融合多维超声时频域特征的锂离子电池荷电状态估计

doi:10.19595/j.cnki.1000-6753.tces.221046

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摘要准确地估计锂离子电池的荷电状态（SOC）对电动汽车的安全运行至关重要。传统方法通过电池电压、电流、温度等参数估计SOC,但参数依赖对电极行为的测量,且易受阻抗、充放电速率等因素影响。相对于传统信号,超声信号可区分电池材料物理性质的微小变化,从而准确地表征电池状态。该文首先通过连续均匀分层介质模型研究了超声波在电池内部的传播过程,进而分析了电池材料物理性质对超声波传播特性的影响;其次,搭建了锂离子电池超声检测平台,提取了多维超声时频域特征,并利用超声特征解释了电池内部的电化学过程;最后,通过长短时记忆神经网络建立了融合多维超声时频域特征的锂离子电池SOC估计模型,对比了融合不同特征对SOC估计精度的影响。实验结果表明,融合多维超声时频域特征可以有效提高SOC估计的精度。在动态工况下,SOC估计的方均根误差在1.46%以内,平均绝对误差在1.15%以内,验证了方法的有效性和准确性。

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关键词 ：锂离子电池, 荷电状态, 超声检测, 长短时记忆神经网络, 多维超声特征

Abstract：The state of charge (SOC) of lithium-ion batteries is an essential parameter of the battery management system. Accurately estimating the SOC of lithium-ion batteries is crucial for the safe operation of electric vehicles. However, SOC cannot be measured directly, and it can only be estimated by parameters related to the working state of the battery. Meanwhile, due to the highly nonlinear and time-varying characteristics of the battery, the accurate estimation of battery SOC has become a difficult issue. Conventional methods estimate the SOC by using battery voltage, current, temperature, and other parameters. However, the acquisition of these parameters depends on the measurement of electrode behavior. And they are susceptible to factors such as impedance and charge-discharge rate. Compared with conventional signals, ultrasonic signals can discriminate minor changes in the battery materials' physical properties, so they can characterize the battery states accurately. At present, the research on estimating SOC by ultrasound only utilizes the time-domain features of ultrasonic signals, which lacks multi-dimensional analysis. Moreover, due to the nonlinear and non-stationary characteristics of ultrasonic signals, using the time-domain features cannot reflect the changes of ultrasonic signals at different scales, which will reduce the accuracy of SOC estimation. In order to solve the above problems, this paper proposes a lithium-ion battery SOC estimation method that integrates multi-dimensional ultrasonic time-frequency domain features. Multi-dimensional ultrasonic time-frequency domain features which have high correlations with SOC are extracted through joint time-frequency domain analysis of signals. The lithium-ion battery SOC estimation model is proposed by long-short-term memory neural network (LSTM), which realizes the accurate estimation of battery SOC.
Firstly, the propagation process of ultrasonic waves in the battery was studied through the continuous uniform layered medium model. The influence of the battery materials' physical properties on the ultrasonic propagation characteristics was analyzed. Secondly, the ultrasonic testing platform for lithium-ion batteries was built and the multi-dimensional ultrasonic time-frequency domain features were extracted. Based on the ultrasonic features, the electrochemical process inside the battery was explained. Finally, considering the special processing ability of LSTM for time series data, the SOC estimation model of lithium-ion batteries fused with multi-dimensional ultrasonic time-frequency domain features was proposed by LSTM. The effects of different fusion features on SOC estimation accuracy were compared.
Experimental results show that the accuracy of SOC estimation can be effectively improved by the integration of multi-dimensional ultrasonic time-frequency features. During battery charging and discharging, the root mean square error of SOC estimation is within 0.85% and 0.37% respectively, and the mean absolute error is within 0.58% and 0.24%. At the initial stage of battery charging and discharging, the error of SOC estimation is relatively large. But as the process goes on, the estimation error decreases gradually, which also reflects that LSTM can capture the change of SOC and its dependence on battery historical states through time series data. In order to verify the applicability of this method under dynamic conditions further, dynamic stress test (DST) conditions are used for verification. The results show that under DST conditions, the root mean square error of SOC estimation is within 1.46%, and the average absolute error is within 1.15%, which verifies the effectiveness and accuracy of the method.

Key words： Lithium-ion batteries state of charge ultrasonic testing long-short-term memory neural network multi-dimensional ultrasonic features

收稿日期: 2022-06-06

PACS:

TM911

基金资助:中央引导地方科技发展项目（216Z4406G）和清华大学国家重点实验室开放课题（SKLD21KZ04）资助

通讯作者: 刘素贞女,1969年生,博士,教授,博士生导师,研究方向为工程电磁场与磁技术。E-mail：szliu@hebut.edu.cn

作者简介: 陈云龙男,1998年生,硕士研究生,研究方向为锂离子电池超声检测。E-mail：202021401050@stu.hebut.edu.cn

引用本文:

刘素贞, 陈云龙, 张闯, 金亮, 杨庆新. 融合多维超声时频域特征的锂离子电池荷电状态估计[J]. 电工技术学报, 2023, 38(17): 4539-4550. Liu Suzhen, Chen Yunlong, Zhang Chuang, Jin Liang, Yang Qingxin. State of Charge Estimation of Lithium-Ion Batteries Fused with Multi-Dimensional Ultrasonic Time-Frequency Domain Features. Transactions of China Electrotechnical Society, 2023, 38(17): 4539-4550.

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