基于等效电路模型和数据驱动模型融合的SOC和SOH联合估计方法

doi:10.19595/j.cnki.1000-6753.tces.230261

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (1540 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The accuracy of state of charge (SOC)can be significantly affected by battery aging, leading to misguidance in the calibration of state of health(SOH). Existing studies often estimate SOC and SOH separately, neglecting their close relationship and resulting in reduced estimation accuracy. This paper proposes a joint estimation method for SOC and SOH based on the fusion of an equivalent circuit model and a data-driven model. The influence mechanism between battery SOC and SOH is revealed, mitigating their mutual influence and enhancing the accuracy of SOC and SOH estimation.
Firstly, by constructing a second-order RC equivalent circuit model of the battery considering aging and SOC, the recursive least square method with a forgetting factor isused to identify battery parameters online under different SOC and SOH conditions. Secondly, the required time from 20%SOC to the end of the constant-current charging stage is extracted. Pearson and Spearman relationships between constant current charge time and SOH of lithium-ion batteries arecalculated. Thirdly, the actual time required from 20%SOC to the end of the constant-current charging phase of lithium-ion batteries is taken as input and battery SOH as output to train the GPR model offline. The trained GPR model is optimized by hyper parameters and used for SOH prediction. Finally, the estimated SOH output ismultiplied by the rated capacity of the cell to obtain the actual cell capacity, which is used to update the second-order RC state space equation. Based on the second-order RC equivalent circuit model, the battery SOC was estimated by the EKF.
The Oxford University battery degradation data set and NASA random battery data set are used to verify the joint estimation method. The results show that the proposed method achieves low average MAE and RMSE for SOC estimation (typicallyless than 0.04). In aging experiments of Cell 1~Cell 8 and RW 3~RW 6 under different working conditions, the average MAE and average RMSE are stable. The actual initial SOC value is 1, and the initial value is set to 0.7 in this paper. With the decline in battery capacity, the joint estimate of battery SOC can follow the actual SOC more accurately. The joint estimation algorithm is robust and accurate. Meanwhile, the reservation-one method is used to verify the Gaussian process regression model. The MAE and RMSE predicted by SOH for Cell 1~Cell 8 are less than 0.5%, and the MAE and RMSE predicted by SOH for RW 3~RW 6 are about 0.05. All the predicted SOH values are in a narrow confidence interval.
The following conclusions can be drawn from the simulation analysis: (1) Compared with the existing battery model, the dynamic second-order RC equivalent circuit model considering battery aging and SOC is constructed. In the case of battery aging, the voltage obtained by fitting the identified circuit parameters can track the actual voltage well. (2) The joint estimation method applies the real-time online modified battery parameters and battery SOH to ensure that the battery SOC is adjusted with battery aging. The SOC estimation is accurate. (3) The combined method applies the estimated SOC to ensure effective health feature extraction and improve the accuracy of SOH prediction.

Key words： Lithium-ion battery state of charge state of health Gaussian process regression forgetting factor recursive least squares

Received: 07 March 2023 Published: 07 June 2024

PACS:

TM912

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Liu Ping
	Li Zewen
	Cai Yusi
	Wang Wen
	Xia Xiangyang

Cite this article:

Liu Ping,Li Zewen,Cai Yusi等. Joint Estimation Method of SOC and SOH Based on Fusion of Equivalent Circuit Model and Data-Driven Model[J]. Transactions of China Electrotechnical Society, 2024, 39(10): 3232-3243.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.230261 OR https://dgjsxb.ces-transaction.com/EN/Y2024/V39/I10/3232

[1] 甘露雨, 陈汝颂, 潘弘毅, 等. 锂电池安全性多尺度研究策略: 实验与模拟方法[J]. 储能科学与技术, 2022, 11(3): 852-865.
Gan Luyu, Chen Rusong, Pan Hongyi, et al.Multiscale research strategy of lithium ion battery safety issue: experimental and simulation methods[J]. Energy Storage Science and Technology, 2022, 11(3): 852-865.
[2] 王义军, 左雪. 锂离子电池荷电状态估算方法及其应用场景综述[J]. 电力系统自动化, 2022, 46(14): 193-207.
Wang Yijun, Zuo Xue.Review on estimation methods for state of charge of lithium-ion battery and their application scenarios[J]. Automation of Electric Power Systems, 2022, 46(14): 193-207.
[3] Rahimi-Eichi H, Ojha U, Baronti F, et al.Battery management system: an overview of its application in the smart grid and electric vehicles[J]. IEEE Industrial Electronics Magazine, 2013, 7(2): 4-16.
[4] 谭必蓉, 杜建华, 叶祥虎, 等. 基于模型的锂离子电池SOC估计方法综述[J]. 储能科学与技术, 2023, 12(6): 1995-2010.
Tan Birong, Du Jianhua, Ye Xianghu, et al.Overview of SOC estimation methods for lithium-ion batteries based on model[J]. Energy Storage Science and Technology, 2023, 12(6): 1995-2010.
[5] How D N T, Hannan M A, Hossain Lipu M S, et al. State of charge estimation for lithium-ion batteries using model-based and data-driven methods: a review[J]. IEEE Access, 2019, 7: 136116-136136.
[6] 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.
[7] Yu Quanqing, Wan Changjiang, Li Junfu, et al.An open circuit voltage model fusion method for state of charge estimation of lithium-ion batteries[J]. Energies, 2021, 14(7): 1797.
[8] Somakettarin N, Funaki T.Study on factors for accurate open circuit voltage characterizations in Mn-type Li-ion batteries[J]. Batteries, 2017, 3(4): 8.
[9] 于海芳, 逯仁贵, 朱春波, 等. 基于安时法的镍氢电池SOC估计误差校正[J]. 电工技术学报, 2012, 27(6): 12-18.
Yu Haifang, Lu Rengui, Zhu Chunbo, et al.State of charge estimation calibration for Ni-MH battery based on ampere-hour method[J]. Transactions of China Electrotechnical Society, 2012, 27(6): 12-18.
[10] 程泽, 杨磊, 孙幸勉. 基于自适应平方根无迹卡尔曼滤波算法的锂离子电池SOC和SOH估计[J]. 中国电机工程学报, 2018, 38(8): 2384-2393, 2548.
Cheng Ze, Yang Lei, Sun Xingmian.State of charge and state of health estimation of Li-ion batteries based on adaptive square-root unscented Kalman filters[J]. Proceedings of the CSEE, 2018, 38(8): 2384-2393, 2548.
[11] 郝文美, 张立伟, 彭博, 等. 动车组钛酸锂电池荷电状态估计[J]. 电工技术学报, 2021, 36(增刊1): 362-371.
Hao Wenmei, Zhang Liwei, Peng Bo, et al.State of charge estimation of lithium titanate battery for EMU[J]. Transactions of China Electrotechnical Society, 2021, 36(S1): 362-371.
[12] Luo Jiayi, Peng Jiankun, He Hongwen.Lithium-ion battery SOC estimation study based on Cubature Kalman filter[J]. Energy Procedia, 2019, 158: 3421-3426.
[13] 魏克新, 陈峭岩. 基于自适应无迹卡尔曼滤波算法的锂离子动力电池状态估计[J]. 中国电机工程学报, 2014, 34(3): 445-452.
Wei Kexin, Chen Qiaoyan.States estimation of Li-ion power batteries based on adaptive unscented Kalman filters[J]. Proceedings of the CSEE, 2014, 34(3): 445-452.
[14] 李超然, 肖飞, 樊亚翔, 等. 基于门控循环单元神经网络和Huber-M估计鲁棒卡尔曼滤波融合方法的锂离子电池荷电状态估算方法[J]. 电工技术学报, 2020, 35(9): 2051-2062.
Li Chaoran, Xiao Fei, Fan Yaxiang, et al.A hybrid approach to lithium-ion battery SOC estimation based on recurrent neural network with gated recurrent unit and huber-M robust Kalman filter[J]. Transactions of China Electrotechnical Society, 2020, 35(9): 2051-2062.
[15] 李宁, 何复兴, 马文涛, 等. 基于经验模态分解的门控循环单元神经网络的锂离子电池荷电状态估计[J]. 电工技术学报, 2022, 37(17): 4528-4536.
Li Ning, He Fuxing, Ma Wentao, et al.State-of- charge estimation of lithium-ion battery based on gated recurrent unit using empirical mode decom- position[J]. Transactions of China Electrotechnical Society, 2022, 37(17): 4528-4536.
[16] Jiao Meng, Wang Dongqing, Qiu Jianlong.A GRU- RNN based momentum optimized algorithm for SOC estimation[J]. Journal of Power Sources, 2020, 459: 228051.
[17] 周才杰, 汪玉洁, 李凯铨, 等. 基于灰色关联度分析-长短期记忆神经网络的锂离子电池健康状态估计[J]. 电工技术学报, 2022, 37(23): 6065-6073.
Zhou Caijie, Wang Yujie, Li Kaiquan, et al.State of health estimation for lithium-ion battery based on gray correlation analysis and long short-term memory neural network[J]. Transactions of China Electro- technical Society, 2022, 37(23): 6065-6073.
[18] 姚芳, 张楠, 黄凯. 锂离子电池状态估算与寿命预
测综述[J]. 电源学报, 2020, 18(3): 175-183.
19 Yao Fang, Zhang Nan, Huang Kai.Review of state estimation and life prediction for lithiumion batteries[J]. Journal of Power Supply, 2020, 18(3): 175-183.
[19] 陈霖华, 陈剑, 徐志强, 等. 基于实时电路模型的储能系统锂离子电池状态估算[J]. 中南大学学报(自然科学版), 2021, 52(2): 458-464.
Chen Linhua, Chen Jian, Xu Zhiqiang, et al.State estimation of lithium ion battery in energy storage system based on real time circuit model[J]. Journal of Central South University (Science and Technology), 2021, 52(2): 458-464.
[20] Spotnitz R.Simulation of capacity fade in lithium-ion batteries[J]. Journal of Power Sources, 2003, 113(1): 72-80.
[21] Bloom I, Cole B W, Sohn J J, et al.An accelerated calendar and cycle life study of Li-ion cells[J]. Journal of Power Sources, 2001, 101(2): 238-247.
[22] Matsushima T.Deterioration estimation of lithium- ion cells in direct current power supply systems and characteristics of 400-Ah lithium-ion cells[J]. Journal of Power Sources, 2009, 189(1): 847-854.
[23] Remmlinger J, Buchholz M, Meiler M, et al.State- of-health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimation[J]. Journal of Power Sources, 2011, 196(12): 5357-5363.
[24] Liu Enhui, Niu Guangxing, Wang Xuan, et al.SOH diagnostic and prognostic based on external health indicator of lithium-ion batteries[C]//IECON 2021- 47th Annual Conference of the IEEE Industrial Electronics Society, Toronto, ON, Canada, 2021: 1-6.
[25] 樊欣欣, 丁晖, 陈秀国, 等. 基于模糊逻辑的变电站蓄电池在线健康状态评估[J]. 电子器件, 2021, 44(1): 136-140.
Fan Xinxin, Ding Hui, Chen Xiuguo, et al.On-line health assessment of substation battery based on fuzzy logic[J]. Chinese Journal of Electron Devices, 2021, 44(1): 136-140.
[26] 申江卫, 高承志, 舒星, 等. 基于迁移模型的锂离子电池宽温度全寿命SOC与可用容量联合估计[J]. 电工技术学报, 2023, 38(11): 3052-3063.
Shen Jiangwei, Gao Chengzhi, Shu Xing, et al.Joint estimation of SOC and usable capacity of lithium-ion battery with wide temperature and full life based on migration model[J]. Transactions of China Electro- technical Society, 2023, 38(11): 3052-3063.
[27] 赵靖英, 胡劲, 张雪辉, 等. 基于锂电池模型和分数阶理论的SOC-SOH联合估计[J]. 电工技术学报, 2023, 38(17): 4551-4563.
Zhao Jingying, Hu Jin, Zhang Xuehui, et al.Joint estimation of the SOC-SOH based on lithium battery model and fractional order theory[J]. Transactions of China Electrotechnical Society, 2023, 38(17): 4551-4563.
[28] Birkl C.Oxford battery degradation dataset 1[DB/OL]. University Oxford, Oxford, UK, 2017.
[29] Bole B, Kulkarni C, Daigle M.Randomized battery usage data set, NASA prognostics data repo- sitory[DB/OL]. NASA Ames Research Center, Moffett Field, CA, USA.
[30] Yun Xiang, Zhang Xin, Wang Chao, et al.Online parameters identification and state of charge esti- mation for lithium-ion batteries based on improved central difference particle filter[J]. Journal of Energy Storage, 2023, 70: 107987.
[31] Abu-Sharkh S, Doerffel D.Rapid test and non-linear model characterisation of solid-state lithium-ion batteries[J]. Journal of Power Sources, 2004, 130(1/2): 266-274.
[32] 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.
[33] 廖根兴, 赵盈盈, 高雁凤, 等. 锂离子电池模型参数辨识与荷电状态估算[J]. 电源技术, 2021, 45(9): 1136-1139.
Liao Genxing, Zhao Yingying, Gao Yanfeng, et al.Parameter identification and SOC estimation of lithium-ion battery model[J]. Chinese Journal of Power Sources, 2021, 45(9): 1136-1139.