Battery PACK heating threshold calibration method

Abstract

The invention discloses a battery PACK heating threshold calibration method, and belongs to the technical field of battery production. The method comprises the following steps: S100, acquiring simulation steady-state temperature values of a plurality of feature point locations of a welding part, a tightening part and a connector plug-in part on a battery simulation model through a simulation method; 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 deviation of the obtained heating standard threshold value is less than or equal to 5%, so that a scientific, reasonable and uniform judgment basis is provided for subsequently judging whether a welding part, a tightening part and a connector plug-in part are abnormal or not, and a foundation is laid for automatically detecting and judging whether overcurrent parts such as the welding part, the tightening part and the connector plug-in part are abnormal or not; and the abnormal detection rate of the overflowing part can be increased from 70%-80% of traditional manual work to 99% or above.

Description

TECHNICAL FIELD

[0001] The application relates to a battery PACK heat generation threshold calibration method 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 abnormal phenomena such as virtual welding, explosive welding and welding through will occur. 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 abnormalities 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 overcurrent welding position, tightening position and connector plug-in position, and then whether each charging and discharging heating position is normal is judged by manual experience.

[0004] However, the above abnormality judgment method lacks scientific, reasonable and unified judgment basis, and is judged only by manual experience. Influenced by factors such as manual experience deviation and visual fatigue, it is inevitable that there will be abnormal misjudgment of the overcurrent position such as welding position, tightening position and connector plug-in position, resulting in that the abnormal detection rate of the overcurrent position is only 70% to 80%. SUMMARY

[0005] To solve the above technical problems, the application provides a battery PACK heat generation threshold calibration method.

[0006] The application is realized by the following technical scheme: 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 welding positions, tightening positions and connector plug-in positions on a battery simulation model by a simulation method; S200, performing charging and discharging test on a plurality of battery pack samples, and after corresponding the feature points on the battery pack samples with the feature points on the battery simulation model one by one, obtaining test steady-state temperature values of each feature point on the battery pack samples; 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.

[0007] The step S100 comprises the following steps: S101, import the battery pack 3D design model into the CFD simulation software, and divide the battery pack 3D design model by using tetrahedral unstructured mesh; 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 mode of each core component of the battery pack; S103, simulate the heat generation and heat dissipation to reach a dynamic balance state 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; 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.

[0008] The parameter values of the key thermal physical property parameters of the core components of the battery pack in step 102 are obtained through a detection report provided by a material supplier or experimental testing.

[0009] The key thermal physical property parameters of the core components of the battery pack 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.

[0010] 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.

[0011] 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 the 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.

[0012] The step S200 comprises 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.

[0013] 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°. ​​​​​

[0014] 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.

[0015] 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.

[0016] 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, reasonable, and unified basis for subsequent judgment on whether welding parts, tightening parts, and connector mating parts are abnormal. This lays a solid foundation for automatically detecting and judging whether overcurrent parts such as welding parts, tightening parts, and connector mating parts are abnormal, which is conducive to increasing the detection rate of overcurrent parts abnormalities from 70%~80% by traditional manual methods to over 99%.

[0017] 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. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the battery PACK thermal imaging anomaly detection system in an embodiment of the present invention. Detailed Implementation

[0019] The technical solution of the present invention is further described below, but the scope of protection is not limited to what is described.

[0020] The battery pack heating threshold calibration method of the present invention includes the following steps: S100. Using simulation methods, obtain the simulation steady-state temperature values ​​of multiple feature points on the battery simulation model, including the welding parts, tightening parts, and connector mating parts.

[0021] Step S100 includes the following steps: S101. Import the 3D design model of the battery pack into the CFD simulation software, and use a tetrahedral unstructured mesh to divide the 3D design model of the battery pack.

[0022] Specifically, the CFD (Computational Fluid Dynamics) simulation software used is either ANSYS Fluent or STAR-CCM+. ANSYS Fluent supports mesh generation for complex geometric models and has excellent compatibility with the Spalart-Allmaras turbulence model, enabling efficient coupled thermal analysis of multiple battery pack components (cells, liquid cooling plates, housings, etc.). STAR-CCM+, on the other hand, has significant advantages in automated mesh generation and multiphysics coupling calculations, making it suitable for batch simulation verification. The 3D design model of the battery pack is imported into the selected software, and a "tetrahedral unstructured mesh" is used to mesh the 3D design model (balancing computational accuracy and efficiency). Key parameters such as mesh node coordinates and the number of elements are output (e.g., a typical 10kWh energy storage PACK battery pack has approximately 500,000 to 800,000 mesh elements and approximately 100,000 to 150,000 nodes; the specific number needs to be adjusted according to the model complexity).

[0023] S102. Define the key thermophysical property parameters of the core components of the battery pack in order to build a three-dimensional thermophysical model of the battery pack in the CFD simulation software, and clarify the geometric boundaries and physical property association methods of each core component of the battery pack.

[0024] Specifically, clarify the physical property relationships of each core component of the battery pack, that is, clarify the connection methods of each core component of the battery pack, such as the contact between the battery cell and the current collector, and the connection between the liquid cooling plate and the battery cell through thermally conductive adhesive, etc.

[0025] The key thermophysical property parameters of the core components of the battery pack in step 102 are obtained through test reports or experimental tests provided by the material supplier to ensure the authenticity of the data.

[0026] The key thermophysical property parameters of the core components of the battery pack defined in step 102 include: Define the thermal conductivity, specific heat capacity, and density of the battery cell and the current collector respectively; Define the thermal conductivity of the liquid cooling plate, and the thermal conductivity and dynamic viscosity of the coolant; Define the thermal conductivity of the enclosure; The contact thermal resistances of the welding points, tightening points, and connector mating points are defined separately. See the table below for details:

[0027] Based on the above parameters, a three-dimensional thermophysical model of the battery pack was constructed in CFD simulation software.

[0028] S103. The steady-state calculation mode is used to simulate the dynamic balance between heat generation and heat dissipation during the charging and discharging process of the battery pack. The Spalart-Allmaras turbulence model is selected to simulate the influence of coolant flow on heat distribution, and the iteration steps, convergence tolerance and boundary conditions are set.

[0029] Specifically, a steady-state calculation mode is used to simulate the dynamic balance between heat generation and heat dissipation during the charging and discharging process of the battery pack. The steady-state calculation is suitable for static charging and discharging scenarios and eliminates the interference of transient thermal fluctuations on the threshold measurement.

[0030] The Spalart-Allmaras turbulence model is a one-equation model, which has the advantages of high computational efficiency and strong stability. It is suitable for medium Reynolds numbers (Re=10³~10⁻¹⁰) in liquid-cooled channels (such as serpentine channels with liquid-cooled plates) within battery packs. 5 Flow simulation can accurately simulate the effect of coolant flow on heat distribution.

[0031] In step 103, the number of iterations is set to 100, and the convergence tolerance is 10. -3 The boundary conditions include: setting the ambient temperature to be consistent with the charging and discharging test temperature of the battery pack sample; setting the heat dissipation method to liquid cooling, air cooling, or natural cooling; and setting the charging and discharging current to nC times the rated current of the battery pack sample (such as 1C or 2C times), where n is a positive integer, to simulate different charging and discharging conditions.

[0032] Specifically, the iteration step count is set to 100 steps. The first 50 steps are coarse calculations used to gradually optimize the parameters; the last 50 steps are fine calculations used to approximate the actual thermal equilibrium state. The convergence tolerance is set to 10. -3 Using the residual of the energy equation as the core criterion, the residual is ≤10. -3 When the time reaches a certain point, it indicates that the simulation results no longer change significantly and have reached computational stability.

[0033] The battery pack sample charge / discharge test environment was: temperature 23℃±2℃, humidity 50%±5%. When the heat dissipation method was natural convection cooling, the surface heat transfer coefficient was h=5~10 W / (m²・K).

[0034] S104. Run the CFD simulation software to perform the simulation and output the temperature distribution cloud map and the temperature distribution tangent plane map.

[0035] Specifically, the temperature distribution cloud map can intuitively display the overall temperature field distribution of the battery pack, with the red area representing the high-temperature zone and the blue area representing the low-temperature zone; the temperature distribution tangent plane map is cut along the central axis of the welding points, tightening parts, and connectors to show the internal temperature gradient of the battery pack.

[0036] S105. Extract the simulated steady-state temperature values ​​of each characteristic point of the welding part, tightening part and connector mating part from the temperature distribution cloud map and temperature distribution tangent plane map.

[0037] Step S105 includes the following steps: S1051. Determine key feature points: Mark the welding parts, tightening parts, and connector mating parts on the three-dimensional thermophysical model of the battery PACK. For the welding parts, extract the welding point and three points within a 3mm radius around the welding point as key feature points. For the tightening parts, extract the screw center and two points where the washer contacts the substrate as key feature points. For the connector mating parts, extract two pin contact points and two shell contact points as key feature points. For each welding part, tightening part, and connector mating part, extract a total of 5 to 8 feature points, including key feature points, to ensure coverage of the core heat-generating area. S1052. Data Averaging Processing: For each feature point, after the simulation reaches steady state, the average temperature of the five adjacent grid nodes of that feature point is extracted as its simulation steady-state temperature value. The calculation formula is as follows: , in, For the first Simulated steady-state temperature values ​​at each characteristic point. For the first The feature point is located around the first feature point. Temperature of each grid node.

[0038] S200. Perform charge and discharge tests on multiple battery pack samples, and after matching the feature points on the battery pack samples with the feature points on the battery simulation model, obtain the test steady-state temperature values ​​of each feature point on the battery pack samples.

[0039] Step S200 includes the following steps: S201. Prepare ≥10 sets of battery pack samples, and select 3 sets of battery pack samples to place in an environment with a temperature of 23℃±2℃ and a humidity of 50%±5%. Then, run the 3 sets of battery pack samples according to the charging and discharging current set for the battery simulation model during the simulation process (such as 1C charging and 1C discharging).

[0040] In the early stages of manufacturing after the design of a battery pack is completed (such as the pilot production stage), after ensuring that the production process has been verified by small-scale testing, prepare ≥10 sets of battery pack samples, 2 sets of thermal imagers (resolution ≥640×512, temperature measurement range -20℃~150℃, accuracy ±2%), 2 sets of charging and discharging cabinets (current accuracy ±0.5% FS, voltage accuracy ±0.2% FS), and 1 temperature and humidity controllable laboratory (temperature control accuracy ±1℃, humidity control accuracy ±5%).

[0041] S202. Position the lens of the thermal imager directly at the feature points on the battery pack sample, and ensure that the feature points on the battery pack sample correspond one-to-one with the feature points on the battery simulation model.

[0042] In step S202, the lens of the thermal imager is aligned with the key feature points on the battery pack sample, and the distance deviation between the lens and the key feature points is 1mm~1.5mm, and the angle deviation is ≤10°.

[0043] S203. Temperature data of the welded, tightened, and connector mating points on the battery pack sample are collected in real time using a thermal imager. The average steady-state temperature of each feature point is then extracted as the test steady-state temperature value. The calculation formula is as follows: , in, For the first The test steady-state temperature values ​​of each characteristic point. For thermal equilibrium time, for The instantaneous temperature at any given moment.

[0044] 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.

[0045] S300. Compare the measured steady-state temperature values ​​of each feature point with the simulated steady-state temperature values, and determine the heating standard threshold for each feature point.

[0046] 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 there any loose test points (S3013, PACK) that could cause data anomalies?

[0047] 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.

[0048] 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.

[0049] Specifically, the battery pack heating threshold calibration method provided by this invention has the following significant advantages compared with the anomaly judgment methods commonly used in the industry: 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, reasonable, and unified basis for subsequent judgment on whether welding parts, tightening parts, and connector mating parts are abnormal. This lays a solid foundation for automatically detecting and judging whether overcurrent parts such as welding parts, tightening parts, and connector mating parts are abnormal, which is conducive to increasing the detection rate of overcurrent parts abnormalities from 70%~80% by traditional manual methods to over 99%.

[0050] 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 (such as heat dissipation coefficient and charge / discharge rate) and the charge / 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.

[0051] like Figure 1As shown, 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. They are used to acquire thermal image data of the welded parts, tightened parts and connector mating parts on the PACK battery pack in real time.

[0052] Specifically, the two thermal imagers serve as backups for each other to avoid single points of failure. The thermal imagers are fixed using industrial camera brackets, ensuring they are positioned according to the relative positional relationship between the thermal imager and the battery pack sample during charge / discharge testing. The thermal imagers output 16-bit grayscale images, with each pixel corresponding to a grayscale value G ranging from 0 to 65535. The data from the thermal imagers is transmitted in real-time via Ethernet to the data preprocessing module (transmission rate ≥ 100 Mbps, latency ≤ 100 ms).

[0053] Data preprocessing module: Receives thermal image data transmitted from the thermal imaging data acquisition module, and sequentially performs image noise reduction, image registration, and temperature calibration on the original thermal image, outputting a temperature image for analysis.

[0054] The data preprocessing module uses Gaussian filtering to perform image noise reduction on the original thermal image.

[0055] Specifically, the original thermal image is affected by ambient light and electronic noise, resulting in random noise. Gaussian filtering is used for noise reduction. The two-dimensional Gaussian filter kernel function is: ,in, This represents the Gaussian standard deviation, ranging from 1.5 to 2.0. The specific value should be adjusted based on the noise intensity. , The coordinates of the pixels within the filter kernel relative to the center (e.g., for a 3×3 filter kernel x,y∈{-1,0,1}).

[0056] For each pixel in the original grayscale image Its denoised grayscale value is ( (Using a 3×3 filter kernel), image denoising is used to smooth noise while preserving temperature gradient information.

[0057] The data preprocessing module performs image registration on thermal images 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 during the charge and discharge test, thereby achieving image registration.

[0058] Specifically, due to installation errors of the thermal imager or placement deviations of the battery pack, it is necessary to align the feature points on the thermal image with the feature points on the preset 3D model of the battery pack. This is achieved using the SIFT (Scale Invariant Feature Transform) algorithm, and includes the following steps: Feature point extraction: In the thermal image and the 3D model projection of the PACK battery pack, features of the welding parts, tightening parts or connector mating parts are extracted respectively (extreme points are detected by Gaussian difference pyramid to determine the location and scale of feature points).

[0059] Feature matching: Calculate the descriptor (128-dimensional vector) of feature points and filter matching pairs by Euclidean distance (distance threshold ≤ 50, discard false matches).

[0060] Coordinate transformation: Solve for the homography matrix H (3×3 matrix) based on the matching pairs, and perform an affine transformation on the thermal image. The formula is as follows: , in The coordinates of the original thermal image. To ensure that the registered coordinates are aligned, the key points in the thermal image completely coincide with the points in the simulation / test.

[0061] The data preprocessing module uses a linear piecewise fitting method to establish the relationship between the output grayscale value G of the thermal imager and the actual temperature T, and uses the least squares method to solve it in order to achieve temperature calibration.

[0062] Specifically, the grayscale value G output by the thermal imager has a non-linear relationship with the actual temperature T. It needs to be calibrated using a standard heat source (such as a blackbody furnace, with a temperature range of 20℃ to 100℃ and an accuracy of ±0.1℃) to establish a grayscale-temperature calibration model. A linear piecewise fitting method is used (due to its good linearity within a small temperature range), with the formula: when G∈[G1,G2], T=a⋅G+b, where a and b are calibration coefficients, solved using the least squares method. (M represents the number of calibration points, ≥5, such as 20℃, 40℃, 60℃, 80℃, 100℃). For example, for a thermal imager operating within the 20℃~80℃ range, a=0.015, b=-50, then T=0.015G-50 (this needs to be adjusted based on the actual equipment calibration results).

[0063] Data parsing and threshold comparison module: Extracts the temperature of feature points in the temperature image, compares it with the corresponding heating standard threshold, and outputs the judgment result of normal or abnormal.

[0064] The data processing procedure of the data parsing 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. .

[0065] 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.

[0066] 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.

[0067] 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).

[0068] 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.

[0069] 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.

[0070] Human-Computer Interaction (HCI) Module: This module displays real-time temperature images, temperature values ​​at key points, and judgment results. It also supports historical data queries and anomaly alarms. Anomaly alarms in the HCI module include audible and visual alarms and SMS notifications.

[0071] Specifically, the data preprocessing module is connected to the thermal imaging data acquisition module, the data parsing and threshold comparison module is connected to the data preprocessing module, and the human-computer interaction module is connected to the data parsing and threshold comparison module.

[0072] The battery pack thermal imaging anomaly detection system provided by this invention has the following technical advantages: Significantly improved detection accuracy: 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 of 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 with a temperature judgment method that combines individual feature points and regional collaboration to judge and output 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 of 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%.

[0073] Significantly improved detection efficiency: The battery pack thermal imaging anomaly detection system automatically completes data acquisition (real-time transmission), data preprocessing (time ≤ 1s / frame), data parsing and comparison (time ≤ 0.5s / point), reducing the detection time of a single battery pack from 2-3 hours in the traditional manual process to 1-1.5 hours. It also supports multiple parallel detections (expandable to 4-8 groups running simultaneously), reducing manual input by 80% and significantly improving the detection efficiency of battery packs.

[0074] Process stability assurance: The data analysis and threshold comparison module outputs the coordinates, temperature values, and anomaly types of abnormal feature points, which are then displayed on the human-machine interface module. This allows for reverse tracing of PACK process issues (such as poor soldering or insufficient tightening torque), providing closed-loop data for process optimization (such as adjusting welding power and tightening torque parameters based on the frequency of anomalies). This reduces the PACK process defect rate (target from 5% to below 1%), solving the problem that traditional manual inspection can only determine "whether it is abnormal" but cannot pinpoint "why it is abnormal".

[0075] This invention focuses solely on the static charge-discharge model of battery packs. Actual battery packs vary in type, including liquid-cooled plate, air-cooled, immersion-type, and natural heat dissipation methods, and differ in application scenarios for power and energy storage battery packs. The testing conditions can simulate actual operating conditions to improve the accuracy of the tests. For example, a vibration tester can be added to the testing of power batteries to perform simultaneous charging and discharging. This invention only tests the overcurrent and heat-generating areas during static charge-discharge, making the testing simpler, providing more intuitive results, and offering more targeted feedback on whether the heating at the soldering / tightening / wiring harness connection points in the PACK manufacturing process is normal.

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%, then make 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, 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 of the battery pack sample in real time through the thermal imager, 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 of the battery pack sample is 1Hz, and the temperature data is continuously collected until the charge and discharge 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 charge and discharge 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 detection of the battery pack sample is completed, the battery pack sample is subjected to charge and discharge retest, and steps S301 and S302 are repeated until all the charge and discharge test results of the battery pack samples 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.

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