Disassembling system and method for new energy automobile battery

Abstract

The invention relates to the technical field of new energy automobile batteries, and particularly discloses a system and a method for disassembling a new energy automobile battery. According to the method, battery structure topological data are obtained through three-dimensional laser scanning and industrial CT, electrochemical state data are synchronously collected, firstly, a basic disassembling path is planned based on a shell fragility coefficient and a battery cell distance, secondly, the path is optimized according to a bolt connection rigidity coefficient, a rigidity weak area is preferentially disassembled to release stress, and finally, the reliability of the battery is improved. The method comprises the following steps: firstly removing a bolt stable structure, then positioning an active abnormal cell through an internal resistance spectrum phase angle abrupt change point, mapping the active abnormal cell to a three-dimensional space to form a risk point, and finally fusing the structure risk and the electrical risk to generate a dual optimization path: firstly removing the bolt stable structure, and then avoiding an abnormal cell area. The risks of shell breakage, cell extrusion and thermal runaway are recognized in advance, and the disassembly safety and the recovery rate are improved through two-time path optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicle batteries, and in particular to a disassembly system and method for a new energy vehicle battery. Background Art

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

[0003] Existing new energy vehicle batteries are usually disassembled using a mechanical disassembly mode, and during the battery disassembly process, the outer shell is usually only cut along a preset path. This will increase the risk of disassembly due to battery damage, and thus lead to a reduced battery pack recycling rate. Therefore, a new energy vehicle battery disassembly method is needed to solve the above problems. Summary of the Invention

[0004] The object of the present invention is to provide a disassembly system and method for new energy vehicle batteries to solve the technical problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for disassembling a new energy vehicle battery, comprising:

[0007] Obtaining physical data of new energy vehicle batteries, wherein the physical data includes structural topology data and electrochemical state data;

[0008] Obtaining the shell point cloud coordinates, multiple bolt position coordinates, and battery cell spatial arrangement topology information according to the structural topology data, and obtaining a basic disassembly path according to the shell point cloud coordinates and battery cell spatial arrangement topology information;

[0009] Obtaining a connection stiffness coefficient of the new energy vehicle battery according to the plurality of 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] Acquire internal resistance spectra of a plurality of battery cells according to the electrochemical state data, and acquire a plurality of phase angle mutation points of the internal resistance spectra according to the plurality of internal resistance spectra;

[0011] Acquire multiple active abnormal points according to the multiple phase angle mutation points, and rearrange the first optimized disassembly path according to the multiple active abnormal points to obtain a second optimized disassembly path;

[0012] The new energy vehicle battery is disassembled according to the second optimized disassembly path.

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

[0014] Obtaining a shell surface area of ​​a new energy vehicle battery, and dividing the shell surface area according to a preset value to obtain a plurality of divided region areas;

[0015] Acquire multiple shell curvature features corresponding to the areas of the multiple segmented regions according to the shell point cloud coordinates, and acquire multiple shell fragility coefficients according to the multiple shell curvature features;

[0016] determining in sequence whether the plurality of shell fragility coefficients are less than a preset coefficient;

[0017] If the shell fragility coefficient is less than a preset coefficient, the segmented area corresponding to the shell fragility coefficient less than the preset coefficient is defined as a high-fragility zone, and a high-fragility zone path is established based on the multiple high-fragility zones;

[0018] Acquire a plurality of cell position coordinates according to the cell spatial arrangement topology information, and acquire a plurality of adjacent distances between adjacent cell positions according to the plurality of cell position coordinates;

[0019] sequentially determining whether a plurality of adjacent distances are less than a preset adjacent distance;

[0020] If it is less than the preset coefficient, the area of ​​the segmented region corresponding to the preset adjacent distance is defined as a high-risk area, and a high-risk area path is established based on multiple 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 the 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 vehicle battery according to the plurality of 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 includes:

[0023] Establishing a bolt position Delaunay triangulation according to the plurality of bolt position coordinates, and calculating a plurality of adjacent bolt spacings according to the bolt position Delaunay triangulation;

[0024] sequentially determining whether a plurality of adjacent bolt spacings are less than a preset adjacent bolt spacing;

[0025] If the adjacent bolt spacing is smaller than the preset adjacent bolt spacing, it is determined that there is a stiffness loss in the adjacent bolt spacing, and multiple adjacent bolt spacings smaller than the preset adjacent bolt spacing are marked to obtain multiple marked bolt spacings;

[0026] Comparing the plurality of marked bolt spacings with the preset adjacent bolt spacings in sequence to obtain a plurality of distance ratio values;

[0027] The distance ratio value is used to calculate the connection stiffness coefficient (the stiffness coefficient is inversely proportional to the distance ratio value), and the corresponding marked bolt spacing and the basic disassembly path are re-fitted and arranged according to the connection stiffness coefficient to obtain the first optimized disassembly path.

[0028] Preferably, the step of obtaining internal resistance spectra of a plurality of battery cells according to the electrochemical state data, and obtaining a plurality of phase angle mutation points of the internal resistance spectra according to the plurality of internal resistance spectra, comprises:

[0029] Acquiring multiple single cell positions according to the electrochemical state data, applying a preset AC excitation signal (0.1 Hz to 1000 Hz) to the multiple single cell positions and measuring to obtain multiple voltage response signals and multiple current response signals, and using the multiple voltage response signals and the multiple current response signals as the internal resistance spectrum of the single cell;

[0030] Acquire multiple complex impedances at multiple frequency points according to the internal resistance spectra of the multiple single battery cells;

[0031] Acquire a plurality of phase angles according to the plurality of complex impedances, and generate a phase angle-frequency curve according to the plurality of phase angles and a preset frequency;

[0032] Acquire a plurality of phase angle change rates according to the phase angle-frequency curve and the preset phase angle-frequency curve;

[0033] Determining whether the plurality of phase angle change rates are less than a preset phase angle change rate;

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

[0035] Preferably, the step of obtaining a plurality of active abnormal points according to the plurality of phase angle mutation points, and rearranging the first optimized disassembly path according to the plurality of active abnormal points to obtain the second optimized disassembly path includes:

[0036] Acquire multiple phase angle offsets according to the multiple phase angle mutation points and the preset phase angle points;

[0037] Identifying a plurality of abnormal points of a plurality of first battery cells whose phase angle offset is greater than a preset phase angle offset, and treating the abnormal points of the plurality of first battery cells as a plurality of active abnormal points;

[0038] Associating the plurality of active abnormal points with the point cloud coordinates of the outer shell of the new energy vehicle to obtain the coordinates of the plurality of active abnormal points;

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

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

[0041] Inputting the second optimized disassembly path into a preset PLC control system of the maintenance device, wherein the preset PLC control system is applied to the battery removal device, and the battery removal device includes a gripping mechanism. The gripping mechanism is activated according to the preset PLC control system, and a preset visual sensor of the gripping mechanism scans the battery pack shell and matches the shell point cloud coordinates in the path to achieve 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 the single cells in the new energy vehicle battery according to the multiple active abnormal points.

[0044] The present application also provides a new energy vehicle battery disassembly system, comprising:

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

[0046] A second acquisition module is used to obtain the shell point cloud coordinates, multiple bolt position coordinates and battery cell spatial arrangement topology information according to the structural topology data, and obtain a basic disassembly path according to the shell point cloud coordinates and battery cell spatial arrangement topology information;

[0047] a third acquisition module, configured to acquire a connection stiffness coefficient of the new energy vehicle battery according to the plurality of bolt position coordinates, and re-fit and arrange the basic disassembly path according to the connection stiffness coefficient to obtain a first optimized disassembly path;

[0048] a fourth acquisition module, configured to acquire internal resistance spectra of a plurality of battery cells according to the electrochemical state data, and acquire a plurality of phase angle mutation points of the internal resistance spectra according to the plurality of internal resistance spectra;

[0049] a fifth acquisition module, configured to acquire a plurality of active abnormal points according to the plurality of phase angle mutation points, and rearrange the first optimized disassembly path according to the plurality of active abnormal points to obtain a second optimized disassembly path;

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

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

[0052] a first acquiring unit, configured to acquire a surface area of ​​a shell of a new energy vehicle battery, and segment the shell surface area according to a preset value to obtain areas of a plurality of segmented regions;

[0053] A second acquisition unit is configured to acquire a plurality of shell curvature features corresponding to the areas of the plurality of segmented regions according to the shell point cloud coordinates, and acquire a plurality of shell fragility coefficients according to the plurality of shell curvature features;

[0054] A first judging unit is configured to sequentially judge whether a plurality of shell fragility coefficients are less than a preset coefficient;

[0055] If the shell fragility coefficient is less than a preset coefficient, the segmented area corresponding to the shell fragility coefficient less than the preset coefficient is defined as a high-fragility zone, and a high-fragility zone path is established based on the multiple high-fragility zones;

[0056] a third acquiring unit, configured to acquire a plurality of battery cell position coordinates according to the battery cell spatial arrangement topology information, and acquire a plurality of adjacent distances between adjacent battery cells according to the plurality of battery cell position coordinates;

[0057] A second judging unit is configured to judge in sequence whether the plurality of adjacent distances are less than a preset adjacent distance;

[0058] If it is less than the preset coefficient, the area of ​​the segmented region corresponding to the preset adjacent distance is defined as a high-risk area, and a high-risk area path is established based on 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 areas to obtain a merged path, and to fit and optimize the preset disassembly path according to the merged path to obtain a basic disassembly path.

[0060] The present application also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

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

[0062] The beneficial effects of the present application are as follows: the present invention obtains battery structure topology data (shell point cloud coordinates, bolt position, cell arrangement) through three-dimensional laser scanning and industrial CT, and simultaneously collects electrochemical state data (internal resistance spectrum, phase angle). First, the basic disassembly path is planned based on the shell fragility coefficient (high-fragile areas are divided by curvature analysis) and the cell spacing. Secondly, the path is optimized according to the bolt connection stiffness coefficient, and the weak stiffness areas are disassembled first to release stress. Then, the active abnormal cells are located through the internal resistance spectrum phase angle mutation points, and mapped to three-dimensional space to form risk points. Finally, the structural risks (shell fragility areas, bolt stiffness) and electrical risks (abnormal cells) are integrated to generate a dual optimization path: first remove the bolts to stabilize the structure, and then avoid the abnormal cell area. This method breaks through the limitations of traditional fixed paths, identifies the risks of shell rupture, cell extrusion and thermal runaway in advance, and improves the disassembly safety and recovery rate through two path optimizations. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a schematic diagram of a method flow chart according to an embodiment of the present application.

[0064] Figure 2 This is a schematic diagram of the system structure of an embodiment of the present application.

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

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

[0067] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0068] like Figure 1-Figure 3 As shown, the present application provides a method for disassembling a new energy vehicle battery, comprising:

[0069] S1. Obtaining physical data of a new energy vehicle battery, wherein the physical data includes structural topology data and electrochemical state data;

[0070] S2. Obtaining the outer shell point cloud coordinates, the coordinates of the positions of the multiple bolts, and the topological information of the space arrangement of the battery cells according to the structural topological data, and obtaining a basic disassembly path according to the outer shell point cloud coordinates and the topological information of the space arrangement of the battery cells;

[0071] S3. Obtaining a connection stiffness coefficient of the new energy vehicle battery according to the plurality of 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;

[0072] S4. Obtaining internal resistance spectra of a plurality of single cells according to the electrochemical state data, and obtaining a plurality of phase angle mutation points of the internal resistance spectra according to the plurality of internal resistance spectra;

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

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

[0075] As described in the above steps S1-S6, since the disassembly of existing new energy vehicle batteries usually adopts a mechanical disassembly mode, and in the process of battery disassembly, the shell is usually cut only by a preset path, this will lead to an increase in the risk of disassembly due to battery damage, and thus lead to a decrease in the battery pack recovery rate. The present invention first obtains the body data of the new energy vehicle battery, wherein the body data includes structural topology data and electrochemical state data, so as to accurately collect the structural topology data of the battery (such as shell point cloud coordinates, bolt position, battery cell arrangement, etc.) and electrochemical state data (such as internal resistance spectrum, phase angle, etc.), laying a solid data foundation for subsequent disassembly path planning, and simultaneously collecting structural topology data (shell shape, bolt position, battery cell arrangement) and electrochemical state data (internal resistance, phase angle), providing a dual basis for disassembly path planning, avoiding the one-sidedness caused by single data;

[0076] Data collection process: Use a 3D laser scanner (such as FAROScanArm) to scan the battery shell, generate point cloud data, and extract the shell point cloud coordinates; obtain the bolt position coordinates and battery cell spatial arrangement topology information (such as battery cell row and column distribution and stacking relationship) through industrial CT scanning or CAD model matching before disassembly;

[0077] Secondly, the shell point cloud coordinates, multiple bolt position coordinates, and cell spatial arrangement topology information are obtained based on the structural topology data, and the basic disassembly path is obtained based on the shell point cloud coordinates and cell spatial arrangement topology information. In this way, the disassembly area is divided into "high vulnerability areas" and "high risk areas" through shell fragility coefficient and cell spacing analysis, achieving preliminary optimization of the path, reducing the risks of shell rupture and cell extrusion, and merging the two types of risk paths to avoid path deviation caused by a single factor. For example, weak shell areas and dense cell areas are avoided at the same time, thereby improving path rationality.

[0078] For example: Set the shell surface area to a preset value (such as 50cm 2 / area) is divided into multiple sub-areas, the average curvature of each area is calculated (the curvature value is obtained by fitting the surface equation of the point cloud data and taking the derivative), and the shell fragility coefficient threshold is set (such as curvature>0.2mm -1), areas below the threshold are defined as high-vulnerability areas, and detours are given priority when planning routes;

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

[0080] Then, internal resistance spectra of multiple single battery cells are obtained based on the electrochemical state data, and multiple phase angle mutation points of the internal resistance spectra are obtained based on the multiple internal resistance spectra. In this way, internal defects of the battery cell (such as active material shedding and electrolyte reduction) can be located through internal resistance spectrum analysis without disassembly, thereby improving the efficiency of identifying abnormal battery cells. In addition, the phase angle mutation points can reflect the change in the interface impedance of the battery cell, thereby discovering sub-healthy batteries in advance and avoiding thermal runaway caused by battery cell damage during disassembly.

[0081] Afterwards, multiple active abnormal points are obtained based on the multiple phase angle mutation points, and the first optimized disassembly path is rearranged based on the multiple active abnormal points to obtain a second optimized disassembly path. In this way, the phase angle mutation points are mapped to the battery shell point cloud coordinates to form active abnormal points in three-dimensional space, thereby achieving precise avoidance of the disassembly path. Based on the first optimized path, the path is further optimized in combination with the battery 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. In this way, the positioning of the point cloud coordinates is matched by the visual sensor to adapt to the dimensional deviation of batteries in different batches, and the structural connection is processed first (bolt removal), and then abnormal battery cells are avoided, thereby realizing the safety process of "stabilizing the structure first, then controlling the risk". In turn, it can avoid the blindness of traditional mechanical disassembly that only relies on fixed paths, and can identify high-risk areas (such as fragile areas of the shell, areas with close spacing between battery cells) and active abnormal battery cells in advance, reducing the risk of short circuit and thermal runaway during the disassembly process. The basic path is optimized twice through the connection stiffness coefficient (bolt spacing analysis) and the active abnormal point (battery cell status analysis) to ensure that the disassembly path avoids structural weak points and electrical abnormal areas, thereby improving the disassembly efficiency and battery recovery rate.

[0083] In one embodiment, the step S2 of acquiring a basic disassembly path according to the housing point cloud coordinates and the cell spatial arrangement topology information includes:

[0084] S201, obtaining a shell surface area of ​​a new energy vehicle battery, and dividing the shell surface area according to preset values ​​to obtain a plurality of divided region areas;

[0085] S202, obtaining a plurality of shell curvature features corresponding to the areas of the plurality of segmented regions according to the shell point cloud coordinates, and obtaining a plurality of shell fragility coefficients according to the plurality of shell curvature features;

[0086] S203, sequentially determining whether the shell fragility coefficients are less than a preset coefficient;

[0087] If the shell fragility coefficient is less than a preset coefficient, the segmented area corresponding to the shell fragility coefficient less than the preset coefficient is defined as a high-fragility zone, and a high-fragility zone path is established based on the multiple high-fragility zones;

[0088] S204, obtaining a plurality of battery cell position coordinates according to the battery cell spatial arrangement topology information, and obtaining a plurality of adjacent distances between adjacent battery cells according to the plurality of battery cell position coordinates;

[0089] S205, sequentially determining whether the plurality of adjacent distances are less than a preset adjacent distance;

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

[0091] S206 , dynamically couple the high-risk area path and the high-vulnerability area path in adjacent areas to obtain a merged path, and perform fitting optimization on 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 shell of the new energy vehicle battery, and then divides the shell surface area according to preset values ​​to obtain multiple segmented area areas. In this way, the complex shell surface is converted into quantifiable sub-units through regional segmentation, which facilitates subsequent curvature calculation and vulnerability assessment and avoids the ambiguity of the overall analysis. At the same time, the preset segmentation value can be flexibly adjusted (such as by area or curvature density) to meet the disassembly requirements of battery shells of different shapes such as square, round, and special shapes;

[0093] Next, multiple shell curvature features corresponding to the areas of the multiple 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. This numerically characterizes the steepness of the shell surface through curvature. The greater the curvature (such as a sharp corner), the more likely 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 properties (such as aluminum alloy, plastic), improving the universality of the method.

[0094] Curvature calculation process: For the point cloud data in each segmented area, the moving least squares method is used to fit the local surface, the Gaussian curvature and mean curvature of each point are calculated, and the mean curvature of all points in the area is taken as the shell curvature feature of the area;

[0095] Then, it is determined in turn whether the multiple shell fragility coefficients are less than the preset coefficient. If the shell fragility coefficient is less than the preset coefficient, the segmented area corresponding to the shell fragility coefficient less than the preset coefficient is defined as a high-fragility area, and a high-fragility area path is established based on the multiple high-fragility areas. In this way, the high-fragility area is automatically screened through the preset threshold, avoiding manual subjective misjudgment, improving risk identification efficiency and consistency, and at the same time establishing an independent path label (such as "prohibited cutting area") for the high-fragility area to ensure that the disassembly tool avoids the area first, thereby reducing the risk of shell rupture;

[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 quantitative analysis of cell spacing, short circuit risks caused by physical contact or excessive proximity are identified, and electrical safety is incorporated into the path planning dimension. This method is applicable to serial, matrix, and irregularly arranged cells. Only the topological coordinates need to be input to automatically calculate the spacing, without the need for manual measurement. The adjacent distances are calculated using the Euclidean distance formula.

[0097] Afterwards, it is determined in turn whether the multiple adjacent distances are less than the preset adjacent distances. If they are less than the preset coefficient, the area of ​​the segmented region corresponding to the adjacent distance less than the preset distance is defined as a high-risk area, and a high-risk area path is established based on the multiple high-risk areas. In this way, the internal risk of the battery cells being too close together is mapped to the surface area of ​​the shell, 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 battery cell type (such as ternary lithium, lithium iron phosphate). For example, a stricter threshold is used for high-energy density batteries. For example, the battery cell spacing in the middle of a battery pack is only 3mm, and the corresponding top central area of ​​the shell is marked as a high-risk area. The basic path is adjusted to disassembly from the edges on both sides, and the robotic arm fixture is inserted from the side to avoid the top central area.

[0098] Finally, the high-risk area path and the high-vulnerability area path are dynamically coupled in adjacent areas to obtain a merged path, and the preset disassembly path is fitted and optimized according to the merged path to obtain a basic disassembly path. In this way, the high-vulnerability area and high-risk area paths are merged to generate a global optimal path that avoids both structural weaknesses and electrical risk points. For example, in the three-dimensional model of the shell, the red high-vulnerability area grid and the yellow high-risk area grid are superimposed to generate a composite risk map. The white area is a safe and operable area to avoid the limitations of single risk optimization. At the same time, under the premise of avoiding risks, the disassembly efficiency is ensured through the path fitting algorithm (such as the shortest path) to avoid excessive detours that lead to increased time consumption. Among them, the dynamic coupling process of adjacent areas: the safe area is discretized into nodes, and the lines between the nodes are passable edges (the safety distance from the risk area must be met), and the preset path fitting: the coupled path is compared with the preset disassembly path (such as along the module seam line), and the inflection point is smoothly adjusted through the Bezier curve to ensure smooth movement of the robotic arm and reduce start-stop 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, the step S3 of obtaining a connection stiffness coefficient of the new energy vehicle battery according to the plurality of 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 includes:

[0101] S301, establishing a bolt position Delaunay triangulation according to the plurality of bolt position coordinates, and calculating a plurality of adjacent bolt spacings according to the bolt position Delaunay triangulation;

[0102] S302, sequentially determining whether the distances between the plurality of adjacent bolts are less than a preset distance between adjacent bolts;

[0103] If the adjacent bolt spacing is smaller than the preset adjacent bolt spacing, it is determined that there is a stiffness loss in the adjacent bolt spacing, and multiple adjacent bolt spacings smaller than the preset adjacent bolt spacing are marked to obtain multiple marked bolt spacings;

[0104] S303, comparing the plurality of marked bolt spacings with preset adjacent bolt spacings in sequence to obtain a plurality of distance ratio values;

[0105] S304: Calculate the connection stiffness coefficient using the distance ratio value (the stiffness coefficient is inversely proportional to the distance ratio value), and re-fit and arrange the corresponding marked bolt spacing and the foundation disassembly path according to the connection stiffness coefficient to obtain a first optimized disassembly path.

[0106] As described in steps S301-S304 above, the present invention first establishes a bolt position Delaunay triangulation based on the multiple bolt position coordinates, and calculates multiple adjacent bolt spacings based on the bolt position Delaunay triangulation. In this way, the spatial association of the 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, the natural adjacency of the triangulation is utilized, eliminating the need to fully calculate all bolt spacings. Only adjacent bolts corresponding to the triangulation edges need to be calculated, which greatly reduces the amount of calculation.

[0107] For example, input the bolt position coordinates (2D or 3D coordinates) obtained in step S1 and use a Delaunay triangulation algorithm (such as the Bowyer-Watson algorithm) to generate a triangulated network. Ensure that the circumcircle of any triangle does not contain other bolt points, and that the edges of the triangulated network are the connecting edges of adjacent bolts. Each edge corresponds to a pair of adjacent bolts (such as bolt A and bolt B).

[0108] Then, it is determined in sequence whether the adjacent bolt spacings are less than the preset adjacent bolt spacings. If they are less than the preset adjacent bolt spacings, it is determined that there is stiffness loss in the adjacent bolt spacings, and the adjacent bolt spacings that are less than the preset adjacent bolt spacings are marked to obtain a plurality of marked bolt spacings. In this way, the bolt spacing is converted into a quantitative indicator of stiffness loss by using a preset spacing threshold (such as 80% of the design value), avoiding subjective judgment. Moreover, only the measured spacing needs to be compared with the threshold to batch identify bolt pairs with stiffness risks, thereby improving analysis efficiency.

[0109] Secondly, the multiple marked bolt spacings are sequentially compared with the preset adjacent bolt spacings to obtain multiple distance ratio values. In this way, the stiffness loss is quantified into a value between 0 and 1 through the distance ratio value (measured spacing / preset spacing), which facilitates subsequent sorting by risk level and eliminates the influence of differences in the designed spacings of different bolts, making the stiffness loss of bolts in different positions comparable (for example, bolts with a designed spacing of 20 mm and 15 mm can be uniformly measured using the ratio value);

[0110] Finally, the distance ratio is used to calculate the connection stiffness coefficient (the stiffness coefficient is inversely proportional to the distance ratio), and the corresponding marked bolt spacing and the basic disassembly path are re-fitted and arranged according to the connection stiffness coefficient to obtain the first optimized disassembly path. In this way, the structural importance of the bolt is converted into path priority through the stiffness coefficient (inversely proportional to the ratio), realizing the strategy of "the more serious the stiffness loss, the higher the disassembly priority". At the same time, the path is dynamically fitted based on the stiffness coefficient to avoid stress concentration caused by traditional fixed-order disassembly, and improve the structural stability of the disassembly process. Then, through the process of "bolt topology modeling → spacing threshold screening → stiffness quantification classification → path dynamic fitting", the bolt connection stiffness is deeply bound to the disassembly path, realizing the technical upgrade from "empirical sequential disassembly" to "structural stress release".

[0111] In one embodiment, the step S4 of acquiring internal resistance spectra of a plurality of battery cells according to the electrochemical state data, and acquiring a plurality of phase angle mutation points of the internal resistance spectra according to the plurality of internal resistance spectra, includes:

[0112] S401, acquiring multiple single cell positions based on the electrochemical state data, applying a preset AC excitation signal (0.1 Hz to 1000 Hz) to the multiple single cell positions and measuring to obtain multiple voltage response signals and multiple current response signals, and using the multiple voltage response signals and the multiple current response signals as the internal resistance spectrum of the single cell;

[0113] S402, obtaining a plurality of complex impedances at a plurality of frequency points according to the internal resistance spectra of the plurality of single battery cells;

[0114] S403, acquiring a plurality of phase angles according to the plurality of complex impedances, and generating a phase angle-frequency curve according to the plurality of phase angles and a preset frequency;

[0115] S404, obtaining a plurality of phase angle change rates according to the phase angle-frequency curve and the preset phase angle-frequency curve;

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

[0117] If it is less than the preset phase angle change rate, it is determined that a phase angle mutation point occurs in the single cell corresponding to the phase angle change rate that is less than the preset phase angle change rate, and the corresponding multiple phase angle mutation points are obtained in sequence according to the 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 single cell positions based on the electrochemical state data, and applies a preset AC excitation signal (0.1 Hz to 1000 Hz) to the multiple single cell positions and measures multiple voltage response signals and multiple current response signals, and uses the multiple voltage response signals and the multiple current response signals as the internal resistance spectrum of the single cell. In this way, the internal resistance spectrum is obtained by the AC excitation signal, and the internal state of the single cell can be evaluated without disassembling the battery, avoiding the waste of resources caused by traditional destructive testing. The excitation signal frequency range is 0.1 Hz to 1000 Hz, 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, a plurality of complex impedances at a plurality of frequency points are obtained according to the internal resistance spectra of the plurality of said single cells. Such complex impedances contain information of 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, combined with the complex impedance change trend at different frequency points, construct an equivalent circuit model (such as Randles model) inside the cell, and quantify the parameters of each component (such as charge transfer resistance and double-layer capacitance);

[0120] Secondly, multiple phase angles are obtained according to the multiple complex impedances, and a phase angle-frequency curve is generated according to the multiple phase angles and the preset frequency. In this way, the phase angle-frequency curve intuitively reflects the changing trend of the polarization characteristics inside the battery cell with frequency. Abnormal curve morphology (such as peak offset and slope mutation) can be directly associated with specific failure modes. By comparing with the standard curve of healthy batteries of the same model, the degree of deviation can be quickly identified to avoid 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. Such a change rate reflects the rate of change of the phase angle with frequency, and can identify local anomalies of the curve (such as a sudden change in the slope in a certain frequency interval). It is more accurate than simply comparing numerical values. At the same time, the phase angle deviation speed is quantified by the change rate value, which facilitates the setting of thresholds to distinguish between minor anomalies and serious faults.

[0122] Finally, it is determined whether the multiple phase angle change rates are less than the 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 in turn 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 the preset threshold, the phase angle mutation at a specific frequency point can be locked to achieve accurate positioning 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 the mutation points, the cell is risk graded (such as a single mutation point is low risk, and multiple mutation points are high risk), guiding the disassembly priority, and then through the process of "wideband excitation-complex impedance analysis-phase angle dynamic analysis-mutation point positioning", the internal microscopic failure of the cell (such as electrolyte attenuation, increase in interface impedance) is converted into detectable macroscopic electrical signal characteristics, realizing the full chain control of "non-destructive testing-precise diagnosis-risk grading".

[0123] In one embodiment, the step S5 of obtaining multiple active abnormal points according to the multiple phase angle mutation points, and rearranging the first optimized disassembly path according to the multiple active abnormal points to obtain the second optimized disassembly path includes:

[0124] S501, obtaining a plurality of phase angle offsets according to a plurality of phase angle mutation points and a preset phase angle point;

[0125] S502, identifying a plurality of abnormal points of a plurality of first battery cells whose phase angle offset is greater than a preset phase angle offset, and treating the abnormal points of the plurality of first battery cells as a plurality of active abnormal points;

[0126] S503, associating the plurality of active abnormal points with the point cloud coordinates of the outer shell of the new energy vehicle to obtain the coordinates of the plurality of active abnormal points;

[0127] S504: Map and merge the coordinates of the plurality of active abnormal points with the first optimized disassembly path to obtain a second optimized disassembly path.

[0128] As described in steps S501-S504 above, the present invention first obtains multiple phase angle offsets based on the multiple phase angle mutation points and the preset phase angle points. In this way, the cell activity abnormality is converted from "qualitative judgment" to "quantitative analysis" through the phase angle offset (the difference between the measured value and the standard value), which facilitates the risk comparison between different cells. At the same time, the failure type can be preliminarily judged in combination with 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, multiple abnormal points of multiple first single cells whose phase angle offset is greater than a preset phase angle offset are identified, and the abnormal points of the multiple first single cells are used as multiple active abnormal points. In this way, single cells with active abnormalities are screened out by an offset threshold (such as >10°), thereby avoiding misjudging normal cells as risk points and improving positioning accuracy. In addition, the abnormal points can be preliminarily classified according to the size and direction of the offset, and subsequent disassembly strategies can be guided.

[0130] Secondly, multiple active anomaly points are associated with the outer shell point cloud coordinates of the new energy vehicle to obtain multiple active anomaly point coordinates. In this way, the active anomaly inside the battery cell is mapped 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 anomaly is converted into three-dimensional space coordinates, facilitating subsequent geometric operations with the disassembly path (such as distance calculation and area avoidance), thereby achieving physical risk avoidance.

[0131] Finally, the coordinates of the multiple active anomaly points are mapped and merged with the first optimized disassembly path to obtain the second optimized disassembly path. In this way, the structural risk path (first optimized path) is combined with the electrical risk point (active anomaly point) to generate a composite path that avoids both mechanical damage and electrical risks, thereby 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 the disassembly process (such as the discovery of abnormalities in adjacent battery cells during disassembly). Then, through the process of "phase angle offset quantification → abnormal point positioning → spatial coordinate mapping → dynamic path avoidance", the electrical activity anomaly inside the battery cell is converted into an operational spatial avoidance instruction, realizing cross-dimensional linkage from "battery cell status assessment" to "physical path optimization".

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

[0133] S601: Inputting the second optimized disassembly path into a preset PLC control system of the maintenance device, wherein the preset PLC control system is applied to the battery removal device, and the battery removal device includes a gripping mechanism. The gripping mechanism is activated according to the preset PLC control system, and a preset visual sensor of the gripping mechanism scans the battery pack shell and matches the shell point cloud coordinates in the path to achieve 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 disassembles the single cells in the new energy vehicle battery in an evasive manner according to the plurality of 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, wherein 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 visual sensor of the clamping mechanism scans the battery pack shell and matches the shell point cloud coordinates in the path to achieve positioning of the device and the battery pack. In this way, through the linkage of the PLC control system and the visual sensor, sub-millimeter positioning of the battery pack and the disassembly device is achieved, manual alignment errors are eliminated, and disassembly consistency is improved. At the same time, the preset PLC system can read the second optimized path data in real time, automatically adjust the motion trajectory of the robotic arm, and be compatible with the path differences of battery packs of different models;

[0137] Secondly, the clamping mechanism separates the new energy vehicle battery according to the bolt position coordinates to obtain a separated structure. In this way, the bolts are first removed to release the structural stress, and then the shell and the module are separated, thereby avoiding the structural collapse 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 the tool head according to the bolt type (such as plum bolts, hexagon socket bolts), thereby improving disassembly efficiency.

[0138] Finally, the clamping mechanism evades and disassembles the single cells in the new energy vehicle battery according to the multiple active abnormal points. In this way, the coordinates of the active abnormal points are used to guide the avoidance movement of the robotic arm, avoiding direct contact of the disassembly tools with high-risk cells, reducing the risk of internal short circuits caused by squeezing or puncture, and automatically switching the disassembly mode (such as gentle clamping and frozen disassembly) for abnormal points of different risk levels, realizing the refined processing of "one type of abnormality, one strategy", thereby avoiding relying on the preset path to cut the shell, and avoiding the increase in disassembly risk due to battery damage, thereby improving the battery pack recovery rate.

[0139] The present application also provides a new energy vehicle battery disassembly system, comprising:

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

[0141] A second acquisition module is used to obtain the shell point cloud coordinates, multiple bolt position coordinates and battery cell spatial arrangement topology information according to the structural topology data, and obtain a basic disassembly path according to the shell point cloud coordinates and battery cell spatial arrangement topology information;

[0142] a third acquisition module, configured to acquire a connection stiffness coefficient of the new energy vehicle battery according to the plurality of bolt position coordinates, and re-fit and arrange the basic disassembly path according to the connection stiffness coefficient to obtain a first optimized disassembly path;

[0143] a fourth acquisition module, configured to acquire internal resistance spectra of a plurality of battery cells according to the electrochemical state data, and acquire a plurality of phase angle mutation points of the internal resistance spectra according to the plurality of internal resistance spectra;

[0144] a fifth acquisition module, configured to acquire a plurality of active abnormal points according to the plurality of phase angle mutation points, and rearrange the first optimized disassembly path according to the plurality of active abnormal points to obtain a second optimized disassembly path;

[0145] A 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] a first acquiring unit, configured to acquire a surface area of ​​a shell of a new energy vehicle battery, and segment the shell surface area according to a preset value to obtain areas of a plurality of segmented regions;

[0148] A second acquisition unit is configured to acquire a plurality of shell curvature features corresponding to the areas of the plurality of segmented regions according to the shell point cloud coordinates, and acquire a plurality of shell fragility coefficients according to the plurality of shell curvature features;

[0149] A first judging unit is configured to sequentially judge whether a plurality of shell fragility coefficients are less than a preset coefficient;

[0150] If the shell fragility coefficient is less than a preset coefficient, the segmented area corresponding to the shell fragility coefficient less than the preset coefficient is defined as a high-fragility zone, and a high-fragility zone path is established based on the multiple high-fragility zones;

[0151] a third acquiring unit, configured to acquire a plurality of battery cell position coordinates according to the battery cell spatial arrangement topology information, and acquire a plurality of adjacent distances between adjacent battery cells according to the plurality of battery cell position coordinates;

[0152] A second judging unit is configured to judge in sequence whether the plurality of adjacent distances are less than a preset adjacent distance;

[0153] If it is less than the preset coefficient, the area of ​​the segmented region corresponding to the preset adjacent distance is defined as a high-risk area, and a high-risk area path is established based on 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 areas to obtain a merged path, and to fit and optimize the preset disassembly path according to the merged path to obtain a basic disassembly path.

[0155] The present application also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

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

[0157] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0158] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0159] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for disassembling a new energy vehicle battery, characterized in that: include: Obtaining physical data of new energy vehicle batteries, wherein the physical data includes structural topology data and electrochemical state data; Obtaining the shell point cloud coordinates, multiple bolt position coordinates, and battery cell spatial arrangement topology information according to the structural topology data, and obtaining a basic disassembly path according to the shell point cloud coordinates and battery cell spatial arrangement topology information; Obtaining a connection stiffness coefficient of the new energy vehicle battery according to the plurality of 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; Acquire internal resistance spectra of a plurality of battery cells according to the electrochemical state data, and acquire a plurality of phase angle mutation points of the internal resistance spectra according to the plurality of internal resistance spectra; Acquire multiple active abnormal points according to the multiple phase angle mutation points, and rearrange the first optimized disassembly path according to the multiple active abnormal points to obtain a 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 a basic disassembly path according to the outer shell point cloud coordinates and the cell spatial arrangement topology information includes: Obtaining a shell surface area of ​​a new energy vehicle battery, and dividing the shell surface area according to a preset value to obtain a plurality of divided region areas; Acquire multiple shell curvature features corresponding to the areas of the multiple segmented regions according to the shell point cloud coordinates, and acquire multiple shell fragility coefficients according to the multiple shell curvature features; determining in sequence whether the plurality of shell fragility coefficients are less than a preset coefficient; If the shell fragility coefficient is less than a preset coefficient, the segmented area corresponding to the shell fragility coefficient less than the preset coefficient is defined as a high-fragility zone, and a high-fragility zone path is established based on the multiple high-fragility zones; Acquire a plurality of cell position coordinates according to the cell spatial arrangement topology information, and acquire a plurality of adjacent distances between adjacent cell positions according to the plurality of cell position coordinates; sequentially determining whether a plurality of adjacent distances are less than a preset adjacent distance; If it is less than the preset coefficient, the area of ​​the segmented region corresponding to the preset adjacent distance is defined as a high-risk area, and a high-risk area path is established based on multiple high-risk areas; The high-risk area path and the high-vulnerability area path are dynamically coupled in adjacent areas to obtain a merged path, and the preset disassembly path is fitted and optimized according to 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 a connection stiffness coefficient of the new energy vehicle battery according to the plurality of 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 includes: Establishing a bolt position Delaunay triangulation according to the plurality of bolt position coordinates, and calculating a plurality of adjacent bolt spacings according to the bolt position Delaunay triangulation; sequentially determining whether a plurality of adjacent bolt spacings are less than a preset adjacent bolt spacing; If the adjacent bolt spacing is smaller than the preset adjacent bolt spacing, it is determined that there is a stiffness loss in the adjacent bolt spacing, and multiple adjacent bolt spacings smaller than the preset adjacent bolt spacing are marked to obtain multiple marked bolt spacings; Comparing the plurality of marked bolt spacings with the preset adjacent bolt spacings in sequence to obtain a plurality of distance ratio values; The connection stiffness coefficient is calculated using the distance ratio value, and the corresponding marked bolt spacing and the basic disassembly path are re-fitted and arranged according to the connection stiffness coefficient to obtain a 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 internal resistance spectra of a plurality of battery cells according to the electrochemical state data, and obtaining a plurality of phase angle mutation points of the internal resistance spectra according to the plurality of internal resistance spectra, comprises: Acquiring multiple single cell positions according to the electrochemical state data, applying a preset AC excitation signal to the multiple single cell positions and measuring to obtain multiple voltage response signals and multiple current response signals, and using the multiple voltage response signals and the multiple current response signals as the internal resistance spectrum of the single cell; Acquire multiple complex impedances at multiple frequency points according to the internal resistance spectra of the multiple single battery cells; Acquire a plurality of phase angles according to the plurality of complex impedances, and generate a phase angle-frequency curve according to the plurality of phase angles and a preset frequency; Acquire a plurality of phase angle change rates according to the phase angle-frequency curve and the preset phase angle-frequency curve; Determining whether the plurality of phase angle change rates are less than a preset phase angle change rate; If it is less than the preset phase angle change rate, it is determined that a phase angle mutation point occurs in the single cell corresponding to the phase angle change rate that is less than the preset phase angle change rate, and the corresponding multiple phase angle mutation points are obtained in sequence according to the 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 a plurality of active abnormal points according to the plurality of phase angle mutation points, and rearranging the first optimized disassembly path according to the plurality of active abnormal points to obtain a second optimized disassembly path includes: Acquire multiple phase angle offsets according to the multiple phase angle mutation points and the preset phase angle points; Identifying a plurality of abnormal points of a plurality of first battery cells whose phase angle offset is greater than a preset phase angle offset, and treating the abnormal points of the plurality of first battery cells as a plurality of active abnormal points; Associating the plurality of active abnormal points with the point cloud coordinates of the outer shell of the new energy vehicle to obtain the coordinates of the plurality of active abnormal points; The coordinates of the plurality of active abnormal points are mapped and merged with the first optimized disassembly path to obtain a 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: Inputting the second optimized disassembly path into a preset PLC control system of the maintenance device, wherein the preset PLC control system is applied to the battery removal device, and the battery removal device includes a gripping mechanism. The gripping mechanism is activated according to the preset PLC control system, and a preset visual sensor of the gripping mechanism scans the battery pack shell and matches the shell point cloud coordinates in the path to achieve 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 the single cells in the new energy vehicle battery according to the multiple active abnormal points.

7. A disassembly system for new energy vehicle batteries, characterized in that: include: A first acquisition module is used to acquire physical data of the new energy vehicle battery, wherein the physical data includes structural topology data and electrochemical state data; A second acquisition module is used to obtain the shell point cloud coordinates, multiple bolt position coordinates and battery cell spatial arrangement topology information according to the structural topology data, and obtain a basic disassembly path according to the shell point cloud coordinates and battery cell spatial arrangement topology information; a third acquisition module, configured to acquire a connection stiffness coefficient of the new energy vehicle battery according to the plurality of bolt position coordinates, and re-fit and arrange the basic disassembly path according to the connection stiffness coefficient to obtain a first optimized disassembly path; a fourth acquisition module, configured to acquire internal resistance spectra of a plurality of battery cells according to the electrochemical state data, and acquire a plurality of phase angle mutation points of the internal resistance spectra according to the plurality of internal resistance spectra; a fifth acquisition module, configured to acquire a plurality of active abnormal points according to the plurality of phase angle mutation points, and rearrange the first optimized disassembly path according to the plurality of active abnormal points to obtain a second optimized disassembly path; A disassembly module is used to disassemble the new energy vehicle battery according to the second optimized disassembly path.

8. A disassembly system for new energy vehicle batteries according to claim 7, characterized in that: The second acquisition module includes: a first acquiring unit, configured to acquire a surface area of ​​a shell of a new energy vehicle battery, and segment the shell surface area according to a preset value to obtain areas of a plurality of segmented regions; A second acquisition unit is configured to acquire a plurality of shell curvature features corresponding to the areas of the plurality of segmented regions according to the shell point cloud coordinates, and acquire a plurality of shell fragility coefficients according to the plurality of shell curvature features; A first judging unit is configured to sequentially judge whether a plurality of shell fragility coefficients are less than a preset coefficient; If the shell fragility coefficient is less than a preset coefficient, the segmented area corresponding to the shell fragility coefficient less than the preset coefficient is defined as a high-fragility zone, and a high-fragility zone path is established based on the multiple high-fragility zones; a third acquiring unit, configured to acquire a plurality of battery cell position coordinates according to the battery cell spatial arrangement topology information, and acquire a plurality of adjacent distances between adjacent battery cells according to the plurality of battery cell position coordinates; A second judging unit is configured to judge in sequence whether the plurality of adjacent distances are less than a preset adjacent distance; If it is less than the preset coefficient, the area of ​​the segmented region corresponding to the preset adjacent distance is defined as a high-risk area, and a high-risk area path is established based on 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 areas to obtain a merged path, and to fit and optimize the preset disassembly path according to 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, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

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

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