Fault diagnosis method for automotive lithium battery based on multi-classification support vector machine algorithm

Abstract

The vehicle lithium battery fault diagnosis method based on the multi-classification support vector machine algorithm belongs to the field of vehicle lithium battery fault diagnosis; this application solves the problem that the existing vehicle lithium battery fault diagnosis technology requires high training data volume, which makes it difficult to realize real-time online faults The problem of detection; the method of the present invention includes battery sample grouping experiment, and the collection data sorting forms training set and test set; Stipulate battery failure standard; Adopt cross-validation and network search method parameter optimization; Construct kernel function support vector machine; Build The partial binary tree five-category support vector machine obtains a vehicle lithium battery fault diagnosis model capable of identifying different states of the lithium battery; the invention can quickly and accurately complete the vehicle lithium battery fault diagnosis.

Description

technical field [0001] A vehicle lithium battery fault diagnosis method based on a multi-classification support vector machine algorithm belongs to the field of vehicle lithium battery fault diagnosis. Background technique [0002] Lithium batteries have high discharge power, long life, no pollution and mature preparation technology, and are widely used in electric vehicles, mobile power supplies, factory power supplies, etc. Due to the complex working environment of the battery, abnormal phenomena such as overvoltage, overcurrent, and overtemperature often occur, and in severe cases, the battery will be damaged; and the differences in the parameters of the single battery will also affect the safe operation of the battery system as a whole. Therefore, in order to ensure the safe use of lithium batteries, its fault diagnosis has become an important task. [0003] In the fault diagnosis method of the battery, the fault diagnosis based on the battery model is commonly used. Th...

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Problems solved by technology

[0005] In order to solve the above problems, the present invention discloses a vehicle lithium battery fault diagnosis method based on a multi-classification support vector machine algorithm to solve the problem that the vehicle lithium battery fault mode is complex and the difficulty of data acquisition in the fault state affects the fault diagnosis results. Can quickly and accurately complete the fault diagnosis of lithium batteries for vehicles

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