上海交通大学学报 ›› 2024, Vol. 58 ›› Issue (12): 1925-1934.doi: 10.16183/j.cnki.jsjtu.2023.141
收稿日期:
2023-04-17
修回日期:
2023-07-25
接受日期:
2023-09-18
出版日期:
2024-12-28
发布日期:
2025-01-06
通讯作者:
陈自强,研究员,博士生导师;E-mail:作者简介:
朱浩然(1999—),硕士生,从事锂离子电池健康状态监测研究.
ZHU Haoran1,2, CHEN Ziqiang1(), YANG Deqing1,2
Received:
2023-04-17
Revised:
2023-07-25
Accepted:
2023-09-18
Online:
2024-12-28
Published:
2025-01-06
摘要:
锂离子电池在工作过程中会发生容量衰退甚至恶化等现象,实现电池健康状态(SOH)的有效估计是电池管理系统发展的关键挑战.提出一种数据驱动模型与特征参数相融合的锂离子电池健康状态估计方法,使用差分热伏安(DTV)法对锂离子电池实验数据进行预处理,提取6个有用的特征,建立以不同核函数的两步高斯过程回归(GPR)为核心的SOH估计模型.结果表明,建立的模型能在更好地逼近实验值的同时缩短训练和预测时间,SOH估计的平均绝对误差在0.67%~0.97%之间,相比单步GPR降低了20%~30%.因此,该模型对锂离子电池健康状态的估计有较高的鲁棒性和准确性.
中图分类号:
朱浩然, 陈自强, 杨德庆. 基于差分热伏安法和高斯过程回归的锂离子电池健康状态估计[J]. 上海交通大学学报, 2024, 58(12): 1925-1934.
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.
[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. |
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