基于柔性压电纤维阵列的锂离子电池荷电状态声学表征  

Acoustic Characterization of Lithium-Ion Batteries State of Charge Based on Flexible Piezoelectric Fiber Array

作　　者：刘轩 吕炎[1] 高杰[1] LIU Xuan;LYU Yan;GAO Jie(School of Information Science and Technology,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]北京工业大学信息科学技术学院,北京100124

出　　处：《湖南电力》2025年第1期29-36,共8页Hunan Electric Power

基　　金：国家电网有限公司科技项目(52020124001C)。

摘　　要：复杂服役工况下的锂离子电池极易出现局部荷电状态异常问题,严重影响其安全性与稳定性。传统电学测量方法难以充分评价多区域锂离子电池健康状态的分布情况,易造成单体局部性能薄弱区或潜在异常状态区的漏检。针对上述问题,提出基于压电纤维阵列的超声导波检测方法,实验提取不同区域锂离子电池的声学特征参数,以探究不同区域锂离子电池荷电状态的分布差异性。随后,联合声学与电学特征参量形成完整数据库,利用神经网络智能反演模型对荷电状态进行量化评估,结果误差低于0.25%。所提检测方法可为锂离子电池荷电状态的无损量化评估提供新的技术方案。Lithium-ion batteries under complex service conditions are prone to local abnormal state of charge(SOC)problems,which seriously affects their safety and stability.Traditional electrical measurement methods are difficult to fully evaluate the distribution of health status of lithium-ion batteries in multiple regions,which can easily lead to missed detection of local weak performance areas or potential abnormal state areas of individual cells.To address the above issues,a ultrasonic guided wave detection method based onpiezoelectric fiber array is proposed.The acoustic characteristic parameters of lithium-ion batteries in different regions are experimentally extracted to explore the distribution differences of lithium-ion batteries SOC in different regions.Subsequently,a complete database is formed by combining acoustic and electrical characteristic parameters,and a neural network intelligent inversion model is used to quantitatively evaluate SOC with an error of less than 0.25%.The proposed detection method can provide a new technical solution for nondestructive quantitative evaluation of lithium-ion batteries SOC.

关 键 词：锂离子电池 荷电状态 超声导波 压电纤维阵列 反演 

分 类 号：TM911[电气工程—电力电子与电力传动]