基于数据驱动模型融合的锂离子电池多时间尺度状态联合估计方法

doi:10.19562/j.chinasae.qcgc.2022.03.007

Abstract

Abstract:

This paper proposes a multi-time scale joint state estimation method for lithium-ion batteries based on the fusion of data driven method （DDM） and equivalent circuit model （ECM）. Firstly， the internal resistance is extracted as the health factor （HF）， and the least squares support vector machine （LSSVM） is used to establish the battery aging model to estimate state of health （SOH）. Subsequently， the battery state space equation is established according to the RC parameter identification values and the capacity estimation values， and then the Unscented Kalman Filter （UKF） algorithm is used to estimate state of charge （SOC）. Then， Gaussian process regression （GPR） is used to map the change of HF with the number of cycles to predict the trend of HF change， and long-term remaining useful life （RUL） prediction is realized combined with the LSSVM model. The experimental results show that the proposed method has high accuracy and robustness.

Key words: lithium-ion battery, multi-time scale, joint estimation, fusion method

Ping Wang,Xiangyuan Peng,Ze Cheng,Ji’ang Zhang. A Multi-time Scale Joint State Estimation Method for Lithium-ion Batteries Based on Data-driven Model Fusion[J].Automotive Engineering, 2022, 44(3): 362-371.

Figures/Tables 9

电池	Pearson系数				GRC系数
电池	$R$	$R 0$	$R p$	$C p$	$R$	$R 0$	$R p$	$C p$
Cell1	-0.998 0	-0.985 2	-0.996 0	0.957 8	0.933 3	0.933 3	0.933 3	0.646 0
Cell2	-0.986 3	-0.964 1	-0.981 8	0.964 1	0.879 0	0.879 0	0.879 0	0.734 8
Cell3	-0.998 2	-0.989 3	-0.997 5	0.973 8	0.934 4	0.934 4	0.934 4	0.763 6
Cell4	-0.998 6	-0.980 2	-0.997 8	0.975 9	0.939 0	0.938 9	0.938 9	0.708 4
Cell5	-0.998 9	-0.993 7	-0.998 7	0.984 9	0.947 2	0.947 2	0.947 2	0.662 5
Cell6	-0.992 6	-0.989 3	-0.988 2	0.977 8	0.928 0	0.928 0	0.928 0	0.742 6
Cell7	-0.998 3	-0.991 2	-0.997 2	0.981 4	0.942 1	0.942 0	0.942 0	0.687 6
Cell8	-0.997 0	-0.991 2	-0.995 5	0.979 9	0.930 2	0.930 2	0.930 2	0.759 3

References 19

1	LIN C P， LIN C P， CABRERA J， et al. Battery state of health modeling and remaining useful life prediction through time series model［J］. Applied Energy， 275.P115338.
2	TIAN Huixin， QIN Pengliang， LI Kun， et al. A review of the state of health for lithium-ion batteries： research status and suggestions［J］. Journal of Cleaner Production，2020，261：120813.
3	HU Xiaosong， FENG Fei， LIU Kailong， et al. State estimation for advanced battery management： key challenges and future trends［J］. Renewable and Sustainable Energy Reviews，2019，114：109334.
4	CHEN C， XIONG R， SHEN W. A Lithium-ion battery-in-the-loop approach to test and validate multiscale dual h infinity filters for state-of-charge and capacity estimation［J］. IEEE Transactions on Power Electronics， 2018：1-1.
5	XIA B， CHEN G， ZHOU J， et al. Online parameter identification and joint estimation of the state of charge and the state of health of lithium-ion batteries considering the degree of polarization［J］. Energies， 2019， 12（15）：2939.
6	JAMOOS A， GRIVEL E， SHAKARNEH N， et al. Dual optimal filters for parameter estimation of a multivariate autoregressive process from noisy observations［J］. Iet Signal Processing， 2011， 5（5）：471-479.
7	CHEN Z， SUN H， DONG G， et al. Particle filter-based state-of-charge estimation and remaining- dischargeable-time prediction method for lithium-ion batteries［J］. Journal of Power Sources， 2019， 414：158-166.
8	DUONG V H， BASTAWROUS H A， LIM K C， et al. Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares［J］. Journal of Power Sources， 2015， 296：215-224.
9	SUN F， XIONG R， HE H. A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique［J］. Applied Energy， 2016， 162：1399-1409.
10	HUA Y， CORDOBA-ARENAS A， WARNER N， et al. A multi time-scale state-of-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control［J］. Journal of Power Sources， 2015， 280：293-312.
11	LEE S， KIM J， LEE J， et al. State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge［J］. Journal of Power Sources， 2009，2：1367-1373.
12	WEI Z， TSENG K J， WAI N， et al. Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery［J］. Journal of Power Sources， 2016， 332：389-398.
13	YANG Q， XU J， CAO B， et al. State-of-health estimation of lithium-ion battery based on interval capacity［J］. Energy Procedia， 2017， 105：2342-2347.
14	GUO P， CHENG Z， YANG L. A data-driven remaining capacity estimation approach for lithium-ion batteries based on charging health feature extraction［J］. Journal of Power Sources， 2019， 412：442-450.
15	HOSSAIN L， HANNAN M A， AINI H， et al. A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles： Challenges and recommendations［J］. Journal of Cleaner Production， 2018， 205：115-133.
16	XING Y， MA E， TSUI K L， et al. An ensemble model for predicting the remaining useful performance of lithium-ion batteries［J］. Microelectronics Reliability， 2013， 53（6）：811-820.
17	WANG S， ZHAO L， SU X， et al. Prognostics of lithium-ion batteries based on flexible support vector regression［C］. Proceedings of Prognostics & System Health Management Conference. Zhangiiaijie： IEEE， 2014，7（10）：6492-6508.
18	REZVANI M， LEE S， LEE J. A comparative analysis of techniques for electric vehicle battery prognostics and health management （PHM）［C］. SAE Paper 2011-01-2247.
19	LIU D T， ZHOU J B， LIAO H T，et al．A health indicator extraction and optimization framework for lithium-ion battery degradation modeling and prognostics［J］.IEEE Transactions on Systems，Man，and Cybernetics：Systems，2015，45（6）：915-928．

[1]	ZHOU Wei, ZHANG Cheng-Ning, LI Jun-Qiu. [J]. , 2016, 38(12): 1407 -1414 .
[2]	ZHANG Ying-Chao, ZHAN Da-Peng, ZHAO Jing, ZHANG Zhe, LI Jie. [J]. , 2016, 38(12): 1434 -1439 .
[3]	TANG You-Ming, YAN Ling-Bo, LUO Qian, CAO Li-Bo. [J]. , 2016, 38(12): 1452 -1458 .
[4]	ZHANG Lei, LU Jian-Wei, JIANG Jun-Zhao, YAN Pei-Lei, LI Lei. [J]. , 2016, 38(12): 1477 -1482 .
[5]	ZHAO Liang, ZHANG Qiang, GUO Kong-Hui. [J]. , 2017, 39(1): 86 -91 .
[6]	SHEN Deng-Feng, WANG Chen, YU Hai-Sheng, ZHANG Tong, YI Xian-Ke. [J]. , 2017, 39(1): 15 -22 .
[7]	ZOU Tie-Fang, HU Lin, LI Ping-Fan, YI Liang. [J]. , 2017, 39(1): 41 -46 .
[8]	ZHOU Kai, ZANG Jing-Lun. [J]. , 2017, 39(2): 127 -132 .
[9]	ZUO Wen-Jie, CHEN Ji-Shun, LI Yi-Wen, LIU Nian. [J]. , 2017, 39(2): 145 -149 .
[10]	HU Lin, DAI Xing-Xing, HUANG Jing, CHEN Qiang. [J]. , 2017, 39(2): 159 -167 .

电池	SOH		RUL
电池	MAE	RMSE	MAE	RMSE
Cell 1	0.003 8	0.004 3	0.666 7	0.866 0
Cell 2	0.002 9	0.003 4	0.333 3	0.577 4
Cell 3	0.003 6	0.004 1	0.413 8	0.694 8
Cell 4	0.002 9	0.003 5	0.454 5	0.738 5
Cell 5	0.001 7	0.002 3	0.285 7	0.534 5
Cell 6	0.002 1	0.002 7	0.565 2	0.751 8
Cell 7	0.003 4	0.003 8	0.975 0	1.172 6
Cell 8	0.003 9	0.004 4	0.166 7	0.408 2

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A Multi-time Scale Joint State Estimation Method for Lithium-ion Batteries Based on Data-driven Model Fusion

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