Electrochemical impedance feature selection and gaussian process regression based on the state-of-health estimation method for lithium-ion batteries

doi:10.19799/j.cnki.2095-4239.2022.0150

Abstract

Abstract:

Electrochemical impedance spectroscopy (EIS) contains rich information about the battery state of health (SOH). However, due to the correlation of EIS data at different frequencies, the SOH estimation model constructed from the whole frequency range of EIS data may have poor performance and high complexity. Therefore, this study proposes an SOH estimation method with feature selection and Gaussian process regression by combining sequential forward search and cross-validation to seek the feature set. A level diagram method was adopted to formulate model performance evaluation indicators based on the number of features and the estimation error, which aimed to balance model complexity and model estimation accuracy. A public dataset was used to validate the proposed method, and the results showed that the proposed model with feature selection could achieve higher accuracy and less time for the EIS test than the model constructed from the whole frequency range of EIS data. This study provides theoretical and technical support for applying EIS to online SOH estimation.

Key words: lithium-ion battery, state of health, electrochemical impedance spectroscopy, feature selection, gaussian process regression

CLC Number:

TM 912

Xiaoyu CHEN, Mengmeng GENG, Qiankun WANG, Jiani SHEN, Yijun HE, Zifeng MA. Electrochemical impedance feature selection and gaussian process regression based on the state-of-health estimation method for lithium-ion batteries[J]. Energy Storage Science and Technology, 2022, 11(9): 2995-3002.

Figures/Tables 9

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Table 1

Table 2

References 23

1	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.
2	HAN X B, LU L G, ZHENG Y J, et al. A review on the key issues of the lithium ion battery degradation among the whole life cycle[J]. eTransportation, 2019, 1: 100005.
3	CHENG K W E, DIVAKAR B P, WU H J, et al. Battery-management system (BMS) and SOC development for electrical vehicles[J]. IEEE Transactions on Vehicular Technology, 2011, 60(1): 76-88.
4	NG M F, ZHAO J, YAN Q Y, et al. Predicting the state of charge and health of batteries using data-driven machine learning[J]. Nature Machine Intelligence, 2020, 2(3): 161-170.
5	ZHENG L F, ZHANG L, ZHU J G, et al. Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model[J]. Applied Energy, 2016, 180: 424-434.
6	LAI X, ZHENG Y J, SUN T. A comparative study of different equivalent circuit models for estimating state-of-charge of lithium-ion batteries[J]. Electrochimica Acta, 2018, 259: 566-577.
7	KIM J, CHO B H. State-of-charge estimation and state-of-health prediction of a Li-ion degraded battery based on an EKF combined with a per-unit system[J]. IEEE Transactions on Vehicular Technology, 2011, 60(9): 4249-4260.
8	BI J, ZHANG T, YU H Y, et al. State-of-health estimation of lithium-ion battery packs in electric vehicles based on genetic resampling particle filter[J]. Applied Energy, 2016, 182: 558-568.
9	SALKIND A J, FENNIE C, SINGH P, et al. Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology[J]. Journal of Power Sources, 1999, 80(1/2): 293-300.
10	ANDRE D, NUHIC A, SOCZKA-GUTH T, et al. Comparative study of a structured neural network and an extended Kalman filter for state of health determination of lithium-ion batteries in hybrid electricvehicles[J]. Engineering Applications of Artificial Intelligence, 2013, 26(3): 951-961.
11	HE Y J, SHEN J N, SHEN J F, et al. State of health estimation of lithium-ion batteries: A multiscale Gaussian process regression modeling approach[J]. AIChE Journal, 2015, 61(5): 1589-1600.
12	庄全超, 徐守冬, 邱祥云, 等. 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 Q C, XU S D, QIU X Y, et al. Diagnosis of electrochemical impedance spectroscopy in lithium ion batteries[J]. Progress in Chemistry, 2010, 22(6): 1044-1057.
13	HUET F. A review of impedance measurements for determination of the state-of-charge or state-of-health of secondary batteries[J]. Journal of Power Sources, 1998, 70(1): 59-69.
14	刘海洋. 基于阻抗谱时域测量的锂离子电池健康状态估计研究[D]. 哈尔滨: 哈尔滨工业大学, 2018.
	LIU H Y. Research on state of health estimation of lithium ion batteries based on time domian electrochemical impedance spectroscopy measurement[D]. Harbin: Harbin Institute of Technology, 2018.
15	STROE D I, SWIERCZYNSKI M, STAN A I, et al. Diagnosis of lithium-ion batteries state-of-health based on electrochemical impedance spectroscopy technique[C]//2014 IEEE Energy Conversion Congress and Exposition. September 14-18, 2014, Pittsburgh, PA, USA. IEEE, 2014: 4576-4582.
16	GALEOTTI M, CINÀ L, GIAMMANCO C, et al. Performance analysis and SOH (state of health) evaluation of lithium polymer batteries through electrochemical impedance spectroscopy[J]. Energy, 2015, 89: 678-686.
17	ZHU S, SUN X Y, GAO X Y, et al. Equivalent circuit model recognition of electrochemical impedance spectroscopy via machine learning[J]. Journal of Electroanalytical Chemistry, 2019, 855: 113627.
18	ZHANG Y W, TANG Q C, ZHANG Y, et al. Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning[J]. Nature Communications, 2020, 11: 1706.
19	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.
20	SUN Z H, BEBIS G, MILLER R. Object detection using feature subset selection[J]. Pattern Recognition, 2004, 37(11): 2165-2176.
21	何志昆, 刘光斌, 赵曦晶, 等. 高斯过程回归方法综述[J]. 控制与决策, 2013, 28(8): 1121-1129, 1137.
	HE Z K, LIU G B, ZHAO X J, et al. Overview of Gaussian process regression[J]. Control and Decision, 2013, 28(8): 1121-1129, 1137.
22	BLASCO X, HERRERO J M, SANCHIS J, et al. A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization[J]. Information Sciences, 2008, 178(20): 3908-3924.
23	MOMMA T, MATSUNAGA M, MUKOYAMA D, et al. Ac impedance analysis of lithium ion battery under temperature control[J]. Journal of Power Sources, 2012, 216: 304-307.

特征选择策略	优选特征子集包含特征个数	训练集交叉验证RMSE	测试集SOH 估计RMSE
SFS-1	1	0.23%	5.9%
SFS-4	4	0.18%	6.0%
SFS-LD	18	0.03%	4.2%
SFS-MIN	61	0.02%	5.4%

[1]	Linwang DENG, Tianyu FENG, Shiwei SHU, Zifeng ZHANG, Bin GUO. Review of a fast-charging strategy and technology for lithium-ion batteries [J]. Energy Storage Science and Technology, 2022, 11(9): 2879-2890.
[2]	Zhizhan LI, Jinlei QIN, Jianing LIANG, Zhengrong LI, Rui WANG, Deli WANG. High-nickel ternary layered cathode materials for lithium-ion batteries： Research progress， challenges and improvement strategies [J]. Energy Storage Science and Technology, 2022, 11(9): 2900-2920.
[3]	Tao SUN, Tengteng SHEN, Xin LIU, Dongsheng REN, Jinhai LIU, Yuejiu ZHENG, Luyan WANG, Languang LU, Minggao OUYANG. Application of titration gas chromatography technology in the quantitative detection of lithium plating in Li-ion batteries [J]. Energy Storage Science and Technology, 2022, 11(8): 2564-2573.
[4]	Yong MA, Xiaohan LI, Lei SUN, Dongliang GUO, Jinggang YANG, Jianjun LIU, Peng XIAO, Guangjun QIAN. Parameter design of lithium-ion batteries based on a three-dimensional electrochemical thermal coupling lithium precipitation model [J]. Energy Storage Science and Technology, 2022, 11(8): 2600-2611.
[5]	Liang TANG, Xiaobo YIN, Houfu WU, Pengjie LIU, Qingsong WANG. Demand for safety standards in the development of the electrochemical energy storage industry [J]. Energy Storage Science and Technology, 2022, 11(8): 2645-2652.
[6]	Liping HUO, Weiling LUAN, Zixian ZHUANG. Development trend of lithium-ion battery safety technology for energy storage [J]. Energy Storage Science and Technology, 2022, 11(8): 2671-2680.
[7]	Zhicheng CAO, Kaiyun ZHOU, Jiali ZHU, Gaoming LIU, Min YAN, Shun TANG, Yuancheng CAO, Shijie CHENG, Weixin ZHANG. Patent analysis of fire-protection technology of lithium-ion energy storage system [J]. Energy Storage Science and Technology, 2022, 11(8): 2664-2670.
[8]	Yue ZHANG, Depeng KONG, Ping PING. Performance and design optimization of a cold plate for inhibiting thermal runaway propagation of lithium-ion battery packs [J]. Energy Storage Science and Technology, 2022, 11(8): 2432-2441.
[9]	Chengshan XU, Borui LU, Mengqi ZHANG, Huaibin WANG, Changyong JIN, Minggao OUYANG, Xuning FENG. Study on thermal runaway gas evolution in the lithium-ion battery energy storage cabin [J]. Energy Storage Science and Technology, 2022, 11(8): 2418-2431.
[10]	Yang WANG, Xu LU, Yuxin ZHANG, Long LIU. Thermal runaway exhaust strategy of power battery [J]. Energy Storage Science and Technology, 2022, 11(8): 2480-2487.
[11]	Qingsong ZHANG, Yang ZHAO, Tiantian LIU. Effects of state of charge and battery layout on thermal runaway propagation in lithium-ion batteries [J]. Energy Storage Science and Technology, 2022, 11(8): 2519-2525.
[12]	Wei KONG, Jingtao JIN, Xipo LU, Yang SUN. Study on cooling performance of lithium ion batteries with symmetrical serpentine channel [J]. Energy Storage Science and Technology, 2022, 11(7): 2258-2265.
[13]	Yuzuo WANG, Jin WANG, Yinli LU, Dianbo RUAN. Study on the effects of pore structure on lithium-storage performances for soft carbon [J]. Energy Storage Science and Technology, 2022, 11(7): 2023-2029.
[14]	Shunmin YI, Linbo XIE, Li PENG. Remaining useful life prediction of lithium-ion batteries based on VF-DW-DFN [J]. Energy Storage Science and Technology, 2022, 11(7): 2305-2315.
[15]	Qingwei ZHU, Xiaoli YU, Qichao WU, Yidan XU, Fenfang CHEN, Rui HUANG. Semi-empirical degradation model of lithium-ion battery with high energy density [J]. Energy Storage Science and Technology, 2022, 11(7): 2324-2331.