Integrating model- and data-driven methods for accurate state estimation of lithium-ion batteries

doi:10.19799/j.cnki.2095-4239.2022.0508

Abstract

Abstract:

Addressing the inadequacies of the conventional model- and data-driven methods, an integrating strategy combining both methods, for accurate state estimation of lithium-ion batteries is proposed for estimating battery state-of-charge. After establishing the classical second-order battery model, a dual-Kalman filter, composed of an extended Kalman filter and an unscented Kalman filter, was used to estimate the status of the lithium battery system preliminarily. Then, the preliminary estimation results were input into the LSTM neural network to correct the errors and complete the data-driven part. Datasets from NASA PCoE were used to test the performance of the single-and dual-driven methods. Results show that the integrating method reduces the dependence of the estimation system on the data while improving the estimation accuracy and robustness because it combines the advantages of the model-and data-driven methods and makes up for their shortcomings. Satisfactory results were obtained.

Key words: lithium battery, state of charge, state of health, model-driven method, data-driven method, extended Kalman filter, unscented Kalman filter, long-short-term neural network

CLC Number:

TK 02

Qingyang CHEN, Yinghui HE, Guanding YU, Mingyang LIU, Chong XU, Zhenming LI. Integrating model- and data-driven methods for accurate state estimation of lithium-ion batteries[J]. Energy Storage Science and Technology, 2023, 12(1): 209-217.

Figures/Tables 18

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Table 1

Parameters of equivalent model of battery"

参数名	参数值
$η$	0.991
$τ 1$	210.056
$τ 2$	28.185
$R 0$	75 $m Ω$
$R 1$	76.3 $m Ω$
$R 2$	28.3 $m Ω$

Table 1

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Table 2

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Table 3

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References 17

1	宁娜. 2019上半年中国储能十大事件回顾[J]. 能源, 2019(9): 28-32.
2	HE Y, LIU X T, ZHANG C B, et al. A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries[J]. Applied Energy, 2013, 101: 808-814.
3	冯飞, 宋凯, 逯仁贵, 等. 磷酸铁锂电池组均衡控制策略及荷电状态估计算法[J]. 电工技术学报, 2015, 30(1): 22-29.
	FENG F, SONG K, LU R G, et al. Equalization control strategy and SOC estimation for LiFePO₄ battery pack[J]. Transactions of China Electrotechnical Society, 2015, 30(1): 22-29.
4	LU L G, HAN X B, LI J Q, et al. A review on the key issues for lithium-ion battery management in electric vehicles[J]. Journal of Power Sources, 2013, 226: 272-288.
5	UNGUREAN L, CÂRSTOIU G, MICEA M V, et al. Battery state of health estimation: A structured review of models, methods and commercial devices[J]. International Journal of Energy Research, 2017, 41(2): 151-181.
6	DUBARRY M, SVOBODA V, HWU R, et al. Capacity loss in rechargeable lithium cells during cycle life testing: The importance of determining state-of-charge[J]. Journal of Power Sources, 2007, 174(2): 1121-1125.
7	孙冬, 许爽. 梯次利用锂电池健康状态预测[J]. 电工技术学报, 2018, 33(9): 2121-2129.
	SUN D, XU S. State of health prediction of second-use lithium-ion battery[J]. Transactions of China Electrotechnical Society, 2018, 33(9): 2121-2129.
8	CAI Y F, WANG Q T, QI W. D-UKF based state of health estimation for 18650 type lithium battery[C]//2016 IEEE International Conference on Mechatronics and Automation. Harbin, China. IEEE: 754-758.
9	HUSSEIN A A. Capacity fade estimation in electric vehicle Li-ion batteries using artificial neural networks[J]. IEEE Transactions on Industry Applications, 2015, 51(3): 2321-2330.
10	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: 20-27.
11	MICEA M V, UNGUREAN L, CÂRSTOIU G N, et al. Online state-of-health assessment for battery management systems[J]. IEEE Transactions on Instrumentation and Measurement, 2011, 60(6): 1997-2006.
12	许巧巧. 锂离子动力电池剩余容量估计算法研究与实现[D]. 重庆: 重庆大学, 2013.
	XU Q Q. Research and realization of state of charge estimation of Li-ion power battery[D]. Chongqing: Chongqing University, 2013.
13	WANG Q, LI F, TANG Y, et al. Integrating model-driven and data-driven methods for power system frequency stability assessment and control[J]. IEEE Transactions on Power Systems, 2019, 34(6): 4557-4568.
14	Ribeiro M I. Kalman and extended kalman filters: Concept, derivation and properties[J]. Institute for Systems and Robotics, 2004, 43: 46.
15	WAN E A, VAN DER MERWE R. The unscented Kalman filter for nonlinear estimation[C]//Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373). Lake Louise, AB, Canada. IEEE: 153-158.
16	林成涛, 王军平, 陈全世. 电动汽车SOC估计方法原理与应用[J]. 电池, 2004, 34(5): 376-378.
	LIN C T, WANG J P, CHEN Q S. Methods for state of charge estimation of EV batteries and their application[J]. Battery Bimonthly, 2004, 34(5): 376-378.
17	BOLE B, KULKARNI C S, DAIGLE M. Adaptation of an electrochemistry-based Li-ion battery model to account for deterioration observed under randomized use[C]// Annual Conference of the PHM Society. 2014.

估测方法	估测参数	绝对误差	相对误差
模型驱动法	SOC(大于0.4)	±0.01	±3%
模型驱动法	SOC(小于0.4)	0.02+	/
数据驱动法	SOC	0.015+	/
双驱动法	SOC(大于0.4)	±0.005	±1.5%
双驱动法	SOC(小于0.4)	±0.01	/

估测方法	估测参数	绝对误差	相对误差
模型驱动法	SOC(大于0.4)	±0.02	±7%
模型驱动法	SOC(小于0.4)	0.02+	/
数据驱动法	SOC	0.015+	/
双驱动法	SOC(大于0.4)	±0.01	±2%
双驱动法	SOC(小于0.4)	±0.015	/

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