The invention discloses a high-sensitivity detection method for the safety of a
battery cell, and the method comprises the following steps: S1, collecting the data of the
battery cell in real time through a tunnel
magnetoresistive sensor, and outputting a preliminary detection
data set; s2, generating a micro-
short circuit risk identification signal based on the preliminary detection
data set; s3, generating a temperature anomaly distribution map by using the preliminary detection
data set; s4, generating a
thermal runaway early warning
signal based on the temperature anomaly distribution map; s5, performing
magnetic field scanning on the
pole piece to generate a magnetic
distortion data atlas; s6, constructing
battery cell multi-source detection data, inputting the data into the
physical information neural network, and training to obtain a final battery
cell aging evolution model; s7, outputting a charging and discharging strategy optimization instruction based on the final
cell aging evolution model; and S8, uniformly fusing the micro
short circuit risk identification signal, the
thermal runaway early warning signal and the charging and discharging strategy optimization instruction to generate a comprehensive
response control instruction. According to the method, magnetic resistance sensing and physical modeling are fused, and intelligent prediction and
response control optimization of the risk of the battery
cell are realized.