Mode clustering method of battery alarm characteristic data and accident characteristic identification technology

A feature data, clustering method technology, applied in character and pattern recognition, instruments, computer parts and other directions, can solve the problem of difficult to accurately distinguish the accident car and so on

Active Publication Date: 2020-10-30
CHINA AUTOMOTIVE ENG RES INST
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The invention provides a pattern clustering method of battery alarm feature data and accident feature recognition technology, which solves the technical problem that it is difficult to accurately distinguish accident vehicles in the prior art

Method used

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  • Mode clustering method of battery alarm characteristic data and accident characteristic identification technology

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0047] A pattern clustering method for battery alarm feature data and an embodiment of accident feature recognition technology in the present invention are basically as attached figure 1 shown, including steps:

[0048] S1. Collect the operation data of the battery before and after the alarm during the operation of the accident vehicle and the normal vehicle;

[0049] S2. Perform dimensionality reduction processing on the operating data to obtain pattern characteristics;

[0050] S3. Carrying out cluster analysis on the pattern features after dimensionality reduction to obtain the classification features of the operating data;

[0051] S4, analyze the statistical difference between the accident car and the normal car according to the classification feature;

[0052] S5. Determine whether the vehicle is an accident vehicle by taking the statistical difference as a standard.

[0053] In this embodiment, taking a new energy vehicle as an example, some important operating param...

Embodiment 2

[0066] The only difference from Example 1 is that

[0067] The probability density distribution curve is used to analyze statistical differences, and it is judged whether the degree of deviation of the probability density distribution curve satisfies the preset condition. If the preset condition is met, the vehicle is judged to be an accident vehicle. The preset condition may be a deviation percentage, such as 2%. The probability density function is a function that describes the possibility of the output value of a random variable near a certain value point, and the probability that the value of the random variable falls within a certain area is the integral of the probability density function on this area . It can be seen that analyzing the statistical difference through the probability density distribution curve is more intuitive on the graph. Usually, the probability density distribution curve of a normal car satisfies the normal distribution. If a certain car deviates gr...

Embodiment 3

[0069] The only difference from Embodiment 2 is that the surface temperature of the battery cell is also used to assist in judging whether the battery cell is abnormal. The data of each battery cell uploaded by the new energy vehicle to the enterprise platform includes temperature data. These temperature data are collected by a temperature sensor. The probe or probe of the temperature sensor is in contact with the battery cell to measure the temperature of the battery cell in real time. surface temperature data.

[0070] In this embodiment, each battery cell has a preset number, and these numbers correspond to the location information of the battery cell, and the location information is specifically the horizontal distance and the vertical distance; where the horizontal distance refers to the distance between the battery cell and the The straight-line distance of the cabin, that is, the distance between the geometric center of the cockpit and the geometric center of the batter...

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Abstract

The invention relates to the technical field of batteries, in particular to a mode clustering method of battery alarm characteristic data and an accident characteristic recognition technology. The method comprises the following steps: S1, collecting operation data of a battery before and after alarm in the operation of an accident vehicle and a normal vehicle; S2, performing dimension reduction processing on the operation data to obtain mode features; S3, performing clustering analysis on the mode features after dimension reduction to obtain classification features of the operation data; S4, analyzing the statistical difference between the accident vehicle and the normal vehicle according to the classification features; S5, judging whether the vehicle is an accident vehicle or not by taking the statistical difference as a standard. The method has the advantages that compared with the prior art, the judgment standard in the scheme is not single and fuzzy, the mode characteristics, the classification characteristics and the statistical difference are obtained in sequence by analyzing the operation data of the battery, the accident vehicle can be accurately identified, and the technical problem that the accident vehicle is difficult to accurately identify in the prior art is solved.

Description

technical field [0001] The invention relates to the technical field of batteries, in particular to a pattern clustering method of battery alarm characteristic data and an accident characteristic recognition technology. Background technique [0002] Lithium batteries have the advantages of small size, light weight, high energy, long cycle life, and no pollution, and are widely used in the field of new energy vehicles. In order to ensure driving safety, it is necessary to detect the working status of the lithium battery in real time, and to provide timely and effective early warning before a dangerous situation may occur. [0003] In this regard, the document CN109143085A discloses a method and system for early warning of lithium batteries based on artificial intelligence algorithms, wherein the method includes: collecting the operation data of each of the multiple conventional parameters of each lithium battery in multiple lithium batteries ; Classify and store the collected...

Claims

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Application Information

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IPC IPC(8): G06F30/27G06K9/62G06F111/08G06F119/08
CPCG06F30/27G06F2111/08G06F2119/08G06F18/23213G06F18/241
Inventor 严中红抄佩佩马敬轩陈悟果张玉兰杨若浩
Owner CHINA AUTOMOTIVE ENG RES INST
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