结合高斯过程回归与特征选择的锂离子电池容量估计方法

doi:10.19799/j.cnki.2095-4239.2021.0109

储能科学与技术 ›› 2021, Vol. 10 ›› Issue (4): 1432-1438.doi: 10.19799/j.cnki.2095-4239.2021.0109

结合高斯过程回归与特征选择的锂离子电池容量估计方法

韩云飞(), 谢佳, 蔡涛(), 程时杰

华中科技大学电气与电子工程学院，强电磁工程与新技术国家重点实验室，湖北武汉 430074

收稿日期:2021-03-16 修回日期:2021-03-18 出版日期:2021-07-05 发布日期:2021-06-25
通讯作者: 蔡涛 E-mail:M201871373@hust.edu.cn;caitao@hust.edu.cn
作者简介:韩云飞（1995—），男，硕士研究生，研究方向为电池管理、机器学习在储能系统中的应用，E-mail：M201871373@hust.edu.cn。
基金资助:
国家自然科学基金项目(U196620053)

Capacity estimation of lithium-ion batteries based on Gaussian process regression and feature selection

Yunfei HAN(), Jia XIE, Tao CAI(), Shijie CHENG

State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China

Received:2021-03-16 Revised:2021-03-18 Online:2021-07-05 Published:2021-06-25
Contact: Tao CAI E-mail:M201871373@hust.edu.cn;caitao@hust.edu.cn

摘要/Abstract

摘要：

由于锂离子电池本身复杂的老化特性，准确预测电池的健康状态和剩余寿命是一个尚未解决的挑战，这限制了消费电子、电动汽车和电网储能等技术的发展。电池的老化机制复杂且相互耦合，难以采用基于模型的方法进行准确的建模。本工作提出了一种基于数据驱动的锂离子电池容量估计方法，通过分析电池的电压-放电容量曲线随循环老化的演变模式，提取具有电化学意义的特征，采用高斯过程回归(Gaussian process regression，GPR)对电池的容量进行预测。该模型的输入特征可以在线获取，不需要对电池进行完整的充放电循环即可估计容量。在钴酸锂电池和磷酸铁锂电池数据集上分别进行了实验验证，结果表明该方法具有较好的泛化能力，对不同类型的电池均能实现准确的容量估计。将本文的方法与阻抗谱作为输入的GPR模型进行对比试验，结果表明该特征能获得更好的估计精度。这一结果说明了合适的特征选择能显著影响锂离子电池的数据驱动模型性能，为电池的状态预测与诊断提供了参考。

关键词: 锂离子电池, 容量估计, 高斯过程回归, 特征选择

Abstract:

Because of the complex degradation characteristics of Li-ion batteries, it is a continuing challenge to accurately predict the state of health and remaining useful life of batteries, which limits the development of consumer electronics, electric vehicles, and grid energy storage technologies. The degradation mechanism of the batteries is complex and coupled with each other. Therefore, it is difficult to use a model-based method for accurate modeling. This study proposes a data-driven capacity estimation method for Li-ion batteries. By analyzing the evolution pattern of the voltage-discharge capacity curve with cycle aging, features with an electrochemical concept are selected as the model input, and the capacity of Li-ion batteries can be predicted by the Gaussian process regression (GPR) model. The input features of the model can be obtained online, and the capacity of the battery can be estimated without a full charge-discharge. The experimental verification was completed with the data sets for LCO/graphite batteries and LFP/graphite batteries. The results show that the method has a good generalization ability and can accurately estimate the capacity of different types of batteries. The proposed method is compared to the GPR model with electrochemical impedance spectroscopy as the input, and the results indicate that the proposed method can obtain better estimation accuracy. This highlights that the appropriate selection of various features can significantly affect the performance of the data-driven model for Li-ion batteries and can provide a reference for battery state prediction and diagnosis.

Key words: lithium-ion battery, capacity estimation, Gaussian process regression, feature selection

中图分类号:

TM 912

韩云飞, 谢佳, 蔡涛, 程时杰. 结合高斯过程回归与特征选择的锂离子电池容量估计方法[J]. 储能科学与技术, 2021, 10(4): 1432-1438.

Yunfei HAN, Jia XIE, Tao CAI, Shijie CHENG. Capacity estimation of lithium-ion batteries based on Gaussian process regression and feature selection[J]. Energy Storage Science and Technology, 2021, 10(4): 1432-1438.

图/表 12

图1

图2

图3

图4

图5

图6

表1

图7

表2

图8

表3

表4

参考文献 16

1	MENG J H, STROE D, RICCO M, et al. A simplified model-based state-of-charge estimation approach for lithium-ion battery with dynamic linear model[J]. IEEE Transactions on Industrial Electronics, 2019, 66(10): 7717-7727.
2	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: 161-170.
3	HUANG W, ATTIA P M, WANG H S, et al. Evolution of the solid-electrolyte interphase on carbonaceous anodes visualized by atomic-resolution cryogenic electron microscopy[J]. Nano Letters, 2019, 19(8): 5140-5148.
4	DIAO W P, SAXENA S, PECHT M. Accelerated cycle life testing and capacity degradation modeling of LiCoO₂-graphite cells[J]. Journal of Power Sources, 2019, 435: doi: 10.1016/j.jpowsour.2019. 226830.
5	PALACIN M R, DE GUIBERT A. Why do batteries fail?[J]. Science, 2016, 351: 1253292.
6	HARRIS S J, LU P. Effects of inhomogeneities-nanoscale to mesoscale on the durability of Li-ion batteries[J]. The Journal of Physical Chemistry, 2013, 117: 6481-6492.
7	ATALAY S, SHEIKH M, MARIANI A, et al. Theory of battery ageing in a lithium-ion battery: capacity fade, nonlinear ageing and lifetime prediction[J]. Journal of Power Sources, 2020, 478:doi: 10.1016/j.jpowsour.2020. 229026.
8	朱元富, 贺文武, 李建兴, 等. 基于Bi-LSTM/Bi-GRU循环神经网络的锂电池SOC估计[J]. 储能科学与技术, 2021, 10(3): 1163-1176.ZHU Y F, HE W W, LI J X, et al. SOC Estimation for Li-ion batteries based on Bi-LSTM and Bi-GRU[J]. Energy Storage Science and Technology, 2021, 10(3): 1163-1176.
9	易灵芝, 张宗光, 范朝冬, 等. 基于EEMD-GSGRU的锂电池寿命预测[J]. 储能科学与技术, 2020, 9(5): 1566-1573.YI L Z, ZHANG Z G, FAN C D, et al. Life prediction of lithium battery based on EEMD-GSGRU[J]. Energy Storage Science and Technology, 2020, 9(5): 1566-1573.
10	LI X Y, WAN Z P, ZHANG L, et al. State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis[J]. Journal of Power Sources, 2019, 410/411: 106-114.
11	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.
12	李超然, 肖飞, 樊亚翔, 等. 基于深度学习的锂离子电池SOC和SOH联合估算[J]. 中国电机工程学报, 2021, 41(2): 681-691.LI C R, XIAO F, FAN Y X, et al. Joint estimation of the state of charge and the state of health based on deep learning for lithium-ion batteries[J]. Proceedings of the Chinese Society for Electrical Engineering, 2021, 41(2): 681-691.
13	ATTIA P M, GROVER A, JIN N, et al. Closed-loop optimization of fast-charging protocols for batteries with machine learning[J]. Nature, 2020, 578: 397-402.
14	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.
15	SEVERSON K. Machine learning for applications in chemical and biological engineering[D]. Cambridge: Massachusetts Institute of Technology, 2018.
16	RASMUSSEN C E, WILLIAMS C K I. Gaussian processes for machine learning[M]. Cambridge: The MIT Press, 2006.

电池编号	RMSE	MAPE/%
C02	0.3172	1.18
C06	0.5553	1.88
C08	0.3310	1.02

电池编号	RMSE	MAPE/%
C02	3.999	14.43
C06	5.003	18.50
C08	3.342	11.28

电池编号	RMSE	MAPE/%
b3c18	0.0018	1.41
b3c34	0.0035	2.80

电压区间/V	RMSE	MAPE/%
3.5～3.275	0.0551	1.17
3.2～2.975	0.0075	0.57
2.75～2.525	0.0047	0.40

[1]	李海涛, 孔令丽, 张欣, 余传军, 王纪威, 徐琳. N/P设计对高镍NCM/Gr电芯性能的影响[J]. 储能科学与技术, 2022, 11(7): 2040-2045.
[2]	刘显茜, 孙安梁, 田川. 基于仿生翅脉流道冷板的锂离子电池组液冷散热[J]. 储能科学与技术, 2022, 11(7): 2266-2273.
[3]	陈龙, 夏权, 任羿, 曹高萍, 邱景义, 张浩. 多物理场耦合下锂离子电池组可靠性研究现状与展望[J]. 储能科学与技术, 2022, 11(7): 2316-2323.
[4]	易顺民, 谢林柏, 彭力. 基于VF-DW-DFN的锂离子电池剩余寿命预测[J]. 储能科学与技术, 2022, 11(7): 2305-2315.
[5]	祝庆伟, 俞小莉, 吴启超, 徐一丹, 陈芬放, 黄瑞. 高能量密度锂离子电池老化半经验模型[J]. 储能科学与技术, 2022, 11(7): 2324-2331.
[6]	王宇作, 王瑨, 卢颖莉, 阮殿波. 孔结构对软碳负极储锂性能的影响[J]. 储能科学与技术, 2022, 11(7): 2023-2029.
[7]	孔为, 金劲涛, 陆西坡, 孙洋. 对称蛇形流道锂离子电池冷却性能[J]. 储能科学与技术, 2022, 11(7): 2258-2265.
[8]	霍思达, 薛文东, 李新丽, 李勇. 基于CiteSpace知识图谱的锂电池复合电解质可视化分析[J]. 储能科学与技术, 2022, 11(7): 2103-2113.
[9]	邓健想, 赵金良, 黄成德. 高能量锂离子电池硅基负极黏结剂研究进展[J]. 储能科学与技术, 2022, 11(7): 2092-2102.
[10]	欧宇, 侯文会, 刘凯. 锂离子电池中的智能安全电解液研究进展[J]. 储能科学与技术, 2022, 11(6): 1772-1787.
[11]	韩俊伟, 肖菁, 陶莹, 孔德斌, 吕伟, 杨全红. 致密储能：基于石墨烯的方法学和应用实例[J]. 储能科学与技术, 2022, 11(6): 1865-1873.
[12]	辛耀达, 李娜, 杨乐, 宋维力, 孙磊, 陈浩森, 方岱宁. 锂离子电池植入传感技术[J]. 储能科学与技术, 2022, 11(6): 1834-1846.
[13]	燕乔一, 吴锋, 陈人杰, 李丽. 锂离子电池负极石墨回收处理及资源循环[J]. 储能科学与技术, 2022, 11(6): 1760-1771.
[14]	沈秀, 曾月劲, 李睿洋, 李佳霖, 李伟, 张鹏, 赵金保. γ射线辐照交联原位固态化阻燃锂离子电池[J]. 储能科学与技术, 2022, 11(6): 1816-1821.
[15]	丁奕, 杨艳, 陈锴, 曾涛, 黄云辉. 锂离子电池智能消防及其研究方法[J]. 储能科学与技术, 2022, 11(6): 1822-1833.

结合高斯过程回归与特征选择的锂离子电池容量估计方法

Capacity estimation of lithium-ion batteries based on Gaussian process regression and feature selection

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 16

相关文章 15

编辑推荐

Metrics

本文评价