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| Multi-Source Data Feature Extraction Method for State of Charge Estimation of LiFePO4 Battery |
| Liu Suzhen1,2, Ren Jiale1,2, Yuan Luhang1,2, Xu Zhicheng1,2, Zhang Chuang1,2 |
1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin 300130 China
2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province Hebei University of Technology Tianjin 300130 China |
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Abstract The flat open-circuit voltage versus state of charge (SOC) curve of LiFePO4 batteries leads to difficulties in achieving an accurate estimation of SOC using only electrical signals. In addition, there are limitations in SOC estimation methods for single electrical, thermal, and acoustic data sources. In view of this, a multi-source data feature extraction method for SOC estimation of LiFePO4 battery was proposed. A comprehensive feature extraction was carried out on the electro-thermal-acoustic multi-source data obtained from different angles. Considering the advantages of different feature selection methods, a new feature selection method integrating Spearman correlation coefficient, mutual information, category boosting and least absolute shrinkage and selection operator regression was proposed. The joint selection of electro-thermal-acoustic key features was realized to improve the accuracy of SOC estimation.
Firstly, an experimental platform for LiFePO4 batteries was built. Electro-thermal-acoustic multi-source data were acquired. The transient features and short-term variation features of electrical and thermal signals, as well as the time-domain, frequency-domain, and time-frequency-domain features of ultrasonic signals were extracted, respectively. Secondly, in order to select the key features more accurately, a new method of feature selection incorporating Spearman correlation coefficient, mutual information, category boosting, and least absolute shrinkage and selection operator regression was proposed. In order to verify the performance of the proposed method, the proposed method was compared with SOC estimation results using all features and SOC estimation results under different feature selection methods. The effect of SOC estimation using single data source features versus multi-source data features was compared. The feasibility of the proposed method was verified at different magnifications and under different operating conditions. Finally, Gaussian white noise with different signal-to-noise ratios was added to the raw ultrasound signals acquired under dynamic stress test (DST) conditions and new european driving cycle (NEPC) conditions, respectively, to verify the applicability of the proposed method under high-intensity noise.
The results show that using the new method of feature selection proposed can effectively select the features that are important for SOC estimation with higher accuracy than SOC estimation using all features. With the same number of features, the SOC estimation accuracy of this method is improved compared with that of a single feature selection method. The model constructed using electric-thermal-acoustic multi-source key features has higher SOC estimation accuracy compared to single data source features. When using the BiGRU model, the mean absolute error and root-mean-square error of SOC estimates are 0.58% and 0.72%, respectively. The method performs well under a single operating condition. The method also shows good applicability at different discharge multipliers and under multiple operating conditions. Under DST conditions and NEDC conditions, the mean absolute error of SOC estimation is 0.91% and 0.98%, and the root mean square error is 1.03% and 1.13%, respectively, which verifies the validity and accuracy of the method. After adding noise with different signal-to-noise ratios to the original signals of different working conditions, the wavelet noise reduction can resist the noise interference in the actual environment to a certain extent and maintain the accuracy of SOC estimation.
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Received: 21 May 2024
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[1] Tian Jinpeng, Xiong Rui, Shen Weixiang, et al.State-of-charge estimation of LiFePO4 batteries in electric vehicles: a deep-learning enabled approach[J]. Applied Energy, 2021, 291(3): 116812.
[2] 宋磊, 陆春光, 刘琳, 等. 基于修正安时积分法的磷酸铁锂电池荷电状态估计[J]. 郑州大学学报(工学版), 2023, 44(6): 84-90.
Song Lei, Lu Chunguang, Liu Lin, et al.State of charge estimation of LiFePO4 battery based on modified amper-hour integral method[J]. Journal of Zhengzhou University (Engineering Science), 2023, 44(6): 84-90.
[3] Hou Jiayang, Xu Jun, Lin Chuanping, et al.State of charge estimation for lithium-ion batteries based on battery model and data-driven fusion method[J]. Energy, 2024, 290: 130056.
[4] 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.
[5] 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 decomposition[J]. Transactions of China Electrotechnical Society, 2022, 37(17): 4528-4536.
[6] 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.
[7] Javid G, Ould Abdeslam D, Basset M.Adaptive online state of charge estimation of EVs lithium-ion batteries with deep recurrent neural networks[J]. Energies, 2021, 14(3): 758.
[8] Chung D W, Ko J H, Yoon K Y.State-of-charge estimation of lithium-ion batteries using LSTM deep learning method[J]. Journal of Electrical Engineering & Technology, 2022, 17(3): 1931-1945.
[9] 余杰, 廖思阳, 徐箭, 等. 考虑环境温度的磷酸铁锂电池SOC实时修正及频率控制方法[J]. 电工技术学报, 2023, 38(17): 4564-4573.
Yu Jie, Liao Siyang, Xu Jian, et al.Real-time SOC correction and frequency control method for LFP batteries considering ambient temperature[J]. Transactions of China Electrotechnical Society, 2023, 38(17): 4564-4573.
[10] Hsieh A G, Bhadra S, Hertzberg B J, et al.Electrochemical-acoustic time of flight: in operando correlation of physical dynamics with battery charge and health[J]. Energy & Environmental Science, 2015, 8(5): 1569-1577.
[11] Gold L, Bach T, Virsik W, et al.Probing lithium-ion batteries' state-of-charge using ultrasonic transmission-concept and laboratory testing[J]. Journal of Power Sources, 2017, 343: 536-544.
[12] Davies G, Knehr K W, Van Tassell B, et al.State of charge and state of health estimation using electrochemical acoustic time of flight analysis[J]. Journal of the Electrochemical Society, 2017, 164(12): A2746-A2755.
[13] Ladpli P, Liu Chen, Kopsaftopoulos F, et al.Estimating lithium-ion battery state of charge and health with ultrasonic guided waves using an efficient matching pursuit technique[C]//2018 IEEE Transpo-rtation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific), Bangkok, Thailand, 2018: 1-5.
[14] Li Xiaoyu, Hua Wen, Wu Chuxin, et al.State estimation of a lithium-ion battery based on multi-feature indicators of ultrasonic guided waves[J]. Journal of Energy Storage, 2022, 56: 106113.
[15] 刘素贞, 袁路航, 张闯, 等. 基于超声时域特征及随机森林的磷酸铁锂电池荷电状态估计[J]. 电工技术学报, 2022, 37(22): 5872-5885.
Liu Suzhen, Yuan Luhang, Zhang Chuang, et al.State of charge estimation of LiFeO4 batteries based on time domain features of ultrasonic waves and random forest[J]. Transactions of China Electrotechnical Society, 2022, 37(22): 5872-5885.
[16] Cai Zhiduan, Pan Tianle, Jiang Haoye, et al.State-of-charge estimation of lithium-ion batteries based on ultrasonic detection[J]. Journal of Energy Storage, 2023, 65: 107264.
[17] Shi Na, Chen Zewang, Niu Mu, et al.State-of-charge estimation for the lithium-ion battery based on adaptive extended Kalman filter using improved parameter identification[J]. Journal of Energy Storage, 2022, 45: 103518.
[18] Zhang Runnan, Li Xiaoyu, Sun Chuanyu, et al.State of charge and temperature joint estimation based on ultrasonic reflection waves for lithium-ion battery applications[J]. Batteries, 2023, 9(6): 335.
[19] Yang Sijia, Zhang Caiping, Jiang Jiuchun, et al.Review on state-of-health of lithium-ion batteries: characterizations, estimations and applications[J]. Journal of Cleaner Production, 2021, 314: 128015.
[20] 徐文军, 胡芃. 基于电化学-热耦合模型的21700型锂离子电池充放电过程热行为分析[J]. 中国科学技术大学学报, 2020, 50(5): 645-653.
Xu Wenjun, Hu Peng.Thermal behavior analysis of 21700 type lithium ion battery during charge-discharge cycles based on electrochemical-thermal model[J]. Journal of University of Science and Technology of China, 2020, 50(5): 645-653.
[21] Sun Tao, Shen Tengteng, Zheng Yuejiu, et al.Modeling the inhomogeneous lithium plating in lithium-ion batteries induced by non-uniform temperature distribution[J]. Electrochimica Acta, 2022, 425: 140701.
[22] Wu Yi, Wang Youren, Yung W K C, et al. Ultrasonic health monitoring of lithium-ion batteries[J]. Electronics, 2019, 8(7): 751.
[23] 邓哲, 黄震宇, 刘磊, 等. 超声技术在锂离子电池表征中的应用[J]. 储能科学与技术, 2019, 8(6): 1033-1039.
Deng Zhe, Huang Zhenyu, Liu Lei, et al.Applications of ultrasound technique in characterization of lithium-ion batteries[J]. Energy Storage Science and Technology, 2019, 8(6): 1033-1039.
[24] 张闯, 孙博, 金亮, 等. 基于声波时域特征的锂离子电池荷电状态表征[J]. 电工技术学报, 2021, 36(22): 4666-4676.
Zhang Chuang, Sun Bo, Jin Liang, et al.Characterization of the state of charge of lithium-ion batteries based on the time-domain characteristics of acoustic waves[J]. Transactions of China Electro-technical Society, 2021, 36(22): 4666-4676.
[25] 李永刚, 王月, 吴滨源. 基于双重Q学习的动态风速预测模型[J]. 电工技术学报, 2022, 37(7): 1810-1819.
Li Yonggang, Wang Yue, Wu Binyuan.Dynamic wind speed prediction model based on double Q learning[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1810-1819.
[26] Moslemi A.A tutorial-based survey on feature selection: Recent advancements on feature selection[J]. Engineering Applications of Artificial Intelligence, 2023, 126: 107136.
[27] Zhang Mei, Yin Jun, Chen Wanli.SOH estimation and RUL prediction of lithium batteries based on multidomain feature fusion and CatBoost model[J]. Energy Science & Engineering, 2023, 11(9): 3082-3101.
[28] Vilsen S B, Stroe D I.Battery state-of-health modelling by multiple linear regression[J]. Journal of Cleaner Production, 2021, 290: 125700. |
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2025, Vol. 40 
