Fault diagnosis method of vehicle lithium battery based on multi-class support vector machine algorithm

Abstract

A fault diagnosis method of a vehicle lithium battery based on a multi-class support vector machine algorithm belongs to the field of vehicle lithium battery fault diagnosis. In order to solve the problem that the existing vehicle lithium battery fault diagnosis technology requires high amount of training data, which leads to the difficulty of realizing real-time on-line fault detection, the method of the invention comprises the following steps of: grouping battery samples into experiments, and sorting the collected data to form a training set and a test set; specifying battery failure criteria; using cross-validation and network search methods to optimize the parameters; constructing a kernel function support vector machine; constructing as biased binary tree five-class support vector machine to get the fault diagnosis model of vehicle lithium battery which can identify different states of lithium battery. The method can quickly and accurately complete the fault diagnosis of a lithiumbattery for a vehicle.

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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