State of Charge Estimation of LiFeO4 Batteries Based on Time Domain Features of Ultrasonic Waves and Random Forest
Liu Suzhen1,2, Yuan Luhang1,2, 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
Abstract：State of charge (SOC) is an important monitoring parameter in the battery management system. Due to the flat open circuit voltage and SOC curve, SOC of LiFeO4 (LFP) batteries is not sensitive to changes in electrical signals. Therefore, it is difficult to accurately estimate the SOC of LFP batteries. Ultrasonic wave signals can detect changes in the physical properties of electrode materials, and establish a structure-activity relationship to characterize the battery state. In this paper, a SOC estimation method of LFP batteries is proposed based on high-correlation ultrasound features and a low-complexity regression model. Firstly, the consistency and correlation between commonly used ultrasonic features and SOC are analyzed under different conditions such as ultrasonic transmission frequency, current rate, and temperature. Secondly, the time domain ultrasound features of high- correlation are further extended based on the structural features of ultrasound envelope line. After the comparison of data-driven and model-driven methods, an accurate estimation method of SOC is proposed based on random forest model. The experimental results show that the root mean square error and mean absolute error of SOC estimation under different dynamic conditions are lower than 1.9% and 1.6%, respectively, which verifies the reliability and accuracy of this method.
刘素贞, 袁路航, 张闯, 金亮, 杨庆新. 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): 5872-5882.
Liu Suzhen, Yuan Luhang, Zhang Chuang, Jin Liang, Yang Qingxin. State of Charge Estimation of LiFeO4 Batteries Based on Time Domain Features of Ultrasonic Waves and Random Forest. Transactions of China Electrotechnical Society, 2022, 37(22): 5872-5882.
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