State of Health Estimation of Li-Ion Batteries Based on Differential Thermal Voltammetry and Gaussian Process Regression

doi:10.16183/j.cnki.jsjtu.2023.141

Abstract

Abstract:

Lithium-ion batteries experience capacity decline or even deterioration during the working process. Effective estimation of battery health status is a key challenge in the development of battery management systems. This paper proposes a method for estimating the state of health (SOH) of lithium-ion batteries based on the fusion of data-driven models and characteristic parameters. Using differential thermal voltammetry(DTV) to preprocess the experimental data of lithium-ion batteries, this method extracts six useful features, and establishes a SOH estimation model based on two-step Gaussian process regression (GPR) with different kernel functions. The results show that the established model can better approximate the experimental value and shorten the training and prediction time. The average absolute error of SOH estimation is 0.67%—0.97%, which is 20%—30% lower than that of single-step GPR. Therefore, the model has a high robustness and accuracy in estimating the state of health of lithium-ion batteries.

Key words: lithium-ion battery, state of health (SOH), differential thermal voltammetry (DTV), Gaussian process regression (GPR)

CLC Number:

TM912

ZHU Haoran, CHEN Ziqiang, YANG Deqing. State of Health Estimation of Li-Ion Batteries Based on Differential Thermal Voltammetry and Gaussian Process Regression[J]. Journal of Shanghai Jiao Tong University, 2024, 58(12): 1925-1934.

Figures/Tables 7

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References 25

[1]	WANG Z P, YUAN C G, LI X Y. Lithium battery state-of-health estimation via differential thermal voltammetry with Gaussian process regression[J]. IEEE Transactions on Transportation Electrification, 2021, 7(1): 16-25.
[2]	崔显, 陈自强, 卢地华, 等. 基于KCC-PF的锂离子电池剩余使用寿命预测[J]. 装备环境工程, 2022, 19(4): 86-94.
	CUI Xian, CHEN Ziqiang, LU Dihua, et al. Remaining useful life prediction of lithium-ion battery based on Kendall rank correlation coefficient particle filter[J]. Equipment Environmental Engineering, 2022, 19(4): 86-94.
[3]	SANTHANAGOPALAN S, RAMADASS P, ZHANG J Z. Analysis of internal short-circuit in a lithium ion cell[J]. Journal of Power Sources, 2009, 194(1): 550-557.
[4]	FERNANDES Y, BRY A, DE PERSIS S. Identification and quantification of gases emitted during abuse tests by overcharge of a commercial Li-ion battery[J]. Journal of Power Sources, 2018, 389: 106-119.
[5]	陈晓宇, 耿萌萌, 王乾坤, 等. 基于电化学阻抗特征选择和高斯过程回归的锂离子电池健康状态估计方法[J]. 储能科学与技术, 2022, 11(9): 2995-3002. doi: 10.19799/j.cnki.2095-4239.2022.0150
	CHEN Xiaoyu, GENG Mengmeng, WANG Qiankun, et al. 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. doi: 10.19799/j.cnki.2095-4239.2022.0150
[6]	WU M Y, QIN L L, WU G. State of power estimation of power lithium-ion battery based on an equivalent circuit model[J]. Journal of Energy Storage, 2022, 51: 104538.
[7]	CHEN J X, ZHANG Y, WU J, et al. SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output[J]. Energy, 2023, 262: 125375.
[8]	CHEN M Z, MA G J, LIU W B, et al. An overview of data-driven battery health estimation technology for battery management system[J]. Neurocomputing, 2023, 532: 152-169.
[9]	ZHANG L S, WANG W T, YU H Q, et al. Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep learning model[J]. iScience, 2022, 25(12): 105638.
[10]	MA B, YANG S C, ZHANG L S, et al. Remaining useful life and state of health prediction for lithium batteries based on differential thermal voltammetry and a deep-learning model[J]. Journal of Power Sources, 2022, 548: 232030.
[11]	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.
[12]	MAURES M, CAPITAINE A, DELÉTAGE J Y, et al. Lithium-ion battery SoH estimation based on incremental capacity peak tracking at several current levels for online application[J]. Microelectronics Reliability, 2020, 114: 113798.
[13]	卢地华, 陈自强. 基于双充电状态的锂离子电池健康状态估计[J]. 上海交通大学学报, 2022, 56(3): 342-352. doi: 10.16183/j.cnki.jsjtu.2021.027
	LU Dihua, CHEN Ziqiang. State of health estimation of lithium-ion batteries based on dual charging state[J]. Journal of Shanghai Jiao Tong University, 2022, 56(3): 342-352.
[14]	ZHU X T, LIN Q B, YOU S, et al. A review of battery state of health estimation[C]// 2019 4th International Conference on Intelligent Green Building and Smart Grid. Hubei, China: IEEE, 2019: 456-460.
[15]	庞辉. 基于电化学模型的锂离子电池多尺度建模及其简化方法[J]. 物理学报, 2017, 66(23): 238801.
	PANG Hui. Multi-scale modeling and its simplification method of Li-ion battery based on electrochemical model[J]. Acta Physica Sinica, 2017, 66(23): 238801.
[16]	OBREGON J, HAN Y R, HO C W, et al. Convolutional autoencoder-based SOH estimation of lithium-ion batteries using electrochemical impedance spectroscopy[J]. Journal of Energy Storage, 2023, 60: 106680.
[17]	DU X H, MENG J H, ZHANG Y M, et al. An information appraisal procedure: Endows reliable online parameter identification to lithium-ion battery model[J]. IEEE Transactions on Industrial Electronics, 2022, 69(6): 5889-5899.
[18]	SIHVO J, ROINILA T, STROE D I. SOH analysis of Li-ion battery based on ECM parameters and broadband impedance measurements[C]// IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society. Singapore: IEEE, 2020: 1923-1928.
[19]	李超然, 肖飞, 樊亚翔, 等. 基于卷积神经网络的锂离子电池SOH估算[J]. 电工技术学报, 2020, 35(19): 4106-4119.
	LI Chaoran, XIAO Fei, FAN Yaxiang, et al. An approach to lithium-ion battery SOH estimation based on convolutional neural network[J]. Transactions of China Electrotechnical Society, 2020, 35(19): 4106-4119.
[20]	SUI X, HE S, GISMERO A, et al. Robust fuzzy entropy-based SOH estimation for different lithium-ion battery chemistries[C]// 2022 IEEE Energy Conversion Congress and Exposition. Detroit, USA: IEEE, 2022: 1-8.
[21]	BIRKL C. Diagnosis and prognosis of degradation in lithium-ion batteries[D]. Oxford: University of Oxford, 2017.
[22]	BIRKL C. Oxford battery degradation database 1[DB/OL]. (2017-06-13)[2023-03-22]. https://ora.ox.ac.uk/objects/uuid:03ba4b01-cfed-46d3-9b1a-7d4a7bdf6fac.
[23]	WU B, YUFIT V, MERLA Y, et al. Differential thermal voltammetry for tracking of degradation in lithium-ion batteries[J]. Journal of Power Sources, 2015, 273: 495-501.
[24]	SCHAFER R W. What is a savitzky-golay filter?[A]. USA: IEEE Signal Processing Magazine, 2011: 111-117.
[25]	WILLIAM C K I, RASMUSSEN C E. Gaussian processes for regression[C]// Proceedings of the 8th International Conference on Neural Information Processing Systems. Cambridge, MA, USA: MIT, 1995, 514-520.