Internal thermal imaging method, device and equipment for battery module and medium
By combining a segmented robotic arm with a thermal imaging probe and using convolutional neural network analysis, the problem of comprehensiveness and accuracy in detecting the internal temperature of battery modules has been solved. This achieves full coverage of the internal temperature distribution of battery modules and precise location of fault points, thereby improving the safety and efficiency of battery thermal management.
Patent Information
- Application Number
- CN202511669179.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-14
- Publication Date
- 2026-01-13
AI Technical Summary
In existing technologies, external thermal imaging and fixed temperature sensors are insufficient to achieve comprehensive and accurate detection of the internal temperature distribution of battery modules, especially in detecting temperature dead zones in complex structures.
By combining a segmented robotic arm and a thermal imaging probe, the probe can acquire the structural and dimensional parameters of the battery module, plan the scanning trajectory, and achieve dynamic movement of the probe. Combined with a pre-trained convolutional neural network to analyze the thermal distribution map, abnormal hot spots can be accurately located.
It achieves full-coverage detection of the internal temperature of the battery module, accurately locates fault points, and improves the proactive safety and detection efficiency of battery thermal management.
Smart Images
Figure CN121332003A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of battery thermal management technology, and specifically to an internal thermal imaging method, system, device, and medium for battery modules. Background Technology
[0002] With advancements in battery mechanism research and thermal management technology, engineers have discovered that the internal temperature of a battery is the most critical parameter determining battery thermal runaway. Consequently, battery temperature monitoring has become an important technical feature for improving battery thermal runaway management.
[0003] In existing technologies, external thermal imaging or fixed temperature sensors are typically used to monitor the temperature of the battery module inside the battery. However, external thermal imaging technology is easily affected by the battery module's casing material, surface coating, and environmental heat dissipation conditions, resulting in significant errors in the monitored data. It can only reflect the temperature state of the module's surface and cannot accurately obtain the temperature distribution inside the battery module. While fixed temperature sensors can enter the battery module, their fixed position typically only detects temperature changes in localized key areas, making it difficult to achieve comprehensive detection of the module's internal space, especially failing to detect temperature dead zones in complex internal structures. Summary of the Invention
[0004] This invention provides a method, apparatus, device, and medium for internal thermal imaging of battery modules, which can solve the technical problem that existing technologies cannot perform comprehensive temperature detection of the inside of battery modules.
[0005] In a first aspect, the present invention provides an internal thermal imaging method for a battery module, applicable to an image acquisition device including a segmented robotic arm and a thermal imaging probe; wherein, the fixed end of the segmented robotic arm is mounted on the inner wall of the end plate of the battery module under test; the thermal imaging probe is rotatably fixed to the free end of the segmented robotic arm; the method includes: Obtain the structural dimension parameters of the battery module under test, and determine the scanning trajectory of the thermal imaging probe based on the structural dimension parameters; Based on the scanning trajectory, motion parameters corresponding to each motion axis in the segmented robotic arm are generated respectively, so as to obtain full-coverage thermal imaging data of the battery module under test based on the motion parameters and the thermal imaging probe. Obtain the thermal distribution map corresponding to the full-coverage thermal imaging data, and extract the abnormal hot spots in the thermal distribution map; The abnormal movement parameters corresponding to the abnormal hotspots are matched from the movement parameters to obtain the coordinates of the abnormal heating area of the battery module under test based on the abnormal movement parameters and the structural size parameters.
[0006] This invention discloses an internal thermal imaging method for battery modules. By mounting a segmented robotic arm's fixed end to the inner wall of the endplate of the battery module under test and acquiring full-coverage thermal imaging data of the module, a thermal imaging probe can be directly inserted into the module for scanning, accurately capturing the internal temperature distribution. Secondly, the scanning trajectory of the thermal imaging probe is determined based on the structural dimensional parameters, and the movement parameters of the robotic arm are generated, enabling dynamic movement of a single probe to achieve full-coverage scanning of the entire internal space of the module, completely eliminating the defect of temperature blind spots. Finally, by extracting abnormal hot spots from the thermal distribution map and obtaining the coordinates of the abnormal heating areas, the fault point is accurately located from massive amounts of data, providing direct and reliable data support for subsequent thermal runaway early warning and precise maintenance, greatly improving the proactive safety of battery thermal management.
[0007] As a preferred example, the step of obtaining the structural dimension parameters of the battery module under test, and determining the scanning trajectory of the thermal imaging probe based on the structural dimension parameters, includes: Obtain the three-dimensional dimensional parameters and appearance structure type of the battery module under test; wherein, the three-dimensional dimensional parameters include the module length, module width and module height of the battery module under test; Match the scanning trajectory type corresponding to the appearance structure type; wherein, the scanning trajectory type includes spiral scanning trajectory, matrix scanning trajectory and partitioned scanning trajectory; The scanning trajectory of the thermal imaging probe is generated based on the scanning trajectory type and the three-dimensional size parameters.
[0008] The above solution obtains the three-dimensional dimensional parameters and appearance structure type of the battery module under test and matches the corresponding scanning trajectory type, such as spiral or matrix, avoiding the problem of a large number of invalid scans or blind spots that may occur when using a single scanning mode. For cylindrical battery packs, the spiral trajectory can efficiently cover their curved surfaces; for square battery packs, the matrix trajectory can systematically traverse their planes. This customized trajectory planning based on the module's own characteristics ensures that the acquisition of full-coverage thermal imaging data is both complete and avoids redundant actions, significantly improving detection efficiency.
[0009] As a preferred example, before generating the movement parameters corresponding to each motion axis in the segmented robotic arm based on the scanning trajectory, the following steps are included: The initial height of the thermal imaging probe relative to the inner wall of the end plate is obtained based on the three-dimensional dimensional parameters and the preset first safety distance. The initial angles of each motion axis in the segmented robotic arm relative to the inner wall of the end plate are determined based on the three-dimensional dimensional parameters and the appearance structure type. The segmented robotic arm is controlled to perform corresponding movements on each of its motion axes based on the initial height and the initial angle.
[0010] The above scheme, based on the three-dimensional dimensional parameters and a preset first safety distance, obtains the initial height of the thermal imaging probe relative to the inner wall of the end plate and determines the initial angles of each motion axis relative to the inner wall of the end plate. This ensures that the robotic arm can start from a known and safe position before executing complex scanning trajectories. This initial pose is determined based on calculations of the physical constraints and operational safety requirements of the module's internal space, laying the foundation for smooth and collision-free movement of the robotic arm within the confined and complex internal environment of the module, and ensuring the safety and reliability of the detection process.
[0011] As a preferred example, the step of generating motion parameters corresponding to each motion axis in the segmented robotic arm based on the scanning trajectory, and acquiring full-coverage thermal imaging data of the battery module under test based on the motion parameters and the thermal imaging probe, includes: Based on the three-dimensional dimensional parameters and the preset second spacing, determine the rotation angle constraint and extension length constraint corresponding to each motion axis in the segmented robotic arm; The rotation axis parameters that drive the thermal imaging probe to move along the scanning trajectory are generated based on the rotation angle constraint and the telescopic length constraint; wherein, the rotation axis parameters include the movement parameters corresponding to each of the motion axes.
[0012] The above scheme determines the rotation angle constraint and extension length constraint corresponding to each motion axis based on the three-dimensional dimensional parameters and the preset second spacing, thereby transforming the abstract safety requirements into specific and quantified motion limit parameters for each motion axis of the robotic arm. Thus, when generating the rotation axis parameters that drive the probe movement, all motion commands are consciously restricted within safety boundaries, effectively preventing the robotic arm or probe from touching the inner wall or other internal structures of the battery module during movement. This perfectly achieves obstacle avoidance while acquiring full-coverage thermal imaging data of the battery module under test, improving the safety of the detection.
[0013] As a preferred example, the step of obtaining the thermal distribution map corresponding to the full-coverage thermal imaging data and extracting abnormal hotspots from the thermal distribution map includes: The full-coverage thermal imaging data is input into a pre-trained convolutional neural network to output a thermal distribution map corresponding to the full-coverage thermal imaging data; wherein, the thermal distribution map includes the temperature value, temperature distribution density and temperature gradient corresponding to each pixel. For any given pixel: When the temperature value corresponding to the pixel is greater than or equal to a preset temperature threshold or the temperature difference gradient is greater than or equal to a preset temperature difference threshold, the pixel is marked as an abnormal hotspot.
[0014] The above scheme uses a pre-trained convolutional neural network to output a heat map and comprehensively analyzes multiple dimensions of information, such as the temperature value, temperature distribution density, and temperature gradient corresponding to each pixel, overcoming the potential for misjudgment when relying solely on a single temperature threshold. By setting dual judgment criteria of temperature and temperature difference thresholds, it can sensitively capture two typical anomalies: excessively high absolute temperatures and drastic temperature changes. This intelligent identification method makes the process of extracting abnormal hotspots from the heat map more accurate and efficient, providing high-quality data input for subsequent fault location.
[0015] As a preferred example, the step of matching the abnormal movement parameters corresponding to the abnormal hotspot from the movement parameters to obtain the coordinates of the abnormal heating area of the battery module under test based on the abnormal movement parameters and the structural size parameters includes: The spatial pose of the thermal imaging probe when it acquires the abnormal hotspot is obtained; wherein, the spatial pose includes the depth, horizontal position and rotation angle of the thermal imaging probe; Based on the spatial pose, the abnormal movement parameters corresponding to the abnormal hotspots collected by each motion axis in the robotic arm are obtained through coordinate mapping. Based on the abnormal movement parameters and the structural dimension parameters, the coordinates of the abnormal heating area on the battery module under test are obtained.
[0016] The above solution establishes a precise correspondence between thermal image pixels and the historical pose of the robotic arm by acquiring the spatial pose of the abnormal hotspot when captured by the thermal imaging probe and obtaining the abnormal movement parameters corresponding to each motion axis of the robotic arm through coordinate mapping. Finally, based on the abnormal movement parameters and the structural dimension parameters, the coordinates of the abnormal heating area on the battery module under test are accurately obtained, enabling precise diagnosis of faulty battery cells and guiding maintenance personnel to quickly locate the problem. This improves battery maintenance efficiency.
[0017] In a second aspect, the present invention discloses an internal thermal imaging device for a battery module, comprising a segmented robotic arm, a thermal imaging probe, and a controller. The fixed end of the segmented robotic arm is mounted on the inner wall of the end plate of the battery module under test; the thermal imaging probe is rotatably fixed on the free end of the segmented robotic arm. The segmented robotic arm and the thermal imaging probe are respectively connected to the controller via signals, so that the controller performs the following steps: Obtain the structural dimension parameters of the battery module under test, and determine the scanning trajectory of the thermal imaging probe based on the structural dimension parameters; Based on the scanning trajectory, movement parameters corresponding to each motion axis in the segmented robotic arm are generated respectively, so as to control the movement of the segmented robotic arm according to the movement parameters, so that the thermal imaging probe can acquire full-coverage thermal imaging data of the battery module under test. Obtain the thermal distribution map corresponding to the full-coverage thermal imaging data, and extract the abnormal hot spots in the thermal distribution map; The abnormal movement parameters corresponding to the abnormal hotspots are matched from the movement parameters to obtain the coordinates of the abnormal heating area of the battery module under test based on the abnormal movement parameters and the structural size parameters.
[0018] This invention discloses an internal thermal imaging device for battery modules. By mounting a segmented robotic arm's fixed end to the inner wall of the endplate of the battery module under test and acquiring full-coverage thermal imaging data of the module, it achieves direct insertion of the thermal imaging probe into the module for scanning, enabling accurate and realistic acquisition of the internal temperature distribution. Secondly, the scanning trajectory of the thermal imaging probe is determined based on the structural dimensional parameters, and the movement parameters of the robotic arm are generated, allowing a single probe to move dynamically and achieve full-coverage scanning of the entire internal space of the module, completely eliminating the defect of temperature blind spots. Finally, by extracting abnormal hot spots from the thermal distribution map and obtaining the coordinates of the abnormal heating areas, it achieves precise location of fault points from massive amounts of data, providing direct and reliable data support for subsequent thermal runaway early warning and precise maintenance, greatly improving the proactive safety of battery thermal management.
[0019] As a preferred example, the segmented robotic arm includes a first motion axis, a second motion axis, and a third motion axis; the first free end of the first motion axis is mounted on the inner wall of the end plate of the battery module under test; the second free end of the first motion axis is rotatably connected to the first free end of the second motion axis; the second free end of the second motion axis is rotatably connected to the first free end of the third motion axis; and the thermal imaging probe is rotatably fixed to the second free end of the third motion axis.
[0020] The aforementioned solution, through a specific three-axis robotic arm configuration design, provides the probe with extremely high flexibility and freedom of movement within the complex space inside the module. The series connection of the first, second, and third motion axes, along with the rotatable connection points, allows the probe, fixed at the end, to achieve multi-angle attitude adjustments and deep penetration capabilities. This ensures the probe can effectively avoid internal obstacles and reach structurally complex corners within the module, thereby enabling a comprehensive scan of the battery module's interior.
[0021] Thirdly, the present invention discloses a terminal device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements an internal thermal imaging method for a battery module as described in the first aspect.
[0022] Fourthly, the present invention discloses a computer-readable storage medium comprising: a stored computer program, wherein, when the computer program is executed, the device in which the computer-readable storage medium is located is controlled to perform an internal thermal imaging method for a battery module as described in the first aspect. Attached Figure Description
[0023] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0024] Figure 1 This is a flowchart illustrating an internal thermal imaging method for a battery module provided in an embodiment of the present invention. Figure 2 This is a top view structural diagram of an image acquisition device including a segmented robotic arm and a thermal imaging probe provided in an embodiment of the present invention; Figure 3 This is a front view structural diagram of an image acquisition device including a segmented robotic arm and a thermal imaging probe provided in an embodiment of the present invention; Figure 4 This is a side view structural diagram of an image acquisition device including a segmented robotic arm and a thermal imaging probe provided in an embodiment of the present invention; Figure 5 This is a top view schematic diagram of a thermal imaging probe provided in an embodiment of the present invention; Figure 6 This is a side view structural diagram of a thermal imaging probe provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of an internal thermal imaging device for a battery module provided in an embodiment of the present invention. Detailed Implementation
[0025] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.
[0027] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.
[0028] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.
[0029] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character "" in this document generally indicates that the preceding and following related objects have an "or" relationship.
[0030] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).
[0031] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.
[0032] See Figure 1 To address the technical problem of existing technologies being unable to comprehensively detect the internal temperature of battery modules, an embodiment of the present invention provides an internal thermal imaging method for battery modules, applicable to an image acquisition device including a segmented robotic arm and a thermal imaging probe; wherein, the fixed end of the segmented robotic arm is mounted on the inner wall of the end plate of the battery module under test; the thermal imaging probe is rotatably fixed to the free end of the segmented robotic arm; the method includes: Step 101: Obtain the structural dimension parameters of the battery module under test, so as to determine the scanning trajectory of the thermal imaging probe based on the structural dimension parameters.
[0033] Step 102: Generate movement parameters corresponding to each motion axis in the segmented robotic arm based on the scanning trajectory, so as to obtain full-coverage thermal imaging data of the battery module under test based on the movement parameters and the thermal imaging probe.
[0034] Step 103: Obtain the thermal distribution map corresponding to the full-coverage thermal imaging data, and extract the abnormal hot spots in the thermal distribution map.
[0035] Step 104: Match the abnormal movement parameters corresponding to the abnormal hotspot from the movement parameters, so as to obtain the coordinates of the abnormal heating area of the battery module under test according to the abnormal movement parameters and the structural size parameters.
[0036] In some embodiments of this example, in order to quickly and accurately detect abnormal heating inside the battery module under test using an image acquisition device installed on the inner wall of the end plate of the battery module under test, an image acquisition device that can move freely to perform comprehensive inspection of the inner surface of the battery module under test is provided. Specifically, the specific structure of the image acquisition device is as follows: Figures 2 to 4 As shown.
[0037] from Figure 2 It is known that the image acquisition device includes a segmented robotic arm with a three-segment structure, and a thermal imaging probe is fixed at the free end of the segmented robotic arm to acquire temperature data through the thermal imaging probe.
[0038] from Figures 3 to 4 It is known that the thermal imaging probe is integrated onto the segmented robotic arm; the segmented robotic arm adopts a three-section structure and can be mounted on the front panel of the battery module under test. Specifically, the segmented robotic arm can be adjusted... Figure 2 The extension and retraction length of the second motion axis (1-2) shown controls the detection depth of the thermal imaging probe, while the first motion axis (1-1) enables the thermal imaging probe to perform a 360-degree omnidirectional temperature scan of the battery module under test. The overall scanning range is controlled by controlling the third motion axis (1-3). It should be noted that... Figures 2 to 4 The segmented robotic arm shown needs to maintain a certain distance from the top of the battery module under test after it is fully extended, such as a safe distance of 2.5 to 3 cm. This safe distance is to prevent the segmented robotic arm from colliding with the battery module and thus affecting its safety. Simultaneously, an initial depth can be preset based on the three-dimensional dimensions of the battery module, and the depth can be dynamically adjusted through the telescopic function to ensure coverage of the entire testing area inside the battery module.
[0039] In some embodiments of this example, the thermal imaging probe is rotatably connected to the free end of the segmented robotic arm, meaning the thermal imaging probe can rotate 360 degrees at the free end to acquire thermal imaging data from different angles, thereby improving the comprehensiveness of temperature detection. Preferably, to allow the thermal imaging probe to move freely inside the battery module, a miniature thermal imaging probe can be used to acquire thermal imaging data. Preferably, the structure of a miniature thermal imaging probe is as follows: Figures 5 to 6 As shown. From Figure 5 and Figure 6 It is known that the miniature thermal imaging probe measures 19mm × 17.6mm × 8mm and weighs less than 2.5g. Furthermore, to ensure the accuracy of thermal imaging data acquisition, a vanadium oxide uncooled infrared focal plane detector can be placed inside the probe. This vanadium oxide uncooled infrared focal plane detector has a 25Hz frame rate, a probe resolution ≥ 256 × 192 pixels, a spectral range of 8–14μm, an accuracy of ±2℃, and automatic shutter correction. The industrial temperature measurement range is -15℃ to +150℃, with a frame rate not exceeding 25Hz, and image output of 10bit / 14bit. It provides multiple focal length and viewing angle options to adapt to different application needs, enabling it to accurately capture minute temperature changes inside the battery module with high sensitivity and high resolution. It is important to note that the miniature thermal imaging probe generates thermal imaging data (10bit / 14bit image output) by capturing infrared radiation inside the battery module, rather than directly acquiring the specific temperature value at each location.
[0040] In some implementations of this embodiment, such as Figures 2 to 6 The image acquisition device shown also features a three-level detection mode: "fixed point - area - global". Different detection modes can be selected according to the specific application scenario requirements: the fixed point mode is used to quickly and accurately identify local abnormal hot spots, the area mode is used to quickly inspect key areas, and the global mode is used to fully evaluate the overall module status to adapt to the detection needs of different scenarios and stages.
[0041] In some embodiments of this example, step 101 further includes the following steps: Step 1011: Obtain the three-dimensional dimensional parameters and appearance structure type of the battery module under test; wherein, the three-dimensional dimensional parameters include the module length, module width and module height of the battery module under test; Step 1012: Match the scanning trajectory type corresponding to the appearance structure type; wherein, the scanning trajectory type includes spiral scanning trajectory, matrix scanning trajectory and partitioned scanning trajectory; Step 1013: Generate the scanning trajectory of the thermal imaging probe according to the scanning trajectory type and the three-dimensional size parameters.
[0042] Specifically, in some embodiments of this example, the scanning trajectory of the thermal imaging probe can be adaptively planned according to the appearance characteristics and three-dimensional size parameters of the battery module under test. For example, in one embodiment, a spiral scanning trajectory is used for the battery module under test based on a cylindrical battery pack; a matrix scanning trajectory is used for the battery module under test based on a square battery pack; and a partitioned scanning trajectory is used for the battery module under test based on an irregularly arranged battery pack. The trajectory covers all areas of the inner surface of the battery module under test, with no blind spots.
[0043] In this embodiment, the above steps, by acquiring the three-dimensional dimensional parameters and appearance structure type of the battery module under test and matching the scanning trajectory type corresponding to the appearance structure type, such as spiral or matrix, avoid the problem of a large number of invalid scans or blind spots that may occur when using a single scanning mode. For cylindrical battery packs, spiral trajectories can efficiently cover their curved surfaces; for square battery packs, matrix trajectories can systematically traverse their planes. This customized trajectory planning based on the module's own characteristics ensures that the acquisition process of full-coverage thermal imaging data is both complete and avoids redundant actions, significantly improving detection efficiency.
[0044] In this embodiment, step 102 further includes the following steps: Step 1021: Obtain the initial height of the thermal imaging probe relative to the inner wall of the end plate based on the three-dimensional dimensional parameters and the preset first safety distance.
[0045] Step 1022: Determine the initial angles of each motion axis in the segmented robotic arm relative to the inner wall of the end plate based on the three-dimensional dimensional parameters and the appearance structure type.
[0046] Step 1023: Control each motion axis in the segmented robotic arm to perform corresponding movements according to the initial height and the initial angle.
[0047] Step 1024: Based on the three-dimensional dimensional parameters and the preset second spacing, determine the rotation angle constraint and extension length constraint corresponding to each motion axis in the segmented robotic arm.
[0048] Step 1025: Generate rotation axis parameters to drive the thermal imaging probe to move along the scanning trajectory based on the rotation angle constraint and the telescopic length constraint; wherein, the rotation axis parameters include the movement parameters corresponding to each of the motion axes.
[0049] Specifically, in this embodiment, Figures 2 to 4 After the image acquisition device is placed on the front panel of the battery module under test, the power of the image acquisition device is turned on and a self-test is completed. First, the initial posture of the segmented robotic arm is adjusted according to the three-dimensional dimensional parameters of the battery module under test, namely, its length L, width W, and height H. First, the second motion axis 1-2 is adjusted to the specified initial depth. The determination of the initial depth needs to be combined with the three-dimensional dimensional parameters to ensure the starting position of the thermal imaging probe entering the detection area of the battery pack under test. The initial depth satisfies... (A 3cm safety clearance is reserved); simultaneously, the initial angles of the first and third motion axes are preset according to the shape of the battery pack under test. Preferably, for a cylindrical battery module under test, the diameter of the battery module is obtained. Then, the initial angle of the first motion axis is set to 0°, and the initial angle of the third motion axis is... satisfy To avoid touching the sidewall of the battery module under test; for square battery packs under test, adjust the initial angle of the third motion axis. This ensures that the rotation range is within the width of the battery module under test, while the first motion axis is set to maintain an initial angle that is horizontal towards the surface of the battery module; all axis parameters are based on preset three-dimensional dimensions and do not exceed their respective rotation limits, laying the foundation for complete coverage of the subsequent scanning trajectory.
[0050] Next, after determining the scanning trajectory of the miniature thermal imaging probe based on the appearance characteristics and three-dimensional structural parameters of the battery module under test, the movement parameters corresponding to each motion axis can be determined based on the scanning trajectory. In determining these movement parameters, the rotational limitations and dimensional calculation formulas of each motion axis must be considered. Specifically, for a cylindrical battery module under test, a spiral trajectory is planned based on the 360-degree rotation capability of the first motion axis, while the extension / retraction length of the second motion axis... satisfy (Maintain a safe distance of ≥2.5cm from the top of the battery pack), rotation angle range of the third motion axis (Covering the radial range of a cylinder); For a square battery module under test, relying on the rotation of the first motion axis and the extension / retraction of the second motion axis, a trajectory is planned according to the matrix row and column spacing, and the single extension / retraction amount of the second motion axis is... (Ensure uniform scanning spacing and avoid contact with the inner wall), third motion axis rotation angle (Limited to the square edge area), thus ensuring that the trajectory covers the entire surface of the battery pack within the rotation limit of the shaft, with no blind spots in monitoring.
[0051] In this embodiment, the above steps, based on the three-dimensional dimensional parameters and the preset first safety distance, determine the initial height of the thermal imaging probe relative to the inner wall of the end plate and the initial angle of each motion axis relative to the inner wall of the end plate. This ensures that the robotic arm can start from a known and safe position before executing complex scanning trajectories. This initial pose determination is calculated based on the physical constraints and operational safety requirements of the module's internal space, laying the foundation for smooth and collision-free movement of the robotic arm in the confined and complex internal environment of the module, ensuring the safety and reliability of the detection process. Next, based on the three-dimensional dimensional parameters and the preset second distance, the rotation angle constraint and extension length constraint corresponding to each motion axis are determined, transforming the abstract safety requirements into specific and quantified motion limit parameters for each motion axis of the robotic arm. Thus, when generating the rotation axis parameters that drive the probe movement, all motion commands are consciously limited within the safety boundaries, effectively preventing the robotic arm or probe from touching the inner wall or other internal structures of the battery module during movement. This perfectly achieves obstacle avoidance while acquiring full-coverage thermal imaging data of the battery module under test, improving the safety of the detection.
[0052] In this embodiment, step 103 includes: Step 1031: Input the full-coverage thermal imaging data into a pre-trained convolutional neural network to output a thermal distribution map corresponding to the full-coverage thermal imaging data through the convolutional neural network; wherein, the thermal distribution map includes the temperature value, temperature distribution density and temperature gradient corresponding to each pixel. Step 1032: For any one of the pixel points: when the temperature value corresponding to the pixel point is greater than or equal to a preset temperature threshold or the temperature difference gradient is greater than or equal to a preset temperature difference threshold, mark the pixel point as an abnormal hot spot.
[0053] Specifically, in this embodiment, thermal imaging data collected by a thermal imaging probe, such as infrared temperature data, etc., is received, and the thermal imaging data is input into a pre-trained convolutional neural network model to standardize the temperature value through the input layer of the convolutional neural network model, extract features such as temperature gradient and hot spot clustering through the hidden layer (including convolutional layer and pooling layer), and the output layer generates a thermal distribution map including temperature value (°C), temperature distribution density, and temperature difference gradient. When it is detected that the temperature exceeds a preset temperature threshold such as 60 °C or the temperature difference gradient is greater than or equal to a preset temperature difference threshold such as 10 °C, it is determined as an abnormal hot spot, and its three-dimensional coordinates (depth, horizontal position, rotation angle) are marked. It should be noted that when the abnormal hot spot is detected, an alarm instruction can be actively sent to the battery management system (BMS) according to the linked communication with the BMS to assist the BMS to quickly take safety response measures and achieve early prediction and efficient disposal of battery safety hazards.
[0054] In this embodiment, this step outputs a thermal distribution map by using a pre-trained convolutional neural network, and comprehensively analyzes multiple-dimensional information such as the temperature value, temperature distribution density, and temperature difference gradient corresponding to each pixel point, overcoming the misjudgment that may occur by relying only on a single temperature threshold. By setting a dual judgment criterion of temperature threshold and temperature difference threshold, two typical abnormal phenomena of too high absolute temperature and剧烈 temperature change can be sensitively captured. This intelligent recognition method makes the process of extracting abnormal hot spots in the thermal distribution map more accurate and efficient, providing high-quality data input for subsequent fault location.
[0055] In an implementation manner of this embodiment, step 104 includes: Step 1041: Obtain the spatial pose corresponding to the thermal imaging probe when collecting the abnormal hot spot; wherein, the spatial pose includes the depth, horizontal position, and rotation angle of the thermal imaging probe; Step 1042: According to the spatial pose, obtain the abnormal movement parameters corresponding to each movement axis of the robotic arm when collecting the abnormal hot spot through coordinate mapping; Step 1043: According to the abnormal movement parameters and the structural dimension parameters, obtain the coordinates of the abnormal heating area of the abnormal hot spot on the待测 battery module.
[0056] Specifically, in this embodiment, after completing the scanning task, the movement parameters corresponding to each motion axis in the segmented robotic arm are back-matched based on the three-dimensional coordinates (depth, horizontal position, and rotation angle) of the abnormal hotspots marked on the thermal distribution map. The depth corresponds to the extension length of the second motion axis, the horizontal position parameter corresponds to the rotation angle of the third motion axis, and the rotation angle parameter directly maps to the 360° rotation value of the first motion axis. After determining the movement parameters corresponding to each motion axis, combined with the three-dimensional dimensions of the battery module, the extension length of the second motion axis is mapped to the depth coordinates of the battery module, the rotation angle of the third motion axis corresponds to the horizontal plane coordinates, and the rotation angle of the first motion axis corresponds to its relative orientation to the surface of the battery module. These three coordinates work together to locate the specific coordinates of the abnormal hotspots on the battery module. It should be noted that during temperature detection, the coordinates of the abnormal heating area of the abnormal hotspots on the battery module under test, the temperature value of the abnormal point, and the handling measures can all be recorded in the detection report. Subsequently, the detection data is uploaded to the cloud monitoring system, and the robotic arm, carrying the probe, exits the module and returns to its initial position.
[0057] In this embodiment, the above steps establish a precise correspondence between pixels in the thermal distribution map and the historical poses of the robotic arm by acquiring the spatial pose corresponding to the abnormal hotspot when the thermal imaging probe collects the data and obtaining the abnormal movement parameters corresponding to each motion axis in the robotic arm through coordinate mapping. Finally, based on the abnormal movement parameters and the structural dimension parameters, the coordinates of the abnormal heating area on the battery module under test are accurately obtained, enabling precise diagnosis of faulty battery cells and guiding maintenance personnel to quickly locate the problem. This improves the efficiency of battery maintenance.
[0058] like Figure 7 As shown, based on the above method embodiments, corresponding device embodiments are provided; this embodiment provides an internal thermal imaging device for a battery module, including a segmented robotic arm 201, a thermal imaging probe 202, and a controller 203.
[0059] The fixed end of the segmented robotic arm 201 is mounted on the inner wall of the end plate of the battery module under test; the thermal imaging probe 202 is rotatably fixed on the free end of the segmented robotic arm 201.
[0060] The segmented robotic arm 201 and the thermal imaging probe 202 are respectively connected to the controller 203 via signals, so that the controller 203 performs the following steps: Obtain the structural dimension parameters of the battery module under test, and determine the scanning trajectory of the thermal imaging probe 202 based on the structural dimension parameters; Based on the scanning trajectory, movement parameters corresponding to each motion axis in the segmented robotic arm 201 are generated respectively, so as to control the movement of the segmented robotic arm 201 according to the movement parameters, so that the thermal imaging probe 202 can acquire full-coverage thermal imaging data of the battery module under test. Obtain the thermal distribution map corresponding to the full-coverage thermal imaging data, and extract the abnormal hot spots in the thermal distribution map; The abnormal movement parameters corresponding to the abnormal hotspots are matched from the movement parameters to obtain the coordinates of the abnormal heating area of the battery module under test based on the abnormal movement parameters and the structural size parameters.
[0061] In this embodiment, the segmented robotic arm 201 includes a first motion axis, a second motion axis, and a third motion axis; the first free end of the first motion axis is mounted on the inner wall of the end plate of the battery module under test; the second free end of the first motion axis is rotatably connected to the first free end of the second motion axis; the second free end of the second motion axis is rotatably connected to the first free end of the third motion axis; and the thermal imaging probe is rotatably fixed to the second free end of the third motion axis.
[0062] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.
[0063] Based on the above embodiments of the internal thermal imaging method for battery modules, this embodiment provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the internal thermal imaging method for battery modules of the method embodiment.
[0064] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete this embodiment. The one or more module units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.
[0065] The terminal device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and memory. The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.
[0066] Based on the above-described method embodiments, this embodiment also provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the internal thermal imaging method for a battery module described in the method embodiments.
[0067] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.
[0068] This embodiment provides a method, apparatus, device, and medium for internal thermal imaging of battery modules. A segmented robotic arm can penetrate deep into the battery module to a specific depth for internal temperature detection. By adjusting the swing angle of the segmented robotic arm, comprehensive scanning and detection of various parts of the battery module within the same depth range can be achieved, resulting in efficient coverage of the battery module and improved scanning and detection accuracy and comprehensiveness. The high resolution of the thermal imaging probe enables it to accurately capture subtle temperature changes inside the battery module and promptly identify potential hotspots, significantly improving the accuracy of internal temperature monitoring. The probe's frame rate ensures smooth image transmission during dynamic scanning, allowing real-time tracking of the battery module's thermal runaway process and rapid anomaly detection. The probe's compact size and built-in shutter design facilitate easy integration into the robotic arm, ensuring efficient internal scanning and monitoring. It supports comprehensive temperature data output, providing rich temperature information to help accurately analyze the battery's health status, predict thermal runaway risks in advance, and thus improve battery safety during use.
[0069] Next, the thermal imaging data fed back from the probe is processed by a convolutional neural network model to generate a heat map and mark abnormal hot spots. When a problem is detected, an early warning is immediately issued and the system coordinates with the battery management system to take safety measures. Finally, after the scan is completed, a detection report is generated and uploaded to the cloud. The robotic arm then exits the module and returns to its initial position, completing the detection. The method described in this embodiment can accurately determine if a module has a high risk of failure and promptly send instructions to the BMS, requesting the BMS to issue an alarm to the upstream system outside the module. Simultaneously, the BMS controls the liquid cooling device to increase the liquid cooling flow rate, reducing the temperature of the cells in the module. This effectively improves system safety and alerts operation and maintenance personnel to take appropriate measures, effectively preventing safety accidents such as fires, protecting personnel and property, and ensuring the stable operation of the energy storage system.
[0070] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.
Claims
1. An internal thermal imaging method for battery modules, applicable to image acquisition devices including segmented robotic arms and thermal imaging probes; wherein, The fixed end of the segmented robotic arm is mounted on the inner wall of the end plate of the battery module under test; The thermal imaging probe is rotatably fixed to the free end of the segmented robotic arm; characterized in that the method includes: Obtain the structural dimension parameters of the battery module under test, and determine the scanning trajectory of the thermal imaging probe based on the structural dimension parameters; Based on the scanning trajectory, motion parameters corresponding to each motion axis in the segmented robotic arm are generated respectively, so as to obtain full-coverage thermal imaging data of the battery module under test based on the motion parameters and the thermal imaging probe. Obtain the thermal distribution map corresponding to the full-coverage thermal imaging data, and extract the abnormal hot spots in the thermal distribution map; The abnormal movement parameters corresponding to the abnormal hotspots are matched from the movement parameters to obtain the coordinates of the abnormal heating area of the battery module under test based on the abnormal movement parameters and the structural size parameters.
2. The method for internal thermal imaging of a battery module according to claim 1, characterized in that, The step of acquiring the structural dimension parameters of the battery module under test, and determining the scanning trajectory of the thermal imaging probe based on the structural dimension parameters, includes: Obtain the three-dimensional dimensional parameters and appearance structure type of the battery module under test; wherein, the three-dimensional dimensional parameters include the module length, module width and module height of the battery module under test; Match the scanning trajectory type corresponding to the appearance structure type; wherein, the scanning trajectory type includes spiral scanning trajectory, matrix scanning trajectory and partitioned scanning trajectory; The scanning trajectory of the thermal imaging probe is generated based on the scanning trajectory type and the three-dimensional size parameters.
3. The method for internal thermal imaging of a battery module according to claim 2, characterized in that, Before generating the movement parameters corresponding to each motion axis in the segmented robotic arm based on the scanning trajectory, the following steps are included: The initial height of the thermal imaging probe relative to the inner wall of the end plate is obtained based on the three-dimensional dimensional parameters and the preset first safety distance. The initial angles of each motion axis in the segmented robotic arm relative to the inner wall of the end plate are determined based on the three-dimensional dimensional parameters and the appearance structure type. The segmented robotic arm is controlled to perform corresponding movements on each of its motion axes based on the initial height and the initial angle.
4. The method for internal thermal imaging of a battery module according to claim 2, characterized in that, The step of generating motion parameters corresponding to each motion axis in the segmented robotic arm based on the scanning trajectory, and acquiring full-coverage thermal imaging data of the battery module under test based on the motion parameters and the thermal imaging probe, includes: Based on the three-dimensional dimensional parameters and the preset second spacing, determine the rotation angle constraint and extension length constraint corresponding to each motion axis in the segmented robotic arm; The rotation axis parameters that drive the thermal imaging probe to move along the scanning trajectory are generated based on the rotation angle constraint and the telescopic length constraint; wherein, the rotation axis parameters include the movement parameters corresponding to each of the motion axes.
5. The method for internal thermal imaging of a battery module according to claim 1, characterized in that, The step of acquiring the thermal distribution map corresponding to the full-coverage thermal imaging data and extracting abnormal hotspots from the thermal distribution map includes: The full-coverage thermal imaging data is input into a pre-trained convolutional neural network to output a thermal distribution map corresponding to the full-coverage thermal imaging data; wherein, the thermal distribution map includes the temperature value, temperature distribution density and temperature gradient corresponding to each pixel. For any given pixel: When the temperature value corresponding to the pixel is greater than or equal to a preset temperature threshold or the temperature difference gradient is greater than or equal to a preset temperature difference threshold, the pixel is marked as an abnormal hotspot.
6. The method for internal thermal imaging of a battery module according to claim 5, characterized in that, The step of matching the abnormal movement parameters corresponding to the abnormal hotspots from the movement parameters, and obtaining the coordinates of the abnormal heating area of the battery module under test based on the abnormal movement parameters and the structural size parameters, includes: The spatial pose of the thermal imaging probe when it acquires the abnormal hotspot is obtained; wherein, the spatial pose includes the depth, horizontal position and rotation angle of the thermal imaging probe; Based on the spatial pose, the abnormal movement parameters corresponding to the abnormal hotspots collected by each motion axis in the robotic arm are obtained through coordinate mapping. Based on the abnormal movement parameters and the structural dimension parameters, the coordinates of the abnormal heating area on the battery module under test are obtained.
7. An internal thermal imaging device for a battery module, characterized in that, Includes a segmented robotic arm, a thermal imaging probe, and a controller; The fixed end of the segmented robotic arm is mounted on the inner wall of the end plate of the battery module under test; the thermal imaging probe is rotatably fixed on the free end of the segmented robotic arm. The segmented robotic arm and the thermal imaging probe are respectively connected to the controller via signals, so that the controller performs the following steps: Obtain the structural dimension parameters of the battery module under test, and determine the scanning trajectory of the thermal imaging probe based on the structural dimension parameters; Based on the scanning trajectory, movement parameters corresponding to each motion axis in the segmented robotic arm are generated respectively, so as to control the movement of the segmented robotic arm according to the movement parameters, so that the thermal imaging probe can acquire full-coverage thermal imaging data of the battery module under test. Obtain the thermal distribution map corresponding to the full-coverage thermal imaging data, and extract the abnormal hot spots in the thermal distribution map; The abnormal movement parameters corresponding to the abnormal hotspots are matched from the movement parameters to obtain the coordinates of the abnormal heating area of the battery module under test based on the abnormal movement parameters and the structural size parameters.
8. An internal thermal imaging device for a battery module according to claim 7, characterized in that, The segmented robotic arm includes a first motion axis, a second motion axis, and a third motion axis; the first free end of the first motion axis is mounted on the inner wall of the end plate of the battery module under test; the second free end of the first motion axis is rotatably connected to the first free end of the second motion axis; the second free end of the second motion axis is rotatably connected to the first free end of the third motion axis; and the thermal imaging probe is rotatably fixed to the second free end of the third motion axis.
9. A terminal device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements an internal thermal imaging method for a battery module as described in any one of claims 1-6.
10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform an internal thermal imaging method for a battery module as described in any one of claims 1-6.