedings of the CSEE, 2017, 37(15): 4514-4520.
19	SUN Quan, ZHANG Hong, ZHANG Jianrong, et al. Adaptive unscented Kalman filter with correntropy loss for robust state of charge estimation of lithium-ion battery[J]. Energies, 2018, 11(11): doi: 10.3390/en.11113123.

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