基于简单循环单元的储能锂离子电池SOC和SOH联合估计方法*

doi:10.11985/2023.03.035

电气工程学报 ›› 2023, Vol. 18 ›› Issue (3): 332-340.doi: 10.11985/2023.03.035

• 新能源发电与电能存储 • 上一篇下一篇

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基于简单循环单元的储能锂离子电池SOC和SOH联合估计方法^*

周航¹(), 刘晓龙¹(), 张梦迪¹(), 孙金磊¹(), 程泽²()

1.天津大学建筑设计规划研究总院有限公司天津 300073
2.天津大学电气自动化与信息工程学院天津 300072

收稿日期:2023-04-14 修回日期:2023-05-22 出版日期:2023-09-25 发布日期:2023-10-23
通讯作者: 刘晓龙，男，1975年生，硕士，高级工程师。主要研究方向为建筑电气。E-mail：tdjzsjy4@163.com
作者简介:周航，男，1984年生，硕士，高级工程师。主要研究方向为建筑电气。E-mail：hunter.chou@163.com
张梦迪，男，1995年生，助理工程师。主要研究方向为建筑电气。E-mail：m13021393168@163.com
孙金磊，男，1993年生，硕士，助理工程师。主要研究方向为建筑电气。E-mail：495101706@qq.com
程泽，男，1959年生，博士，教授。主要研究方向为现代电力电子技术及储能技术。E-mail：chengze@tju.edu.cn
基金资助:
* 国家自然科学基金资助项目(61873180)

Joint SOC and SOH Estimation Method for Energy Storage Lithium-ion Batteries Based on Simple Recurrent Unit

ZHOU Hang¹(), LIU Xiaolong¹(), ZHANG Mengdi¹(), SUN Jinlei¹(), CHENG Ze²()

1. Tianjin University Research Institute of Architectural Design and Urban Planning Co., Ltd., Tianjin 300073
2. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072

Received:2023-04-14 Revised:2023-05-22 Online:2023-09-25 Published:2023-10-23

摘要/Abstract

摘要：

锂离子电池的荷电状态(State of charge，SOC)和健康状态(State of health，SOH)是电池储能系统在运维过程中所需要估算的重要参数。为了能够对电池状态进行可靠估计，采用深度学习方法中的简单循环单元(Simple recurrent unit，SRU)来实现对电池SOC和SOH的联合估计。首先，通过利用SRU在处理时序问题上的优势，建立了基于SRU的电池SOC估计模型；接着，给模型引入了数据单元的输入形式，并使用含有电池老化信息的样本数据来对模型进行训练，使得训练好的模型能够实现任意电池老化程度下的SOC估计；最后，通过对该模型输出的SOC估计值中所隐含的老化信息进行挖掘，实现对电池SOH的估计。试验结果表明，该联合估计方法可以实现电池SOC与SOH的准确估计，并且对不同种类的电池也有较好的适用能力。

关键词: 锂离子电池, 荷电状态, 健康状态, 简单循环单元, 联合估计

Abstract:

The state of charge(SOC) and state of health(SOH) of lithium-ion battery are important parameters to be estimated during the operation and maintenance of battery energy storage systems. In order to reliably estimate battery states, the simple recurrent unit(SRU) in deep learning is used to achieve joint estimation of SOC and SOH. Firstly, a SOC estimation model based on SRU is established by taking advantage of SRU in dealing with timing problems. Then, the input form of data unit is introduced to the model, and the sample data containing battery aging information is used to train the model, so that the trained model can achieve SOC estimation at any degree of battery aging. Finally, the SOH of the battery is estimated by mining the aging information contained in the SOC estimate output by the model. The experimental results show that the proposed method can accurately estimate SOC and SOH of batteries, and has good applicability to different types of batteries.

Key words: Lithium-ion batteries, state of charge, state of health, simple recurrent unit, joint estimation

中图分类号:

TM911

周航, 刘晓龙, 张梦迪, 孙金磊, 程泽. 基于简单循环单元的储能锂离子电池SOC和SOH联合估计方法^*[J]. 电气工程学报, 2023, 18(3): 332-340.

ZHOU Hang, LIU Xiaolong, ZHANG Mengdi, SUN Jinlei, CHENG Ze. Joint SOC and SOH Estimation Method for Energy Storage Lithium-ion Batteries Based on Simple Recurrent Unit[J]. Journal of Electrical Engineering, 2023, 18(3): 332-340.

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参考文献 23

[1]	武龙星, 庞辉, 晋佳敏, 等. 基于电化学模型的锂离子电池荷电状态估计方法综述[J]. 电工技术学报, 2022, 37(7):1703-1725.
	WU Longxing, PANG Hui, JIN Jiamin, et al. A review of SOC estimation methods for lithium-ion batteries based on electrochemical model[J]. Transactions of China Electrotechnical Society, 2022, 37(7):1703-1725.
[2]	TIAN Huixin, QIN Pengliang, LI Kun, et al. A review of the state of health for lithium-ion batteries:Research status and suggestions[J]. Journal of Cleaner Production, 2020, 261(1):120813. doi: 10.1016/j.jclepro.2020.120813
[3]	李哲, 卢兰光, 欧阳明高. 提高安时积分法估算电池SOC精度的方法比较[J]. 清华大学学报, 2010, 50(8):1293-1296,1301.
	LI Zhe, LU Languang, OUYANG Minggao. Comparison of methods for improving SOC estimation accuracy through an ampere-hour integeration approach[J]. Journal of Tsinghua University, 2010, 50(8):1293-1296,1301.
[4]	CHEN Xiaokai, LEI Hao, XIONG Rui, et al. A novel approach to reconstruct open circuit voltage for state of charge estimation of lithium ion batteries in electric vehicles[J]. Applied Energy, 2019, 255:113758. doi: 10.1016/j.apenergy.2019.113758
[5]	DONG Xile, ZHANG Caiping, JIANG Jiuchun. Evaluation of SOC estimation method based on EKF/AEKF under noise interference[J]. Energy Procedia, 2018, 152:520-525. doi: 10.1016/j.egypro.2018.09.204
[6]	WU Chunling, HU Wenbo, MENG Jinhao, et al. State-of-charge estimation of lithium-ion batteries based on MCC-AEKF in non-Gaussian noise environment[J]. Energy, 2023, 274:127316. doi: 10.1016/j.energy.2023.127316
[7]	郭向伟, 邢程, 司阳, 等. RLS锂电池全工况自适应等效电路模型[J]. 电工技术学报, 2022, 37(16):4029-4037.
	GUO Xiangwei, XING Cheng, SI Yang, et al. RLS adaptive equivalent circuit model of lithium battery under full working condition[J]. Transactions of China Electrotechnical Society, 2022, 37(16):4029-4037.
[8]	DENG Zhongwei, HU Xiaosong, LIN Xianke, et al. Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression[J]. Energy, 2020, 205:118000. doi: 10.1016/j.energy.2020.118000
[9]	LUO Kai, CHEN Xiang, ZHENG Huiru, et al. A review of deep learning approach to predicting the state of health and state of charge of lithium-ion batteries[J]. Journal of Energy Chemistry, 2022, 74(11):159-173. doi: 10.1016/j.jechem.2022.06.049
[10]	JIAO Meng, WANG Dongqing, QIU Jianlong. A GRU-RNN based momentum optimized algorithm for SOC estimation[J]. Journal of Power Sources, 2020, 459:228051. doi: 10.1016/j.jpowsour.2020.228051
[11]	SHEN Liyuan, LI Jingjing, LIU Jieyan, et al. Temperature adaptive transfer network for cross-domain state-of-charge estimation of Li-ion batteries[J]. IEEE Transactions on Power Electronics, 2023, 38(3):3857-3869. doi: 10.1109/TPEL.2022.3220760
[12]	WANG Jia, ZHAO Rui, HUANG Qiuan, et al. High-efficient prediction of state of health for lithium-ion battery based on AC impedance feature tuned with Gaussian process regression[J]. Journal of Power Sources, 2023, 561:232737. doi: 10.1016/j.jpowsour.2023.232737
[13]	HAN Qiaoni, JIANG Fan, CHENG Ze. The state of health estimation framework for lithium-ion batteries based on health feature extraction and construction of mixed model[J]. Journal of the Electrochemical Society, 2021, 168(7):070509. doi: 10.1149/1945-7111/ac0e4c
[14]	TSENG K, LIANG Jinwei, CHANG W, et al. Regression models using fully discharged voltage and internal resistance for state of health estimation of lithium-ion batteries[J]. Energies, 2015, 8(4):2889-2907. doi: 10.3390/en8042889
[15]	LIU Datong, ZHOU Jianbao, LIAO Haitao, et al. A health indicator extraction and optimization framework for lithium-ion battery degradation modeling and prognostics[J]. IEEE Transactions on Systems Man & Cybernetics Systems, 2015, 45(6):915-928.
[16]	LIU Jiazhi, LIU Xintian. An improved method of state of health prediction for lithium batteries considering different temperature[J]. Journal of Energy Storage, 2023, 63:107028. doi: 10.1016/j.est.2023.107028
[17]	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.
[18]	王萍, 彭香园, 程泽, 等. 基于数据驱动模型融合的锂离子电池多时间尺度状态联合估计方法[J]. 汽车工程, 2022, 44(3):362-371,378.
	WANG Ping, PENG Xiangyuan, CHENG Ze, et al. A multi-time scale joint state estimation method for lithium-ion batteries based on data-driven model fusion[J]. Automotive Engineering, 2022, 44(3):362-371,378.
[19]	杨爽, 曾向阳. 基于多尺度稀疏简单循环单元模型的水声目标识别方法[J]. 哈尔滨工程大学学报, 2022, 43(7):958-964.
	YANG Shuang, ZENG Xiangyang. Underwater acoustic target recognition method based on multi-scale sparse simple recurrent unit model[J]. Journal of Harbin Engineering University, 2022, 43(7):958-964.
[20]	GONG Qingrui, WANG Ping, CHENG Ze. A data-driven model framework based on deep learning for estimating the states of lithium-ion batteries[J]. Journal of the Electrochemical Society, 2022, 169:030532. doi: 10.1149/1945-7111/ac5bac
[21]	BIRKL C. Oxford battery degradation dataset 1[Z]. Oxford:University of Oxford, 2017.
[22]	DONG Guangzhong, CHEN Zonghai, WEI Jingwen, et al. Battery health prognosis using Brownian motion modeling and particle filtering[J]. IEEE Transactions on Industrial Electronics, 2018, 65(11):8646-8655. doi: 10.1109/TIE.41
[23]	HONG J, LEE D, JEONG E R, et al. Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning[J]. Applied Energy, 2020, 278:115646. doi: 10.1016/j.apenergy.2020.115646

数据集	训练集	测试集
牛津	Cell1，Cell2，Cell3，Cell4，Cell5，Cell6	Cell7，Cell8
NASA	B0005，B0006	B0007

电池编号	RMSE(%)	MAE(%)	MAX(%)
Cell7	0.93	0.78	3.01
Cell8	0.92	0.73	3.40
B0007	0.95	0.79	4.05

电池编号	RMSE(%)	MAE(%)	MAX(%)
Cell7	0.76	0.71	1.29
Cell8	0.79	0.70	1.70
B0007	0.93	0.84	2.30

基于简单循环单元的储能锂离子电池SOC和SOH联合估计方法^*

Joint SOC and SOH Estimation Method for Energy Storage Lithium-ion Batteries Based on Simple Recurrent Unit

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