CN-121656326-B - Method and system for evaluating performance of damping heat insulation material for battery pack

CN121656326BCN 121656326 BCN121656326 BCN 121656326BCN-121656326-B

Abstract

The invention discloses a method and a system for evaluating the performance of a damping heat-insulating material for a battery pack, and relates to the technical field of battery pack heat management, wherein the method comprises the steps of extracting a calibration sample set, applying transient thermal excitation and collecting an infrared thermal image sequence to extract thermal response characteristics; the method comprises the steps of carrying out industrial CT scanning to obtain defect quantification indexes and thermal response characteristics to form a calibration data set, establishing a quantitative association model based on the calibration data set, determining a batch quality threshold according to model output distribution, carrying out the same excitation and collection on samples to be tested on line to extract characteristics of the samples, inputting the model to obtain defect quantification predicted values, comparing the defect quantification predicted values with the batch quality threshold, outputting an evaluation result, and supplementing and extracting new samples and obtaining new calibration data when a preset period or trigger condition is met to update the model and the threshold. According to the invention, accurate material performance evaluation is provided by establishing a quantitative model of thermal response characteristics and defect quantitative indexes, and evaluation consistency and stability are enhanced through a dynamic updating mechanism.

Inventors

JIN XINGGUO
XU PING
Luan Linsen
GUO XINXIANG

Assignees

艾华(浙江)新材料有限公司

Dates

Publication Date: 20260505
Application Date: 20260209

Claims (8)

1. A method for evaluating the performance of a damping heat insulating material for a battery pack, comprising: S1, extracting a calibration sample set from a production batch corresponding to a material to be evaluated, applying preset transient thermal excitation to each sample in the calibration sample set, collecting an infrared thermal image sequence, and extracting thermal response characteristics based on the infrared thermal image sequence; s2, carrying out industrial CT scanning on the calibration sample set to obtain a defect quantification index, and forming a calibration data set by the defect quantification index and thermal response characteristics of the corresponding sample; s3, establishing a quantitative association model from the thermal response characteristic to the defect quantitative index based on the calibration data set, and determining a batch quality threshold according to the output distribution of the quantitative association model; S4, applying the transient thermal excitation to an online sample to be detected, collecting an infrared thermal image sequence, and extracting thermal response characteristics of the sample to be detected; S5, inputting the thermal response characteristics of the sample to be tested into the quantitative association model to obtain a defect quantitative predicted value, comparing the defect quantitative predicted value with the batch quality threshold, and outputting a performance evaluation result of the sample to be tested; S6, when a preset period or a trigger condition is met, new calibration samples are additionally extracted, the S1 and the S2 are executed to obtain new calibration data, and the quantitative association model and the batch quality threshold are updated based on the new calibration data; the determining the batch quality threshold from the output distribution of the quantitative association model comprises: inputting the thermal response characteristics of the calibration sample set into the quantitative association model to obtain a corresponding defect quantitative predicted value set; Calculating a specific statistical quantile of the predicted value set, and determining a predicted value corresponding to the quantile as the batch quality threshold; The updating the quantitative association model and the lot quality threshold based on the newly added calibration data includes: When a preset period or a trigger condition is met, new calibration samples are extracted from the current and subsequent production batches, wherein the trigger condition comprises an abnormal condition dynamically determined based on online detection data, and the abnormal condition comprises that defect quantitative predicted values of a plurality of samples to be detected continuously show unidirectional variation trend and exceed a control limit, the proportion of samples with the prediction confidence of model output lower than a threshold value is continuously abnormal, and the deviation between the predicted values and true values of the samples which are judged to be in an edge state on line is found to exceed a tolerance after CT sampling inspection; S1 and S2 are executed on the new calibration sample to obtain new calibration data, wherein the new calibration data is formed by pairing thermal response characteristics and corresponding defect quantization indexes; and updating parameters of the quantitative association model and the batch quality threshold by using the newly-added calibration data.
2. The method for evaluating the performance of a damping heat insulating material for a battery pack according to claim 1, wherein in step S1, applying a preset transient thermal excitation to each sample in the calibration sample set and acquiring an infrared thermal image sequence comprises: Sampling samples from the production batch in a layered sampling mode to form a calibration sample set with statistical representativeness; Applying transient thermal excitation to each sample in the calibration sample set according to the unified excitation parameters by adopting a pulse heat source; simultaneously, the transient thermal excitation is applied, an infrared thermal imager is used for synchronously acquiring infrared thermal image sequences of the surfaces of the samples according to preset acquisition parameters, and a baseline thermal image is acquired for background subtraction before acquisition.
3. The method for evaluating the performance of a damping heat insulating material for a battery pack according to claim 2, wherein in step S1, the extracting a thermal response characteristic based on the infrared thermal image sequence comprises: preprocessing the infrared thermal image sequence, wherein the preprocessing comprises dead point correction, denoising filtering, background subtraction and baseline normalization; Extracting time variation characteristics, space distribution characteristics and space-time comprehensive characteristics from the preprocessed infrared thermal image sequence to form thermal response characteristics.
4. The method for evaluating the performance of a damping heat insulating material for a battery pack according to claim 3, wherein in step S2, said composing the defect quantization index and the thermal response characteristics of the corresponding sample into a calibration data set comprises: Carrying out industrial CT scanning on the calibration sample set to obtain internal three-dimensional structure data of each sample; identifying and calculating defect quantification indexes based on the three-dimensional structure data, wherein the defect quantification indexes comprise debonding area rate representing abnormal interface thermal resistance, layering gap thickness and porosity representing heat conduction heterogeneity of a bulk material; and (2) matching the defect quantification index of each sample obtained through calculation with the thermal response characteristics of the same physical sample obtained in the step (S1) one by one to form a calibration data set.
5. The method for evaluating the performance of a damping heat insulating material for a battery pack according to claim 4, wherein in step S3, the establishing a quantitative association model of thermal response characteristics to defect quantization indexes based on the calibration data set comprises: dividing the calibration data set into a training subset and a verification subset; Taking the thermal response characteristics in the training subset as input, taking the corresponding defect quantization indexes as output, and training through a machine learning regression algorithm to obtain the quantitative association model; and verifying the trained model by using the verification subset to evaluate the prediction precision and generalization capability of the model.
6. The method for evaluating the performance of a damping heat insulating material for a battery pack according to claim 5, wherein in step S4, the applying the transient thermal excitation to the on-line sample to be measured and acquiring an infrared thermal image sequence comprises: Applying the transient thermal excitation to the on-line sample to be detected by adopting the excitation parameters the same as those in the step S1 at an on-line detection station of the production line; synchronously acquiring an infrared thermal image sequence of the on-line sample to be detected by adopting the same acquisition parameters as in the step S1; And (2) extracting the thermal response characteristics of the on-line sample to be detected from the infrared thermal image sequence by adopting the same characteristic extraction caliber and flow as in the step (S1).
7. The method for evaluating the performance of a damping heat insulating material for a battery pack according to claim 6, wherein step S5 specifically comprises: Inputting the thermal response characteristics of the sample to be detected into the quantitative association model, and outputting a defect quantitative predicted value of the sample; Comparing the defect quantitative predicted value with a batch quality threshold value, and obtaining a classification judgment result according to a preset judgment logic; and generating and outputting a structured evaluation result comprising the sample identification to be detected, the defect quantification predicted value, the batch quality threshold value and the classification judgment result.
8. A damping heat insulating material performance evaluation system for a battery pack for implementing the damping heat insulating material performance evaluation method for a battery pack according to any one of claims 1 to 7, comprising: the sample extraction and thermal excitation module is used for extracting a calibration sample set from a production batch corresponding to the material to be evaluated, applying preset transient thermal excitation to each sample in the calibration sample set, collecting an infrared thermal image sequence, and extracting thermal response characteristics based on the infrared thermal image sequence; the industrial CT scanning and calibration module is used for carrying out industrial CT scanning on the calibration sample set to obtain a defect quantification index, and forming a calibration data set by the defect quantification index and the thermal response characteristic of the corresponding sample; The model establishing and threshold determining module is used for establishing a quantitative association model from the thermal response characteristic to the defect quantification index based on the calibration data set and determining a batch quality threshold according to the output distribution of the quantitative association model; The on-line detection and thermal response acquisition module is used for applying the transient thermal excitation to an on-line sample to be detected, acquiring an infrared thermal image sequence and extracting the thermal response characteristics of the sample to be detected; the performance evaluation and prediction module inputs the thermal response characteristics of the sample to be tested into the quantitative association model to obtain a defect quantitative predicted value, compares the defect quantitative predicted value with the batch quality threshold, and outputs a performance evaluation result of the sample to be tested; And the calibration data updating module is used for supplementing and extracting new calibration samples when a preset period or a trigger condition is met, executing the S1 and the S2 to obtain new calibration data, and updating the quantitative association model and the batch quality threshold value based on the new calibration data.

Description

Method and system for evaluating performance of damping heat insulation material for battery pack Technical Field The invention relates to the technical field of battery pack thermal management, in particular to a method and a system for evaluating performance of a damping heat insulation material for a battery pack. Background With the popularization of energy-intensive equipment and the rapid development of the electric vehicle industry, a battery pack is becoming more and more important as an electric vehicle core component. In order to ensure the safety and reliability of the battery pack, performance evaluation of the internal materials thereof becomes an important link in battery design and manufacture. In recent years, the thermal management function of the damping heat insulating material of the battery pack has been the focus of research. The heat management material has the main functions of adjusting the internal temperature change of the battery pack, avoiding the influence of high temperature on the battery performance and improving the working efficiency of the battery system. With advances in measurement technology and computer simulation technology, industrial CT and infrared thermography techniques have been widely used in performance evaluation of thermal management materials. The techniques provide a more accurate means for obtaining performance data such as heat conduction, heat diffusion and the like inside and outside the battery pack. However, the existing battery pack damping heat insulation material performance evaluation method still has certain defects. Firstly, the traditional thermal response characteristic extraction method often depends on manual experience, and lacks efficient and reproducible technical means, so that the accuracy and consistency of the evaluation result are not high. Secondly, current performance evaluation methods rely mainly on single test means, such as infrared thermal imaging or industrial CT scanning, and lack comprehensive analysis based on multiple data sources, resulting in limited ability to identify and predict material defects. In addition, most of the prior art fails to realize dynamic adaptability update, lacks intelligent calibration capability for material performance difference between different production batches, which results in insufficient stability and reliability of evaluation results, and particularly cannot sufficiently cope with complex and changeable working environments in mass production and long-term use. In view of these problems, the prior art cannot provide a systematic and iterative evaluation method, and a system for integrating multiple data sources and dynamically updating the system is needed to solve the technical problems. Disclosure of Invention The present invention has been made in view of the above-described problems. Therefore, the technical problem solved by the invention is that the existing battery pack thermal management material evaluation method has the problems of low evaluation result precision and consistency, dependence on a single test means, lack of a dynamic update mechanism, incapability of effectively identifying material defects and the like, and how to realize more accurate and reliable performance evaluation based on multiple data sources and a dynamic calibration mechanism. In order to solve the technical problems, the invention provides the following technical scheme: In a first aspect, an embodiment of the present invention provides a method for evaluating performance of a damping heat insulation material for a battery pack, including the steps of: S1, extracting a calibration sample set from a production batch corresponding to a material to be evaluated, applying preset transient thermal excitation to each sample in the calibration sample set, collecting an infrared thermal image sequence, and extracting thermal response characteristics based on the infrared thermal image sequence; s2, carrying out industrial CT scanning on the calibration sample set to obtain a defect quantification index, and forming a calibration data set by the defect quantification index and thermal response characteristics of the corresponding sample; s3, establishing a quantitative association model from the thermal response characteristic to the defect quantitative index based on the calibration data set, and determining a batch quality threshold according to the output distribution of the quantitative association model; S4, applying the transient thermal excitation to an online sample to be detected, collecting an infrared thermal image sequence, and extracting thermal response characteristics of the sample to be detected; S5, inputting the thermal response characteristics of the sample to be tested into the quantitative association model to obtain a defect quantitative predicted value, comparing the defect quantitative predicted value with the batch quality threshold, and outputting a performance evaluation result