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CN-121995226-A - Power battery test data abnormity early warning method and system

CN121995226ACN 121995226 ACN121995226 ACN 121995226ACN-121995226-A

Abstract

The invention relates to the technical field of lithium battery manufacturing, in particular to a method and a system for early warning abnormality of test data of a power battery, comprising the following steps of collecting operation parameters of test equipment and test data of a tested power battery in the test process of the power battery in real time; based on the battery state information in the test data, inputting the battery state information into a preset virtual battery model, calculating the relative deviation rate of the measured voltage of the tested power battery and the analog voltage value, and executing an equipment fault diagnosis step if the data abnormality is judged not to be caused by a battery problem, wherein the step comprehensively uses at least two judgment bases for fault diagnosis and grade judgment, namely executing corresponding early warning operation according to the fault grade judged by the fault diagnosis step. The invention obviously improves the reliability of the test data and the reliability of the test process, and avoids the misjudgment of the battery performance caused by equipment problems.

Inventors

  • WU WENDI
  • LI WEI
  • SUN ZEKUN
  • REN YI
  • DONG QIAN

Assignees

  • 中汽新能(天津)电池科技有限公司

Dates

Publication Date
20260508
Application Date
20260120

Claims (10)

  1. 1. The power battery test data abnormality early warning method is characterized by comprising the following steps of: S1, collecting operation parameters of test equipment in the power battery test process and test data of a tested power battery in real time; s2, inputting the battery state information in the test data into a preset virtual battery model, and calculating to obtain an analog voltage value under the current working condition; S3, calculating the relative deviation rate of the measured voltage of the measured power battery and the analog voltage value, and judging whether the data abnormality is caused by a battery problem according to whether the relative deviation rate exceeds a preset threshold corresponding to the battery type; S4, if the data abnormality is judged not to be caused by the battery problem, performing a device fault diagnosis step, wherein the fault diagnosis and grade judgment are performed by comprehensively using at least two of the following judgment bases: (a) Judging a basic fault mode based on the data recording characteristics, wherein the basic fault mode comprises at least one of data missing, data repetition and data jump; (b) Judging the matching of a historical fault base based on the historical fault information of the equipment; (c) Based on the comparison and judgment of the calibration data of the device measured data and the calibration reference data, the device measuring device is used for identifying the precision attenuation of the device; s5, executing corresponding early warning operation according to the fault grade determined in the fault diagnosis step.
  2. 2. The method for early warning of abnormal power battery test data according to claim 1, wherein in the step S2, the battery state information comprises initial SOC, real-time current and temperature of the battery, and the virtual battery model is constructed based on an advanced equivalent circuit model and an SOC correction algorithm.
  3. 3. The method for early warning of abnormal power battery test data according to claim 1, wherein in step S3, the preset threshold is configured differently according to the battery type, and includes a basic threshold, a charge-discharge switching threshold, a multi-scene superposition threshold, and a continuous super-threshold determination abnormal time.
  4. 4. The power battery test data abnormality pre-warning method according to claim 3, wherein the battery types comprise a lithium iron phosphate battery and a ternary lithium battery, the basic threshold is 1% for the lithium iron phosphate battery, and the basic threshold is 1.5% for the ternary lithium battery.
  5. 5. The method for early warning of abnormal power battery test data according to claim 1, wherein the basic fault mode judgment specifically comprises judging time continuity of data records according to a preset sampling frequency to identify data missing or repeated, and/or calculating parameter change rates of adjacent data points and comparing the parameter change rates with a preset jump threshold to identify data jumps.
  6. 6. The power battery test data abnormality early warning method according to claim 1 is characterized in that the calibration data comparison and judgment specifically comprises the steps of periodically comparing measured data of test equipment under standard working conditions with reference data obtained by high-precision calibration equipment to establish an equipment precision health baseline, and judging whether equipment precision is attenuated and exceeds a confidence threshold value by comparing deviation trend of current measured data with the health baseline in real-time test.
  7. 7. The power battery test data abnormality early warning method according to claim 1, characterized in that the failure level determined in the step S4 at least includes a reminding level and an alarm level, wherein the reminding level corresponds to an abnormality which does not affect a core test service and can be recorded through a log, and the alarm level corresponds to an abnormality which has affected or possibly affects the validity of test data and needs immediate intervention.
  8. 8. The method for warning abnormality of power battery test data according to claim 7, wherein in step S5, system log marking and/or periodic summary notification is performed for alert level warning, and platform real-time popup and/or instant messaging tool pushing is performed for alert level warning.
  9. 9. A power battery test data anomaly early warning system for implementing the method of any one of claims 1-8, comprising: The data acquisition system (1) is used for acquiring actual measurement data in the power battery testing process in real time; the data analysis system (2) is connected with the data acquisition system (1), is internally provided with a virtual battery model and is used for receiving the actual measurement data and calculating battery simulation data; the fault judging system (3) is connected with the data analyzing system (2) and is used for comparing the measured data with the simulation data, judging whether the source of the data abnormality is from the test equipment or not based on a preset rule and determining an abnormality grade; And the fault alarm system (4) is connected with the fault judging system (3) and is used for executing corresponding early warning operation according to the abnormal grade.
  10. 10. The system according to claim 9, wherein the data acquisition system (1) comprises a power battery test cabinet (11) and an industrial personal computer (12), and the functions of the data analysis system (2), the fault judgment system (3) and the fault alarm system (4) are cooperatively realized by a conversion protocol module (13), a database module (14), a gateway (15) and a data processing system (16) which are sequentially connected.

Description

Power battery test data abnormity early warning method and system Technical Field The invention belongs to the technical field of lithium batteries, and particularly relates to a power battery test data abnormality early warning method and system. Background With the rapid development of the new energy automobile industry, the power battery is used as a core component, and the performance and the safety of the power battery directly influence the quality of the whole automobile, so that the testing link of the power battery is important. In the power battery testing process, multiple indexes such as capacity, charge and discharge efficiency, cycle life, high and low temperature performance and the like of the battery are detected through testing equipment, and a large amount of test data are generated, wherein the data are key bases for evaluating the performance of the battery, optimizing the design of the battery and guaranteeing the quality of products. However, in the existing power battery testing process, two types of data problems caused by equipment are often faced, namely, firstly, the data recording abnormality caused by equipment hardware faults (such as poor contact of a sensor and a data acquisition module) or software abnormality (such as error of a data transmission protocol and failure of a storage module) are represented as data missing, data jump, data repetition and the like, if the problems are not found in time, the test data are incomplete, the subsequent battery performance evaluation is influenced, secondly, the accuracy of the equipment after long-term use is reduced (such as drift of a current sensor, ageing of a voltage measurement module and accuracy deviation of temperature control), the deviation of the test data and a true value is beyond an allowable range, namely, the data is inaccurate, the problems are hidden, the unqualified battery is easily caused to flow into a market or the quality battery is misjudged in a short term, and economic loss and safety risks are brought to enterprises. At present, the processing of test data of the power battery in the industry is mostly focused on data analysis and verification after the test is completed, and a real-time monitoring and early warning mechanism is lacked. Part of enterprises adopt a mode of manually checking the state of equipment periodically, so that the efficiency is low, real-time data abnormality in the testing process cannot be covered, few systems with simple early warning functions can only warn aiming at a single data index (such as a voltage exceeding range), whether the data abnormality is caused by a problem of a battery or a problem of the equipment cannot be distinguished, and the problem of inaccurate data caused by the reduction of the equipment precision cannot be early warned in advance. Therefore, a technical scheme capable of accurately identifying the related data problem of the equipment and performing early warning in real time is needed to make up for the defects of the prior art. Disclosure of Invention The invention aims to provide a power battery test data abnormality early warning method and system, which can monitor power battery test data in real time, intelligently distinguish battery abnormality from equipment abnormality and early warn equipment accuracy degradation. In order to achieve the above purpose, the present invention provides the following technical solutions: The invention provides a power battery test data abnormality early warning method, which comprises the following steps: S1, collecting operation parameters of test equipment in the power battery test process and test data of a tested power battery in real time; s2, inputting the battery state information in the test data into a preset virtual battery model, and calculating to obtain an analog voltage value under the current working condition; S3, calculating the relative deviation rate of the measured voltage of the measured power battery and the analog voltage value, and judging whether the data abnormality is caused by a battery problem according to whether the relative deviation rate exceeds a preset threshold corresponding to the battery type; S4, if the data abnormality is judged not to be caused by the battery problem, performing a device fault diagnosis step, wherein the fault diagnosis and grade judgment are performed by comprehensively using at least two of the following judgment bases: (a) Judging a basic fault mode based on the data recording characteristics, wherein the basic fault mode comprises at least one of data missing, data repetition and data jump; (b) Judging the matching of a historical fault base based on the historical fault information of the equipment; (c) Based on the comparison and judgment of the calibration data of the device measured data and the calibration reference data, the device measuring device is used for identifying the precision attenuation of the device; s5, executing corresponding e