A new energy battery environmental adaptability detection system and detection method

Abstract

The application discloses a new energy battery environmental adaptability detection system and a detection method, relates to the technical field of new energy battery detection, and is used for solving the problem that the thermal mismatch type hidden fault cannot be accurately predicted. The system obtains the environmental pressure changes of the battery at different heights through ultrasonic ranging, extracts the thermal physical parameters of the battery surface in combination with thermal infrared imaging, calculates the thermal inertia value and adjusts the preset area, collects the internal and external temperature difference by using the embedded thermocouple, evaluates the thermal risk in combination with the temperature gradient, generates a risk score, adopts the flexible strain array to monitor the micro-strain for the high-risk area, identifies the thermal mismatch phenomenon based on the Pearson coefficient and the frequency domain features in combination with the support vector machine, finally accurately locates the mismatch position through the structural similarity index and the barycenter trajectory analysis, realizes the high-precision thermal risk identification and the area screening, and improves the reliability of the battery environmental adaptability detection.

Description

Technical Field

[0001] This invention relates to the field of new energy battery testing technology, and more specifically, to a new energy battery environmental adaptability testing system and testing method. Background Technology

[0002] With the widespread application of new energy vehicles, energy storage devices, and high-performance electric equipment, the stability and safety of new energy batteries under complex environmental conditions have become a key research focus. Especially in extreme environments such as high altitudes, frigid zones, and high temperatures, battery systems are easily affected by factors such as external air pressure, temperature differences, and thermal inertia disturbances, which can easily lead to phenomena such as thermal runaway, capacity decay, and shortened lifespan, thereby endangering the overall operational safety and energy efficiency of the system.

[0003] The existing technology has the following shortcomings:

[0004] Currently, the environmental adaptability assessment of new energy batteries mainly relies on conventional temperature acquisition devices, such as infrared thermal imagers and thermocouple probes, combined with static thermal distribution characteristics for surface temperature analysis. However, using surface temperature distribution alone as a criterion is insufficient to effectively identify potential thermal anomaly risks within the battery, especially under complex conditions where changes in ambient air pressure cause alterations in thermodynamic properties. Existing detection methods lack the comprehensive ability to assess the effects of thermal inertia, micro-strain response characteristics, and thermal-strain coupling anomalies, making it difficult to accurately predict latent faults such as "thermal mismatch." Therefore, this paper proposes a new energy battery environmental adaptability testing system and method.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a new energy battery environmental adaptability testing system and method, which solves the problems mentioned in the background art by employing different product testing methods.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a new energy battery environmental adaptability testing system and method, comprising the following steps:

[0008] Step S1: Obtain the battery climbing height and the corresponding ambient pressure change value and the thermal inertia value of the preset area on the battery surface. Combine the ambient pressure change value and the thermal inertia value to further divide the preset area on the battery surface to obtain the battery surface temperature area.

[0009] Step S2: According to the battery surface temperature area, the surface temperature distribution gradient is obtained, the embedded thermocouple is combined with the external infrared thermometer to collect, the temperature difference inside and outside the battery core is obtained, the surface temperature distribution gradient and the temperature difference inside and outside the battery core are comprehensively analyzed, the thermal risk score is obtained, and whether there is thermal risk is determined;

[0010] Step S3: After determining that there is thermal risk, the strain-thermal response consistency abnormal mechanism is entered, the micro-strain temperature correlation coefficient and the strain signal frequency spectrum energy change frequency are obtained, the micro-strain temperature correlation coefficient and the strain signal frequency spectrum energy change frequency are comprehensively determined, and whether there is thermal mismatch phenomenon is determined;

[0011] Step S4: When there is thermal mismatch phenomenon, the normal temperature control and abnormal temperature control model are obtained, the thermal map similarity and the thermal map response center gravity trajectory difference of the model under normal temperature control and abnormal temperature control are compared, the misalignment position is obtained, the key is marked, and the misalignment position is screened out when drawing the subsequent preset battery surface area.

[0012] Preferably, in step S1, the height difference absolute value of the battery between different height positions is obtained, and the battery climbing height is obtained.

[0013] The environmental pressure change value corresponding to the battery climbing height is calculated based on the international standard atmospheric model formula;

[0014] The battery surface is preliminarily divided based on the surface area of the battery, and the preset area of the battery surface is obtained.

[0015] The specific heat capacity, density and thermal conductivity of the battery surface preset area are obtained, and the thermal inertia value of the battery surface preset area is calculated.

[0016] Preferably, in step S1, the environmental pressure change value and the thermal inertia value are defined as input variables, the adjustment amplitude is defined as an output variable, and a fuzzy set is divided;

[0017] Fuzzy rules are formulated for the fuzzy set;

[0018] Fuzzy reasoning is performed according to the fuzzy rules, and the centroid method is used to solve, and the adjustment amplitude is obtained;

[0019] According to the adjustment amplitude, the adjusted area of the battery surface preset area is calculated, and the battery surface temperature area is obtained.

[0020] Preferably, in step S2, the temperature data of each collection point in each battery surface temperature area is collected, and the average value is taken as the battery surface temperature;

[0021] The difference value of the battery surface temperature of the adjacent battery surface temperature area is calculated, and the temperature distribution gradient is obtained;

[0022] Detect the temperature inside the battery core, and calculate the difference with the surface temperature of the battery to obtain the temperature difference between the inside and outside of the battery core;

[0023] Normalize the temperature difference between the inside and outside of the battery core and the temperature distribution gradient, and calculate the thermal risk score based on the weighted average formula.

[0024] Preferably, in step S2, the thermal risk score is compared with the thermal risk threshold value;

[0025] If the thermal risk score is greater than or equal to the thermal risk threshold value, it is determined that a thermal risk area is obtained;

[0026] If the thermal risk score is less than the thermal risk threshold value, it is determined that there is no thermal risk.

[0027] Preferably, in step S3, the strain data of each monitoring point in the thermal risk area is collected, and the average strain value of each monitoring point is calculated as the strain value of the thermal risk area;

[0028] The correlation coefficient between the strain value and the temperature in the thermal risk area is calculated as the micro-strain temperature correlation coefficient;

[0029] By performing frequency domain analysis on the micro-strain signals before and after the thermal field loading, the energy change characteristics of the strain signals are captured, and the strain signal sequence before the thermal field loading and the strain signal sequence after the thermal field loading are obtained;

[0030] The strain signal sequence before the thermal field loading and the strain signal sequence after the thermal field loading are subjected to fast Fourier transform processing to obtain the strain signal spectrum before the thermal field loading and the strain signal spectrum after the thermal field loading;

[0031] Based on the strain signal spectrum before the thermal field loading and the strain signal spectrum after the thermal field loading, the signal energy spectrum before and after the thermal field loading is calculated, and compared with the preset energy change threshold value to obtain the strain signal spectrum energy change frequency.

[0032] Preferably, in step S3, the micro-strain temperature correlation coefficient and the strain signal spectrum energy change frequency are normalized to construct a support vector machine model, a linear kernel function is used for modeling, and a decision value is output;

[0033] The decision value is compared with the decision threshold value;

[0034] If the decision value is greater than or equal to the decision threshold value, it is determined that the current thermal risk area has a thermal mismatch phenomenon;

[0035] If the decision value is less than the decision threshold value, it is determined that the current thermal risk area has no thermal mismatch phenomenon.

[0036] Preferably, in step S4, based on the battery surface temperature region division result, temperature distribution data and strain distribution data in each battery surface temperature region are collected to generate a reference thermal map as a normal temperature control model;

[0037] Based on the battery surface temperature region data obtained in the current detection period, combined with the thermal mismatch region, an abnormal temperature control model is obtained;

[0038] The similarity between the normal temperature control model and the abnormal temperature control thermal model is calculated using the structural similarity index to obtain a thermal map similarity;

[0039] The thermal map center of gravity trajectory difference analysis is performed on the normal temperature control model and the abnormal temperature control model to obtain the center of gravity coordinates;

[0040] The center of gravity trajectory deviation of the normal temperature control model and the abnormal temperature control model is compared to obtain the thermal map response center of gravity trajectory difference.

[0041] Preferably, in step S4, the thermal map similarity and the thermal map response center of gravity trajectory difference are weighted and averaged to obtain a misalignment score;

[0042] The misalignment score is compared with a misalignment score threshold value;

[0043] If the misalignment score is greater than the misalignment score threshold value, it is marked as a misalignment point, and the misalignment point is excluded in the subsequent battery surface region drawing process;

[0044] Otherwise, it is not marked.

[0045] A new energy battery environmental adaptability detection system includes a region division module, a risk assessment module, an anomaly detection module, and a mismatch calibration module, and the modules are signal connected;

[0046] The region division module obtains the battery climbing height, and combines the environmental pressure change value corresponding to the battery climbing height and the thermal inertia value of the preset battery surface region to redivide the preset battery surface region to obtain the battery surface temperature region;

[0047] The risk assessment module obtains the surface temperature distribution gradient based on the battery surface temperature region, uses embedded thermocouples and external infrared thermometers to jointly collect to obtain the temperature difference inside and outside the battery core, and comprehensively analyzes the surface temperature distribution gradient and the temperature difference inside and outside the battery core to obtain a thermal risk score to determine whether there is a thermal risk;

[0048] The anomaly detection module enters the strain-thermal response consistency anomaly mechanism after determining that there is a thermal risk, obtains the micro-strain temperature correlation coefficient and the strain signal spectrum energy change frequency, and comprehensively determines whether there is a thermal mismatch phenomenon based on the micro-strain temperature correlation coefficient and the strain signal spectrum energy change frequency;

[0049] After the mismatch calibration module detects the thermal mismatch phenomenon, the normal temperature control and abnormal temperature control models are obtained, the thermal map similarity and the thermal map response center gravity trajectory difference of the models under normal temperature control and abnormal temperature control are compared, the misalignment position is obtained, key marking is performed, and the misalignment position is screened out when drawing the subsequent preset battery surface area.

[0050] The technical effects and advantages of the present application are:

[0051] 1. The present application obtains the environmental pressure change of the battery at different heights by an ultrasonic range finder, and uses a thermal infrared imaging device to scan the battery surface to obtain thermal physical parameters such as heat capacity, density and thermal conductivity, and then calculates the thermal inertia value to calibrate the adjustment amplitude of the preset area of the battery surface. The temperature inside the battery core is monitored by the embedded thermocouple, the temperature difference between the inside and outside of the battery is calculated, and the thermal risk is evaluated in combination with the temperature distribution gradient to finally generate a potential thermal risk score and an overall thermal risk score. For the identified potential thermal risk area, further use a flexible strain array to monitor the micro-strain signal, analyze the correlation between temperature change and strain through the Pearson correlation coefficient, and capture the energy change of the strain signal through frequency domain analysis, and determine whether there is a thermal mismatch phenomenon through a support vector machine model. Finally, the thermal mismatch position is accurately positioned through structural similarity index and barycenter trajectory difference analysis, key marking and screening are performed, thereby improving the accuracy and reliability of the battery environmental adaptability detection. BRIEF DESCRIPTION OF DRAWINGS

[0052] Fig. 1 The present application is a flowchart for realizing a new energy battery environmental adaptability detection method.

[0053] Fig. 2 The present application is a flowchart for realizing a new energy battery environmental adaptability detection method.

[0054] Fig. 3 The present application is a module schematic diagram of a new energy battery environmental adaptability detection system. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0056] Embodiment 1

[0057] A new energy battery environmental adaptability detection method, as shown in Figs. 1-2 includes the following steps:

[0058] Step S1: Obtain the battery climbing height, and obtain the corresponding environmental pressure change value and the thermal inertia value of the preset region on the battery surface. Combine the environmental pressure change value and the thermal inertia value to redivide the preset region on the battery surface, and obtain the battery surface temperature region;

[0059] Step S2: According to the battery surface temperature region, obtain the surface temperature distribution gradient, use the embedded thermocouple and the external infrared thermometer to jointly collect, obtain the temperature difference inside and outside the battery core, and comprehensively analyze the surface temperature distribution gradient and the temperature difference inside and outside the battery core to obtain the thermal risk score to determine whether there is a thermal risk;

[0060] Step S3: After determining that there is a thermal risk, enter the strain-thermal response consistency abnormal mechanism, obtain the micro-strain temperature correlation coefficient and the strain signal spectrum energy change frequency, and comprehensively determine whether there is a thermal mismatch phenomenon by combining the micro-strain temperature correlation coefficient and the strain signal spectrum energy change frequency;

[0061] Step S4: When there is a thermal mismatch phenomenon, obtain the normal temperature control and abnormal temperature control model, compare the thermal map similarity and the thermal map response center gravity trajectory difference of the model under the normal temperature control and the abnormal temperature control, obtain the misalignment position, perform key marking, and perform screening operation on the misalignment position when drawing the subsequent preset battery surface region;

[0062] In step S1, the ultrasonic range finder is used to obtain the height difference absolute value between different height positions of the battery as the battery climbing height, and the environmental pressure change value corresponding to the battery climbing height is calculated based on the international standard atmospheric model formula, and the calculation formula is as follows: , wherein, represents the environmental pressure change value, represents the initial environmental pressure, represents the temperature decrement rate, represents the battery climbing height, represents the initial temperature, represents the gravitational acceleration, represents the air molar mass, represents the gas constant.

[0063] The battery surface is preliminarily divided based on the battery surface area, and is divided into uniform battery surface preset regions. In the preliminary division process, each battery surface preset region is divided equally, and the specific area of the battery surface preset region is determined according to the battery shell size, which is not described here.

[0064] A high-resolution thermal infrared imaging medium is used to comprehensively scan each battery surface preset region to obtain the thermal physical parameters of each battery surface preset region, including specific heat capacity , density and thermal conductivity . Based on the above thermal physical parameters, the thermal inertia value of each preset region on the battery surface is calculated according to the following formula : wherein, represents the thermal inertia value, represents the thermal conductivity, represents the density, represents the specific heat capacity, and the thermal inertia value is used to characterize the response speed of the preset region on the battery surface to the change of the heat flow.

[0065] It should be noted that the ultrasonic range finder is a device based on the principle of ultrasonic reflection, which is used to measure the distance between the battery and the ground; the international standard atmospheric model is a mathematical model used to simulate the change of pressure with height under standard atmospheric conditions, and the calculation formula is obtained by experiments of meteorological professionals; the high-resolution thermal infrared imaging medium is a device based on the principle of infrared thermal radiation, which realizes the visualization of temperature field by detecting the infrared thermal radiation emitted by the battery and converting it into an electrical signal, which will not be described here.

[0066] The environmental pressure change value and the thermal inertia value are comprehensively analyzed, the preset region on the battery surface is dynamically adjusted based on the fuzzy logic method, and the adjustment amplitude is determined. First, the fuzzy set is constructed based on the environmental pressure change value and the thermal inertia value , and is divided as follows:

[0067] The environmental pressure change value : low, medium, and high;

[0068] The thermal inertia value : low, medium, and high;

[0069] The adjustment amplitude : reduction, maintenance, and increase.

[0070] The fuzzy rules are set based on the combined relationship of the environmental pressure change value and the thermal inertia value :

[0071] Rule 1: when is high and is high, the region temperature response is sensitive, and the corresponding region is reduced to avoid local region temperature abnormally rising.

[0072] Rule 2: when is medium and is medium, the region division is moderate, and the corresponding region is kept unchanged.

[0073] Rule 3: when is low and is low, the region temperature response is slow, and the corresponding region is increased to avoid local region temperature abnormally falling.For low, the temperature change inside the region is not obvious, and the corresponding region is increased to reduce the amount of data collection.

[0074] It should be noted that the division of fuzzy sets can be adjusted according to actual conditions, for example, three fuzzy sets are taken as examples in the embodiment, and in fact, the environmental pressure change value and the thermal inertia value can be divided into more than three sets to facilitate more accurate adjustment according to the adjustment amplitude.

[0075] For the judgment of high, medium and low of the environmental pressure change value and the thermal inertia value, quantile method can be used to set threshold to judge, for example, when the environmental pressure change value exceeds the 70% quantile of the data set, it is marked as high, and when the thermal inertia value is lower than the 40% quantile of the data set, it is marked as low, which will not be repeated here.

[0076] The centroid method is used to determine the adjustment amplitude, and the specific calculation formula is wherein, is the adjustment amplitude, is the discrete value of the adjustment amplitude, is the corresponding membership value, is the index value of the battery surface preset region, is the total number of the battery surface preset region.

[0077] Finally, according to the adjustment amplitude, the final area of each battery surface preset region after adjustment is calculated to obtain the battery surface temperature region, and the calculation formula is wherein, is the area of the battery surface temperature region, is the area of the battery surface preset region, is the adjustment amplitude of the battery surface preset region, is the index value of the battery surface preset region.

[0078] In step S2, according to the battery surface temperature region determined in step S1, the temperature data in each battery surface temperature region is collected. Since there is a difference in temperature inside the battery surface temperature region, the temperature data of each collection point is collected quickly multiple times using an external infrared thermometer, and the average value is taken as the surface temperature value of the battery surface temperature region, and is recorded as the battery surface temperature. Further, by calculating the difference between the temperatures of adjacent regions, the temperature distribution gradient is obtained, which is defined as follows: wherein, is the temperature distribution gradient between the th battery surface temperature region and the adjacent th battery surface temperature region, is the battery surface temperature of the th battery surface temperature region, is the adjacent battery surface temperature of the i-th battery surface temperature region.

[0079] The battery core internal temperature is detected by an embedded thermocouple, denoted as , combined with the battery surface temperature, to calculate the battery core internal and external temperature difference, defined as follows: , wherein is the battery core internal and external temperature difference of the i-th battery surface temperature region, is the battery core internal temperature, is the battery surface temperature of the i-th battery surface temperature region. It should be noted that the external infrared thermometer is a non-contact temperature measuring device, which calculates the temperature of an object by detecting the intensity of its infrared radiation. In this case, it is used to collect the temperature of the battery surface temperature region. The embedded thermocouple is a direct contact temperature sensor, which is used to measure the battery core internal temperature in this case, and will not be described here.

[0080] For each battery surface temperature region, the battery core internal and external temperature difference and the temperature distribution gradient are analyzed comprehensively to evaluate the thermal risk.

[0081] For each battery surface temperature region, the battery core internal and external temperature difference and the temperature distribution gradient are analyzed comprehensively to evaluate the thermal risk.

[0082] The battery core internal and external temperature difference and the temperature distribution gradient are respectively constructed into the battery core internal and external temperature difference dataset and the temperature distribution gradient dataset. To ensure that each data dimension is in the same order of magnitude, the battery core internal and external temperature difference and the temperature distribution gradient are normalized. The calculation method is to divide the battery core internal and external temperature difference and the temperature distribution gradient by the maximum value in the dataset. The thermal risk score is calculated based on the weighted average formula after normalizing the battery core internal and external temperature difference and the temperature distribution gradient. The specific formula is as follows:

[0083] ;

[0084] , wherein is the thermal risk score, and are the weight coefficients of the battery core internal and external temperature difference and the temperature distribution gradient, respectively, and satisfy , is the battery core internal and external temperature difference, is the maximum value in the battery core internal and external temperature difference dataset, is the temperature distribution gradient, is the maximum value in the temperature distribution gradient dataset.

[0085] Compare the thermal risk score with the thermal risk threshold;

[0086] ​The setting of the thermal risk threshold is based on a statistical analysis method, and the thermal risk scores of all battery surface temperature regions are calculated and averaged, and the thermal risk threshold is obtained according to a 3 times standard deviation method, and the specific calculation formula is ;

[0087] wherein, is the thermal risk threshold, is the average of the thermal risk scores of all battery surface temperature regions, is the standard deviation of the thermal risk scores.

[0088] If , it is determined that the battery surface temperature region is a thermal risk region;

[0089] If , it is determined that the battery surface temperature region has no thermal risk.

[0090] In step S3, after determining the thermal risk region, the strain-thermal response consistency anomaly detection mechanism is entered to investigate the correlation between strain and temperature change in the thermal risk region and identify the thermal mismatch phenomenon.

[0091] First, the strain data of each monitoring point in the thermal risk region is quickly collected by using a flexible strain array, and the average strain value of each monitoring point is calculated as the strain value of the thermal risk region, denoted as . The Pearson correlation coefficient is used to measure the linear correlation between the strain value and the temperature, and the correlation coefficient between the strain value and the temperature in the thermal risk region is calculated as the micro-strain temperature correlation coefficient, and the calculation formula is as follows:

[0092] ;

[0093] wherein, is the micro-strain temperature correlation coefficient, and are the strain value and the battery surface temperature of the thermal risk region, and are the average strain value and the average battery surface temperature of the thermal risk region, is the total number of thermal risk regions.

[0094] It should be noted that the flexible strain array is a multi-point strain sensor array prepared based on a flexible substrate material, which is used to measure strain at multiple positions simultaneously, and will not be described here.

[0095] By performing frequency domain analysis on the micro-strain signals before and after the thermal field loading, the energy change characteristics of the strain signals are captured, and the strain signal frequency spectrum energy change frequency is obtained.

[0096] The micro-strain signals before and after the thermal field loading are collected to obtain the strain signal sequence before the thermal field loading and the strain signal sequence after the thermal field loading.

[0097] The strain signal sequence before the thermal field loading and the strain signal sequence after the thermal field loading are respectively subjected to fast Fourier transform processing to convert the time domain signals into frequency domain signals to obtain corresponding frequency spectrums, and a specific expression is wherein, is the strain signal frequency spectrum before the thermal field loading, is the strain signal sequence before the thermal field loading, is the strain signal frequency spectrum after the thermal field loading, is the strain signal sequence after the thermal field loading, is the frequency domain strain signal before and after the thermal field loading, is the time domain strain signal before and after the thermal field loading.

[0098] It should be noted that the thermal field is a function of three-dimensional space and time domain, and is used to reflect the overall state of temperature distribution in a certain spatial region; the Nyquist sampling theorem is a basic theorem in signal processing; and the fast Fourier transform is an algorithm for converting a discrete time domain signal into a frequency domain signal.

[0099] Based on the above spectrum results, the signal energy spectrums before and after the thermal field loading are calculated, and are defined as follows: , wherein, is the signal energy spectrum before the thermal field loading, is the signal energy spectrum after the thermal field loading, and are the strain signal spectrums before and after the thermal field loading, respectively.

[0100] The frequency domain energy difference before and after the thermal field loading is calculated, and a calculation formula is wherein, is the frequency domain energy difference before and after the thermal field loading, are the signal energy spectrums before and after the thermal field loading, respectively, based on the distribution characteristics of the frequency domain energy difference before and after the thermal field loading, an energy change frequency set is defined, and a selection criterion is: wherein, is a frequency component, i.e., a frequency value corresponding to each frequency point, is an index value of the energy change frequency, is a preset energy change threshold value, which is determined by a professional based on experimental data, and will not be described herein, and a frequency point set The frequency of the strain signal spectrum energy change is calculated for each thermal risk region, defined as the change frequency of the strain signal spectrum energy, denoted as .

[0101] The micro-strain temperature correlation coefficient and the strain signal spectrum energy change frequency are used as input features to build a support vector machine model to identify whether a thermal mismatch phenomenon exists. To ensure the consistency of the data range of each input feature dimension, the micro-strain temperature correlation coefficient and the strain signal spectrum energy change frequency are normalized, and the normalization formula is as follows: , wherein, is the normalized result of the micro-strain temperature correlation coefficient , and and represent the mean and standard deviation of the strain temperature correlation coefficient, respectively; is the normalized result of the strain signal spectrum energy change frequency , and and represent the mean and standard deviation of the strain signal spectrum energy change frequency, respectively.

[0102] The support vector machine model uses a linear kernel function for modeling, and the decision function is defined as follows: wherein, is the input feature vector, is the weight vector, and b is the bias term.

[0103] The support vector machine model maximizes the sample boundary interval by optimizing the following objective function:

[0104] ;

[0105] wherein, is a slack variable used to tolerate abnormal data points; is a penalty coefficient used to balance the boundary interval and classification error.

[0106] According to the decision value output by the support vector machine model, the following decision criteria are used to determine the thermal mismatch phenomenon:

[0107] If the decision value is greater than or equal to the decision threshold, it is determined that a thermal mismatch phenomenon exists in the current thermal risk region, and it is marked as a thermal mismatch region.

[0108] If the decision value is less than the decision threshold, it is determined that there is no thermal mismatch phenomenon in the current thermal risk region, and it is not marked.

[0109] The setting of the decision threshold is based on experimental data, and the best segmentation point is selected as the determination threshold by performing ROC curve analysis on the prediction results of the support vector machine model on the validation set, which will not be described here.

[0110] In step S4, based on the battery surface temperature region division result, a normal temperature control model and an abnormal temperature control model are constructed.

[0111] The normal temperature control model is a baseline model established under the condition of no abnormal temperature control, which is established based on temperature and strain data collected under the condition that the battery is in a normal working state. The temperature distribution data and strain distribution data in each battery surface temperature region are collected to generate a reference heat map as the normal temperature control model.

[0112] Based on the battery surface temperature region data obtained in the current detection period, the abnormal temperature control model is obtained in combination with the thermal mismatch region.

[0113] The similarity between the normal temperature control model and the abnormal temperature control model is calculated using the structural similarity index to obtain the heat map similarity, and the calculation formula is as follows:

[0114] ;

[0115] wherein, is the heat map similarity, are the average values of the normal temperature control model and the abnormal temperature control model, respectively, is the normal temperature control model, is the abnormal temperature control model, are the variances of the normal temperature control model and the abnormal temperature control model, respectively; is the covariance of the normal temperature control model and the abnormal temperature control model; , is a constant for avoiding zero denominator.

[0116] The heat map barycenter trajectory difference analysis is performed on the normal temperature control model and the abnormal temperature control model, and the barycenter coordinate formula is calculated as follows:

[0117] ;

[0118] wherein, , is the coordinate of the vth pixel point in the normal temperature control model and the abnormal temperature control model; is the temperature value of the pixel point; r is the total number of pixel points.

[0119] wherein, the pixel points in the normal temperature control model and the abnormal temperature control model are the same;

[0120] Based on the above center of gravity coordinate calculation result, the center of gravity trajectory offset of each area in the normal temperature control model and the abnormal temperature control model is compared, the center of gravity trajectory difference of the heat map response center is obtained, and the calculation formula is as follows:

[0121] ;

[0122] Wherein, is the center of gravity trajectory difference of the heat map response center; , respectively the center of gravity coordinates of the abnormal model heat map; respectively the center of gravity coordinates of the normal model heat map.

[0123] It should be noted that the center of gravity coordinates of the abnormal model heat map are calculated according to the above center of gravity coordinate formula, which is not repeated here;

[0124] For each pixel point, based on the calculation results of the heat map similarity and the center of gravity trajectory difference of the heat map response center, weighted average is carried out, and the misalignment score is defined, and the specific calculation formula is

[0125] ;

[0126] Wherein, is the misalignment score, and the weight coefficients of the heat map similarity and the center of gravity trajectory difference of the heat map response center, and satisfy .

[0127] The misalignment score threshold is set, and the calculation of the misalignment score threshold is based on statistical analysis method. When the battery is in normal working state, the misalignment score of each area is calculated, and the misalignment score threshold is set as the sum of the mean value and twice the standard deviation of the misalignment score of each area.

[0128] If , the pixel point is identified as a misalignment point, and the misalignment point is screened out in the subsequent battery surface area drawing process to avoid its interference on the area division and temperature distribution evaluation.

[0129] Otherwise, it is not marked.

[0130] Embodiment 2

[0131] Please refer to Fig. 3 , a new energy battery environmental adaptability detection system, comprising a region division module, a risk assessment module, an abnormality detection module and a mismatch calibration module, and the modules are signal connected;

[0132] The region division module obtains the battery climbing height, and re-divides the preset region on the battery surface by combining the battery climbing height with the corresponding environmental pressure change value and the thermal inertia value of the preset region on the battery surface, to obtain a battery surface temperature region;

[0133] The risk assessment module obtains a surface temperature distribution gradient according to the battery surface temperature region, uses an embedded thermocouple and an external infrared thermometer to jointly collect, obtains an internal-external temperature difference of the battery core, and comprehensively analyzes the surface temperature distribution gradient and the internal-external temperature difference of the battery core to obtain a thermal risk score, and determines whether there is a thermal risk;

[0134] The abnormality detection module enters a strain-thermal response consistency abnormality mechanism after determining that there is a thermal risk, obtains a micro-strain temperature correlation coefficient and a strain signal spectrum energy change frequency, and comprehensively determines whether there is a thermal mismatch phenomenon by using the micro-strain temperature correlation coefficient and the strain signal spectrum energy change frequency.

[0135] After the mismatch calibration module detects the thermal mismatch phenomenon, the normal temperature control and the abnormal temperature control model are obtained, the thermal map similarity and the thermal map response center gravity trajectory difference of the model under the normal temperature control and the abnormal temperature control are compared, the misalignment position is obtained, the misalignment position is marked, and the misalignment position is screened out when the preset battery surface region is drawn.

[0136] The above formulas are dimensionless values, and the formulas are obtained by software simulation of a large amount of data to obtain a formula closest to the actual situation. The preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0137] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another, for example, the computer instructions can be transferred from one website, computer, server or data center to another website, computer, server or data center through a wired or wireless (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0138] It should be understood that the term "and / or" herein merely describes an association relationship of associated objects, which means that there can be three relationships, for example, A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone, where A and B can be singular or plural. In addition, the character " / " herein generally represents an "or" relationship between the front and rear associated objects, but it can also represent an "and / or" relationship, which can be understood according to the context before and after.

[0139] In this application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can represent a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0140] It should be understood that in various embodiments of the present application, the size of the sequence number of the above-described processes does not mean the order of execution, and the execution order of the processes should be determined according to their functions and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0141] Those skilled in the art can clearly understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0142] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0143] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0144] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0145] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit.

[0146] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0147] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A new energy battery environmental adaptability detection method, characterized in that: The method comprises the following steps: Step S1: Obtain the battery climbing height, and obtain the corresponding environmental pressure change value and the thermal inertia value of the preset region on the battery surface. The preset region on the battery surface is re-divided according to the environmental pressure change value and the thermal inertia value, and the battery surface temperature region is obtained; Step S2: According to the battery surface temperature region, the surface temperature distribution gradient is obtained, the embedded thermocouple and the external infrared thermometer are combined to collect, the internal and external temperature difference of the battery core is obtained, the surface temperature distribution gradient and the internal and external temperature difference of the battery core are comprehensively analyzed, the thermal risk score is obtained, and whether there is a thermal risk is determined; Step S3: After determining that there is a thermal risk, the strain-thermal response consistency abnormal mechanism is entered, the micro-strain temperature correlation coefficient and the strain signal spectrum energy change frequency are obtained, the micro-strain temperature correlation coefficient and the strain signal spectrum energy change frequency are comprehensively determined, and whether there is a thermal mismatch phenomenon is determined; Step S4: When there is a thermal mismatch phenomenon, the normal temperature control and the abnormal temperature control model are obtained, the thermal map similarity and the thermal map response center gravity trajectory difference of the model under the normal temperature control and the abnormal temperature control are compared, the misalignment position is obtained, the misalignment position is marked, and the misalignment position is excluded when drawing the preset battery surface region.

2. The new energy battery environmental adaptability detection method according to claim 1, wherein: in step S1, the height difference absolute value between the battery at different height positions is obtained, and the battery climbing height is obtained; The environmental pressure change value corresponding to the battery climbing height is calculated based on the international standard atmospheric model formula; The battery surface is preliminarily divided based on the battery surface area, and the preset region on the battery surface is obtained; The specific heat capacity, density and thermal conductivity of the preset region on the battery surface are obtained, and the thermal inertia value of the preset region on the battery surface is calculated.

3. The new energy battery environmental adaptability detection method according to claim 2, wherein: In step S1, the environmental pressure change value and the thermal inertia value are defined as input variables, the adjustment amplitude is defined as an output variable, and a fuzzy set is divided; Fuzzy rules are formulated for the fuzzy set; According to the fuzzy rule, the fuzzy reasoning is carried out, and the adjustment amplitude is solved by using the centroid method; According to the adjustment amplitude, the adjusted area of the preset region on the battery surface is calculated, and the battery surface temperature region is obtained.

4. The new energy battery environmental adaptability detection method according to claim 3, wherein: In step S2, the temperature data of each collection point in each battery surface temperature region is collected, and the average value is taken as the battery surface temperature; The difference value of the battery surface temperature of the adjacent battery surface temperature region is calculated, and the temperature distribution gradient is obtained; The temperature difference between the battery core inside and outside is detected, and the difference value between the battery core inside and outside is calculated, and the battery core inside and outside temperature difference is obtained; The battery core inside and outside temperature difference and the temperature distribution gradient are normalized, and the thermal risk score is calculated based on the weighted average formula.

5. The new energy battery environmental adaptability detection method according to claim 4, wherein: In step S2, the thermal risk score and the thermal risk threshold are compared; ​ If the thermal risk score is greater than or equal to the thermal risk threshold, it is determined that a thermal risk region is obtained; If the thermal risk score is less than the thermal risk threshold, it is determined that there is no thermal risk.

6. The new energy battery environmental adaptability detection method according to claim 1, wherein: In step S3, strain data of each monitoring point in the thermal risk region is collected, and the average strain value of each monitoring point is calculated as the strain value of the thermal risk region; The correlation coefficient between the strain value in the thermal risk region and the battery surface temperature is calculated as the micro-strain temperature correlation coefficient; Through frequency domain analysis of the micro-strain signals before and after the thermal field loading, the energy change characteristics of the strain signals are captured, and the strain signal sequence before the thermal field loading and the strain signal sequence after the thermal field loading are obtained; The strain signal sequence before the thermal field loading and the strain signal sequence after the thermal field loading are subjected to fast Fourier transform processing to obtain the strain signal spectrum before the thermal field loading and the strain signal spectrum after the thermal field loading; Based on the strain signal spectrum before the thermal field loading and the strain signal spectrum after the thermal field loading, the signal energy spectrum before and after the thermal field loading is calculated, and compared with the preset energy change threshold to obtain the strain signal spectrum energy change frequency.

7. The new energy battery environmental adaptability detection method according to claim 6, wherein: In step S3, the micro-strain temperature correlation coefficient and the strain signal spectrum energy change frequency are normalized to construct a support vector machine model, a linear kernel function is used for modeling, and a decision value is output; The decision value is compared with a decision threshold value; If the decision value is greater than or equal to the decision threshold value, it is determined that the current thermal risk region has a thermal mismatch phenomenon; If the decision value is less than the decision threshold value, it is determined that the current thermal risk region has no thermal mismatch phenomenon.

8. The new energy battery environmental adaptability detection method according to claim 7, wherein: In step S4, based on the battery surface temperature region division result, temperature distribution data and strain distribution data in each battery surface temperature region are collected to generate a reference thermal map as a normal temperature control model; Based on the battery surface temperature region data obtained in the current detection period, an abnormal temperature control model is obtained in combination with the thermal mismatch region; The similarity between the normal temperature control model and the abnormal temperature control model is calculated using the structural similarity index to obtain a thermal map similarity degree; The thermal map response center of gravity trajectory difference is obtained by analyzing the thermal map response center of gravity trajectory difference between the normal temperature control model and the abnormal temperature control model.

9. The new energy battery environmental adaptability detection method according to claim 8, wherein: In step S4, the thermal map similarity degree and the thermal map response center of gravity trajectory difference are weighted and averaged to obtain a misalignment score; The misalignment score is compared with a misalignment score threshold value; If the misalignment score is greater than the misalignment score threshold value, it is marked as a misalignment point, and the misalignment point is excluded in the subsequent battery surface region drawing process; Otherwise, it is not marked. The system comprises a region division module, a risk assessment module, an abnormality detection module, and a mismatch calibration module, and the modules are connected by signals.

10. A new energy battery environmental adaptability detection system for implementing the new energy battery environmental adaptability detection method of any one of claims 1-9. ​ The area division module obtains the battery climbing height, and re-divides the preset battery surface area by combining the battery climbing height with the corresponding environmental pressure change value and the thermal inertia value of the preset battery surface area, to obtain a battery surface temperature area; The risk assessment module obtains a surface temperature distribution gradient according to the battery surface temperature area, uses an embedded thermocouple and an external infrared thermometer to jointly collect, obtains an internal and external temperature difference of the battery core, and comprehensively analyzes the surface temperature distribution gradient and the internal and external temperature difference of the battery core to obtain a thermal risk score, and determines whether there is a thermal risk; The abnormality detection module, after determining that there is a thermal risk, enters a strain-thermal response consistency abnormality mechanism, obtains a micro-strain temperature correlation coefficient and a strain signal spectrum energy change frequency, and comprehensively determines whether there is a thermal mismatch phenomenon according to the micro-strain temperature correlation coefficient and the strain signal spectrum energy change frequency; After the mismatch calibration module detects the thermal mismatch phenomenon, obtains a normal temperature control and an abnormal temperature control model, compares the thermal map similarity and the thermal map response center gravity trajectory difference of the models under the normal temperature control and the abnormal temperature control, obtains a misalignment position, performs key marking, and performs a screening operation on the misalignment position when drawing the subsequent preset battery surface area.

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