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| State of Charge Estimation of Lithium-Ion Batteries Fused with Multi-Dimensional Ultrasonic Time-Frequency Domain Features |
| Liu Suzhen1,2, Chen Yunlong1,2, Zhang Chuang1,2, Jin Liang1,2, Yang Qingxin1 |
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 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.
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Received: 06 June 2022
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2023, Vol. 38 
