A new energy vehicle battery disassembly system and method thereof

Abstract

This invention relates to the field of new energy vehicle battery technology, specifically disclosing a dismantling system and method for new energy vehicle batteries. The invention acquires battery structural topology data through 3D laser scanning and industrial CT, simultaneously collecting electrochemical state data. First, a basic dismantling path is planned based on the shell fragility coefficient and cell spacing. Second, the path is optimized based on the bolt connection stiffness coefficient, prioritizing the dismantling of weak areas to release stress. Third, abnormal active cells are located through abrupt changes in the phase angle of the internal resistance spectrum, mapped to three-dimensional space to form risk points. Finally, a dual-optimized path is generated by integrating structural and electrical risks: first, bolts are removed to stabilize the structure, then abnormal cell areas are avoided. This method overcomes the limitations of traditional fixed paths, identifying shell cracking, cell compression, and thermal runaway risks in advance. The two-step path optimization improves dismantling safety and recycling rate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy automobile battery, and particularly relates to a new energy automobile battery disassembling system and method. BACKGROUND

[0002] New energy automobile power battery recycling has become a key link of green industry chain, and efficient and safe disassembly of single battery cell is a prerequisite for resource regeneration.

[0003] The existing new energy automobile battery disassembly usually adopts a mechanical disassembly mode, and only relies on a preset path to cut the shell during battery disassembly, which may cause the battery to be damaged and increase the disassembly risk, thereby reducing the battery pack recovery rate. SUMMARY

[0004] The present application aims to provide a new energy automobile battery disassembly system and method to solve the technical problems in the background art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0006] A new energy automobile battery disassembly method comprises:

[0007] Obtaining body state data of the new energy automobile battery, wherein the body state data comprises structure topology data and electrochemical state data;

[0008] Obtaining shell point cloud coordinates, a plurality of bolt position coordinates, and cell space arrangement topology information according to the structure topology data, and obtaining a basic disassembly path according to the shell point cloud coordinates and the cell space arrangement topology information;

[0009] Obtaining a connection stiffness coefficient of the new energy automobile battery according to a plurality of the bolt position coordinates, and re-fitting and arranging the basic disassembly path according to the connection stiffness coefficient to obtain a first optimized disassembly path;

[0010] Obtaining internal resistance spectra of a plurality of single battery cells according to the electrochemical state data, and obtaining a plurality of phase angle mutation points of the internal resistance spectra according to a plurality of the internal resistance spectra;

[0011] Obtaining a plurality of active abnormal point positions according to a plurality of the phase angle mutation points, and re-arranging the first optimized disassembly path according to a plurality of the active abnormal point positions to obtain a second optimized disassembly path;

[0012] Disassembling the new energy automobile battery according to the second optimized disassembly path.

[0013] Preferably, the step of obtaining a basic disassembly path according to the shell point cloud coordinates and the cell spatial arrangement topology information comprises:

[0014] An area of a shell surface of the new energy automobile battery is obtained, and the area of the shell surface is segmented according to a preset value to obtain a plurality of segmented area areas;

[0015] A plurality of shell curvature characteristics corresponding to the plurality of segmented area areas are obtained according to the shell point cloud coordinates, and a plurality of shell vulnerability coefficients are obtained according to the plurality of shell curvature characteristics;

[0016] It is judged in turn whether the plurality of shell vulnerability coefficients are less than a preset coefficient;

[0017] If the shell vulnerability coefficient is less than the preset coefficient, the segmented area area corresponding to the shell vulnerability coefficient less than the preset coefficient is defined as a high vulnerability area, and a high vulnerability area path is established according to the plurality of high vulnerability areas;

[0018] A plurality of cell position coordinates are obtained according to the cell spatial arrangement topology information, and a plurality of adjacent distances between adjacent cells are obtained according to the plurality of cell position coordinates;

[0019] It is judged in turn whether the plurality of adjacent distances are less than a preset adjacent distance;

[0020] If less than the preset coefficient, the segmented area area corresponding to the preset adjacent distance is defined as a high-risk area, and a high-risk area path is established according to the plurality of high-risk areas;

[0021] The high-risk area path and the high vulnerability area path are dynamically coupled in adjacent areas to obtain a merged path, and a preset disassembly path is fitted and optimized according to the merged path to obtain a basic disassembly path.

[0022] Preferably, the step of obtaining a connection stiffness coefficient of the new energy automobile battery according to the plurality of bolt position coordinates, re-fitting and arranging the basic disassembly path according to the connection stiffness coefficient to obtain a first optimized disassembly path comprises:

[0023] A bolt position Delaunay triangular net is established according to the plurality of bolt position coordinates, and a plurality of adjacent bolt spacings are calculated according to the bolt position Delaunay triangular net;

[0024] It is judged in turn whether the plurality of adjacent bolt spacings are less than a preset adjacent bolt spacing;

[0025] If less than the preset adjacent bolt spacing, it is determined that the adjacent bolt spacing has a stiffness loss, and a plurality of marked bolt spacings are obtained by marking a plurality of adjacent bolt spacings less than the preset adjacent bolt spacing;

[0026] The spacing between the multiple marked bolts is sequentially compared with the preset spacing between adjacent bolts to obtain multiple distance ratio values;

[0027] The connection stiffness coefficient (which is inversely proportional to the distance ratio) is calculated based on the distance ratio. The corresponding marked bolt spacing is then refitted and rearranged with the foundation disassembly path based on the connection stiffness coefficient to obtain the first optimized disassembly path.

[0028] Preferably, the step of obtaining the internal resistance spectra of multiple individual cells based on the electrochemical state data, and obtaining multiple phase angle abrupt change points of the internal resistance spectra based on the multiple internal resistance spectra, includes:

[0029] Multiple cell locations are obtained based on the electrochemical state data, and a preset AC excitation signal (0.1Hz to 1000Hz) is applied to the multiple cell locations to measure multiple voltage response signals and multiple current response signals. The multiple voltage response signals and multiple current response signals are used as the internal resistance spectrum of the cell.

[0030] Multiple complex impedances at multiple frequency points are obtained based on the internal resistance spectra of multiple individual battery cells;

[0031] Multiple phase angles are obtained based on the multiple complex impedances, and a phase angle-frequency curve is generated based on the multiple phase angles and a preset frequency.

[0032] Multiple phase angle change rates are obtained based on the phase angle-frequency curve and the preset phase angle-frequency curve;

[0033] Determine whether the multiple phase angle change rates are less than a preset phase angle change rate;

[0034] If the change rate is less than a preset phase angle change rate, then the single cell corresponding to the phase angle change rate that is less than the preset phase angle change rate is determined to have a phase angle abrupt change point, and then multiple phase angle abrupt change points are obtained in sequence according to multiple phase angle change rates that are less than the preset phase angle change rate.

[0035] Preferably, the step of obtaining multiple active anomaly points based on multiple phase angle abrupt change points, and rearranging the first optimized disassembly path based on the multiple active anomaly points to obtain the second optimized disassembly path includes:

[0036] Multiple phase angle offsets are obtained based on multiple phase angle abrupt change points and preset phase angle points;

[0037] Identify a plurality of abnormal points in a plurality of first single-cell batteries whose phase angle offsets are greater than a preset phase angle offset, and treat the anomalous points of the plurality of first single-cell batteries as a plurality of active abnormal points.

[0038] The coordinates of multiple active anomaly points are obtained by associating them with the point cloud coordinates of the outer shell of the new energy vehicle.

[0039] The coordinates of multiple active anomaly points are mapped and merged with the first optimized disassembly path to obtain the second optimized disassembly path.

[0040] Preferably, the step of disassembling the new energy vehicle battery according to the second optimized disassembly path includes:

[0041] The second optimized disassembly path is input into the preset PLC control system of the maintenance device. The preset PLC control system is applied to the battery disassembly device, and the battery disassembly device includes a clamping mechanism. The clamping mechanism is activated according to the preset PLC control system, and the preset vision sensor of the clamping mechanism scans the battery pack shell and matches the shell point cloud coordinates in the path to realize the positioning of the device and the battery pack.

[0042] The clamping mechanism separates the new energy vehicle battery according to the bolt position coordinates to obtain a separation structure;

[0043] The clamping mechanism avoids disassembling individual cells in the new energy vehicle battery based on multiple abnormal activity points.

[0044] This application also provides a dismantling system for new energy vehicle batteries, including:

[0045] The first acquisition module is used to acquire the bulk data of new energy vehicle batteries, wherein the bulk data includes structural topology data and electrochemical state data;

[0046] The second acquisition module is used to acquire the shell point cloud coordinates, multiple bolt position coordinates and cell space arrangement topology information based on the structural topology data, and to acquire the basic disassembly path based on the shell point cloud coordinates and cell space arrangement topology information.

[0047] The third acquisition module is used to acquire the connection stiffness coefficient of the new energy vehicle battery based on the coordinates of multiple bolt positions, and to refit and rearrange the basic disassembly path based on the connection stiffness coefficient to obtain the first optimized disassembly path.

[0048] The fourth acquisition module is used to acquire the internal resistance spectrum of multiple individual cells based on the electrochemical state data, and to acquire multiple phase angle abrupt change points of the internal resistance spectrum based on the multiple internal resistance spectra.

[0049] The fifth acquisition module is used to acquire multiple active abnormal points based on multiple phase angle mutation points, and rearrange the first optimized disassembly path based on the multiple active abnormal points to obtain the second optimized disassembly path.

[0050] The disassembly module is used to disassemble the new energy vehicle battery according to the second optimized disassembly path.

[0051] 8. The dismantling system for a new energy vehicle battery according to claim 7, characterized in that the second acquisition module comprises:

[0052] The first acquisition unit is used to acquire the surface area of ​​the casing of the new energy vehicle battery, and to divide the surface area of ​​the casing according to a preset value to obtain multiple segmented area regions.

[0053] The second acquisition unit is used to acquire multiple shell curvature features corresponding to the areas of multiple segmented regions based on the shell point cloud coordinates, and to acquire multiple shell fragility coefficients based on the multiple shell curvature features.

[0054] The first judgment unit is used to sequentially determine whether the multiple shell fragility coefficients are less than a preset coefficient;

[0055] If the shell fragility coefficient is less than the preset coefficient, the area of ​​the segmented region corresponding to the shell fragility coefficient less than the preset coefficient is defined as a high fragility region, and a high fragility region path is established based on multiple high fragility regions.

[0056] The third acquisition unit is used to acquire multiple cell position coordinates based on the cell spatial arrangement topology information, and to acquire multiple adjacent distances between adjacent cells based on the multiple cell position coordinates.

[0057] The second judgment unit is used to sequentially judge whether the plurality of adjacent distances are less than a preset adjacent distance;

[0058] If the area is less than a preset coefficient, the area of ​​the segmented region corresponding to the distance between adjacent regions is defined as a high-risk area, and a high-risk area path is established based on the multiple high-risk areas.

[0059] The dynamic coupling unit is used to dynamically couple the high-risk area path and the high-vulnerability area path in adjacent regions to obtain a merged path, and to fit and optimize the preset disassembly path based on the merged path to obtain a basic disassembly path.

[0060] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0061] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0062] The beneficial effects of this application are as follows: This invention acquires battery structural topology data (shell point cloud coordinates, bolt positions, and cell arrangement) through three-dimensional laser scanning and industrial CT, and simultaneously collects electrochemical state data (internal resistance spectrum and phase angle). First, it plans a basic dismantling path based on the shell fragility coefficient (highly vulnerable areas are divided through curvature analysis) and cell spacing. Second, it optimizes the path based on the bolt connection stiffness coefficient, prioritizing the dismantling of weak stiffness areas to release stress. Then, it locates active abnormal cells through the phase angle mutation points of the internal resistance spectrum, maps them to three-dimensional space to form risk points, and finally integrates structural risks (shell fragility areas and bolt stiffness) and electrical risks (abnormal cells) to generate a dual-optimized path: first, the bolts are removed to stabilize the structure, and then the abnormal cell area is avoided. This method breaks through the limitations of traditional fixed paths, identifies shell cracking, cell extrusion, and thermal runaway risks in advance, and improves dismantling safety and recycling rate through two path optimizations. Attached Figure Description

[0063] Fig. 1 This is a schematic diagram of a method flow according to an embodiment of this application.

[0064] Fig. 2 This is a schematic diagram of the system structure according to an embodiment of this application.

[0065] Fig. 3 This is a schematic diagram of the internal structure of a computer device according to an embodiment of this application.

[0066] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0067] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0068] like Figs. 1-3 As shown, this application provides a method for disassembling a new energy vehicle battery, including:

[0069] S1. Obtain the bulk data of the new energy vehicle battery, including structural topology data and electrochemical state data;

[0070] S2. Obtain the shell point cloud coordinates, multiple bolt position coordinates, and cell space arrangement topology information based on the structural topology data, and obtain the basic disassembly path based on the shell point cloud coordinates and cell space arrangement topology information;

[0071] S3. Obtain the connection stiffness coefficient of the new energy vehicle battery based on the coordinates of multiple bolt positions, and refit and rearrange the basic disassembly path based on the connection stiffness coefficient to obtain the first optimized disassembly path.

[0072] S4. Obtain the internal resistance spectrum of multiple individual cells based on the electrochemical state data, and obtain multiple phase angle abrupt change points of the internal resistance spectrum based on the multiple internal resistance spectra.

[0073] S5. Obtain multiple active abnormal points based on multiple phase angle mutation points, and rearrange the first optimized disassembly path based on the multiple active abnormal points to obtain the second optimized disassembly path;

[0074] S6. Disassemble the new energy vehicle battery according to the second optimized disassembly path.

[0075] As described in steps S1-S6 above, the disassembly of existing new energy vehicle batteries usually adopts a mechanical disassembly mode. During the battery disassembly process, the shell is usually cut only by a preset path. This will lead to an increase in the disassembly risk due to battery damage, and thus a decrease in the battery pack recycling rate. The present invention first obtains the physical data of the new energy vehicle battery. The physical data includes structural topology data and electrochemical state data. This accurate collection of the battery's structural topology data (such as shell point cloud coordinates, bolt positions, cell arrangement, etc.) and electrochemical state data (such as internal resistance spectrum, phase angle, etc.) lays a solid data foundation for subsequent disassembly path planning. At the same time, the collection of structural topology data (shell shape, bolt positions, cell arrangement) and electrochemical state data (internal resistance, phase angle) provides dual basis for disassembly path planning, avoiding the one-sidedness caused by single data.

[0076] Data acquisition process: The battery casing is scanned using a 3D laser scanner (such as FAROScanArm) to generate point cloud data and extract the coordinates of the casing point cloud; the bolt position coordinates and the topological information of the cell spatial arrangement (such as the cell row and column distribution and stacking relationship) are obtained through industrial CT scanning or matching of the CAD model before disassembly.

[0077] Secondly, based on the structural topology data, the shell point cloud coordinates, multiple bolt position coordinates, and cell spatial arrangement topology information are obtained. Based on the shell point cloud coordinates and cell spatial arrangement topology information, a basic disassembly path is obtained. Thus, by analyzing the shell fragility coefficient and cell spacing, the disassembly area is divided into "high fragility zone" and "high risk zone", achieving preliminary path optimization, reducing the risk of shell breakage and cell squeezing, and merging the two types of risk paths to avoid path deviation caused by a single factor, such as simultaneously avoiding the weak points of the shell and the dense areas of the cells, thus improving the rationality of the path.

[0078] For example: Set the surface area of ​​the shell to a preset value (e.g., 50cm²). 2 The region is divided into multiple sub-regions. The average curvature of each region is calculated (by fitting the surface equation to the point cloud data and taking the derivative to obtain the curvature value), and a shell fragility coefficient threshold is set (e.g., curvature > 0.2 mm). -1Areas below the threshold are defined as high-vulnerability zones and should be detoured during route planning.

[0079] Next, the connection stiffness coefficient of the new energy vehicle battery is obtained based on the coordinates of multiple bolt positions. The basic disassembly path is refitted and rearranged based on the connection stiffness coefficient to obtain the first optimized disassembly path. In this way, the structural strength of each part of the battery pack is evaluated by bolt spacing and connection stiffness coefficient. The weak stiffness area is disassembled first to avoid overall structural instability. Furthermore, the stress in the stiffness loss area (loose bolts) is released in advance to reduce component splashing or cell damage caused by sudden structural fracture during disassembly.

[0080] Then, based on the electrochemical state data, the internal resistance spectra of multiple individual cells are obtained, and multiple phase angle abrupt change points of the internal resistance spectra are obtained. In this way, through internal resistance spectrum analysis, internal defects of the cell (such as active material shedding and electrolyte reduction) can be located without disassembly, improving the efficiency of abnormal cell identification. Moreover, the phase angle abrupt change points can reflect the changes in the cell interface impedance, allowing for early detection of sub-healthy cells and avoiding thermal runaway caused by cell damage during disassembly.

[0081] Subsequently, multiple active anomaly points are obtained based on the multiple phase angle mutation points. The first optimized disassembly path is rearranged based on the multiple active anomaly points to obtain the second optimized disassembly path. In this way, the phase angle mutation points are mapped to the point cloud coordinates of the battery casing to form active anomaly points in three-dimensional space, so as to achieve precise avoidance of the disassembly path. Furthermore, based on the first optimized path, it is further optimized by combining the cell status data to ensure that the path avoids both structural and electrical risks.

[0082] Finally, the new energy vehicle battery is disassembled according to the second optimized disassembly path. This method uses visual sensors to match point cloud coordinates for positioning, adapting to the size deviations of different batches of batteries. It first handles structural connections (bolt removal) and then avoids abnormal cells, achieving a safe process of "stabilizing the structure first and then controlling risks." This avoids the blindness of traditional mechanical disassembly that relies solely on fixed paths. It can identify high-risk areas (such as weak areas of the casing and areas where cell spacing is too close) and abnormal cells in advance, reducing the risk of short circuits and thermal runaway during disassembly. Furthermore, the basic path is optimized twice by using connection stiffness coefficients (bolt spacing analysis) and abnormal cell locations (cell status analysis) to ensure that the disassembly path avoids structural weak points and electrical abnormal areas, improving disassembly efficiency and battery recycling rate.

[0083] In one embodiment, step S2, which involves obtaining the basic disassembly path based on the shell point cloud coordinates and the cell spatial arrangement topology information, includes:

[0084] S201. Obtain the surface area of ​​the casing of the new energy vehicle battery, and divide the surface area of ​​the casing according to a preset value to obtain multiple segmented area regions.

[0085] S202. Obtain multiple shell curvature features corresponding to the areas of multiple segmented regions based on the shell point cloud coordinates, and obtain multiple shell fragility coefficients based on the multiple shell curvature features.

[0086] S203. Sequentially determine whether the multiple shell fragility coefficients are less than a preset coefficient;

[0087] If the shell fragility coefficient is less than the preset coefficient, the area of ​​the segmented region corresponding to the shell fragility coefficient less than the preset coefficient is defined as a high fragility region, and a high fragility region path is established based on multiple high fragility regions.

[0088] S204. Obtain multiple cell position coordinates based on the cell spatial arrangement topology information, and obtain multiple adjacent distances between adjacent cells based on the multiple cell position coordinates;

[0089] S205. Sequentially determine whether the multiple adjacent distances are less than a preset adjacent distance;

[0090] If the area is less than a preset coefficient, the area of ​​the segmented region corresponding to the distance between adjacent regions is defined as a high-risk area, and a high-risk area path is established based on the multiple high-risk areas.

[0091] S206. Dynamically couple the high-risk area path and the high-vulnerability area path in adjacent regions to obtain a merged path, and fit and optimize the preset disassembly path according to the merged path to obtain a basic disassembly path.

[0092] As described in steps S201-S206 above, the present invention first obtains the surface area of ​​the casing of the new energy vehicle battery, and then divides the surface area of ​​the casing according to a preset value to obtain multiple segmented area regions. In this way, the complex casing surface is transformed into quantifiable sub-units through region segmentation, which facilitates subsequent curvature calculation and vulnerability assessment, avoids the ambiguity of the overall analysis, and the preset segmentation value can be flexibly adjusted (such as by area or curvature density) to adapt to the disassembly requirements of battery casings of different shapes such as square, round, and irregular shapes.

[0093] Next, multiple shell curvature features corresponding to the areas of the segmented regions are obtained based on the shell point cloud coordinates, and multiple shell fragility coefficients are obtained based on the multiple shell curvature features. In this way, the curvature is numerically characterized to represent the steepness of the shell surface. The greater the curvature (such as acute corners), the easier the shell is to break during disassembly, providing a scientific basis for path avoidance. At the same time, the fragility coefficient threshold can be adjusted according to the shell material characteristics (such as aluminum alloy, plastic) to improve the universality of the method.

[0094] Curvature calculation process: For the point cloud data in each segmented region, the moving least squares method is used to fit the local surface, calculate the Gaussian curvature and average curvature of each point, and take the average curvature of all points in the region as the shell curvature feature of the region.

[0095] Then, it is determined whether the multiple shell fragility coefficients are less than a preset coefficient. If the shell fragility coefficient is less than the preset coefficient, the area of ​​the segmented region corresponding to the shell fragility coefficient is defined as a high fragility area. A high fragility area path is established based on the multiple high fragility areas. In this way, high fragility areas are automatically filtered by preset thresholds to avoid human subjective misjudgment, improve the efficiency and consistency of risk identification, and establish independent path labels (such as "prohibited cutting area") for high fragility areas to ensure that disassembly tools prioritize avoiding the area and reduce the risk of shell breakage.

[0096] Next, multiple cell position coordinates are obtained based on the cell spatial arrangement topology information, and multiple adjacent distances between adjacent cells are obtained based on the multiple cell position coordinates. In this way, through the quantitative analysis of cell spacing, the risk of short circuit caused by physical contact or being too close is identified, and electrical safety is incorporated into the path planning dimension. It is applicable to cells arranged in series, matrix, or irregularly. Only the topology coordinates need to be input to automatically calculate the spacing without manual measurement. The adjacent distance is calculated using the Euclidean distance formula.

[0097] Next, it is determined whether multiple adjacent distances are less than a preset adjacent distance. If they are less than a preset coefficient, the area of ​​the segmented region corresponding to the smaller adjacent distance is defined as a high-risk area. A high-risk area path is established based on multiple high-risk areas. This maps the internal risks of the cells being too close together to the surface area of ​​the casing, so that the disassembly path can intuitively avoid the corresponding external operation area. At the same time, the preset adjacent distance threshold can be adjusted according to the cell type (such as ternary lithium, lithium iron phosphate). For example, a stricter threshold is used for high energy density cells. For example, if the cell spacing in the middle of a battery pack is only 3mm, the corresponding central area at the top of the casing is marked as a high-risk area. The basic path is adjusted to disassemble from the two side edges, and the robotic arm clamp is inserted from the side to avoid the central area at the top.

[0098] Finally, the high-risk area path and the high-vulnerability area path are dynamically coupled in adjacent regions to obtain a merged path. The preset dismantling path is then fitted and optimized based on the merged path to obtain a basic dismantling path. This merges the high-vulnerability area and high-risk area paths to generate a globally optimal path that avoids both structural weak points and electrical risk points. For example, in the shell 3D model, the red high-vulnerability area mesh is superimposed with the yellow high-risk area mesh to generate a composite risk map. The white area is the safe and operable area, avoiding the limitations of single risk optimization. At the same time, under the premise of avoiding risks, the dismantling efficiency is ensured by the path fitting algorithm (such as the shortest path) to avoid excessive detours that increase the time consumption. The adjacent region dynamic coupling process involves discretizing the safe area into nodes, and the lines between nodes are passable edges (which must meet the safe distance from the risk area). The preset path fitting compares the coupled path with the preset dismantling path (such as along the module seam line), and the inflection point is smoothly adjusted by Bézier curves to ensure smooth movement of the robotic arm and reduce start-up and shutdown losses.

[0099] Furthermore, through the process of "shell surface area segmentation → curvature quantification of vulnerability → cell spacing risk identification → path dynamic coupling", multi-level risk management from physical structure to electrical safety is achieved, transforming the traditional experience-driven disassembly model into data-driven scientific decision-making.

[0100] In one embodiment, step S3, which involves obtaining the connection stiffness coefficient of the new energy vehicle battery based on the coordinates of multiple bolt positions, and refitting and arranging the basic disassembly path based on the connection stiffness coefficient to obtain a first optimized disassembly path, includes:

[0101] S301. Establish a Delaunay triangulation of bolt positions based on multiple bolt position coordinates, and calculate the spacing between multiple adjacent bolts based on the Delaunay triangulation of bolt positions.

[0102] S302. Sequentially determine whether the spacing between multiple adjacent bolts is less than the preset spacing between adjacent bolts;

[0103] If the distance between adjacent bolts is less than the preset distance between adjacent bolts, it is determined that there is a stiffness loss in the distance between adjacent bolts, and multiple adjacent bolt distances that are less than the preset distance between adjacent bolts are marked to obtain multiple marked bolt distances.

[0104] S303. The spacing between the multiple marked bolts is sequentially compared with the preset spacing between adjacent bolts to obtain multiple distance ratio values;

[0105] S304. Calculate the connection stiffness coefficient (the stiffness coefficient is inversely proportional to the distance ratio) based on the distance ratio, and refit and arrange the corresponding marked bolt spacing and the basic disassembly path according to the connection stiffness coefficient to obtain the first optimized disassembly path.

[0106] As described in steps S301-S304 above, the present invention first establishes a Delaunay triangulation of bolt positions based on multiple bolt position coordinates, and calculates the spacing between multiple adjacent bolts based on the Delaunay triangulation of bolt positions. In this way, the spatial relationship of bolt positions is constructed through the Delaunay triangulation, which intuitively presents the distribution density and connection relationship of the bolt group, providing a geometric basis for stiffness analysis. At the same time, by utilizing the natural adjacency of the triangulation, it is not necessary to calculate all bolt spacings in the full combination; only the adjacent bolts corresponding to the edges of the triangulation need to be calculated, which greatly reduces the amount of calculation.

[0107] For example: Input the bolt position coordinates (two-dimensional or three-dimensional coordinates) obtained in step S1, and use the Delaunay triangulation algorithm (such as the Bowyer-Watson algorithm) to generate a triangulation network, ensuring that the circumcircle of any triangle does not contain other bolt points, and the edges of the triangulation network are the connecting edges of adjacent bolts, with each edge corresponding to a pair of adjacent bolts (such as bolt A and bolt B).

[0108] Then, it is determined whether the spacing between multiple adjacent bolts is less than the preset spacing between adjacent bolts. If it is less than the preset spacing between adjacent bolts, it is determined that there is a stiffness loss in the spacing between adjacent bolts. Multiple adjacent bolt spacings that are less than the preset spacing between adjacent bolts are marked to obtain multiple marked bolt spacings. In this way, by using a preset spacing threshold (such as 80% of the design value), the bolt spacing is converted into a quantitative indicator of stiffness loss, avoiding subjective judgment. Moreover, by simply comparing the measured spacing with the threshold, bolt pairs with stiffness risks can be identified in batches, improving analysis efficiency.

[0109] Secondly, the spacing of the marked bolts is compared with the preset spacing of adjacent bolts in sequence to obtain multiple distance ratio values. In this way, the stiffness loss is quantified into a value between 0 and 1 by the distance ratio value (actual spacing / preset spacing), which is convenient for subsequent sorting according to risk level and eliminates the influence of the difference in the design spacing of different bolts, so that the stiffness loss of bolts in different positions is comparable (e.g., bolts with a design spacing of 20mm and 15mm can be uniformly measured by the ratio value).

[0110] Finally, the connection stiffness coefficient (inversely proportional to the distance ratio) is calculated based on the distance ratio. The corresponding marked bolt spacing and the basic disassembly path are then refitted and rearranged according to the connection stiffness coefficient to obtain the first optimized disassembly path. This transforms the structural importance of the bolts into path priority through the stiffness coefficient (inversely proportional to the ratio), achieving a strategy of "the more severe the stiffness loss, the higher the disassembly priority." Simultaneously, the dynamic path fitting based on the stiffness coefficient avoids stress concentration caused by traditional fixed-sequence disassembly, improving the structural stability of the disassembly process. Furthermore, through the process of "bolt topology modeling → spacing threshold screening → stiffness quantification and grading → dynamic path fitting," the bolt connection stiffness is deeply bound to the disassembly path, achieving a technological upgrade from "empirical sequential disassembly" to "structural stress release."

[0111] In one embodiment, step S4, which involves obtaining the internal resistance spectra of multiple individual cells based on the electrochemical state data and obtaining multiple phase angle abrupt change points of the internal resistance spectra based on the multiple internal resistance spectra, includes:

[0112] S401. Obtain multiple individual cell positions based on the electrochemical state data, apply a preset AC excitation signal (0.1Hz to 1000Hz) to the multiple individual cell positions and measure multiple voltage response signals and multiple current response signals, and use the multiple voltage response signals and multiple current response signals as the internal resistance spectrum of the individual cell.

[0113] S402. Obtain multiple complex impedances at multiple frequency points based on the internal resistance spectrum of multiple individual cells;

[0114] S403. Obtain multiple phase angles based on the multiple complex impedances, and generate a phase angle-frequency curve based on the multiple phase angles and a preset frequency.

[0115] S404. Obtain multiple phase angle change rates based on the phase angle-frequency curve and the preset phase angle-frequency curve;

[0116] S405. Determine whether the multiple phase angle change rates are less than a preset phase angle change rate;

[0117] If the change rate is less than a preset phase angle change rate, then the single cell corresponding to the phase angle change rate that is less than the preset phase angle change rate is determined to have a phase angle abrupt change point, and then multiple phase angle abrupt change points are obtained in sequence according to multiple phase angle change rates that are less than the preset phase angle change rate.

[0118] As described in steps S401-S405 above, the present invention first obtains multiple individual cell locations based on the electrochemical state data, applies a preset AC excitation signal (0.1Hz to 1000Hz) to the multiple individual cell locations, and measures multiple voltage response signals and multiple current response signals. The multiple voltage response signals and multiple current response signals are then used as the internal resistance spectrum of the individual cell. In this way, by obtaining the internal resistance spectrum through the AC excitation signal, the internal state of the individual cell can be evaluated without disassembling the battery, avoiding the waste of resources caused by traditional destructive testing. Moreover, the frequency range of the excitation signal is 0.1Hz to 1000Hz, covering the impedance characteristics of different levels of the cell (such as high frequency corresponding to electrolyte impedance, low frequency corresponding to active material impedance), realizing full-band health assessment.

[0119] Then, based on the internal resistance spectrum of multiple individual cells, multiple complex impedances at multiple frequency points are obtained. These complex impedances contain information on resistance (real part) and reactance (imaginary part), which can separate the ohmic impedance and polarization impedance of the cell, accurately locate resistive faults (such as poor contact) or reactive faults (such as capacitor aging), and combine the changing trends of complex impedances at different frequency points to construct an equivalent circuit model inside the cell (such as the Randles model) and quantify the parameters of each component (such as charge transfer resistance and double-layer capacitance).

[0120] Secondly, multiple phase angles are obtained based on multiple complex impedances, and a phase angle-frequency curve is generated based on multiple phase angles and a preset frequency. In this way, the phase angle-frequency curve intuitively reflects the trend of the polarization characteristics inside the cell with the frequency. Abnormal curve shapes (such as peak shift, slope change) can be directly associated with specific failure modes. Moreover, by comparing with the standard curve of a healthy cell of the same model, the degree of deviation can be quickly identified, avoiding misjudgment of a single data point.

[0121] Next, multiple phase angle change rates are obtained based on the phase angle-frequency curve and the preset phase angle-frequency curve. These change rates reflect the rate of change of the phase angle with frequency, which can identify local anomalies in the curve (such as sudden changes in the slope of a certain frequency range), and are more accurate than simply comparing numerical values. At the same time, the phase angle offset speed is quantified by the change rate values, which makes it easier to set thresholds to distinguish between minor anomalies and serious faults.

[0122] Finally, it is determined whether the multiple phase angle change rates are less than a preset phase angle change rate. If they are less than the preset phase angle change rate, it is determined that the single cell corresponding to the phase angle change rate less than the preset phase angle change rate has a phase angle mutation point. And the corresponding multiple phase angle mutation points are obtained sequentially according to the multiple phase angle change rates less than the preset phase angle change rate. In this way, by comparing the change rate with a preset threshold, the phase angle mutation at a specific frequency point can be locked, realizing the precise location of the faulty cell (such as a failure of a specific physical process corresponding to a certain frequency point). And according to the number and severity of mutation points, the cell is risk-classified (such as a single mutation point is low risk, multiple mutation points are high risk), guiding the disassembly priority. Then, through the process of "wideband excitation - complex impedance analysis - phase angle dynamic analysis - mutation point location", the microscopic failure inside the cell (such as electrolyte decay, interface impedance increase) is converted into detectable macroscopic electrical signal characteristics, realizing the whole chain control of "non-destructive testing - precise diagnosis - risk classification".

[0123] In one embodiment, step S5, which involves obtaining multiple active anomaly points based on multiple phase angle abrupt change points and rearranging the first optimized disassembly path based on the multiple active anomaly points to obtain a second optimized disassembly path, includes:

[0124] S501. Obtain multiple phase angle offsets based on multiple phase angle abrupt change points and preset phase angle points;

[0125] S502. Identify the abnormal points of multiple first single cells whose phase angle offset is greater than a preset phase angle offset, and regard the abnormal points of multiple first single cells as multiple active abnormal points.

[0126] S503. Associate the multiple active anomaly points with the point cloud coordinates of the outer shell of the new energy vehicle to obtain the coordinates of multiple active anomaly points;

[0127] S504. Map and merge the coordinates of multiple active anomaly points with the first optimized disassembly path to obtain the second optimized disassembly path.

[0128] As described in steps S501-S504 above, the present invention first obtains multiple phase angle offsets based on multiple phase angle abrupt change points and preset phase angle points. In this way, the abnormal activity of the battery cell is transformed from "qualitative judgment" to "quantitative analysis" by using the phase angle offset (the difference between the measured value and the standard value), which facilitates risk comparison between different battery cells. At the same time, the failure type can be preliminarily judged by combining the offset direction (positive or negative). For example, a positive offset may correspond to a decrease in capacitive impedance (such as a decrease in double-layer capacitance), and a negative offset may correspond to an increase in resistive impedance (such as an increase in contact resistance).

[0129] Then, abnormal points of multiple first single cell cells with phase angle offsets greater than a preset phase angle offset are identified. These abnormal points of multiple first single cell cells are identified as multiple active abnormal points. In this way, active abnormal single cells are screened out by offset threshold (e.g., >10°), avoiding misjudging normal cells as risk points, improving positioning accuracy. Furthermore, based on the magnitude and direction of the offset, abnormal points can be preliminarily classified and guide subsequent disassembly strategies.

[0130] Secondly, multiple active anomaly points are associated with the point cloud coordinates of the outer shell of the new energy vehicle to obtain multiple active anomaly point coordinates. This maps the active anomalies inside the cell to the surface of the battery shell, allowing operators or automated systems to intuitively identify risk areas through shell markings, reducing the difficulty of disassembly planning. At the same time, the abstract electrical anomalies are transformed into three-dimensional spatial coordinates, which facilitates subsequent geometric calculations (such as distance calculation and area avoidance) with the disassembly path, thereby achieving physical-level risk avoidance.

[0131] Finally, the coordinates of multiple active anomaly points are mapped and merged with the first optimized disassembly path to obtain the second optimized disassembly path. This combines the structural risk path (first optimized path) with the electrical risk points (active anomaly points) to generate a composite path that avoids both mechanical damage and electrical risks, improving disassembly safety. At the same time, based on the real-time updated active anomaly point data, the generated first optimized path is corrected online to adapt to new risks that may be exposed during disassembly (such as the discovery of anomalies in adjacent cells during disassembly). Then, through the process of "phase angle offset quantization → anomaly point location → spatial coordinate mapping → path dynamic avoidance", the electrical active anomalies inside the cell are transformed into operable spatial avoidance commands, realizing cross-dimensional linkage from "cell status assessment" to "physical path optimization".

[0132] In one embodiment, step S6, which involves disassembling the new energy vehicle battery according to the second optimized disassembly path, includes:

[0133] S601. Input the second optimized disassembly path into the preset PLC control system of the maintenance device. The preset PLC control system is applied to the battery disassembly device, and the battery disassembly device includes a clamping mechanism. The clamping mechanism is started according to the preset PLC control system, and the preset vision sensor of the clamping mechanism scans the battery pack shell and matches the shell point cloud coordinates in the path to realize the positioning of the device and the battery pack.

[0134] S602. The clamping mechanism separates the new energy vehicle battery according to the bolt position coordinates to obtain a separation structure;

[0135] S603, the clamping mechanism avoids disassembling individual cells in the new energy vehicle battery according to multiple active abnormal points.

[0136] As described in steps S601-S603 above, the present invention first inputs the second optimized disassembly path into the preset PLC control system of the maintenance device. The preset PLC control system is applied to the battery disassembly device, and the battery disassembly device includes a clamping mechanism. The clamping mechanism is activated according to the preset PLC control system, and the preset vision sensor of the clamping mechanism scans the battery pack shell and matches the shell point cloud coordinates in the path to achieve the positioning of the device and the battery pack. In this way, through the linkage of the PLC control system and the vision sensor, the battery pack and the disassembly device are positioned at the sub-millimeter level, eliminating manual alignment errors and improving disassembly consistency. At the same time, the preset PLC system can read the second optimized path data in real time and automatically adjust the movement trajectory of the robotic arm to accommodate the path differences of different battery pack models.

[0137] Secondly, the clamping mechanism separates the new energy vehicle battery according to the bolt position coordinates to obtain a separated structure. This first removes the bolts to release structural stress, and then separates the shell and module, avoiding structural cracking caused by traditional violent disassembly. It is especially suitable for battery packs with uneven connection stiffness. At the same time, the clamping mechanism integrates torque control and cutting functions, and can automatically switch tool heads according to the bolt type (such as Torx bolts and hex socket head cap screws) to improve disassembly efficiency.

[0138] Finally, the clamping mechanism avoids disassembling individual cells in the new energy vehicle battery based on multiple active abnormality points. In this way, the coordinates of the active abnormality points guide the robotic arm to avoid movement, preventing the disassembly tools from directly contacting high-risk cells and reducing the risk of internal short circuits caused by squeezing or puncturing. At the same time, it automatically switches the disassembly mode (such as gentle clamping or frozen disassembly) for abnormality points of different risk levels, realizing a refined handling of "one strategy for one type of abnormality". This can avoid relying on preset paths to cut the outer shell, and also avoid the increased disassembly risk due to battery damage, thereby improving the battery pack recycling rate.

[0139] This application also provides a dismantling system for new energy vehicle batteries, including:

[0140] The first acquisition module is used to acquire the bulk data of new energy vehicle batteries, wherein the bulk data includes structural topology data and electrochemical state data;

[0141] The second acquisition module is used to acquire the shell point cloud coordinates, multiple bolt position coordinates and cell space arrangement topology information based on the structural topology data, and to acquire the basic disassembly path based on the shell point cloud coordinates and cell space arrangement topology information.

[0142] The third acquisition module is used to acquire the connection stiffness coefficient of the new energy vehicle battery based on the coordinates of multiple bolt positions, and to refit and rearrange the basic disassembly path based on the connection stiffness coefficient to obtain the first optimized disassembly path.

[0143] The fourth acquisition module is used to acquire the internal resistance spectrum of multiple individual cells based on the electrochemical state data, and to acquire multiple phase angle abrupt change points of the internal resistance spectrum based on the multiple internal resistance spectra.

[0144] The fifth acquisition module is used to acquire multiple active abnormal points based on multiple phase angle mutation points, and rearrange the first optimized disassembly path based on the multiple active abnormal points to obtain the second optimized disassembly path.

[0145] The disassembly module is used to disassemble the new energy vehicle battery according to the second optimized disassembly path.

[0146] In one embodiment, the second acquisition module includes:

[0147] The first acquisition unit is used to acquire the surface area of ​​the casing of the new energy vehicle battery, and to divide the surface area of ​​the casing according to a preset value to obtain multiple segmented area regions.

[0148] The second acquisition unit is used to acquire multiple shell curvature features corresponding to the areas of multiple segmented regions based on the shell point cloud coordinates, and to acquire multiple shell fragility coefficients based on the multiple shell curvature features.

[0149] The first judgment unit is used to sequentially determine whether the multiple shell fragility coefficients are less than a preset coefficient;

[0150] If the shell fragility coefficient is less than the preset coefficient, the area of ​​the segmented region corresponding to the shell fragility coefficient less than the preset coefficient is defined as a high fragility region, and a high fragility region path is established based on multiple high fragility regions.

[0151] The third acquisition unit is used to acquire multiple cell position coordinates based on the cell spatial arrangement topology information, and to acquire multiple adjacent distances between adjacent cells based on the multiple cell position coordinates.

[0152] The second judgment unit is used to sequentially judge whether the plurality of adjacent distances are less than a preset adjacent distance;

[0153] If the area is less than a preset coefficient, the area of ​​the segmented region corresponding to the distance between adjacent regions is defined as a high-risk area, and a high-risk area path is established based on the multiple high-risk areas.

[0154] The dynamic coupling unit is used to dynamically couple the high-risk area path and the high-vulnerability area path in adjacent regions to obtain a merged path, and to fit and optimize the preset disassembly path based on the merged path to obtain a basic disassembly path.

[0155] This application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0156] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0157] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in this application and in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0158] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0159] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for disassembling a new energy vehicle battery, characterized in that, include: Acquire bulk data of new energy vehicle batteries, including structural topology data and electrochemical state data; Based on the structural topology data, obtain the shell point cloud coordinates, multiple bolt position coordinates, and cell space arrangement topology information, and obtain the basic disassembly path based on the shell point cloud coordinates and cell space arrangement topology information; The connection stiffness coefficient of the new energy vehicle battery is obtained based on the coordinates of multiple bolt positions. The basic disassembly path is then refitted and rearranged based on the connection stiffness coefficient to obtain the first optimized disassembly path. The internal resistance spectra of multiple individual cells are obtained based on the electrochemical state data, and multiple phase angle abrupt change points of the internal resistance spectra are obtained based on the multiple internal resistance spectra. Multiple active anomaly points are obtained based on multiple phase angle mutation points, and the first optimized disassembly path is rearranged based on the multiple active anomaly points to obtain the second optimized disassembly path; The new energy vehicle battery is disassembled according to the second optimized disassembly path.

2. The method for disassembling a new energy vehicle battery according to claim 1, characterized in that, The step of obtaining the basic disassembly path based on the shell point cloud coordinates and the cell spatial arrangement topology information includes: The surface area of ​​the battery casing of a new energy vehicle is obtained, and the surface area of ​​the casing is divided according to a preset value to obtain multiple segmented areas. Based on the shell point cloud coordinates, obtain multiple shell curvature features corresponding to the areas of the segmented regions, and obtain multiple shell fragility coefficients based on the multiple shell curvature features; Sequentially determine whether the multiple shell fragility coefficients are less than a preset coefficient; If the shell fragility coefficient is less than the preset coefficient, the area of ​​the segmented region corresponding to the shell fragility coefficient less than the preset coefficient is defined as a high fragility region, and a high fragility region path is established based on multiple high fragility regions. Multiple cell position coordinates are obtained based on the cell spatial arrangement topology information, and multiple adjacent distances between adjacent cells are obtained based on the multiple cell position coordinates; Sequentially determine whether multiple adjacent distances are less than a preset adjacent distance; If the area is less than a preset coefficient, the area of ​​the segmented region corresponding to the distance between adjacent regions is defined as a high-risk area, and a high-risk area path is established based on the multiple high-risk areas. The high-risk area path and the high-vulnerability area path are dynamically coupled in adjacent regions to obtain a merged path. The preset disassembly path is then fitted and optimized based on the merged path to obtain a basic disassembly path.

3. The method for disassembling a new energy vehicle battery according to claim 1, characterized in that, The step of obtaining the connection stiffness coefficient of the new energy vehicle battery based on the coordinates of multiple bolt positions, and refitting and arranging the basic disassembly path based on the connection stiffness coefficient to obtain the first optimized disassembly path includes: A Delaunay triangulation of bolt positions is established based on multiple bolt position coordinates, and the spacing between multiple adjacent bolts is calculated based on the Delaunay triangulation of bolt positions. Sequentially determine whether the spacing between multiple adjacent bolts is less than a preset spacing between adjacent bolts; If the distance between adjacent bolts is less than the preset distance between adjacent bolts, it is determined that there is a stiffness loss in the distance between adjacent bolts, and multiple adjacent bolt distances that are less than the preset distance between adjacent bolts are marked to obtain multiple marked bolt distances. The spacing between the multiple marked bolts is sequentially compared with the preset spacing between adjacent bolts to obtain multiple distance ratio values; The connection stiffness coefficient is calculated based on the distance ratio, and the corresponding marked bolt spacing is refitted and rearranged with the basic disassembly path according to the connection stiffness coefficient to obtain the first optimized disassembly path.

4. The method for disassembling a new energy vehicle battery according to claim 1, characterized in that, The step of obtaining the internal resistance spectra of multiple individual cells based on the electrochemical state data, and obtaining multiple phase angle abrupt change points of the internal resistance spectra based on the multiple internal resistance spectra, includes: Multiple individual cell positions are obtained based on the electrochemical state data, and a preset AC excitation signal is applied to the multiple individual cell positions to measure multiple voltage response signals and multiple current response signals. The multiple voltage response signals and multiple current response signals are used as the internal resistance spectrum of the individual cell. Multiple complex impedances at multiple frequency points are obtained based on the internal resistance spectra of multiple individual battery cells; Multiple phase angles are obtained based on the multiple complex impedances, and a phase angle-frequency curve is generated based on the multiple phase angles and a preset frequency. Multiple phase angle change rates are obtained based on the phase angle-frequency curve and the preset phase angle-frequency curve; Determine whether the multiple phase angle change rates are less than a preset phase angle change rate; If the change rate is less than a preset phase angle change rate, then the single cell corresponding to the phase angle change rate that is less than the preset phase angle change rate is determined to have a phase angle abrupt change point, and then multiple phase angle abrupt change points are obtained in sequence according to multiple phase angle change rates that are less than the preset phase angle change rate.

5. The method for disassembling a new energy vehicle battery according to claim 1, characterized in that, The step of obtaining multiple active anomaly points based on multiple phase angle abrupt change points, and rearranging the first optimized disassembly path based on the multiple active anomaly points to obtain the second optimized disassembly path includes: Multiple phase angle offsets are obtained based on multiple phase angle abrupt change points and preset phase angle points; Identify a plurality of abnormal points in a plurality of first single-cell batteries whose phase angle offsets are greater than a preset phase angle offset, and treat the anomalous points of the plurality of first single-cell batteries as a plurality of active abnormal points. The coordinates of multiple active anomaly points are obtained by associating them with the point cloud coordinates of the outer shell of the new energy vehicle. The coordinates of multiple active anomaly points are mapped and merged with the first optimized disassembly path to obtain the second optimized disassembly path.

6. The method for disassembling a new energy vehicle battery according to claim 1, characterized in that, The step of disassembling the new energy vehicle battery according to the second optimized disassembly path includes: The second optimized disassembly path is input into the preset PLC control system of the maintenance device. The preset PLC control system is applied to the battery disassembly device, and the battery disassembly device includes a clamping mechanism. The clamping mechanism is activated according to the preset PLC control system, and the preset vision sensor of the clamping mechanism scans the battery pack shell and matches the shell point cloud coordinates in the path to realize the positioning of the device and the battery pack. The clamping mechanism separates the new energy vehicle battery according to the bolt position coordinates to obtain a separation structure; The clamping mechanism avoids disassembling individual cells in the new energy vehicle battery based on multiple abnormal activity points.

7. A dismantling system for new energy vehicle batteries, characterized in that, include: The first acquisition module is used to acquire the bulk data of new energy vehicle batteries, wherein the bulk data includes structural topology data and electrochemical state data; The second acquisition module is used to acquire the shell point cloud coordinates, multiple bolt position coordinates and cell space arrangement topology information based on the structural topology data, and to acquire the basic disassembly path based on the shell point cloud coordinates and cell space arrangement topology information. The third acquisition module is used to acquire the connection stiffness coefficient of the new energy vehicle battery based on the coordinates of multiple bolt positions, and to refit and rearrange the basic disassembly path based on the connection stiffness coefficient to obtain the first optimized disassembly path. The fourth acquisition module is used to acquire the internal resistance spectrum of multiple individual cells based on the electrochemical state data, and to acquire multiple phase angle abrupt change points of the internal resistance spectrum based on the multiple internal resistance spectra. The fifth acquisition module is used to acquire multiple active abnormal points based on multiple phase angle mutation points, and rearrange the first optimized disassembly path based on the multiple active abnormal points to obtain the second optimized disassembly path. The disassembly module is used to disassemble the new energy vehicle battery according to the second optimized disassembly path.

8. The dismantling system for a new energy vehicle battery according to claim 7, characterized in that, The second acquisition module includes: The first acquisition unit is used to acquire the surface area of ​​the casing of the new energy vehicle battery, and to divide the surface area of ​​the casing according to a preset value to obtain multiple segmented area regions. The second acquisition unit is used to acquire multiple shell curvature features corresponding to the areas of multiple segmented regions based on the shell point cloud coordinates, and to acquire multiple shell fragility coefficients based on the multiple shell curvature features. The first judgment unit is used to sequentially determine whether the multiple shell fragility coefficients are less than a preset coefficient; If the shell fragility coefficient is less than the preset coefficient, the area of ​​the segmented region corresponding to the shell fragility coefficient less than the preset coefficient is defined as a high fragility region, and a high fragility region path is established based on multiple high fragility regions. The third acquisition unit is used to acquire multiple cell position coordinates based on the cell spatial arrangement topology information, and to acquire multiple adjacent distances between adjacent cells based on the multiple cell position coordinates. The second judgment unit is used to sequentially judge whether the plurality of adjacent distances are less than a preset adjacent distance; If the area is less than a preset coefficient, the area of ​​the segmented region corresponding to the distance between adjacent regions is defined as a high-risk area, and a high-risk area path is established based on the multiple high-risk areas. The dynamic coupling unit is used to dynamically couple the high-risk area path and the high-vulnerability area path in adjacent regions to obtain a merged path, and to fit and optimize the preset disassembly path based on the merged path to obtain a basic disassembly path.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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