A novel approach for state-of-charge estimation of lithium-ion batteries by quasi-static component generation of ultrasonic waves

超声波传感器 锂(药物) 组分(热力学) 离子 声学 荷电状态 材料科学 国家(计算机科学) 电荷(物理) 计算机科学 物理 电池(电) 热力学 功率(物理) 算法 心理学 精神科 量子力学
作者
Xinyi Yuan,Yiyu Wang,Weibin Li,Mingxi Deng
出处
期刊:Measurement Science and Technology [IOP Publishing]
卷期号:35 (9): 096003-096003
标识
DOI:10.1088/1361-6501/ad4e54
摘要

Abstract Lithium-ion batteries content complex internal components, such as porous media and electrolytes, which result in strong scattering and high attenuation of ultrasonic waves in these batteries. The low attenuative feature of the quasi-static components (QSCs) of ultrasonic waves offers great potential for nondestructive assessment of highly attenuating and porous materials. This paper presents an innovative approach for estimating the state-of-charge (SOC) of lithium-ion batteries using QSC of ultrasonic waves. Experimental results demonstrate a clear and repeatable linear relationship between the amplitudes of the generated QSC and the SOC of lithium-ion batteries. In addition, the relationships between different SOCs of the battery and the conventional linear ultrasonic parameters, second harmonic generation (SHG), and the QSC were compared to verify the improved sensitivity of the proposed approach. Notably, compared to linear ultrasonic features and the SHG, the generated QSC shows much higher sensitivity to the variations of SOC. We employ the phase-reversal method to further enhance the signal-to-noise ratio of measured QSC signals. The experimental results demonstrate that the proposed method exhibits a heightened sensitivity to changes in the SOC of batteries, resulting in significantly enhanced detection accuracy and resolution. This method effectively addresses the deficiencies observed in the current detection methods such as limited accuracy and sluggish response times. This method provides a new solution to overcome this challenge. Meanwhile, it also confirms that nonlinear ultrasound promises an alternative method for SOC assessment, providing a foundation for efficient and safe battery management practices.
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