SOH estimation of lithium-ion batteries using a convolutional Fastformer

doi:10.19799/j.cnki.2095-4239.2023.0735

Abstract

Abstract:

The state of health (SOH) of lithium-ion battery batteries is a crucial parameter for battery management systems, playing a substantial role in ensuring reliable operation and extending battery lifespan. This study aims to introduce an estimation method based on convolutional Fastformer model to improve the accuracy of data-driven SOH estimation for lithium-ion batteries. Initially, voltage and current curves from various charging stages of the lithium-ion battery are extracted for each cycle. These curves are then transformed into statistical health features, providing insights into the battery's aging characteristics. The Pearson correlation coefficient is used to analyze the relationship between selected statistical features and capacity, facilitating the identification of highly correlated health features and the elimination of redundant ones. Based on the strengths of convolutional neural networks and the linear complexity of Fastformer neural network, our approach combines the feature extraction capability of the former with local information mining of health features. The Fastformer's multihead attention mechanism efficiently summarizes contextual information within lengthy sequences. Model training time is reduced by optimizing hyperparameters using an orthogonal experimental method. Finally, a publicly available dataset is used for comparative evaluations, pitting our approach against other models such as CNN, GRU, and RNN. The results validate the accuracy of the convolutional Fastformer model, with maximum mean absolute error and root mean square error at only 0.25% and 0.29%, respectively, and a relative error within 0.08%. These findings demonstrate the high accuracy and stability achieved by the proposed method for SOH estimation.

Key words: lithium-ion battery, SOH estimation, orthogonal experiment, convolutional Fastformer

CLC Number:

TM 912

Xiaoyu SHEN, Congbo YIN. SOH estimation of lithium-ion batteries using a convolutional Fastformer[J]. Energy Storage Science and Technology, 2024, 13(3): 990-999.

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References 24

1	ZHANG Y, LI Y F. Prognostics and health management of Lithium-ion battery using deep learning methods: A review[J]. Renewable and Sustainable Energy Reviews, 2022, 161: 112282.
2	TIAN J Q, LIU X H, LI S Q, et al. Lithium-ion battery health estimation with real-world data for electric vehicles[J]. Energy, 2023, 270: 126855.
3	LIN M Q, WU D G, MENG J H, et al. Health prognosis for lithium-ion battery with multi-feature optimization[J]. Energy, 2023, 264: 126307.
4	SHAO J Y, LI J F, YUAN W Z, et al. A novel method of discharge capacity prediction based on simplified electrochemical model-aging mechanism for lithium-ion batteries[J]. Journal of Energy Storage, 2023, 61: 106788.
5	YANG L, CAI Y S, YANG Y X, et al. Supervisory long-term prediction of state of available power for lithium-ion batteries in electric vehicles[J]. Applied Energy, 2020, 257: 114006.
6	KIM S, PARK H J, CHOI J H, et al. A novel prognostics approach using shifting kernel particle filter of Li-ion batteries under state changes[J]. IEEE Transactions on Industrial Electronics, 2021, 68(4): 3485-3493.
7	ZHANG Y H, YANG Y, XIU X C, et al. A remaining useful life prediction method in the early stage of stochastic degradation process[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2021, 68(6): 2027-2031.
8	DONG G Z, HAN W J, WANG Y J. Dynamic Bayesian network-based lithium-ion battery health prognosis for electric vehicles[J]. IEEE Transactions on Industrial Electronics, 2021, 68(11): 10949-10958.
9	LIU K L, SHANG Y L, OUYANG Q, et al. A data-driven approach with uncertainty quantification for predicting future capacities and remaining useful life of lithium-ion battery[J]. IEEE Transactions on Industrial Electronics, 2021, 68(4): 3170-3180.
10	GOH H H, LAN Z T, ZHANG D D, et al. Estimation of the state of health (SOH) of batteries using discrete curvature feature extraction[J]. Journal of Energy Storage, 2022, 50: 104646.
11	XING L K, LIU X, LUO W F, et al. State of health estimation for lithium-ion batteries using IAO–SVR[J]. World Electric Vehicle Journal, 2023, 14(5): 122.
12	WANG S L, FERNANDEZ C, YU C M, et al. A novel charged state prediction method of the lithium ion battery packs based on the composite equivalent modeling and improved splice Kalman filtering algorithm[J]. Journal of Power Sources, 2020, 471: 228450.
13	CHEN Z, XUE Q, WU Y T, et al. Capacity prediction and validation of lithium-ion batteries based on long short-term memory recurrent neural network[J]. IEEE Access, 2020, 8: 172783-172798.
14	高德欣, 刘欣, 杨清. 基于卷积神经网络与双向长短时融合的锂离子电池剩余使用寿命预测[J]. 信息与控制, 2022, 51(3): 318-329, 360.
	GAO D X, LIU X, YANG Q. Remaining useful life prediction of lithium-ion battery based on CNN and BiLSTM fusion[J]. Information and Control, 2022, 51(3): 318-329, 360.
15	陈锐, 丁凯, 祖连兴, 等. 基于AED-CEEMD-Transformer的锂离子电池健康状态估计[J]. 储能科学与技术, 2023, 12(10): 3242-3253.
	CHEN R, DING K, ZU L X, et al. Prediction of state of health of lithium-ion batteries based on the AED-CEEMD-Transformer network[J]. Energy Storage Science and Technology, 2023, 12(10): 3242-3253.
16	LIN Z C, HU H P, LIU W, et al. State of health estimation of lithium-ion batteries based on remaining area capacity[J]. Journal of Energy Storage, 2023, 63: 107078.
17	KONG J Z, YANG F F, ZHANG X, et al. Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries[J]. Energy, 2021, 223: 120114.
18	JIA J F, LIANG J Y, SHI Y H, et al. SOH and RUL prediction of lithium-ion batteries based on Gaussian process regression with indirect health indicators[J]. Energies, 2020, 13(2): 375.
19	ROMAN D, SAXENA S, ROBU V, et al. Machine learning pipeline for battery state-of-health estimation[J]. Nature Machine Intelligence, 2021, 3: 447-456.
20	ZHU J G, WANG Y X, HUANG Y, et al. Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation[J]. Nature Communications, 2022, 13: 2261.
21	WU C H, WU F Z, QI T, et al. Fastformer: Additive attention can be all you need[J]. ArXiv e-Prints, 2021: arXiv: .
22	SEVERSON K A, ATTIA P M, JIN N, et al. Data-driven prediction of battery cycle life before capacity degradation[J]. Nature Energy, 2019, 4: 383-391.
23	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. December 4 - 9, 2017, Long Beach, California, USA. ACM, 2017: 6000–6010.
24	徐建民, 郭紫芯, 肖涛, 等.基于正交试验的水平换热器充气模拟实验研究[J]. 化学工程与装备, 2023(9): 10-12, 16.
	XU J M, GUO Z X,XIAO T, et al. Experimental study on air-simulated level heat exchanger based on orthogonal experiment[J]. Chemical Engineering and Equipment, 2023(9): 10-12, 16.

参数	B01	B02	B03	B04
额定容量/Ah	1.1	1.1	1.1	1.1
充电速率/C	5.3-4-1	5.6-4.6-1	5.6-4.3-1	5.6-4.6-1
充电切换SOC值	54%,80%	19%,80%	36%,80%	19%,80%
放电速率/C	4	4	4	4
最大截止电压/V	3.6	3.6	3.6	3.6
最小截止电压/V	2.0	2.0	2.0	2.0
循环次数	1062	1266	1114	1047

实验次数	输入步长L	卷积核尺寸K	头数H	网络深度N	RMSE
1	10	3	4	3	0.00304
2	10	4	6	4	0.00269
3	10	5	8	5	0.00303
4	15	3	6	5	0.00314
5	15	4	8	3	0.00291
6	15	5	4	4	0.00368
7	20	3	8	4	0.00300
8	20	4	4	5	0.00283
9	20	5	6	3	0.00265

因素	输入步长m	卷积核尺寸k	头数h	网络深度n
A₁	0.0029	0.00306	0.00318	0.00286
A₂	0.00324	0.00281	0.00282	0.00312
A₃	0.00282	0.00312	0.00298	0.00300
R	0.00042	0.00031	0.00036	0.00025

参数	模型	B01	B03	B04
	RNN	0.296%	0.325%	0.317%
MAE	CNN	0.204%	0.276%	0.261%
	GRU	0.244%	0.251%	0.298%
	卷积Fastformer	0.084%	0.080%	0.255%

	RNN	0.362%	0.396%	0.360%
RMSE	CNN	0.271%	0.311%	0.409%
	GRU	0.314%	0.362%	0.371%
	卷积Fastformer	0.133%	0.102%	0.291%

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