The change of electrodes’ conductivity is a crucial parameter during battery aging process, non-contact detection of battery electrodes’ defects through conductivity reconstruction is an innovative technology. In this paper, the magnetic induction tomography (MIT) was applied to reconstruct the conductivity of electrodes, the simplified battery models with complete and broken electrodes were chosen as target A and target Brespectively, and an eight-channel MIT system was designed to measure the change of mutual induced voltage. A method of computing the forward problem called ‘A·E’ was adopted to compute the coefficient matrix for the forward problem and the frequency 12.665kHz was chosen as the excitation frequency. Based on the conductivity reconstruction images, relative errors of reconstructed conductivity and objective function, ‘A·E’ method exhibited advantages in terms of computation time and accuracy compared to coefficient sensitivity method with the simulation data. In order to test the accuracy of ‘A·E’ method, data measured by MIT system was applied in the inverse problem with four regularization parameter selection methods. L-curve criterion and generalized cross-validation criterion identified defects successfully as they were more sensitive to the change of conductivity.

电极电导率的变化是电池老化过程中的一个关键参数，通过电导率重构非接触式检测电池电极的缺陷是一项创新技术。本文采用磁感应断层扫描（MIT）重建电极的电导率，分别选择电极完整和破损的简化电池模型作为目标A和目标B，并设计了一个八通道MIT系统来测量电极的电导率。互感电压的变化。一种计算正向问题的方法，称为“A·E”采用计算正演问题的系数矩阵，选择频率12.665kHz作为激励频率。基于电导率重建图像、重建电导率的相对误差和目标函数，' A·E '法在计算时间和精度上均优于基于模拟数据的系数灵敏度法。为了测试' A·E '方法的准确性，将MIT系统测量的数据应用于具有四种正则化参数选择方法的逆问题。L曲线准则和广义交叉验证准则成功识别缺陷，因为它们对电导率的变化更敏感。