Battery PACK heating threshold calibration method and thermal imaging abnormity judgment system

Abstract

The invention discloses a battery PACK heating threshold calibration method and a thermal imaging anomaly judgment system, and belongs to the technical field of battery production. The heating threshold calibration method comprises the following steps: S100, through a simulation method, obtaining simulation steady-state temperature values of a plurality of feature point positions of a welding part, a tightening part and a connector plug-in part on a battery simulation model; s200, carrying out charging and discharging test on the plurality of groups of battery pack samples, and obtaining a test steady-state temperature value of each feature point location on the battery pack samples; and S300, comparing the test steady-state temperature value of each feature point location with the simulation steady-state temperature value, and determining a heating standard threshold value of each feature point location. The heating threshold calibration method provides a judgment basis for automatically judging whether the overcurrent part is abnormal or not, and is high in universality. Through the thermal imaging anomaly judgment system, the detection precision is remarkably improved, the detection efficiency is remarkably improved, closed-loop data is provided for process optimization, and the PACK process reject ratio is reduced.

Description

TECHNICAL FIELD

[0001] The application relates to a battery PACK heat generation threshold calibration method and a thermal imaging abnormality judgment system, and belongs to the technical field of battery production. BACKGROUND

[0002] At present, new energy storage and power batteries are becoming more and more popular. As the core component of power supply, the improvement of production process, product yield and stability is paid more and more attention by battery manufacturing enterprises. At present, laser welding, screw tightening and wire harness connector process have become very important process links in the production process of new energy battery PACK. For example, in the laser welding process, it is necessary to ensure that the welding surfaces are tightly pressed and well contacted, otherwise abnormality such as virtual welding, explosive welding and welding through will be caused. In the screw tightening process, it is necessary to ensure the tightening torque to ensure that the contact surface of the two or more materials being tightened is good. Whether the subsequent process can effectively detect abnormality is the key to ensure product quality.

[0003] At present, the abnormality judgment method commonly used in the industry is that when the battery is tested by charging and discharging, a thermal imager is manually operated to check the over-current welding position, the tightening position and the connector plug-in position, and then whether each charging and discharging heating position is normal is judged by manual experience. However, it is difficult to scientifically and effectively detect the abnormal failure of the battery PACK package tightening, welding and connector plug-in position by using this method, and the labor intensity and workload are large. SUMMARY

[0004] To solve the above technical problems, the application provides a battery PACK heat generation threshold calibration method and a thermal imaging abnormality judgment system.

[0005] The application is realized by the following technical solutions: A battery PACK heat generation threshold calibration method, comprising the following steps: S100, obtaining simulation steady-state temperature values of a plurality of feature points of a welding position, a tightening position and a connector plug-in position on a battery simulation model by a simulation method; S200, performing charging and discharging test on a plurality of battery package samples, and obtaining test steady-state temperature values of each feature point on the battery package samples after corresponding the feature points on the battery package samples with the feature points on the battery simulation model one by one; S300, comparing the test steady-state temperature values of each feature point with the simulation steady-state temperature values, and determining the heat generation standard threshold of each feature point.

[0006] The step S100 comprises the following steps: S101, importing a battery package 3D design model into a CFD simulation software, and dividing the battery package 3D design model by using a tetrahedral unstructured mesh; S102, define the key thermal physical property parameters of the battery pack core components to build a three-dimensional thermal physical model of the battery pack in the CFD simulation software, and determine the geometric boundary and physical property association mode of each core component of the battery pack; S103, simulate the state of heat generation and heat dissipation reaching dynamic balance in the battery pack charging and discharging process by using a steady-state calculation mode, select a Spalart-Allmaras turbulence model to simulate the influence of cooling liquid flow on heat distribution, and set the iteration step number, convergence tolerance and boundary conditions; S104, run the CFD simulation software for simulation, and output the temperature distribution cloud chart and temperature distribution cut plane chart; S105, extract the simulation steady-state temperature values of each feature point of the welding position, tightening position and plug-in position of the connector from the temperature distribution cloud chart and temperature distribution cut plane chart.

[0007] The parameter values of the key thermal physical property parameters of the battery pack core components in step 102 are obtained through a detection report provided by a material supplier or experimental testing.

[0008] The key thermal physical property parameters of the battery pack core components defined in step 102 include: The thermal conductivity, specific heat capacity and density of the battery cell and current collector are defined respectively; The thermal conductivity of the liquid cooling plate, and the thermal conductivity and dynamic viscosity of the cooling liquid are defined; The thermal conductivity of the box is defined; The contact thermal resistance of the welding position, tightening position and plug-in position of the connector is defined respectively.

[0009] In step 103, the iteration step number is set to 100 steps, the convergence tolerance is set to 10 -3 , and the boundary condition setting includes: setting the ambient temperature to be consistent with the sample charging and discharging test temperature of the battery pack, setting the heat dissipation mode to be liquid cooling, air cooling or natural cooling, setting the charging and discharging current to be nC times of the rated current of the battery pack sample, n being a positive integer, to simulate different charging and discharging conditions.

[0010] The step S105 includes the following steps: S1051, determining key feature points: marking the welding position, tightening position and connector pair insertion position on the battery PACK three-dimensional thermal physical model, extracting 3 points around the welding point as key feature points for the welding position, extracting the screw center and 2 points where the gasket contacts the base material as key feature points for the tightening position, and extracting 2 pin contact points and 2 shell contact points as key feature points for the connector pair insertion position, for each welding position, tightening position and connector pair insertion position, a total of 5-8 feature points including key feature points are extracted to ensure coverage of the heat core area; S1052, data average processing: for each feature point, the average temperature of 5 adjacent grid nodes of the feature point after simulation steady state is extracted as its simulation steady state temperature value, the calculation formula is as follows: , wherein, is the simulation steady state temperature value of the i-th feature point, is the temperature of the j-th grid node around the i-th feature point.

[0011] The step S200 includes the following steps: S201, preparing a number of battery pack samples ≥10 groups, and selecting 3 groups of battery pack samples from them to be placed in an environment with a temperature of 23℃±2℃ and a humidity of 50%±5%, and then making the 3 groups of battery pack samples run according to the charge and discharge current set for the battery simulation model in the simulation process; S202, making the lens of the thermal imager directly face the feature points on the battery pack sample, and ensuring that the feature points on the battery pack sample correspond one-to-one to the feature points on the battery simulation model; S203, collecting the temperature data of the welding position, tightening position and connector pair insertion position on the battery pack sample in real time through the thermal imager, and then extracting the steady state temperature average value of each feature point as the test steady state temperature value, the calculation formula is as follows: , wherein, is the test steady state temperature value of the i-th feature point, is the thermal equilibrium time, is the instantaneous temperature at time t.

[0012] In the step S202, the lens of the thermal imager is made to directly face the key feature points on the battery pack sample, and the distance deviation of the lens to the key feature points is 1mm-1.5mm, and the angle deviation is ≤10°. ​​​​​

[0013] In step S203, the thermal imager collects temperature data of the welded parts, tightened parts and connector mating parts on the battery pack sample at a frequency of 1Hz, and continues to collect temperature data until the charging and discharging ends and thermal equilibrium is reached.

[0014] Step S300 includes the following steps: S301. For the same welding location, tightening location, or connector mating location, if the deviation between the measured steady-state temperature value and the simulated steady-state temperature value at a single characteristic point meets the following conditions: If the average test steady-state temperature value of all feature points deviates from the average simulation steady-state temperature value by ≤3%, then the battery simulation model is deemed valid, and the same method is used to perform charge and discharge tests on the remaining battery pack samples. For the same welding point, tightening point, or connector mating point, if the deviation between the tested steady-state temperature value and the simulated steady-state temperature value at a single characteristic point meets the following conditions: The following tests will be performed on the battery pack samples: S3011. Check whether the relevant steps of the charge and discharge test process are carried out according to the original plan, and whether there are any violations. S3012. Are there any abnormalities in the PACK process at the welding points, tightening points, or connector mating points? Are the S3013 and PACK test points loose, causing data anomalies? S302. In step S301, after the battery pack sample test is completed, the battery pack sample is retested for charge and discharge, and steps S301 and S302 are repeated until all battery pack sample charge and discharge test results show that the battery simulation model is effective. S303. Calculate the average test temperature value for each feature point using the following formula. , , in, This represents the number of battery pack sample groups. For the first The first batch of battery pack samples The steady-state temperature values ​​of each characteristic point were measured; then, using... This serves as the standard threshold for the heating of this feature point.

[0015] A battery pack thermal imaging anomaly detection system includes: Thermal imaging data acquisition module: includes two sets of thermal imagers. Both sets of thermal imagers are arranged according to the relative positional relationship between the thermal imager and the battery pack sample when performing charge and discharge tests on the battery pack sample. They are used to acquire thermal image data of the welding parts, tightening parts and connector mating parts on the PACK battery pack in real time. The data preprocessing module receives the thermal image data transmitted by the thermal imaging data acquisition module, and sequentially performs image denoising, image registration and temperature calibration on the original thermal image, and outputs a temperature image for analysis. The data analysis and threshold comparison module extracts the temperature of the feature points in the temperature image and compares it with the corresponding heat generation standard threshold, and outputs the determination result of normal or abnormal. The man-machine interaction module is used to display real-time temperature images, feature point temperature values and determination results, and supports historical data query and abnormal alarm.

[0016] The data preprocessing module uses Gaussian filtering to perform image denoising on the original thermal image. The data preprocessing module performs image registration on the thermal image by using the SIFT algorithm to make the feature points on the thermal image coincide with the feature points on the battery simulation model or the battery pack sample in the charging and discharging test process, so as to realize image registration. The data preprocessing module uses linear piecewise fitting to establish the relationship between the output gray value G of the thermal imager and the actual temperature T, and uses the least squares method to solve it, so as to realize temperature calibration.

[0017] The data processing process of the data analysis and threshold comparison module includes the following steps: Step one, feature point temperature extraction: in the temperature image, according to the coordinates of the feature points on the battery simulation model or the battery pack sample in the charging and discharging test process, extract the temperature value of each feature point Step two, threshold comparison and abnormality determination: including single feature point temperature determination and regional collaborative temperature determination, wherein: Single feature point temperature determination: the heat generation standard threshold of the th feature point is , if , the temperature of the feature point is normal, otherwise the temperature of the feature point is abnormal, and is marked as high temperature abnormality or low temperature abnormality; Regional collaborative temperature determination: calculate the temperature difference of adjacent feature points in the same welding part, tightening part or connector pair insertion part , wherein is the adjacent feature point of the th feature point, and the distance between the two adjacent feature points is ≤5mm, if , it is determined that there is a thermal distribution abnormality in the region; Step three, result output: if all single feature point temperature determination results on the PACK battery pack are normal, and all adjacent feature points in the regional collaborative temperature determination satisfy ​If the temperature of one or more individual feature points is abnormal, or if the temperature of the area is abnormal, the output judgment result is abnormal, and the coordinates, temperature value and abnormality type of the abnormal feature point are output.

[0018] The beneficial effects of this invention are as follows: First, it provides a basis for automatically judging whether overcurrent parts are abnormal: By importing the 3D design model of the battery pack into CFD simulation software for simulation, the simulated steady-state temperature values ​​of each characteristic point of the welding parts, tightening parts, and connector mating parts are obtained. By conducting charge and discharge tests on multiple sets of battery pack samples, the test steady-state temperature values ​​of each characteristic point of the welding parts, tightening parts, and connector mating parts on the battery pack samples are obtained. Then, the test steady-state temperature values ​​of each characteristic point are compared with the simulated steady-state temperature values ​​to determine the heating standard threshold of each characteristic point. The deviation of the obtained heating standard threshold is ≤5%, which provides a scientific and reasonable basis for subsequent judgment on whether the welding parts, tightening parts, and connector mating parts are abnormal. This lays a solid foundation for realizing the automatic detection and judgment of whether overcurrent parts such as welding parts, tightening parts, and connector mating parts are abnormal.

[0019] Second, it has strong versatility: the battery PACK heat dissipation threshold calibration method is applicable to power or energy storage PACK battery packs that use different heat dissipation methods such as liquid cooling, air cooling and natural heat dissipation. By adjusting the boundary conditions and the charge and discharge test conditions of the battery pack sample, it can be quickly adapted to different PACK products without the need to redevelop the battery PACK thermal imaging anomaly judgment system.

[0020] Third, the detection accuracy is significantly improved: Based on the battery pack heating threshold calibration method to obtain the standard threshold for heating at each feature point, the thermal imaging data acquisition module collects thermal image data of the welding parts, tightening parts, and connector mating parts on the battery pack in real time; the data preprocessing module performs image noise reduction, image registration, and temperature calibration on the original thermal images in sequence, and outputs temperature images for analysis; the data analysis and threshold comparison module extracts the temperature of feature points in the temperature images, and combines the standard threshold for heating at each feature point, adopts a temperature judgment method that combines single feature point and area collaboration, and judges and outputs the result of whether the battery pack is abnormal. It automatically, scientifically, and effectively detects abnormal failures of welding parts, tightening parts, and connector mating parts on the battery pack, significantly reducing the workload and labor intensity of workers, and eliminating the situation where human error in judging abnormalities of welding parts, tightening parts, and connector mating parts due to experience bias, visual fatigue, etc., increases the detection rate of abnormalities of current-carrying parts from 70%~80% in traditional manual methods to over 99%.

[0021] Fourth, the detection efficiency is improved significantly: through the battery PACK thermal imaging anomaly judgment system, data acquisition, data preprocessing, data analysis and comparison are automatically completed, the detection time of a single PACK battery pack is shortened from 2-3h of traditional manual to 1-1.5h, and multiple parallel detection is supported, the labor input is reduced by 80%, and the detection efficiency of PACK battery pack is improved significantly.

[0022] Fifth, process stability guarantee: through the data analysis and threshold comparison module, the coordinates, temperature values and abnormal types of abnormal feature points are output, and displayed on the man-machine interaction module, which can trace back the PACK process problem, provide closed-loop data for process optimization, reduce the PACK process failure rate, and solve the problem that the traditional manual detection can only judge whether it is abnormal, but cannot locate why it is abnormal. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 The figure is a structural schematic diagram of the battery PACK thermal imaging anomaly judgment system of the application. DETAILED DESCRIPTION

[0024] The technical scheme of the application will be further described below, but the scope of protection is not limited to the description.

[0025] The battery PACK heat generation threshold calibration method provided by the application comprises the following steps: S100, by simulation method, the simulation steady-state temperature values of the multiple feature points of the welding position, the tightening position and the connector plug-in position on the battery simulation model are obtained.

[0026] The step S100 comprises the following steps: S101, the battery pack 3D design model is imported into the CFD simulation software, and the battery pack 3D design model is divided by tetrahedral unstructured mesh.

[0027] Specifically, the CFD (Computational Fluid Dynamics) simulation software adopts ANSYS Fluent software or STAR-CCM+ software, wherein the ANSYS Fluent supports meshing of complex geometric models, and has excellent compatibility with the Spalart-Allmaras turbulence model, and can efficiently perform coupled thermal analysis on multiple components (battery cells, liquid cooling plates, boxes, etc.) of the battery PACK; STAR-CCM+ has significant advantages in automatic mesh generation and multi-physical field coupling calculation, and is suitable for batch simulation verification. The battery pack 3D design model is imported into the selected software, and the battery pack 3D design model is meshed using “tetrahedral unstructured mesh” (taking into account the calculation accuracy and efficiency), and the key parameters such as grid node coordinates and element number are output (for example, the grid element number of a typical 10kWh energy storage PACK battery pack is about 500,000-800,000, and the node number is about 100,000-150,000, which needs to be adjusted according to the model complexity).

[0028] S102, define the key thermal physical property parameters of the core components of the battery pack to build a three-dimensional thermal physical model of the battery PACK in the CFD simulation software, and clearly define the geometric boundary and physical property association method of each core component of the battery pack.

[0029] Specifically, the physical property association method of each core component of the battery pack is defined, that is, the connection method of each core component of the battery pack is defined, such as the contact between the battery cell and the current collector, and the connection of the liquid cooling plate with the battery cell through the thermal conductive glue.

[0030] The parameter values of the key thermal physical property parameters of the core components of the battery pack in the step 102 are obtained through the detection report provided by the material supplier or experimental test to ensure the authenticity of the data.

[0031] The key thermal physical property parameters of the core components of the battery pack defined in the step 102 include: The thermal conductivity, specific heat capacity and density of the battery cell and the current collector are defined respectively; The thermal conductivity of the liquid cooling plate, and the thermal conductivity and dynamic viscosity of the cooling liquid are defined; The thermal conductivity of the box is defined; The contact thermal resistance of the welding position, the tightening position and the connector to the plug-in position is defined respectively. Specifically, as shown in the following table:

[0032] Based on the above parameters, a three-dimensional thermal physical model of the battery PACK is built in the CFD simulation software.

[0033] S103, simulate the state of dynamic balance of heat generation and dissipation during the charging and discharging process of the battery pack by using a steady-state calculation mode, select a Spalart-Allmaras turbulence model to simulate the influence of cooling liquid flow on heat distribution, and set the iteration step number, convergence tolerance and boundary conditions.

[0034] Specifically, the steady-state calculation mode is used to simulate the state of dynamic balance of heat generation and dissipation during the charging and discharging process of the battery pack. The steady-state calculation is suitable for static charging and discharging scenarios, and excludes the interference of transient thermal fluctuations on threshold determination.

[0035] The Spalart-Allmaras turbulence model is a one-equation model, which has the advantages of high calculation efficiency and strong stability, and is suitable for medium Reynolds number (Re=10³~10 5 ) flow simulation of liquid cooling flow channels (such as liquid cooling plate serpentine flow channels) in the battery PACK, which can accurately simulate the influence of cooling liquid flow on heat distribution.

[0036] The iteration step number is set to 100 steps in the step 103, the convergence tolerance is set to 10 -3 , and the boundary conditions include: setting the ambient temperature to be consistent with the battery pack sample charging and discharging test temperature, setting the heat dissipation mode to be liquid cooling, air cooling or natural cooling, setting the charging and discharging current to be nC rate (such as 1C, 2C rate) of the rated current of the battery pack sample, and n is a positive integer, to simulate different charging and discharging conditions.

[0037] Specifically, the iteration step number is set to 100 steps, the first 50 steps are rough calculation, which is used for step-by-step optimization of parameters; and the last 50 steps are fine calculation, which is used for approaching the real thermal equilibrium state. The convergence tolerance is set to 10 -3 , and the energy equation residual error is used as the core judgment index. When the residual error is less than or equal to 10 -3 , it means that the simulation result no longer changes significantly, and the calculation is stable.

[0038] The battery pack sample charging and discharging test environment is: temperature 23℃±2℃, humidity 50%±5%. When the heat dissipation mode is natural convection cooling, the surface heat transfer coefficient is h=5~10 W / (m²・K).

[0039] S104, run the CFD simulation software to perform simulation, and output the temperature distribution cloud chart and the temperature distribution cut plane chart.

[0040] Specifically, the temperature distribution cloud chart can directly show the overall temperature field distribution of the battery pack, and the red area is the high temperature area, and the blue area is the low temperature area; the temperature distribution cut plane chart is cut along the center axis of the welding point, the tightening part and the connector, to show the temperature gradient inside the battery pack.

[0041] S105, extract the simulation steady-state temperature values of the characteristic point positions of the welding position, the tightening position and the connector pair insertion position from the temperature distribution cloud chart and the temperature distribution cut plane chart.

[0042] The step S105 includes the following steps: S1051, determine the key characteristic point positions: mark the welding position, the tightening position and the connector pair insertion position on the three-dimensional thermal physical model of the battery PACK, extract the welding point and three points within a range of 3mm around the welding point as the key characteristic point positions for the welding position, extract the screw center and two points where the gasket contacts the base material as the key characteristic point positions for the tightening position, and extract two pin contact points and two shell contact points as the key characteristic point positions for the connector pair insertion position, for each welding position, tightening position and connector pair insertion position, a total of 5-8 characteristic point positions including the key characteristic point positions are extracted to ensure covering the heat generation core area; S1052, data average processing: for each characteristic point position, extract the temperature average value of the 5 adjacent grid nodes of the characteristic point position after the simulation steady state as the simulation steady-state temperature value of the characteristic point position, and the calculation formula is as follows: , Wherein, is the simulation steady-state temperature value of the i-th characteristic point position, is the temperature of the j-th grid node around the i-th characteristic point position.

[0043] S200, perform charge-discharge test on multiple groups of battery pack samples, and after corresponding the characteristic point positions on the battery pack samples with the characteristic point positions on the battery simulation model one by one, obtain the test steady-state temperature values of the characteristic point positions on the battery pack samples.

[0044] The step S200 includes the following steps: S201, prepare ≥10 groups of battery pack samples, and select 3 groups of battery pack samples from them to be placed in an environment with a temperature of 23℃±2℃ and a humidity of 50%±5%, and then make the 3 groups of battery pack samples run according to the charge-discharge current set for the battery simulation model in the simulation process (such as 1C charging and 1C discharging).

[0045] At the initial stage of a PACK battery pack design completion and manufacturing (such as the pilot stage), ensure that the production process is verified after the small-scale test, prepare ≥10 groups of battery pack samples, 2 sets of thermal imagers (resolution ≥640×512, temperature measurement range -20℃~150℃, accuracy ±2%), 2 sets of charge-discharge cabinets (current accuracy ±0.5% FS, voltage accuracy ±0.2% FS), and a temperature and humidity controllable laboratory (temperature control accuracy ±1℃, humidity control accuracy ±5%).​​​

[0046] S202, the lens of the thermal imager is directed to the feature point on the battery pack sample, and it is ensured that the feature point on the battery pack sample corresponds to the feature point on the battery simulation model one by one.

[0047] In the step S202, the lens of the thermal imager is directed to the key feature point on the battery pack sample, and the distance deviation of the lens to the key feature point is 1mm-1.5mm, and the angle deviation is ≤10°.

[0048] S203, the temperature data of the welding part, the tightening part and the plug-in part of the battery pack sample are collected in real time by the thermal imager, then the steady-state temperature average value of each feature point is extracted as the test steady-state temperature value, and the calculation formula is as follows: , Among them, is the test steady-state temperature value of the i-th feature point, is the thermal equilibrium time, is the instantaneous temperature at the moment.

[0049] In the step S203, the frequency of collecting the temperature data of the welding part, the tightening part and the plug-in part of the battery pack sample by the thermal imager is 1Hz, and the temperature data is continuously collected until the charging and discharging ends and reaches the thermal equilibrium.

[0050] S300, the test steady-state temperature value of each feature point is compared with the simulation steady-state temperature value, and the heating standard threshold of each feature point is determined.

[0051] The step S300 includes the following steps: S301, for the same welding part, tightening part or plug-in part, if the test steady-state temperature value of a single feature point and the simulation steady-state temperature value deviation satisfies , and the average test steady-state temperature value of all feature points and the average simulation steady-state temperature value deviation ≤3%, it is determined that the battery simulation model is effective, and the same method is used for the remaining battery pack sample to perform the charging and discharging test; For the same welding part, tightening part or plug-in part, if the test steady-state temperature value of a single feature point and the simulation steady-state temperature value deviation satisfies , the following detection is performed on the battery pack sample: S3011, check whether the related steps of the charging and discharging test process are carried out according to the original plan, whether there is a violation step; S3012, whether the PACK process of the welding part, the tightening part or the plug-in part is abnormal; ​​S3013, PACK test point whether there is loose caused by data anomalies.

[0052] S302, in step S301, after the battery pack sample detection is completed, the battery pack sample is retested, and steps S301 and S302 are repeated until all the battery pack sample charging and discharging test results indicate that the battery simulation model is effective.

[0053] S303, the following formula is used to calculate the test average temperature value of each feature point , , Among them, is the number of battery pack sample groups, is the test steady-state temperature value of the i-th feature point of the j-th battery pack sample in the k-th battery pack sample group; then take as the heat standard threshold of the feature point.

[0054] Specifically, compared with the abnormal judgment method commonly used in the industry at present, the battery PACK heat threshold calibration method provided by the application has the following obvious advantages: First, it provides a basis for automatically judging whether the overcurrent part is abnormal: by importing the 3D design model of the battery pack into the CFD simulation software for simulation, the simulation steady-state temperature values of the welding part, the tightening part and the connector plug-in part are obtained. The test steady-state temperature values of the welding part, the tightening part and the connector plug-in part on the battery pack sample are obtained by charging and discharging test of multiple battery pack samples, and then the test steady-state temperature values of each feature point are compared with the simulation steady-state temperature values to determine the heat standard threshold of each feature point. The deviation of the obtained heat standard threshold is ≤5%, which provides a scientific and reasonable basis for subsequent judgment of whether the welding part, the tightening part and the connector plug-in part are abnormal, and lays a solid foundation for realizing automatic detection and judgment of whether the welding part, the tightening part and the connector plug-in part and other overcurrent parts are abnormal.

[0055] Second, it is widely used: the battery PACK heat threshold calibration method is suitable for power or energy storage PACK battery packs using liquid cooling, air cooling and natural cooling and other different cooling methods. By adjusting the boundary conditions (such as heat dissipation coefficient, charging and discharging rate) and the charging and discharging test conditions of the battery pack sample, different models of PACK products can be quickly adapted without the need to redevelop the battery PACK thermal imaging abnormality judgment system.

[0056] As shown in Figure 1 , a battery PACK thermal imaging abnormality judgment system comprises: ​​The thermal imaging data acquisition module includes two sets of thermal imagers, which are arranged according to the relative position relationship between the thermal imager and the battery pack sample during the charging and discharging test of the battery pack sample, and are used to acquire the thermal image data of the welding position, the tightening position and the plug-in position of the PACK battery pack in real time.

[0057] Specifically, the two sets of thermal imagers are backup for each other to avoid single point failure. The thermal imagers are fixed through an industrial camera support to ensure that they are arranged according to the relative position relationship between the thermal imager and the battery pack sample during the charging and discharging test of the battery pack sample. The output format of the thermal imager is a 16-bit grayscale image, and each pixel point corresponds to a grayscale value G, ranging from 0 to 65535. The data of the thermal imager is transmitted to the data preprocessing module in real time through Ethernet (transmission rate ≥ 100 Mbps, delay ≤ 100 ms).

[0058] The data preprocessing module receives the thermal image data transmitted by the thermal imaging data acquisition module, and sequentially performs image denoising, image registration and temperature calibration on the original thermal image to output a temperature image for analysis.

[0059] The data preprocessing module uses Gaussian filtering to perform image denoising on the original thermal image.

[0060] Specifically, the original thermal image is affected by environmental light and electronic noise, and has random noise. Gaussian filtering is used for denoising. The two-dimensional Gaussian filter kernel function is: wherein, is the Gaussian standard deviation, and the value range is 1.5-2.0. The specific value is adjusted according to the noise intensity, , is the coordinate of the pixel in the filter kernel relative to the center (for example, for a 3x3 filter kernel, x,y ∈ {-1,0,1}).

[0061] For each pixel point in the original grayscale image, its denoised grayscale value is ( when it is a 3x3 filter kernel), the image denoising process smooths the noise and preserves the temperature gradient information.

[0062] The method for the data preprocessing module to perform image registration on the thermal image is to use the SIFT algorithm to make the feature points on the thermal image coincide with the feature points on the battery simulation model or the battery pack sample during the charging and discharging test, so as to realize image registration.

[0063] Specifically, due to the thermal imager installation error or PACK battery pack deviation, the feature point position on the thermal image needs to be aligned with the feature point position on the preset PACK battery pack 3D model, and the SIFT (Scale Invariant Feature Transform) algorithm is used to achieve it, which specifically includes the following steps: Extract feature points: In the thermal image and the PACK battery pack 3D model projection image, the features of the welding position, the tightening position or the connector pair insertion position are extracted (the extreme value points are detected by Gaussian difference pyramid to determine the feature point position and scale).

[0064] Feature matching: Calculate the descriptor (128-dimensional vector) of the feature points, and filter the matching pairs by Euclidean distance (distance threshold ≤ 50, eliminate false matches).

[0065] Coordinate transformation: According to the matching pairs, the homography matrix H (3x3 matrix) is solved, and the affine transformation of the thermal image is performed, and the formula is: , Wherein is the coordinate of the original thermal image, is the coordinate after registration, which ensures that the key points in the thermal image are completely overlapped with the points during simulation / test.

[0066] The data preprocessing module adopts a linear piecewise fitting method to establish the relationship between the output gray value G of the thermal imager and the actual temperature T, and uses the least squares method to solve it to realize temperature calibration.

[0067] Specifically, the output gray value G of the thermal imager and the actual temperature T are not in a linear relationship, and need to be calibrated by a standard heat source (such as a blackbody furnace, temperature range of 20℃~100℃, accuracy ±0.1℃) to establish a gray-temperature calibration model. Linear piecewise fitting (good linearity in small temperature range) is adopted, and the formula is: when G∈[G1,G2], T=a⋅G+b, wherein a and b are calibration coefficients, which are solved by the least squares method: (M is the number of calibration points, ≥5, such as 20℃, 40℃, 60℃, 80℃, 100℃). For example, a certain thermal imager in the range of 20℃~80℃, a=0.015, b=-50, then T=0.015G-50 (need to be adjusted according to the actual equipment calibration result).

[0068] Data analysis and threshold comparison module: Extract the temperature of the feature point position in the temperature image, and compare it with the corresponding heat generation standard threshold to output the determination result of normal or abnormal.

[0069] The data processing process of the data analysis and threshold comparison module includes the following steps: Step 1: Temperature Extraction of Feature Points: Based on the coordinates of feature points on the battery simulation model or the battery pack sample during charge / discharge testing, extract the temperature value of each feature point in the temperature image. .

[0070] The average value is calculated over a 3x3 pixel area surrounding the point, using the following formula: ,in Let be the center coordinates of the i-th feature point.

[0071] Step 2, Threshold Comparison and Anomaly Detection: This includes temperature determination at individual feature points and regional collaborative temperature determination, wherein: Temperature determination at a single feature point: The standard threshold for heating at each feature point is: ,like If the temperature at the feature point is normal, it is considered normal; otherwise, it is considered abnormal and marked as either a high-temperature abnormality or a low-temperature abnormality. Low-temperature abnormalities may be caused by poor contact preventing current flow.

[0072] Regional collaborative temperature determination: Calculate the temperature difference between adjacent feature points at the same welding point, tightening point, or connector mating point. ,in For the first Adjacent feature points of a feature point, where the distance between any two adjacent feature points is ≤5mm, if (Set according to the heat dissipation characteristics of the PACK battery pack, which can be adjusted), then it is determined that there is an abnormal heat distribution in this area (such as poor local contact).

[0073] Specifically, considering the correlation of heat generation in the overcurrent section, such as the consistent temperature trend at multiple points in the same welding area, a temperature gradient determination is introduced.

[0074] Step 3, Result Output: If the temperature determination results of all individual feature points on the PACK battery pack are normal, and all adjacent feature points in the regional collaborative temperature determination meet the requirements... If the output judgment result is normal, it means that all overcurrent parts on the PACK battery pack are heating normally; if there is one or more individual feature point temperature judgment results that are abnormal, or the area collaborative temperature judgment result is abnormal, the output judgment result is abnormal, and the coordinates, temperature value and abnormality type of the abnormal feature point are output. For example: the temperature of welding point A is 65℃, which exceeds the threshold of 55℃~60℃, which is a high temperature abnormality.

[0075] The man-machine interaction module is used for displaying real-time temperature images, feature point temperature values and determination results, and supports historical data query and abnormality alarm.

[0076] Specifically, the data preprocessing module is in communication connection with the thermal imaging data acquisition module, the data analysis and threshold comparison module is in communication connection with the data preprocessing module, and the man-machine interaction module is in communication connection with the data analysis and threshold comparison module.

[0077] The battery PACK thermal imaging abnormality determination system provided by the application has the following technical effects: The detection precision is significantly improved: on the basis of obtaining the heating standard threshold of each feature point by using the battery PACK heating threshold calibration method, the thermal image data of the welding position, the tightening position and the plug-in position of the PACK battery pack are collected in real time by the thermal imaging data acquisition module; the original thermal image is sequentially subjected to image noise reduction, image registration and temperature calibration by the data preprocessing module, and the temperature image for analysis is output; the temperature of the feature point in the temperature image is extracted by the data analysis and threshold comparison module, and combined with the heating standard threshold of each feature point, a single feature point and a region are combined to determine the temperature, and the result of whether the PACK battery pack is abnormal is output, the abnormal failure of the welding position, the tightening position and the plug-in position of the PACK battery pack is automatically, scientifically and effectively detected, the workload and labor intensity of workers are significantly reduced, and the misjudgment of the welding position, the tightening position and the plug-in position of the PACK battery pack is excluded due to experience deviation, visual fatigue and other factors, and the detection rate of the abnormality of the overcurrent position is improved from 70%~80% of the traditional manual method to more than 99%.

[0078] The detection efficiency is significantly improved: the data acquisition (real-time transmission), data preprocessing (time consumption ≤1s / frame), data analysis and comparison (time consumption ≤0.5s / point) are automatically completed by the battery PACK thermal imaging abnormality determination system, the detection time of a single PACK battery pack is shortened from 2~3h of the traditional manual method to 1~1.5h, and multiple parallel detections (scalable to 4~8 groups of synchronous operation) are supported, the manual input is reduced by 80%, and the detection efficiency of the PACK battery pack is significantly improved.

[0079] Process stability guarantee: through data analysis and threshold comparison module, the coordinates, temperature values and abnormal types of abnormal feature points are output, and the display is carried out on the man-machine interaction module, the PACK process problems (such as virtual welding of welding, insufficient tightening torque, etc.) can be traced back reversely, the closed loop data (such as adjusting the welding power, tightening torque parameters according to the abnormal frequency, etc.) is provided for process optimization, the PACK process failure rate is reduced (from 5% to 1% below), the problem that the traditional manual detection can only judge "whether abnormal" but cannot locate "why abnormal" is solved.

[0080] The application is only for the static charging and discharging model of the battery pack. The actual battery pack has different ways such as liquid cooling plate type, air cooling, immersion type and natural heat dissipation according to types, and the application scenarios of power and energy storage battery packs are different. The test conditions can simulate the actual operation condition to make the test more perfect. For example, the vibration tester can be added in the test of the power battery, and the test is carried out while charging and discharging. The application is only for testing the overcurrent part and the heating part of the static charging and discharging, the test is more simple, the obtained result is more intuitive, and the heating normality of the welding / tightening / wire harness plug-in part in the PACK process can be more targetedly fed back.

Claims

1. A battery PACK heat generation threshold calibration method, characterized in that: The method comprises the following steps: S100, obtaining simulation steady-state temperature values of a plurality of feature point positions of welding positions, tightening positions and plug-in positions of connectors on a battery simulation model by a simulation method; S200, performing charge-discharge tests on a plurality of battery pack samples, and obtaining test steady-state temperature values of each feature point position on the battery pack samples after corresponding the feature point positions on the battery pack samples to the feature point positions on the battery simulation model; S300, comparing the test steady-state temperature values of each feature point position with the simulation steady-state temperature values, and determining a heat generation standard threshold of each feature point position. 2.The battery PACK heat generation threshold calibration method of claim 1, wherein: The step S100 comprises the following steps: S101, importing a battery pack 3D design model into a CFD simulation software, and performing meshing on the battery pack 3D design model by using a tetrahedral unstructured mesh; S102, defining key thermal physical property parameters of core components of the battery pack, so as to construct a battery pack three-dimensional thermal physical model in the CFD simulation software, and clearly define a geometric boundary and a physical property correlation mode of each core component of the battery pack; S103, simulating a state that heat generation and heat dissipation reach dynamic balance in a battery pack charge-discharge process by using a steady-state calculation mode, selecting a Spalart-Allmaras turbulence model to simulate an influence of cooling liquid flow on heat distribution, and setting an iteration step number, a convergence tolerance and a boundary condition; S104, running the CFD simulation software to perform simulation, and outputting a temperature distribution cloud chart and a temperature distribution cut plane chart; S105, extracting simulation steady-state temperature values of each feature point position of the welding positions, the tightening positions and the plug-in positions of the connectors from the temperature distribution cloud chart and the temperature distribution cut plane chart.

3. The battery PACK heat generation threshold calibration method of claim 2, wherein: The parameter values of the key thermal physical property parameters of the core components of the battery pack in the step 102 are obtained through a detection report provided by a material supplier or experimental tests.

4. The battery PACK heat generation threshold calibration method of claim 2, wherein: The step 102 of defining the key thermal physical property parameters of the core components of the battery pack comprises: respectively defining thermal conductivities, specific heat capacities and densities of the battery cells and the current collectors; defining a thermal conductivity of the liquid cooling plate, and thermal conductivities and dynamic viscosities of the cooling liquid; defining a thermal conductivity of the box body; respectively defining contact thermal resistances of the welding positions, the tightening positions and the plug-in positions of the connectors.

5. The battery PACK heat generation threshold calibration method of claim 2, wherein: The iteration step number is set to 100 steps and the convergence tolerance is set to 10 in the step 103 -3 The boundary condition setting includes: setting the ambient temperature to be consistent with the battery pack sample charging and discharging test temperature, setting the heat dissipation mode to liquid cooling, air cooling or natural cooling, setting the charging and discharging current to be nC times of the rated current of the battery pack sample, n being a positive integer, to simulate different charging and discharging conditions.

6. The battery PACK heat generation threshold calibration method of claim 2, wherein: The step S105 comprises the following steps: S1051, determining key feature point positions: marking the welding positions, the tightening positions and the plug-in positions of the connectors on the battery pack three-dimensional thermal physical model, extracting 3 points of welding points and a 3mm range around the welding points as the key feature point positions for the welding positions, extracting a screw center and 2 points of a gasket and a substrate contact as the key feature point positions for the tightening positions, and extracting 2 pin contact points and 2 shell contact points as the key feature point positions for the plug-in positions of the connectors, and extracting 5-8 feature point positions including the key feature point positions for each welding position, tightening position and plug-in position of the connectors, so as to ensure covering a heat generation core area; S1052, data average processing: for each feature point position, extracting an average value of temperatures of 5 adjacent grid nodes of the feature point position after simulation steady-state as a simulation steady-state temperature value of the feature point position, and a calculation formula is as follows: ， wherein, is the simulated steady state temperature value of the th feature point, is the temperature of the th grid node surrounding the th feature point.

7. The battery PACK heat generation threshold calibration method of claim 1, wherein: The step S200 includes the following steps: S201, prepare a number of battery pack samples ≥10 groups, and select 3 groups of battery pack samples from them and place them in an environment with a temperature of 23℃±2℃ and a humidity of 50%±5%, and then make the 3 groups of battery pack samples run according to the charging and discharging current set for the battery simulation model in the simulation process; S202, make the lens of the thermal imager directly face the feature points on the battery pack sample, and ensure that the feature points on the battery pack sample correspond one-to-one to the feature points on the battery simulation model; S203, collect the temperature data of the welding parts, tightening parts and plug-in parts on the battery pack sample in real time through the thermal imager, and then extract the steady-state temperature average value of each feature point as the test steady-state temperature value, and the calculation formula is as follows: ， wherein, is the test steady state temperature value for the th feature point, is the thermal equilibration time, is the instantaneous temperature at the time instant.

8. The battery PACK heat generation threshold calibration method of claim 7, wherein: In the step S202, the lens of the thermal imager is made to directly face the key feature points on the battery pack sample, and the distance deviation of the lens to the key feature points is 1mm~1.5mm, and the angle deviation is ≤10°. 9.The battery PACK heat generation threshold calibration method of claim 7, wherein: In the step S203, the frequency of the thermal imager collecting the temperature data of the welding parts, tightening parts and plug-in parts on the battery pack sample is 1Hz, and the temperature data is continuously collected until the charging and discharging ends and reaches thermal equilibrium.

10. The battery PACK heat generation threshold calibration method of claim 1, wherein: The step S300 includes the following steps: S301. For the same welding location, tightening location, or connector mating location, if the deviation between the measured steady-state temperature value and the simulated steady-state temperature value at a single characteristic point meets the following conditions: If the average test steady-state temperature value of all feature points deviates from the average simulation steady-state temperature value by ≤3%, then the battery simulation model is deemed valid, and the same method is used to perform charge and discharge tests on the remaining battery pack samples. For the same welding site, tightening site or plug-in site, if the deviation between the test steady-state temperature value and the simulation steady-state temperature value of a single feature point meets the following conditions, the following detection is performed on the battery pack sample: S3011, check whether the related steps of the charging and discharging test process are carried out according to the original plan, whether there are irregular steps; S3012, whether there is an abnormality in the PACK process of the welding parts, tightening parts or plug-in parts; S3013, whether there is a data anomaly caused by loosening of the PACK test point; S302, in step S301, after the battery pack sample detection is completed, the battery pack sample is subjected to charging and discharging retest, and steps S301 and S302 are repeated until all the battery pack sample charging and discharging test results indicate that the battery simulation model is effective; S303、adopt the following formula to calculate the test average temperature value of each feature point , ， wherein, is the number of battery pack sample groups, is the number of battery pack samples in the group, is the test steady-state temperature value of the is the heat generation standard threshold value of the feature point.

11. A battery PACK thermal imaging abnormality determination system established based on the method of any one of claims 1 to 10, characterized by: It includes: A thermal imaging data acquisition module: including two sets of thermal imagers, both of which are arranged according to the relative position relationship between the thermal imager and the battery pack sample when the battery pack sample is subjected to charging and discharging test, for real-time collection of thermal image data of the welding parts, tightening parts and plug-in parts on the PACK battery pack; A data preprocessing module: receiving the thermal image data transmitted by the thermal imaging data acquisition module, and sequentially performing image denoising, image registration and temperature calibration on the original thermal image, and outputting the temperature image for analysis; A data analysis and threshold comparison module: extracting the temperature of the feature points in the temperature image, and comparing it with the corresponding heating standard threshold, and outputting the determination result of normal or abnormal; A man-machine interaction module: for displaying real-time temperature image, feature point temperature value and determination result, and supporting historical data query and abnormal alarm.

12. The battery PACK thermography abnormality determination system according to claim 11, characterized by: The data preprocessing module uses Gaussian filtering to perform image denoising on the original thermal image; The data preprocessing module performs image registration on the thermal image by using the SIFT algorithm to make the feature points on the thermal image coincide with the feature points on the battery simulation model or the battery pack sample in the charging and discharging test process, so as to realize image registration; The data preprocessing module adopts a linear segmented fitting method to establish the relationship between the output gray value G of the thermal imager and the actual temperature T, and utilizes the least square method to solve, so as to realize temperature calibration.

13. The battery PACK thermography abnormality determination system according to claim 11, characterized by: The data processing process of the data analysis and threshold comparison module includes the following steps: Step one, feature point temperature extraction: in the temperature image, according to the feature point coordinates on the battery simulation model or the battery pack sample in the charging and discharging test process, the temperature value of each feature point is extracted ; Step two, threshold comparison and abnormality determination: including single feature point temperature determination and regional collaborative temperature determination, wherein: Single feature point temperature determination: the first feature point temperature determination is as follows: The heating standard threshold value of the first feature point is If , it is determined that the temperature of the feature point is normal, otherwise it is determined that the temperature of the feature point is abnormal, and is marked as high-temperature abnormality or low-temperature abnormality. Regional coordination temperature determination: calculate the temperature difference of adjacent feature points of the same welding site, tightening site or connector pair insertion site wherein is the first adjacent feature point of the feature point, and the distance between the two adjacent feature points is ≤5mm, if , it is determined that the region has abnormal heat distribution; Step three, result output: if all single feature point temperature determination results on the PACK battery pack are normal, and all adjacent feature points in the regional collaborative temperature determination meet the determination result is normal, indicating that all overcurrent parts on the PACK battery pack are normal; if there is one or more single feature point temperature determination results that are abnormal, or the regional collaborative temperature determination result is abnormal, the determination result output is abnormal, and the coordinates, temperature values and abnormal types of the abnormal feature points are output.

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