State-of-health estimation of lithium batteries based on polynomial feature extension of the CNN-transformer model

doi:10.19799/j.cnki.2095-4239.2024.0465

Abstract

Abstract:

To enhance the accuracy of state-of-health (SOH) estimation for lithium-ion batteries, this study proposes a convolutional neural network (CNN)-transformer fusion model based on polynomial feature expansion. The model leverages the powerful local feature extraction capability of CNNs and the sequence processing ability of transformers. Key health factors, highly correlated with battery capacity, such as peak values of incremental capacity curves, corresponding voltages, areas, and charging time, were extracted and expanded using polynomial features. This expansion enhances the model's ability to handle nonlinearities in the input features. Principal component analysis was employed to reduce the dimensionality of the feature space, which aided in capturing adequate data information and reduced training time. The effectiveness and accuracy of the proposed fusion algorithm were validated using open-source datasets from the National Aeronautics and Space Administration (NASA) and the University of Maryland. Comparative analyses of SOH estimation were conducted for the CNN-transformer model with and without polynomial features and for single-model algorithms. The results indicate that the SOH estimation accuracy of the proposed model, compared to the CNN-transformer model without polynomial features, improved by 38.71%, 50.28%, 4.71%, and 17.58% for datasets B0005, B0006, B0007, and B0018, respectively.

Key words: lithium-ion battery, battery state of health prediction, principal component analysis, CNN-Transformer, incremental capacity analysis, polynomial features

CLC Number:

TM 912

Yuan CHEN, Siyuan ZHANG, Yujing CAI, Xiaohe HUANG, Yanzhong LIU. State-of-health estimation of lithium batteries based on polynomial feature extension of the CNN-transformer model[J]. Energy Storage Science and Technology, 2024, 13(9): 2995-3005.

Figures/Tables 11

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References 34

1	TIAN X, GENG Y, SARKIS J, et al. Features of critical resource trade networks of lithium-ion batteries[J]. Resources Policy, 2021, 73: 102177. DOI: 10.1016/j.resourpol.2021.102177.
2	RAUF H, KHALID M, ARSHAD N. Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling[J]. Renewable and Sustainable Energy Reviews, 2022, 156: 111903. DOI: 10.1016/j.rser.2021.111903.
3	JIANG K, LIU X L, LOU G F, et al. Parameter sensitivity analysis and cathode structure optimization of a non-aqueous Li-O₂ battery model[J]. Journal of Power Sources, 2020, 451: 227821. DOI: 10.1016/j.jpowsour.2020.227821.
4	JIANG S D, SONG Z X. A review on the state of health estimation methods of lead-acid batteries[J]. Journal of Power Sources, 2022, 517: 230710. DOI: 10.1016/j.jpowsour.2021.230710.
5	HOSSAIN LIPU M S, ANSARI S, MIAH M S, et al. Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects[J]. Journal of Energy Storage, 2022, 55: 105752. DOI: 10.1016/j.est.2022.105752.
6	YANG S J, ZHANG C P, JIANG J C, et al. Review on state-of-health of lithium-ion batteries: Characterizations, estimations and applications[J]. Journal of Cleaner Production, 2021, 314: 128015. DOI: 10.1016/j.jclepro.2021.128015.
7	XIONG R, KIM J, SHEN W X, et al. Key technologies for electric vehicles[J]. Green Energy and Intelligent Transportation, 2022, 1(2): 100041. DOI: 10.1016/j.geits.2022.100041.
8	ZHAO J, ZHU Y, ZHANG B, et al. Review of state estimation and remaining useful life prediction methods for lithium–ion batteries[J]. Sustainability, 2023, 15(6): 5014.
9	TIAN H X, QIN P L, LI K, 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. DOI: 10.1016/j.jclepro.2020.120813.
10	顾菊平, 蒋凌, 张新松, 等. 基于特征提取的锂离子电池健康状态评估及影响因素分析[J]. 电工技术学报, 2023, 38(19): 5330-5342. DOI: 10.19595/j.cnki.1000-6753.tces.231085.
	GU J P, JIANG L, ZHANG X S, et al. Estimation and influencing factor analysis of lithium-ion batteries state of health based on features extraction[J]. Transactions of China Electrotechnical Society, 2023, 38(19): 5330-5342. DOI: 10.19595/j.cnki.1000-6753.tces.231085.
11	SUI X, HE S, VILSEN S B, et al. A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery[J]. Applied Energy, 2021, 300: 117346. DOI: 10.1016/j.apenergy.2021.117346.
12	TRAN M K, MATHEW M, JANHUNEN S, et al. A comprehensive equivalent circuit model for lithium-ion batteries, incorporating the effects of state of health, state of charge, and temperature on model parameters[J]. Journal of Energy Storage, 2021, 43: 103252. DOI: 10.1016/j.est.2021.103252.
13	陈媛,段文献,何怡刚,等.带降噪自编码器的锂离子电池健康状态估计算法[J/OL].电工技术学报,1-17[2024-05-24].https://doi.org/10.19595/j.cnki.1000-6753.tces.231644.
	CHEN Y,DUAN W X,HE Y G, et al. Predicting the state of charge and health of batteries using data-driven machine learning[J/OL]. Nature Machine Intelligence,1-17[2024-05-24].https://doi.org/10.19595/j.cnki.1000-6753.tces.231644.
14	ZHANG M, YANG D F, DU J X, et al. A review of SOH prediction of Li-ion batteries based on data-driven algorithms[J]. Energies, 2023, 16(7): 3167. DOI: 10.3390/en16073167.
15	REN Z, DU C Q. A review of machine learning state-of-charge and state-of-health estimation algorithms for lithium-ion batteries[J]. Energy Reports, 2023, 9: 2993-3021. DOI: 10.1016/j.egyr.2023.01.108.
16	CHEN K, LIAO Q, LIU K, et al. Capacity degradation prediction of lithium-ion battery based on artificial bee colony and multi-kernel support vector regression[J]. Journal of Energy Storage, 2023, 72: 108160. DOI: 10.1016/j.est.2023.108160.
17	VAN C N, QUANG D T. Estimation of SoH and internal resistances of lithium ion battery based on LSTM network[J]. International Journal of Electrochemical Science, 2023, 18(6): 100166. DOI: 10.1016/j.ijoes.2023.100166.
18	FAN Y X, XIAO F, LI C R, et al. A novel deep learning framework for state of health estimation of lithium-ion battery[J]. Journal of Energy Storage, 2020, 32: 101741. DOI: 10.1016/j.est. 2020.101741.
19	SUN S, LIN Q B, LI H S, et al. Simultaneous estimation of SOH and SOC of batteries based on SVM[C]//2022 4th International Conference on Smart Power & Internet Energy Systems (SPIES). December 9-12, 2022, Beijing, China. IEEE, 2022: 1934-1938. DOI: 10.1109/SPIES55999.2022.10082477.
20	XIAO S, LIU P Y, CHEN K, et al. Battery state of health prediction based on voltage intervals, BP neural network and genetic algorithm[J]. International Journal of Green Energy, 2024, 21(8): 1743-1756. DOI: 10.1080/15435075.2023.2264959.
21	WEN L, BO N, YE X C, et al. A novel auto-LSTM-based state of health estimation method for lithium-ion batteries[J]. Journal of Electrochemical Energy Conversion and Storage, 2021, 18(3): 030902. DOI: 10.1115/1.4050100.
22	JIA C Y, TIAN Y K, SHI Y H, et al. State of health prediction of lithium-ion batteries based on bidirectional gated recurrent unit and transformer[J]. Energy, 2023, 285: 129401. DOI: 10.1016/j.energy.2023.129401.
23	ZHANG Q C, LI X, ZHOU C, et al. State-of-health estimation of batteries in an energy storage system based on the actual operating parameters[J]. Journal of Power Sources, 2021, 506: 230162. DOI: 10.1016/j.jpowsour.2021.230162.
24	BIAN X L, WEI Z B, LI W H, et al. State-of-health estimation of lithium-ion batteries by fusing an open circuit voltage model and incremental capacity analysis[J]. IEEE Transactions on Power Electronics, 2022, 37(2): 2226-2236. DOI: 10.1109/TPEL. 2021.3104723.
25	GISMERO A, NØRREGAARD K, JOHNSEN B, et al. Electric vehicle battery state of health estimation using Incremental Capacity Analysis[J]. Journal of Energy Storage, 2023, 64: 107110. DOI: 10.1016/j.est.2023.107110.
26	WANG Q S, WANG Z P, ZHANG L, et al. A battery capacity estimation framework combining hybrid deep neural network and regional capacity calculation based on real-world operating data[J]. IEEE Transactions on Industrial Electronics, 2023, 70(8): 8499-8508. DOI: 10.1109/TIE.2022.3229350.
27	JENU S, HENTUNEN A, HAAVISTO J, et al. State of health estimation of cycle aged large format lithium-ion cells based on partial charging[J]. Journal of Energy Storage, 2022, 46: 103855. DOI: 10.1016/j.est.2021.103855.
28	ZHANG Q C, LI X Z, DU Z C, et al. Aging performance characterization and state-of-health assessment of retired lithium-ion battery modules[J]. Journal of Energy Storage, 2021, 40: 102743. DOI: 10.1016/j.est.2021.102743.
29	CHEN K, LI J L, LIU K, et al. State of health estimation for lithium-ion battery based on particle swarm optimization algorithm and extreme learning machine[J]. Green Energy and Intelligent Transportation, 2024, 3(1): 100151. DOI: 10.1016/j.geits. 2024.100151.
30	SUN H L, SUN J R, ZHAO K, et al. Data-driven ICA-Bi-LSTM-combined lithium battery SOH estimation[J]. Mathematical Problems in Engineering, 2022, 2022: 9645892. DOI: 10.1155/2022/9645892.
31	SHE C Q, ZHANG L, WANG Z P, et al. Battery state-of-health estimation based on incremental capacity analysis method: Synthesizing from cell-level test to real-world application[J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2023, 11(1): 214-223. DOI: 10.1109/JESTPE.2021.3112754.
32	TIAN J P, XIONG R, SHEN W X. State-of-health estimation based on differential temperature for lithium ion batteries[J]. IEEE Transactions on Power Electronics, 2020, 35(10): 10363-10373. DOI: 10.1109/TPEL.2020.2978493.
33	PARK S, LEE P, KIM D, et al. A SOH estimation method based on ICA peaks on temperature-robust and aging mechanism analysis under high temperature[C]//2021 IEEE Applied Power Electronics Conference and Exposition (APEC). June 14-17, 2021, Phoenix, AZ, USA. IEEE, 2021: 2646-2649. DOI: 10.1109/APEC42165.2021.9487032.
34	HOU X K, GUO X D, YUAN Y P, et al. The state of health prediction of Li-ion batteries based on an improved extreme learning machine[J]. Journal of Energy Storage, 2023, 70: 108044. DOI: 10.1016/j.est.2023.108044.

Method	B0005			B0006			B0007			B0018
Method	MAE	MAPE	RMSE	MAE	MAPE	RMSE	MAE	MAPE	RMSE	MAE	MAPE	RMSE
Poly-CNN-Transformer	0.00711	0.00535	0.00015	0.00796	0.00648	0.00011	0.0081	0.0056	0.0001	0.0075	0.0056	0.0001
CNN-Transformer	0.01160	0.00875	0.00021	0.01601	0.01294	0.00040	0.0085	0.0059	0.0001	0.0091	0.0068	0.0002

Method	B0005			B0006			B0007			B0018
Method	MAE	MAPE	RMSE	MAE	MAPE	RMSE	MAE	MAPE	RMSE	MAE	MAPE	RMSE
Poly-CNN-Transformer	0.0071	0.0053	0.0001	0.0079	0.0064	0.0001	0.0081	0.0056	0.0001	0.0075	0.0056	0.0001
Poly-Transformer	0.0072	0.0054	0.0001	0.0114	0.0093	0.0002	0.0136	0.0087	0.0005	0.0085	0.0064	0.0001
Poly-LSTM	0.0141	0.0160	0.0003	0.0100	0.0081	0.0001	0.0141	0.0093	0.0005	0.0090	0.0063	0.0001

Method	MAE	MAPE	RMSE
Poly-CNN-Transformer	0.01275	0.05605	0.00050
Poly-Transformer	0.01819	0.08535	0.00122
Poly-LSTM	0.01478	0.06916	0.00066
CNN-Transformer	0.01325	0.06539	0.00057

项目	CNN-Transformer	Transformer	LSTM
参数量/个	102	69	124
估算一次时间/ms	0.0276	0.0243	0.0124

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