Construction of a lithium-ion battery degradation model based on ultrasonic detection

Chenwei Qin; Zhiduan Cai; Jiahao Shen

doi:10.1117/12.2684570

19 October 2023 Construction of a lithium-ion battery degradation model based on ultrasonic detection

Chenwei Qin, Zhiduan Cai, Jiahao Shen

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127092L (2023) https://doi.org/10.1117/12.2684570
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Due to the complex physicochemical properties of lithium-ion batteries, it is difficult to identify the internal changes that cause battery degradation and failure. Ultrasonic testing, as a non-destructive characterization method, has the advantages of high sensitivity, low cost, convenient use, and fast speed, and has great potential for application in battery characterization. Nowadays, the application of ultrasonic waves for state characterization of lithium-ion batteries has achieved initial success, but research on building capacity degradation models for lithium-ion batteries using ultrasonic testing is still limited. In this paper, a capacity degradation model for lithium-ion batteries is proposed based on ultrasonic non-destructive testing technology. The established capacity degradation model is used for extrapolation to reach the failure threshold, providing theoretical support for data-driven lithium-ion battery life prediction and health management.

Citation Download Citation

Chenwei Qin, Zhiduan Cai, and Jiahao Shen "Construction of a lithium-ion battery degradation model based on ultrasonic detection", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127092L (19 October 2023); https://doi.org/10.1117/12.2684570

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