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Temperature anomaly probe identification algorithm based on probability mutation rule

An identification algorithm and probe technology, applied in the field of identification algorithm of abnormal temperature probe, can solve the problems of single battery failure, huge amount of operating data, vehicle safety accidents, etc., to improve the effective utilization rate and quickly identify and positioning effect

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

AI Technical Summary

Problems solved by technology

The power battery includes several single batteries. The power battery is used as the energy supply part of the vehicle, and the single battery in it is always in use; when a single battery of a new energy vehicle fails and is not disposed of in time, It is very easy to affect the failure of the surrounding single battery, which will cause the safety accident of the whole vehicle
[0007] At present, the amount of operating data is huge, and the operating data cannot be well utilized after collection and aggregation
These battery data not only take up a very large storage space, but also the effective utilization rate of battery data is very low

Method used

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  • Temperature anomaly probe identification algorithm based on probability mutation rule
  • Temperature anomaly probe identification algorithm based on probability mutation rule
  • Temperature anomaly probe identification algorithm based on probability mutation rule

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0038] Such as figure 1 As shown, an identification algorithm for temperature anomaly probes based on the probability mutation law, including:

[0039] In the data acquisition step, the temperature data of the probe is acquired with the vehicle as a unit. Specifically, the acquired temperature data is 100,000 pieces of previous historical data.

[0040] The analysis and screening step is to carry out statistical analysis on the temperature data; analyze the cumulative temperature frequency distribution smaller than the preset temperature value with the method of cumulative frequency, screen out the corresponding temperature value when the cumulative temperature frequency is less than X, and record it as the low temperature threshold; use The method of accumulative frequency analyzes the accumulative temperature frequency distribution greater than the preset temperature value, screens out the corresponding temperature value when the accumulative temperature frequency is less t...

Embodiment 2

[0063] Different from Embodiment 1, in this embodiment, it also includes:

[0064] The vehicle body state acquisition step is to obtain vehicle body state data through a vehicle speed sensor electrically connected to the vehicle controller, and a plurality of temperature sensors electrically connected to the vehicle controller; wherein, the number of temperature sensors is at least three times the number of batteries, and the temperature sensors have The respective numbers, temperature sensors are evenly installed inside and outside the vehicle body, and the installation positions of each temperature sensor are pre-stored in the on-board controller;

[0065] In the body temperature analysis step, after the on-board controller receives the temperature fed back by the temperature sensor, it retrieves the corresponding installation position according to the number of the temperature sensor, and generates a temperature distribution map of the vehicle based on the temperature value ...

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Abstract

The invention belongs to the technical field of automobile battery detection, and particularly relates to a temperature anomaly probe identification algorithm based on a probability mutation rule. Thealgorithm comprises the following steps of a data acquisition step: acquiring temperature data of a probe by taking a vehicle as a unit; an analysis and screening step: performing statistical analysis on temperature data; analyzing cumulative temperature frequency distribution smaller than a preset temperature value in a cumulative frequency mode, screening out a corresponding temperature value when the cumulative temperature frequency is smaller than X, and recording the temperature value as a low-temperature threshold value; analyzing cumulative temperature frequency distribution greater than a preset temperature value in the cumulative frequency mode, screening out a corresponding temperature value when the cumulative temperature frequency is less than X, and recording the temperaturevalue as a high-temperature threshold value; and a probe inspection step: carrying out statistical analysis on the data of a single probe. By using the algorithm, an effective utilization rate of battery data can be improved, and batteries with an abnormal temperature can be quickly identified and positioned.

Description

technical field [0001] The invention belongs to the technical field of automobile battery detection, and in particular relates to an identification algorithm of an abnormal temperature probe based on a probability mutation rule. Background technique [0002] New energy vehicles refer to the use of unconventional vehicle fuels as power sources (or the use of conventional vehicle fuels, the use of new vehicle power devices), integrated vehicle power control and advanced technology in driving, the formation of advanced technical principles, with Automobiles with new technology and new structure. [0003] New energy vehicles include pure electric vehicles, extended-range electric vehicles, hybrid vehicles, fuel cell electric vehicles, hydrogen engine vehicles, etc. Compared with the existing fuel vehicles, new energy vehicles have the characteristics of zero pollutant emission, high energy utilization rate, simple structure and low noise. The society is also vigorously advocati...

Claims

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Application Information

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IPC IPC(8): B60L58/10B60L58/24G07C5/08G07C5/00
CPCB60L58/10B60L58/24G07C5/006G07C5/0808G07C5/0825Y02T10/70
Inventor 周科松
Owner CHINA AUTOMOTIVE ENG RES INST
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