基于片段充电数据和DEKF-WNN-WLSTM的锂电池健康状态实时估计

doi:10.19595/j.cnki.1000-6753.tces.2222241

摘要
图/表
参考文献
相关文章 (8)

全文: PDF (1219 KB)
输出: BibTeX | EndNote (RIS)

摘要

实时准确评估电动汽车锂电池健康状态（SOH）对电动汽车的稳定行驶至关重要。因此,本文提出了一种基于锂电池日常片段充电数据和双扩展卡尔曼滤波-小波神经网络-小波长短时记忆神经网络（DEKF-WNN-WLSTM）的电池全充时间估计模型,进而提高了片段充电数据评估电池健康状态的准确度。首先设计了双扩展卡尔曼滤波预测-校正算法,分别用来估计片段数据对应的全充时间和校正扩展卡尔曼滤波的状态初值,以提高估计的准确性。然后,设计了小波神经网络-小波长短时神经网络来学习扩展卡尔曼滤波递推过程的观测值。最后,通过实验仿真,验证了本文所提算法在锂电池健康状态实时估算中的准确性和有效性。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	宋显华
	姚全正

关键词 ：电池健康状态, 片段数据, 双扩展卡尔曼滤波, 小波神经网络, 小波长短时记忆神经网络

Abstract：

As a clean technology to solve carbon emissions, electric vehicles have been widely used in modern vehicles. Due to its high energy density, light weight, long life and low self discharge, lithium-ion batteries have become the main energy storage equipment of electric vehicles. Real time and accurate evaluation of the state of health (SOH) of the lithium batteries is critical to the stable driving of electric vehicles. However, most traditional SOH forecast methods are offline, which makes it difficult to obtain the SOH of the batteries in real time. Recently, some methods were presented to forecast the SOH of lithium-ion batteries, but most of them suffered from inconvenient adjustment of battery model parameters and accumulation of errors. To address these issues, this paper proposes a battery full charging time estimation model and dual extended Kalman filters-wavelet neural network-wavelet short memory neural network (DEKF-WNN-WLSTM). By taking the daily segment charging data of lithium batteries as input, to predict the full time charging of the battery, and then get the SOH in real time.
Firstly, based on the strong robustness of Wavelet Neural Network (WNN) and the ability of Long Short Term Memory (LSTM) to extract the time series features of the data, the neural network of WNN-WLSTM is designed. Secondly, two WNN-WLSTM networks are trained with one full charging data and three fragment data of lithium batteries, respectively. Thirdly, a real-time estimation algorithm named DEKF is constructed, in which the first EKF is used to estimate the full charging time corresponding to the segment data, and the second EKF is used to predict the error between the estimated and measured battery full charging time under the current cycle. Then the two trained networks are integrated into DEKF to provide corresponding output values for the cyclic recursion of EKF. Finally, a real-time SOH estimation model based on daily segment charging data is designed. The segment data from constant current charging to full charging at any time is used as the input of DEKF-WNN-WLSTM, to estimate the current full charging time of lithium batteries, then calculate the SOH of the battery at the current time. In this real-time model, the WNN-WLSTM alleviates the inconvenient adjustment of battery model parameters problem, addresses the long-term dependence problem. The DEKF uses the daily segment charging data as the input, which extends the practical application of the model.
Simulation results on the actual battery charging and discharging data show that, the mean relative error of the predictions for the entire 80 cycles is 0.0101, the estimated error for the first 50 cycles is completely less than 2%, and less than 1% at most times. The comparison between DEKF-WNN-WLSTM and Extended Kalman Filter and Gaussian Process Regression (EKF-GPR) shows that, the mean relative error of EKF-GPR is 0.0176, which is higher than DEKF-WNN-WLSTM, especially in the 170-180 cycles, which indicates that the model of DEKF-WNN-WLSTM can alleviate certain error growth with the increase of cycles. The proposed method has a better estimation effect under the condition that no artificial full recharge operation is performed to update the initial full charging time value.
The following conclusions can be drawn from the simulation analysis:(1)The proposed method integrates WNN-WLSTM neural network, which address the problems of long-term dependence and the inconvenient adjustment of battery model parameters. (2) Compared with EKF-GPR, the DEKF-WNN-WLSTM not only improves the prediction accuracy, but also alleviates the error accumulation. (3) The proposed model only needs the daily segment charging data. In this sense, it is practical in the real world.

Key words： State of health segment data dual extended Kalman filter wavelet neural network wavelet long short-term memory

收稿日期: 2022-12-28

PACS:

TM911

基金资助:

黑龙江省自然科学基金联合引导项目(LH2022F032)和山东省自然科学基金联合基金培育项目（ZR2022LLZ003）

通讯作者: 宋显华, 女,1981年生,博士,副教授,博士生导师,研究方向为机器学习和智能状态监测,图像安全和量子计算等。E-mail：songxianhua@hrbust.edu.cn

作者简介: 姚全正男,1997年生,硕士研究生,研究方向为机器学习以及电动汽车动力电池健康状态评估。E-mail：2311884748@qq.com

引用本文:

宋显华, 姚全正. 基于片段充电数据和DEKF-WNN-WLSTM的锂电池健康状态实时估计[J]. 电工技术学报, 0, (): 9002-2. Song Xianhua, Yao Quanzheng. Real-Time State of Health Estimation for Lithium-Ion Batteries Based on Daily Segment Charging Data and Dual Extended Kalman Filters-Wavelet Neural Network-Wavelet Short Memory Neural Network. Transactions of China Electrotechnical Society, 0, (): 9002-2.

链接本文:

https://dgjsxb.ces-transaction.com/CN/10.19595/j.cnki.1000-6753.tces.2222241 https://dgjsxb.ces-transaction.com/CN/Y0/V/I/9002

[1] Al-Ghussain L, Darwish Ahmad A, Abubaker A M, et al.An integrated photovoltaic/wind/biomass and hybrid energy storage systems towards 100% renewable energy microgrids in university campuses[J]. Sustainable Energy Technologies and Assessments, 2021, 46: 101273.
[2] Guo Yuanjun, Yang Zhile, Liu Kailong, et al.A compact and optimized neural network approach for battery state-of-charge estimation of energy storage system[J]. Energy, 2021, 219: 119529.
[3] Zhang Zhengxin, Si Xiaosheng, Hu Changhua, et al.Degradation data analysis and remaining useful life estimation: a review on Wiener-process-based methods[J]. European Journal of Operational Research, 2018, 271(3): 775-796.
[4] Hu Xiaosong, Feng Fei, Liu Kailong, et al.State estimation for advanced battery management: key challenges and future trends[J]. Renewable and Sustainable Energy Reviews, 2019, 114: 109334.
[5] Shrivastava P, Soon T K, Bin Idris M Y I, et al. Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries[J]. Renewable and Sustainable Energy Reviews, 2019, 113: 109233.
[6] Chen Zheng, Xue Qiao, Xiao Renxin, et al.State of health estimation for lithium-ion batteries based on fusion of autoregressive moving average model and Elman neural network[J]. IEEE Access, 2019, 7: 102662-102678.
[7] Lyu Chao, Zhang Tao, Luo Weilin, et al.SOH estimation of lithium-ion batteries based on fast time domain impedance spectroscopy[C]//2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), Xi'an, China, 2019: 2142-2147.
[8] Kwiecien M, Badeda J, Huck M, et al.Determination of SoH of lead-acid batteries by electrochemical impedance spectroscopy[J]. Applied Sciences, 2018, 8(6): 873.
[9] Sauer D U, Wenzl H.Comparison of different approaches for lifetime prediction of electrochemical systems—using lead-acid batteries as example[J]. Journal of Power Sources, 2008, 176(2): 534-546.
[10] Zhang Ji'ang, Wang Ping, Gong Qingrui, et al.SOH estimation of lithium-ion batteries based on least squares support vector machine error compensation model[J]. Journal of Power Electronics, 2021, 21(11): 1712-1723.
[11] Wang Shuai, Zhang Xiaochen, Chen Wengxiang, et al.State of health prediction based on multi-kernel relevance vector machine and whale optimization algorithm for lithium-ion battery[J]. Transactions of the Institute of Measurement and Control, 2021: 014233122110420.
[12] 杨胜杰, 罗冰洋, 王菁, 等. 基于容量增量曲线峰值区间特征参数的锂离子电池健康状态估算[J]. 电工技术学报, 2021, 36(11): 2277-2287.
Yang Shengjie, Luo Bingyang, Wang Jing, et al.State of health estimation for lithium-ion batteries based on peak region feature parameters of incremental capacity curve[J]. Transactions of China Electrotechnical Society, 2021, 36(11): 2277-2287.
[13] 韩乔妮, 姜帆, 程泽. 变温度下IHF-IGPR框架的锂离子电池健康状态预测方法[J]. 电工技术学报, 2021, 36(17): 3705-3720.
Han Qiaoni, Jiang Fan, Cheng Ze.State of health estimation for lithium-ion batteries based on the framework of IHF-IGPR under variable temperature[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3705-3720.
[14] 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.
[15] 郭永芳, 黄凯, 李志刚. 基于短时搁置端电压压降的快速锂离子电池健康状态预测[J]. 电工技术学报, 2019, 34(19): 3968-3978.
Guo Yongfang, Huang Kai, Li Zhigang.Fast state of health prediction of lithium-ion battery based on terminal voltage drop during rest for short time[J]. Transactions of China Electrotechnical Society, 2019, 34(19): 3968-3978.
[16] Fan Yaxiang, Xiao Fei, Li Chaoran, et al.A novel deep learning framework for state of health estimation of lithium-ion battery[J]. Journal of Energy Storage, 2020, 32: 101741.
[17] Bonfitto A.A method for the combined estimation of battery state of charge and state of health based on artificial neural networks[J]. Energies, 2020, 13(10): 2548.
[18] Zhang Sihan, Hosen M S, Kalogiannis T, et al.State of health estimation of lithium-ion batteries based on electrochemical impedance spectroscopy and backpropagation neural network[J]. World Electric Vehicle Journal, 2021, 12(3): 156.
[19] Chang Chun, Wang Qiyue, Jiang Jiuchun, et al.Lithium-ion battery state of health estimation using the incremental capacity and wavelet neural networks with genetic algorithm[J]. Journal of Energy Storage, 2021, 38: 102570.
[20] Jia Jianfang, Wang Keke, Pang Xiaoqiong, et al.Multi-scale prediction of RUL and SOH for lithium-ion batteries based on WNN-UPF combined model[J]. Chinese Journal of Electronics, 2021, 30(1): 26-35.
[21] Zhang J, Gao X P, Li Y Q.Efficient wavelet networks for function learning based on adaptive wavelet neuron selection[J]. IET Signal Processing, 2012, 6(2): 79.
[22] Xia Bizhong, Cui Deyu, Sun Zhen, et al.State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network[J]. Energy, 2018, 153: 694-705.
[23] Chang, Wen-Yeau, Chang, Po-Chuan .Application of radial basis function neural network, to estimate the state of health for LFP battery[J]. International Journal of Electrical and Electronics Engineering (IJEEE), 2018, 7(1): 1-6.
[24] Liu Qiang, Kang Yongzhe, Qu Shaofei, et al.An online SOH estimation method based on the fusion of improved ICA and LSTM[C]//2020 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia), Weihai, China, 2020: 1163-1167.
[25] Yayan U, Arslan A T, Yucel H.A novel method for SoH prediction of batteries based on stacked LSTM with quick charge data[J]. Applied Artificial Intelligence, 2021, 35(6): 421-439.
[26] Kim S J, Kim S H, Lee H M, et al.State of health estimation of Li-ion batteries using multi-input LSTM with optimal sequence length[C]//2020 IEEE 29th International Symposium on Industrial Electronics (ISIE), Delft, Netherlands, 2020: 1336-1341.
[27] 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.
Cheng Ze, Yang Lei, Sun Xingmian.Estimation of SOC and SOH of Li-ion battery based on adaptive square root unscented Kalman filter algorithm[J]. Proceedings of the CSEE, 2018, 38(8): 2384-2393.
[28] 王萍, 弓清瑞, 张吉昂, 等. 一种基于数据驱动与经验模型组合的锂电池在线健康状态预测方法[J]. 电工技术学报, 2021, 36(24)5201-5212.
Wang Ping, Gong Qingrui, Zhang Jiang, et al. An online state of health prediction method for lithium batteries based on combination of data-driven and empirical model[J]. Transactions of China Electrotechnical Society, 2021, 36(24)5201-5212.
[29] 周頔, 宋显华, 卢文斌, 等. 基于日常片段充电数据的锂电池健康状态实时评估方法研究[J]. 中国电机工程学报, 2019, 39(1): 105-111.
Zhou Di, Song Xianhua, Lu Wenbin, et al.Real-time SOH estimation algorithm for lithium-ion batteries based on daily segment charging data[J]. Proceedings of the CSEE, 2019, 39(1): 105-111.
[30] Yang Duo, Zhang Xu, Pan Rui, et al.A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve[J]. Journal of Power Sources, 2018, 384: 387-395.
[31] Julier S J, Uhlmann J K.New extension of the Kalman filter to nonlinear systems[C]//AeroSense '97. Proc SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, Orlando, FL, USA. 1997, 3068: 182-193.