Lithium ion battery monomer fault self-detection method

Abstract

The invention provides a lithium ion battery monomer fault detection method, which comprises the following steps: carrying out a certain-rate constant-current charge-discharge cycle test on multiple groups of normally working lithium ion batteries, collecting voltage and battery surface temperature data in the cycle process, and then processing the collected data to detect the fault of the lithium ion battery monomer. The method comprises the following steps: extracting a lithium ion battery characteristic curve and characteristic curve peak value data, constructing a data set, carrying out data driving model training, calculating to obtain Hotelling T2 statistic and SPE statistic, further obtaining a failure probability of a normally working lithium ion battery, and setting an anomaly judgment probability threshold value; and then carrying out the above operation on the lithium ion battery to be detected to obtain the failure probability of the lithium ion battery to be detected, and judging whether the battery fails or not according to the comparison result of the failure probability of the lithium ion battery to be detected and the abnormal judgment probability threshold. According to the method, a data set containing information such as battery performance evolution and entropy thermal change is selected, so that complex microscopic performance evolution of the battery can be better reflected, and the accuracy of fault diagnosis is ensured.

Description

technical field [0001] The invention relates to the technical field of batteries, in particular to a method for self-detection of a lithium ion battery cell fault. Background technique [0002] As one of the three key core technologies of new energy vehicles, the new power battery system has always been the focus of attention around the world. With the use of electric vehicles, harsh road conditions, dynamic changes in ambient temperature and load can lead to nonlinear degradation of battery system performance, which in turn leads to problems such as liquid leakage, insulation damage, and partial short circuits. If the fault characteristics of the battery are not monitored in time, the battery will accelerate aging and deterioration, which will lead to serious safety accidents such as spontaneous combustion and explosion. Therefore, it is of great significance to achieve accurate fault detection of lithium-ion batteries. [0003] At present, the fault detection of lithium-i...

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