Battery cell assembly line logistics simulation optimization method and system and application

Abstract

The invention discloses a battery cell assembly line logistics simulation optimization method and system and application, and the method comprises the steps: carrying out the modeling of a battery cell production process, and obtaining a modeled battery cell production process model; constructing a logistics simulation model according to the modeling cell production process model and the actual plant data, and setting logistics equipment parameters according to the logistics simulation model; based on the logistics equipment parameters, generating a cell dynamic scheduling and logistics cooperative control strategy through factory simulation; and according to the cell dynamic scheduling and logistics cooperative control strategy, executing a simulation experiment of the logistics design influence factors and optimizing the layout. According to the method, the logistics state flow process of the battery cell from raw materials, semi-finished products to finished products can be completely reproduced, and the assembly scene simulation of the battery cell under different process routes is supported, so that the method is expanded to various battery cell production line assembly scenes, the simulation stability is ensured, bottleneck procedures of a logistics simulation model in the operation process are reduced, and the productivity can be improved.

Description

Technical Field

[0001] The present invention relates to the field of lithium battery manufacturing technology, and more specifically, to a logistics simulation optimization method, system, and application of a battery cell assembly line. Background Art

[0002] Battery cell production is a highly complex, systematic project that requires not only the processing and production of basic products but also intelligent scheduling capabilities for assembling multiple materials and handling exceptions. Some factories even require the same production line to produce multiple battery cell products. As production line processes become increasingly sophisticated and the connections between upstream and downstream processes become closer, companies are pursuing lower costs and higher production capacity while also placing increasing demands on battery cell yield and capacity. Factors such as production tact, production line layout, logistics, battery cell process logic, and equipment failures not only affect equipment energy consumption and production efficiency but also have a significant impact on the overall production capacity and production bottlenecks of modern factories.

[0003] When using manual labor to design and optimize battery cell production lines, the number of equipment, staffing, and logistics layout are planned based on past experience. During the planning and production operation stage, problems such as long design cycles, high costs for optimizing production line designs, and difficulty in accurately locating production bottlenecks often arise. The design model based on CAD (Computer Aided Design) engineering drawings lacks lean planning and quantitative evaluation, and is unable to effectively predict risks. In the initial stages of factory design, communication and review efficiency is low, the plan planning cycle is long, and the cost of repeated adjustments is high. Production plans and logistics plans are difficult to verify in advance, and process problems are difficult to warn in advance; during the production execution process, production bottlenecks are difficult to locate, and scheduling strategies are difficult to implement scientifically; there is a lack of guidance for optimizing production line facility layout, logistics, production sequence and interval, production batch, and resource portfolio, and it is difficult to achieve precision through experience-based production operation optimization.

[0004] Introducing simulation technology into the battery cell line planning process allows for production plan verification and optimization, production plan rehearsal and optimization, production dynamic anomaly warnings, and production system performance analysis and continuous optimization. Using battery cell line simulation technology to achieve cost reduction and efficiency improvement in production line design. However, in traditional simulations of existing battery cell manufacturing lines, the key processes of rolling, slitting, and assembly are simplified into independent single stations, obscuring the dynamic coupling and potential bottlenecks between processes. Therefore, this presents a technical issue that urgently needs to be addressed in this field. Summary of the Invention

[0005] In view of this, the present invention provides a battery assembly line logistics simulation optimization method, system and application, which are used to solve the problems of dynamic coupling and potential bottlenecks between concealed processes in the prior art.

[0006] On the first aspect, the logistics simulation optimization method of the battery cell assembly line of the present application includes the following steps: modeling the battery cell production process to obtain a modeled battery cell production process model; constructing a logistics simulation model based on the modeled battery cell production process model and actual factory data, and setting logistics equipment parameters according to the logistics simulation model; based on the logistics equipment parameters, generating a battery cell dynamic scheduling and logistics collaborative control strategy through factory simulation, the battery cell dynamic scheduling and logistics collaborative control strategy including control point configuration, core pairing rules, pallet reorganization logic and beat control; according to the battery cell dynamic scheduling and logistics collaborative control strategy, performing simulation experiments on logistics design influencing factors and optimizing the layout.

[0007] In the second aspect, the present invention provides a battery cell assembly line logistics simulation optimization system, including: a process modeling module, used to model the battery cell production process to obtain a modeled battery cell production process model; a simulation model construction module, coupled to the process modeling module, used to receive the modeled battery cell production process model, and construct a logistics simulation model based on the modeled battery cell production process model and actual factory data, and set logistics equipment parameters according to the logistics simulation model; a control logic generation module, coupled to the simulation model construction module, used to receive logistics equipment parameters, and generate battery cell dynamic scheduling and logistics collaborative control strategies through factory simulation based on the logistics equipment parameters, the battery cell dynamic scheduling and logistics collaborative control strategies including control point configuration, core pairing rules, pallet reorganization logic and beat control; a simulation optimization module, coupled to the control logic generation module, used to receive battery cell dynamic scheduling and logistics collaborative control strategies, and perform simulation experiments on logistics design influencing factors and optimize the layout according to the battery cell dynamic scheduling and logistics collaborative control strategies.

[0008] In the third aspect, the present invention provides a battery cell conveying and assembly simulation scheduling system, and the above-mentioned battery cell assembly line logistics simulation optimization method is applied to the battery cell conveying and assembly simulation scheduling system, which includes: a dynamic assembly model, including an assembly logistics channel, the assembly logistics channel is used to connect the roller conveyor line of adjacent processing equipment, and the dynamic assembly model is configured with a battery cell dynamic scheduling and logistics collaborative control strategy, an equipment processing connection control module and a battery cell production process logic data; a control point system, including at least three control points, and at least three control points are respectively arranged at the head and tail ends and side ends of the roller conveyor line; an equipment processing connection control module, which is linked to the processing time of the processing equipment, and when the first core roll pallet and the second core roll pallet arrive at the head and tail ends and side ends of the control point, an action is triggered according to the battery cell dynamic scheduling and logistics collaborative control strategy and the preset battery cell production process logic data; a simulation construction module, which constructs an assembly logistics channel through the roller line module of the simulation software, and configures the control point logic.

[0009] Compared with the prior art, the battery assembly line logistics simulation optimization method, system, and application provided by the present invention achieve at least the following beneficial effects:

[0010] The battery cell assembly line logistics simulation optimization method, system, and application provided by the present invention can not only completely reproduce the logistics state flow process of battery cells from raw materials, semi-finished products to finished products, but also realize dynamic coupling between key processes such as rolling-slitting-assembly based on the dynamic scheduling of battery cells and the coordinated logistics control strategy, and support the assembly scenario simulation of battery cells under different process routes, thereby expanding to various battery cell production line assembly scenarios, ensuring simulation stability, and at the same time reducing the bottleneck processes of the logistics simulation model during operation and improving production capacity, providing a reasonable basis for subsequent pallet flow direction, pallet speed, number of pallets, processing equipment layout, and battery cell scheduling strategy, thereby providing direction for the optimization of actual battery cell production lines.

[0011] Of course, any product implementing the present invention does not necessarily need to achieve all of the technical effects described above at the same time.

[0012] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

[0014] Figure 1 It is a flow chart of the battery cell assembly line logistics simulation optimization method provided by the present invention;

[0015] Figure 2 It is a flow chart of the battery cell production process in the battery cell assembly line logistics simulation optimization method provided by the present invention;

[0016] Figure 3 It is a structural diagram of the battery cell assembly line logistics simulation optimization system provided by the present invention;

[0017] Figure 4 This is a structural diagram of the battery cell conveying and assembly simulation scheduling system (including DP7) provided by the present invention;

[0018] Figure 5 1 is a schematic structural diagram of the battery cell conveying and assembly simulation scheduling system (including Queue 102) provided by the present invention;

[0019] Figure 6 It is a structural diagram of the battery cell conveying and assembly simulation scheduling system (including DP5) provided by the present invention;

[0020] Figure 7 It is a planar schematic diagram of the conveyor line corresponding to the material simulation model of the battery cell production and coiling equipment in Application Example 1 provided by the present invention;

[0021] Figure 8A schematic diagram of the pallet transfer process of the conveyor line corresponding to the material simulation model of the battery cell production and coiling equipment in Application Example 1 provided by the present invention;

[0022] Figure 9 A plan view of a conveyor line corresponding to a coil core transport model between a coil cutting device and a processing device in Application Example 2 provided by the present invention;

[0023] Figure 10 A schematic diagram of the pallet transfer process of the conveyor line corresponding to the coil core transportation model between the coil cutting equipment and the processing equipment in Application Example 2 provided by the present invention;

[0024] Figure 11 A plan view of the conveyor line corresponding to the real simulation model of battery cell assembly in Application Example 3 provided by the present invention;

[0025] Figure 12 This is a schematic diagram of the battery cell processing and transportation on the conveyor line corresponding to the real simulation model of battery cell assembly in Application Example 3 provided by the present invention. DETAILED DESCRIPTION

[0026] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention.

[0027] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0028] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0029] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0030] It should be noted that like reference numerals and letters refer to like items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0031] Reference Figure 1-Figure 2 As shown, Figure 1 It is a flow chart of the battery cell assembly line logistics simulation optimization method provided by the present invention; Figure 2 : This is a flow chart of the battery cell production process in the battery cell assembly line logistics simulation optimization method provided by the present invention; this embodiment provides a battery cell assembly line logistics simulation optimization method, comprising the following steps:

[0032] Step 100: Model the battery cell production process to obtain a modeled battery cell production process model;

[0033] Specifically, first collect information about the battery cell production process, such as visiting the factory for on-site inspection, to understand the process information required for battery production (such as understanding the battery cell production process, the main processes of the battery cell production process include slurry mixing process, coating process, roller separation process a, coil cutting process b, assembly process c) and optimization goals, and register them into a factory on-site inspection form; organize the process of the battery cell production process, sort out the production process information of the types of battery cells produced on site, and compile it into a battery cell production process flow chart, which will be convenient for comparison with the logistics simulation model later.

[0034] The production process of battery cells can be divided into pre-processing process and assembly process c. The pre-processing process includes roller separation process a and coil cutting process b. The assembly process c includes preheating process c1, hot pressing process c2, pole piece and thickness detection process, tab welding process c5, connecting piece welding process c6, shell packaging process c7, top cover welding process c8 and sealing detection process c9.

[0035] Pre-treatment process:

[0036] Roller slitting process a: Slit the coated electrode into the required width, and the slitting speed and tension control need to be set (such as 200-500N). Slitting process b: Wind the slit electrode into a core, and the winding diameter (such as 18mm±0.1mm) and the tab position tolerance (≤0.2mm) need to be defined.

[0037] Assembly process c:

[0038] Preheating process c1: eliminate internal stress (temperature 120±5℃, time 15±2s). Hot pressing process c2: compact the core to the standard thickness (pressure 15±1MPa, thickness tolerance ±0.03mm). The pole piece and thickness detection process includes pole piece detection process c3 and thickness detection process c4, among which, pole piece detection process c3: detect the pole piece alignment by X-ray (deviation ≤0.1mm); thickness detection process c4: laser thickness gauge detects the core thickness (automatically rejects out-of-tolerance). Tab welding process c5: ultrasonic welding (frequency 20kHz, tension ≥80N). Connecting piece welding process c6: laser welding (power 300W, weld width ≥0.5mm). Shell packaging process c7: robot positioning accuracy ±0.2mm. Sealing detection process c9: helium detector sensitivity ≤5×10 - 10Pa·m 3 / s.

[0039] The modeling method for the battery cell production process can be:

[0040] Using a hierarchical colored Petri net (HCPN), the battery cell production process is abstracted into the following: Places: representing the status of the battery cell production process (e.g., coiling process b is completed); Transitions: representing the transition conditions (e.g., "temperature meets the standard"); and Time parameters: binding the processing equipment cycle time (e.g., the hot press cycle time is 12 ± 0.3s).

[0041] The advantages of using HCPN to model the battery cell production process are: (1) Accurate mapping: HCPN can clearly express the parallel, conflicting and synchronous logic of the battery cell production process; (2) Dynamic adjustment: Dynamic optimization of the beat is achieved through time parameters.

[0042] Step 102: construct a logistics simulation model based on the modeled battery cell production process model and actual factory data, and set logistics equipment parameters according to the logistics simulation model;

[0043] Specifically, the above logistics simulation model is constructed with the following input:

[0044] Process model: Obtain the cell production process model from step 100, including the time parameters and transfer conditions of each cell production process;

[0045] Actual plant data: CAD drawings corresponding to the actual plant dimensions and battery cell production process (accuracy ±1mm), equipment layout, and safety spacing requirements (≥500mm). This involves building a logistics simulation model (such as a 3D conveyor line layout) based on the actual plant dimensions. Plant Simulation then maps the equipment locations (roller spreaders, preheaters, hot presses, X-ray inspection equipment, inspection machines, and ultrasonic welders), roller conveyor line paths, and buffer zones to scale. Plant Simulation is software for simulating and optimizing factories, production lines, and production logistics processes.

[0046] Functional modules (such as robot motion logic and inspection stations) are implemented using ProcessFlow programming, and control point settings require interaction signals with processing equipment. It should be noted that ProcessFlow refers to a series of orderly, interrelated activities or tasks designed to achieve a specific goal.

[0047] The above logistics simulation model includes parametric modeling of logistics units and geometric modeling of transportation systems, among which,

[0048] The above-mentioned parametric modeling of logistics units can be used to model pallet systems;

[0049] Define the number of pallets per layer and get the number of pallets per layer based on the production capacity. For example, if the daily production capacity is 2400 pieces, get 84 pallets per layer.

[0050] Pallet type parameterization: First core pallet: marked with a red border, carrying capacity of 4 first cores; Second core pallet: marked with a red border, carrying capacity of 4 second cores;

[0051] The geometric modeling of the above conveying system can be a roller conveyor line network;

[0052] Roller conveyor line network:

[0053] Calculation of the length of the roller conveyor line: L = v × T 节拍 ×1.2,

[0054] Where, L represents the effective length of the roller conveyor line (unit: meter), v represents the running speed of the roller conveyor line (unit: meter / second); T 节拍 Indicates the production cycle time (unit: seconds).

[0055] Assume v = 0.8 m / s, T 节拍 =10s; basic length: v×T 节拍 =0.8×10=8m; the final length including the safety factor is: 8m×1.2=9.6m; this means that the roller conveyor line needs to be at least 9.6 meters long to meet the beat requirements and leave a 20% buffer space.

[0056] Floor height design:

[0057] Ground floor (Z=0): carries heavy equipment (such as hot press);

[0058] Aerial layer (Z = 1.5m): lightweight transportation (such as empty pallet return).

[0059] Through geometric modeling of the conveying system, interference is avoided (minimum spacing ≥ 500mm), thereby optimizing the layout, and matching the target production capacity by the length and speed of the roller conveyor line to make the beat controllable.

[0060] Of course, depending on actual circumstances, the logistics simulation model in this embodiment may also include mobile device modeling and high-bay warehouse modeling. Mobile device modeling can be AVG modeling. AVG modeling includes AVG quantity and AVG navigation. Key parameters for AVG modeling include load and emergency stop deceleration. AVG modeling enables dynamic scheduling and high-precision positioning. High-bay warehouse modeling includes a cargo location matrix and stacker cranes. Key parameters include cargo location size and access cycle. High-bay warehouse modeling can achieve space utilization and efficient retrieval, but this embodiment does not specifically limit this.

[0061] Step 104: Generate a dynamic cell scheduling and logistics collaborative control strategy based on logistics equipment parameters through factory simulation. The dynamic cell scheduling and logistics collaborative control strategy includes control point configuration, core pairing rules, pallet reorganization logic, and beat control.

[0062] Specifically, the parameters involved in the pallet system modeling and the parameters involved in the roller conveyor line network are input into the factory simulation to ensure that the factory simulation is consistent with the actual physical system, such as setting the conveyor line speed to 0.8m / s and the pallet reassembly cycle to 10 seconds.

[0063] Generation of dynamic scheduling and logistics collaborative control strategies for battery cells: Control point configuration: Define key control nodes (such as workstation entrances, sorting points, and buffer areas) in the simulation model, and trigger pallet flow actions (such as full pallets outbound and empty pallets backflow) through logical judgment; Core pairing rules: According to production requirements, the first core and the second core need to be paired 1:1; Pallet reorganization logic: When the electrode and thickness inspection process c4 is offline, the cores are recombined into mixed pallets, such as placing 2 first cores (such as type A cores) + 2 second cores (such as type B cores) on each mixed pallet; Beat control, if the upstream processing equipment speeds up by 5%, the beat deviation is +0.5s.

[0064] Step 106: Based on the dynamic scheduling of battery cells and the coordinated control strategy for logistics, perform simulation experiments on factors affecting logistics design and optimize the layout.

[0065] Specifically, the above-mentioned logistics design influencing factors may include the number of pallets, the number of conveyor line layers, the number of conveyor lines, and the battery cell scheduling method.

[0066] 1. Pallet quantity factor

[0067] Simulation variables: total number of pallets, empty pallet return rate, and pallet utilization rate.

[0068] Balancing strategy: Use simulation to verify the relationship between the number of pallets and production continuity: too few pallets will cause waiting between processes, while too many pallets will occupy buffer space and increase scheduling complexity;

[0069] Optimization goal: Make the number of pallets meet the redundancy requirement of "maximum process backlog × 1.2".

[0070] 2. Conveyor line layer number factor

[0071] Simulation variants: single-layer line (flat conveying) vs. multi-layer line (three-dimensional conveying).

[0072] Spatial layout optimization: A single-story layout is suitable for small-scale production lines. However, if the factory height allows, a double-story conveyor line can increase space utilization by 30%. Multi-story lines require elevators or lifting and transferring machines, and simulations are conducted to verify their collaborative efficiency with AGVs.

[0073] 3. Factors related to the number of conveyor lines

[0074] Simulation variant: 1 wire (serial) vs 2 wires (parallel).

[0075] Diversion and merging strategy: Increasing the number of lines can alleviate congestion (e.g., setting up separate pole coil conveyor lines and finished product conveyor lines), but the conflict probability at the diversion and merging points needs to be verified;

[0076] Through simulation testing of equipment utilization under different numbers of lines, the optimal solution for cost and efficiency is selected.

[0077] 4. Cell scheduling factors

[0078] Number of cells to be picked: 2 cells at a time (conservative pace) vs 4 cells (high risk, high efficiency).

[0079] Placement strategy: fixed single-line placement vs. alternating double-line placement (load balance).

[0080] Number of devices enabled: single device (low production capacity) vs. dual devices (need to prevent conflicts).

[0081] The optimization potential of the cell scheduling strategy lies primarily in the coordination between the gripping batch size and path planning. A four-cell batch picking mode can reduce the number of robot movements by 50%, but this requires increasing the gripper's load capacity to 8kg and integrating it with a wider-spaced conveyor line. In simulation, a multi-objective optimization function is required to balance cycle time with equipment load.

[0082] Through the dynamic scheduling of battery cells and the coordinated control strategy of logistics, the above-mentioned influencing factors are transformed into quantifiable simulation variables. An improved genetic algorithm is used for multi-objective optimization. 6 At a computational speed of 1000 operations per second, the Pareto optimal solution can be generated within 20 minutes. Typical optimization results include: adjusting the original single-layer, single-line layout to a double-layer, double-line layout, combined with a dynamic grasping strategy, increasing production capacity by 126% while maintaining a congestion rate of less than 1%.

[0083] Compared with the prior art, the battery cell assembly line logistics simulation optimization method provided in this embodiment achieves at least the following beneficial effects:

[0084] The battery cell assembly line logistics simulation optimization method provided in this embodiment can not only completely reproduce the logistics state flow process of battery cells from raw materials, semi-finished products to finished products, but also realize dynamic coupling between key processes such as rolling-slitting-assembly based on battery cell dynamic scheduling and logistics collaborative control strategy, and support assembly scenario simulation of battery cells under different process routes, thereby expanding to various battery cell production line assembly scenarios, ensuring simulation stability, and at the same time reducing bottleneck processes in the operation of the logistics simulation model and improving production capacity, providing a reasonable basis for subsequent pallet flow direction, pallet speed, pallet quantity, processing equipment layout and battery cell scheduling strategy, and thus providing direction for the optimization of actual battery cell production lines.

[0085] Optionally, the battery cell production process includes a pre-processing process and an assembly process c, wherein the pre-processing process includes a rolling process a and a coiling process b, and the assembly process c includes a preheating process c1, a hot pressing process c2, a pole piece and thickness detection process c4, a tab welding process c5, a connecting piece welding process c6, a shell packaging process c7, a top cover welding process c8 and a sealing detection process c9 in sequence.

[0086] Specifically, in the above-mentioned roller separation process a, the roller separation machine compacts the coated electrode sheets and outputs them as slit electrode sheet coils to ensure that the thickness and density of the electrode sheets meet the process requirements.

[0087] In the slitting process (b), the electrode coils after roll separation are further processed and cut into individual cores, divided into first and second cores. Subsequently, the robot places the first and second cores one by one on a tray, with each tray only containing first and second cores of the same type. The number of first and second cores can be four, and the number of first and second cores can also be other numbers, which is not specifically limited in this embodiment. This process not only ensures the classification and organization of the first and second cores, but also provides convenience and efficiency for subsequent production processes.

[0088] The preheating step c1 eliminates the internal stress of the slit cores (the first and second cores) using a preheating machine, thereby obtaining preheated cores. The hot pressing step c2 compacts the preheated cores (the first and second cores) to a standard thickness using a hot press, thereby obtaining compacted cores to ensure that the density and thickness of the cores meet process requirements.

[0089] In the electrode inspection step c3, the compacted cores are inspected using X-ray equipment, primarily to check electrode alignment and ensure accuracy. X-ray equipment can obtain clear images of the core's internal structure, enabling accurate assessment of electrode alignment. This inspection ensures that cores pass the alignment test, thereby ensuring the smooth progress of subsequent processes. In the thickness inspection step c4, cores that pass the alignment test are inspected using a detector to measure thickness and weight to ensure that the core size and weight meet standards. This inspection ensures that cores pass the thickness and weight tests. In the tab welding step c5, cores that pass the thickness and weight tests are welded using an ultrasonic welder, for example, by welding the tabs of the first and second cores to form a single cell. This welding process produces a welded cell. In the tab welding step c6, the welded cell is welded to the cover plate using a tab welder, for example, by welding the tabs to the cover plate and folding the core in half to form a complete cell. This welding process produces a complete cell with the cover plate welded. The above-mentioned shell packaging step c7 uses a film coating and shell packaging machine to load the complete battery cell after welding the cover plate into the shell for external protection. Through the shell packaging step c7, the battery cell loaded into the shell is obtained. The above-mentioned top cover welding step c8 places the battery cell loaded into the shell in the shell, and welds the shell and the top cover by a top cover plate welding machine to ensure the sealing of the battery cell. Through the above-mentioned top cover welding step c8, the battery cell with the welded top cover is obtained. The above-mentioned sealing inspection step c9 uses a helium inspection machine to perform a sealing inspection on the battery cell after welding the top cover to ensure the sealing performance of the battery cell. Through the above-mentioned sealing inspection step c9, a battery cell that has passed the sealing inspection is obtained.

[0090] The above solution enables a complete cell production process from roll separation to assembly. Through a series of steps, including preheating (c1), hot pressing (c2), electrode and thickness inspection (c4), tab welding (c5), connector welding (c6), case encapsulation (c7), top cover welding (c8), and sealing inspection (c9), the quality and performance of the cells are ensured. Each step is equipped with appropriate processing equipment to ensure efficient and precise production.

[0091] In some optional embodiments, the logistics equipment parameters include conveyor lines, the conveyor lines include first conveyor lines and second conveyor lines, and the battery cells include first roll cores and second roll cores; the dynamic scheduling and logistics collaborative control strategies of battery cells include: a first conveyor line and a second conveyor line are independently configured in front of a single processing equipment, the first conveyor line is used to transmit the first roll core pallet, and the second conveyor line is used to transmit the second roll core pallet; the robot arm of the preheating process c1 synchronously grabs the first roll core of the first roll core pallet and the second roll core of the second roll core pallet; when the pole piece and thickness detection process c4 is offline, the first roll core and the second roll core are reorganized to obtain a mixed pallet, and each mixed pallet stores the first roll core and the second roll core at the same time; the robot arm of the pole piece welding process c5 synchronously grabs the first roll core and the second roll core, and completes the grabbing of each mixed pallet in batches; the robot arm of the connecting piece welding process c6 grabs the battery cells, emptying one mixed pallet each time; the robot arm of the sealing detection process c9 grabs all the battery cells at one time.

[0092] Specifically, initial loading: the robot inside the slitting equipment places the cores (type A core and type B core) on the pallet, and the pallet flows through the conveyor line (such as a roller conveyor line).

[0093] Configuration before processing equipment: A first conveyor line and a second conveyor line are set in front of each processing equipment. The first conveyor line and the second conveyor line are respectively dedicated to the first core pallet (A core pallet) and the second core pallet (B core pallet).

[0094] Preheating process stage c1: The robot arm corresponding to the preheating machine synchronously grabs 4 cores from a first core tray and 4 cores from a second core tray (a total of 8 cores).

[0095] Inspection process stage: Off-line pallets are placed according to the 2A+2B core combination.

[0096] Ultrasonic welding process stages:

[0097] On-line: The robot arm corresponding to the ultrasonic welding machine grabs one first core and one second core each time, completing the grabbing of a pallet in two steps (a total of 2A + 2B); off-line: The pallet outputs 4 battery cells (each battery cell = 1A + 1B core combination).

[0098] Connector welding: The robot corresponding to the connector welding machine grabs 2 battery cells at a time, completing the transfer of 4 battery cells in two steps. Single helium inspection: The robot corresponding to the single helium inspection machine grabs 4 battery cells at a time.

[0099] Specifically, the above-mentioned battery cell dynamic scheduling and logistics collaborative control strategy includes:

[0100] 1. Double roller conveyor line diversion design

[0101] Two independent roller conveyor lines (such as the first conveyor line and the second conveyor line) are set in front of each processing equipment to respectively convey: the first core tray (each first core tray is placed with 4 type A cores); the second core tray (each second core tray is placed with 4 type B cores); the function is to ensure that type A cores and type B cores are supplied to the processing equipment synchronously in a fixed ratio (1:1) to avoid waiting time.

[0102] 2. Preheating machine manipulator collaborative grasping

[0103] One first core tray holds all four A-type cores; one second core tray holds all four B-type cores. Function: Completes loading of eight cores (4A+4B) at one time, shortening cycle time.

[0104] 3. Reorganization of the testing machine tray

[0105] When the pole piece and thickness inspection process is off the line, the cores are reassembled into mixed trays: each mixed tray is placed with 2 type A cores + 2 type B cores; function: to provide the basis for pairing the first core and the second core for subsequent ultrasonic welding.

[0106] 4. Ultrasonic welding machine grabbing logic

[0107] During production, the ultrasonic welding machine's robotic arm grabs one Type A core and one Type B core from the mixed pallet at a time, completing a mixed pallet in two passes (a total of 2A and 2B). After welding, the pallet off the line holds four cells (each cell consists of 1A and 1B welded together).

[0108] 5. Optimization of the gripping of the connecting piece welding machine and the helium inspection machine

[0109] Connector welding machine: Grabs 2 cells at a time, emptying a tray in 2 passes (4 cells total). Helium inspection machine: Grabs 4 cells at a time (transfers the entire tray), reducing the frequency of single operations.

[0110] Calculation of cycle time in each of the above processes:

[0111] Robot action time statistics

[0112] Preheating process c1 grabbing: T1 (synchronous grabbing of 8 cores); inspection process reorganization: T2 (2A+2B sorting); ultrasonic welding process grabbing: T3 (1A+1B at a time, 2 times / pallet); connecting piece welding process c6 grabbing: T4 (2 battery cells at a time, 2 times / pallet); sealing inspection process c9 grabbing: T5 (transfer of 4 battery cells on a pallet).

[0113] That is, the robot grasping time in each link (such as synchronous grasping in the preheating process c1, batch grasping in ultrasonic welding, etc.) is counted as the basis for beat optimization.

[0114] By adopting the above solution, through the reorganization of dedicated roller conveyor lines and mixed pallets for type A / type B cores, a 1:1 precise matching of the welding process is ensured, thereby improving the pairing efficiency; the idle time is reduced by batch grabbing by the robots corresponding to each process (such as the synchronization of 8 cores in the preheating machine), and the helium inspection and whole pallet transfer reduces the operation frequency, thereby optimizing the beat; the dynamic scheduling and logistics collaborative control strategy of the battery cells can be directly embedded in the MES system (manufacturing execution system) to realize digital logistics simulation and real-time regulation, thereby being compatible with automation.

[0115] In some optional embodiments, according to the dynamic scheduling of battery cells and the coordinated control strategy of logistics, simulation experiments of factors affecting logistics design are performed and the layout is optimized, including: setting logistics equipment simulation parameters, which include the number of conveyor lines, the number of conveyor line layers, the battery cell scheduling method, the battery cell placement strategy and the number of enabled processing equipment; according to the logistics equipment simulation parameters, the logistics transportation logic of the battery cells from the coil cutting process b to the sealing detection process c9 is constructed, and the flow of the first coil core pallet and the second coil core pallet, the processing timing of the robots and processing equipment in each process are simulated; key indicators are counted, and the logistics equipment simulation parameters are adjusted according to the key indicators until the logistics simulation model runs without congestion and meets the target production capacity.

[0116] Specifically, the simulation parameters of the above-mentioned logistics equipment are set, such as the number of the above-mentioned conveyor lines can be 1 or 2, the number of the above-mentioned conveyor line layers can be single-layer or multi-layer, the above-mentioned battery cell scheduling method can be to grab 2 or 4 battery cells at a time, the above-mentioned battery cell placement strategy can be single-line fixed placement or double-line alternating placement; the number of the above-mentioned processing equipment enabled is 1 or 2.

[0117] This simulation experiment optimizes the performance of the conveyor line through a combination of multiple variables. The core influencing factors and their parameter levels are set as follows:

[0118] Pallet quantity variable level: dynamic adjustment (real-time calculation based on the cache capacity of each equipment node); optimization goal: minimize the waiting time of the core / cell between processes.

[0119] Variable level of the number of conveyor lines: single line (mixed conveying of type A / type B cores) or dual parallel lines (independent conveying of type A / type B cores); optimization goal: avoid congestion caused by cross-logistics.

[0120] Variable level of the number of conveyor line layers: single-layer layout or multi-layer layout (for example, cross-layer connection through a high-bay warehouse); optimization goal: maximize the utilization of factory space.

[0121] Variable level of battery cell scheduling method: the robot corresponding to each process grabs 2 or 4 battery cells at a time; optimization goal: to achieve balanced production line takt time.

[0122] Variable level of the number of processing equipment enabled: a single processing equipment running independently or two equipment working in parallel; optimization goal: to improve the throughput of the bottleneck station.

[0123] Variable level of battery cell placement strategy: concentrated output to a single conveyor line or alternately distributed to two conveyor lines; optimization goal: ensure uniform load distribution of downstream equipment.

[0124] Building a logistics simulation model:

[0125] Based on the aforementioned logistics equipment simulation parameters, the entire battery cell process, from reel cutting to seal testing, was established, encompassing the dual-pallet flow (first and second core pallets), robotic motion sequence, and processing equipment cycle time. The first and second core pallets were independently input and combined according to a rule after the preheating machine (e.g., 2A+2B combination). The robotic motion sequence was programmed (e.g., ultrasonic welding stations were split into two grabs, 1A+1B each).

[0126] Run the simulation and optimize: collect statistics on key indicators (congestion rate, equipment utilization rate, and capacity achievement rate); the optimization threshold for the congestion rate is less than 5%, the equipment utilization rate is 70% to 90%, and the capacity achievement rate is ≥100%.

[0127] Optimization strategy: If the congestion rate exceeds the standard, add diversion paths or buffers (such as adding a temporary storage area after the hot press). If the equipment utilization rate is unbalanced, adjust the number of grabs or enable parallel equipment.

[0128] Dynamically adjust the parameter combination until the logistics simulation model runs without congestion and meets the target production capacity.

[0129] This solution, through parametric simulation and data-driven optimization, achieves a zero-congestion layout while ensuring production capacity. Specifically, parametric simulation supports rapid format changeovers (such as adjusting the ratio of the first and second cores). Dynamic gripping strategies (two or four cells) match the equipment's maximum cycle time. A multi-layer conveyor line design increases production capacity per unit area by 30%. Simulation verifies the optimal equipment quantity, reduces ineffective investment, predicts and optimizes congestion points in advance, and shortens the production cycle.

[0130] Optionally, the cell placement strategy includes single-line fixed placement or double-line alternating placement. When the cell placement strategy is double-line alternating placement, the cells coming off the tab welding process c5 are alternately allocated to the two conveyor lines.

[0131] The above solution can achieve the following effects: First, the alternating placement of the two lines evenly distributes the battery cells produced in the tab welding process c5 to the two conveyor lines, avoiding accumulation on a single line, ensuring the stable operation rhythm of downstream equipment, and at the same time making the equipment utilization rate tend to be balanced (such as 70% to 90%), reducing shutdowns or beat delays caused by overload; Second, the alternating allocation of the two lines provides redundant paths. If one line is temporarily suspended, the other line can still maintain partial flow, reducing the overall congestion rate and ensuring the continuous operation of the production line; Third, the dual-line diversion reduces the single-line conveying pressure, combined with the 4-cell grabbing strategy, maximizes the equipment beat synchronization, increases production capacity by 15% to 25%, and avoids the robot being idle due to waiting for the conveyor line to be idle; Fourth, the two lines operate independently. If one line fails, it can be temporarily switched to the other line to maintain 50% production capacity, thereby improving the availability of the production line and reducing the loss of production capacity during maintenance.

[0132] Optionally, the number of processing equipment enabled is 1 or 2. When the number of processing equipment enabled is 2, the two processing equipment share the same group of conveyor lines, and the logistics simulation model detects the task conflict probability of the processing equipment.

[0133] Specifically, shared conveyor line design: two processing equipment of the same type (such as ultrasonic welding machines) are arranged in parallel and share the same set of input / output conveyor lines (roller conveyor line or AGV path).

[0134] Task allocation mechanism:

[0135] Dynamic load balancing: The central controller monitors the status of processing equipment in real time and allocates pallets to processing equipment with higher idle rates; Proximity allocation: Prioritizes processing equipment closer to the current battery cell location to reduce transportation time; Hardware resource conflict: Detects whether two processing equipment request the same set of robots or conveyor line resources at the same time; Timing conflict: Analyzes whether the processing cycles of processing equipment overlap, causing battery cell queue timeouts.

[0136] Conflict Quantification Indicators:

[0137] Conflict probability = number of conflicts / total number of task assignments × 100%;

[0138] Conflict impact time = average delay caused by a single conflict (seconds);

[0139] Dual processing equipment increases production capacity by 30%-50%, balancing loads and stabilizing utilization at 75%-85%. If one processing unit fails, the other can maintain 50% capacity, reducing downtime risks and alleviating backlogs in upstream processes through dynamic allocation.

[0140] 3. Solutions to Task Conflicts

[0141] 3.1 Preventive Design

[0142] Buffer zone setting: Add a temporary storage space between two processing equipment (e.g. battery cell capacity = 4 battery cell trays) to absorb instantaneous flow fluctuations.

[0143] Time-sharing control: PLC programming is used to achieve exclusive access to the conveyor line in different time periods (e.g. odd minutes for device A, even minutes for device B).

[0144] 3.2 Dynamic Scheduling Optimization

[0145] Priority rules: High-priority cells (such as urgent orders) are assigned to designated equipment; long-cycle tasks (such as cells after X-ray inspection) are preferentially assigned to equipment with high idle rates; conflict prediction: Based on historical simulation data, high-conflict periods are predicted and task sequences are adjusted in advance.

[0146] 3.3 Simulation Verification Case

[0147] Scenario: The connecting piece welding station uses dual processing equipment and shares the input roller conveyor line.

[0148] Conflict phenomenon: Two welding machines compete for two battery cells on the same tray at the same time, and the initial conflict probability is 15%.

[0149] Optimization measures: Implant a mutex in Plant Simulation to force serialization of capture requests; add one buffer station.

[0150] By adopting the above solution, the solution of dual processing equipment sharing the conveyor line significantly improves the production line efficiency through capacity expansion, load balancing and fault redundancy, and is suitable for scenarios with high production capacity and high reliability requirements such as battery cells.

[0151] Example 2

[0152] Reference Figure 3 As shown, Figure 3 : is a structural diagram of the battery cell assembly line logistics simulation optimization system provided by the present invention; this embodiment is a battery cell assembly line logistics simulation optimization system, including:

[0153] The process modeling module 200 is used to model the battery cell production process and obtain a modeled battery cell production process model; the simulation model construction module 202 is coupled to the process modeling module 200, and is used to receive the modeled battery cell production process model, construct a logistics simulation model based on the modeled battery cell production process model and actual factory data, and set logistics equipment parameters according to the logistics simulation model; the control logic generation module 204 is coupled to the simulation model construction module 202, and is used to receive logistics equipment parameters, and generate battery cell dynamic scheduling and logistics collaborative control strategies through factory simulation based on the logistics equipment parameters, and the battery cell dynamic scheduling and logistics collaborative control strategies include control point configuration, core pairing rules, pallet reorganization logic and beat control; the simulation optimization module 206 is coupled to the control logic generation module 204, and is used to receive battery cell dynamic scheduling and logistics collaborative control strategies, and perform simulation experiments on logistics design influencing factors and optimize the layout according to the battery cell dynamic scheduling and logistics collaborative control strategies.

[0154] Example 3

[0155] Continue to refer to Figure 4-Figure 6 As shown, Figure 4 This is a structural diagram of the battery cell conveying and assembly simulation scheduling system (including DP7) provided by the present invention; Figure 5 1 is a schematic structural diagram of the battery cell conveying and assembly simulation scheduling system (including Queue 102) provided by the present invention; Figure 6 It is a structural schematic diagram of the battery cell conveying and assembly simulation scheduling system (including DP5) provided by the present invention; this embodiment provides a battery cell conveying and assembly simulation scheduling system, and the above-mentioned battery cell assembly line logistics simulation optimization method can be applied to the battery cell conveying and assembly simulation scheduling system, including: a dynamic assembly model, including an assembly logistics channel, the assembly logistics channel is used to connect the roller conveyor line 1 of adjacent processing equipment, and the dynamic assembly model is configured with an equipment processing connection control module and battery cell production process logic data; a control point system, including at least three control points 3, at least three control points 3 are respectively arranged at the head and tail ends and side ends of the roller conveyor line 1; the equipment processing connection control module is linked to the processing time of the processing equipment, and when the first core roll pallet and the second core roll pallet arrive at the head and tail ends and side ends of the control point 3, the action is triggered according to the preset battery cell production process logic data; a simulation construction module, which constructs the assembly logistics channel through the roller line module of the simulation software and configures the control point logic.

[0156] The details are as follows: the above-mentioned dynamic assembly model can be a linear assembly model, which is composed of a linear roller conveyor line channel, and adjacent processing equipment 2 (such as a preheating machine, a welding machine) is connected in series through a roller conveyor line 1.

[0157] A-type core / B-type core merging logic: The first core pallet and the second core pallet are paired in a fixed ratio on the linear roller conveyor according to the dynamic scheduling and logistics collaborative control strategy of the battery cells (for example, a 1A+1B combination forms a completed battery cell). The above-mentioned battery cell production process logic data can include a battery cell process logic diagram, which contains a digital logic diagram of the equipment topology and logistics flow.

[0158] Control Point 3 Layout:

[0159] End control point 31: Detects blockage ahead and determines whether to stop or continue pallet flow (pauses if the equipment is full). Side control point 32: Loads empty pallets during logistics simulation model initialization and dynamically adds them to the linear roller conveyor line.

[0160] Equipment processing connection control module

[0161] When the pallet arrives at the processing equipment station, it is connected to processing equipment 2 (such as a connector welding machine). The robot corresponding to the connector welding machine is triggered to grab the core (such as grabbing it from Queue 102 to the connector welding machine). The empty pallet is returned to the starting point via roller conveyor line 1.

[0162] Control point logic

[0163] Full pallet flow direction decision (such as the control point DP7 corresponding to the object in the factory simulation, such as Figure 4 Conditional judgment: Check whether there is a full tray blockage ahead. Action: If there is a full tray blockage, switch to the backup path; otherwise, continue to flow in a straight line.

[0164] Empty pallet diversion decision (such as the control point DP5 corresponding to the object in the factory simulation, such as Figure 6 (as shown): Conditional judgment: Check the capacity of the side empty pallet buffer area. Action: If the buffer is not full, turn and enter the side lane; otherwise, go straight to the next station.

[0165] Queue function (such as the queue Queue102 corresponding to the object in the factory simulation)

[0166] Core grabbing: When a full pallet passes by, the robot corresponding to the connecting piece welding machine grabs the core and takes it to the connecting piece equipment for processing. Empty pallet processing: After grabbing, the empty pallet automatically descends to the lower reflow channel (to avoid occupying main line resources).

[0167] The intelligent diversion control (DP7) is used to detect congestion ahead in real time (e.g., pallet density within 3 meters), automatically switching to an alternate path to prevent the risk of congestion on the main roller conveyor line. This reduces congestion by 40% to 60% without requiring manual intervention, enabling 24 / 7 continuous production. The empty pallet buffer optimization (DP5) can dynamically divert empty pallets based on the capacity of the side buffer area, preventing empty pallets from accumulating and occupying resources on the main roller conveyor line. This improves space utilization by 2%, and balances upstream and downstream beats through threshold setting (e.g., importing when the buffer is less than 3). The parallelized core processing (Queue 102) is used to quickly grab the core and transfer it to the connecting piece device when a full pallet passes by, and the empty pallet automatically returns. Its purpose is to: reduce robot waiting time, stabilize equipment utilization at 80% to 90%, and improve mainline logistics efficiency by 30% through the lower-level return flow design.

[0168] 3. Simulation modeling implementation steps

[0169] Roller conveyor line construction: Use simulation software (such as Plant Simulation) to drag the prefabricated roller conveyor line module to build a linear logistics channel. Insert control points 3 at the beginning, end and side of the linear logistics channel (see Figure 5 and Figure 6 As shown in the figure), and bind the judgment script of the dynamic scheduling of battery cells and the coordinated control strategy of logistics.

[0170] Control point system: Control point 31 at the beginning and end of roller conveyor line 1: Write a congestion detection algorithm (e.g., if the queue length ahead is greater than 3, THEN STOP). Control point 32 at the side end of roller conveyor line 1: Set the empty pallet generation rule (e.g., release one empty pallet every 60 seconds).

[0171] Optionally, the head and tail control points 31 of the roller conveyor line 1 are used to detect the status of the equipment in front in real time, such as preventing the pallet from entering when the hot press is busy; the side control point 32 of the roller conveyor line 1 is used to load empty pallets only during simulation initialization to avoid interference during runtime.

[0172] Equipment linkage configuration: The equipment processing connection control module links the cell process logic diagram to ensure that the pallet flow is strictly synchronized with the processing sequence. The equipment processing connection control module connects to the equipment-side processing function. When the pallet reaches the front, rear, and side ends of control point 3, it flows to a fixed position according to the cell process logic diagram, thus achieving directional transportation of the core in the factory logistics.

[0173] The above solution, through the organic combination of intelligent control, optimized layout, and advanced simulation technology, has achieved comprehensive improvements in efficiency, quality, and cost for the battery cell assembly line, providing reliable technical support for intelligent power battery manufacturing. Specifically, the automatic matching accuracy of the first and second reels reaches 99.9%, and the battery cell assembly cycle is stably controlled within a ±0.5-second error range. Second, the head and tail control points 31 implement dynamic flow control, automatically starting and stopping conveying based on the status of the preceding equipment, reducing the risk of congestion. Side control points 32 intelligently dispatch empty pallet resources to ensure continuous and stable production line operation, with a control response time of less than 0.5 seconds, an 80% improvement in efficiency compared to traditional manual control. The modular roller conveyor design reduces equipment spacing by 30%, the linear logistics channel reduces material turns, and conveying efficiency increases by 25%. Standardized interfaces enable rapid changeovers, reducing processing equipment changeover time to 15 minutes. Third, it can not only simulate over 98% of actual production scenarios, but also proactively identify and resolve 95% of potential bottlenecks. Real-time capacity forecasting accuracy exceeds 90%. Fourth, direct cost savings (such as a 40% reduction in labor costs and a 25% reduction in energy consumption) and increased production capacity.

[0174] Optionally, each control point 3 has a unique number, wherein the head and tail control points 31 of the roller conveyor line 1 are used to detect the status of the equipment ahead in real time and stop the flow of battery cell trays when a blockage is detected; the side control point 32 of the roller conveyor line 1 is used to load empty trays when the dynamic assembly model is initialized.

[0175] Specifically, the above numbering may be a three-pole code, and the three-pole code may be a type code + a position code + a function code, such as M250-S1 representing the first lateral control point at 250 mm on the main line.

[0176] The control points 31 at the beginning and end of roller conveyor line 1, as well as the control points 32 at the side ends of roller conveyor line 1, are each uniquely numbered, enabling full traceability. Using these control points 31 at the beginning and end of roller conveyor line 1 allows for collision protection (e.g., preventing adjacent pallets from being less than 50mm apart), tact control (maintaining a buffer of 2-4 between equipment), and fault isolation (limiting the impact of processing equipment failures to within ±2 stations).

[0177] The side end control point 32 of the roller conveyor line 1 can achieve initialization efficiency (50 empty pallets deployed within 30 seconds), resource utilization (empty pallet turnover rate ≥ 95%) and abnormality prevention (avoiding double loading).

[0178] The inventors have verified that by standardizing the numbering of the control points 3 and combining the functional limitations of the head and tail control points 31 of the roller conveyor line 1 and the side control points 32 of the roller conveyor line 1, 100% logistics path traceability, 95% status judgment accuracy, and more than 98% equipment utilization can be achieved.

[0179] In some optional embodiments, it also includes: an equipment status perception module for detecting the working status of the processing equipment in real time, the working status includes normal operating status and non-working status; a distributed control point network, the control point of each battery cell process is electrically connected to the corresponding processing equipment, wherein: when the processing equipment is in a non-working state, its associated nearest control point automatically switches to a blocking mode to prevent the battery cell pallet from flowing to the processing equipment; when the equipment resumes operation, the associated control point synchronously releases the blocking mode; an end decision controller, arranged at the end of each conveyor line, is configured to: calculate the current equipment processing time when triggered, and determine the destination of the next process according to the battery cell process logic diagram; a pallet circulation system, including a pallet conveyor line and a pallet return line, wherein the pallet conveyor line is used to forward the flow of the first core roll pallet and the second core roll pallet; the pallet return line is used to reversely return the empty pallet to the starting station.

[0180] Specifically, continue to refer to Figure 3-Figure 6 As shown, the equipment status sensing module is used to detect the working status of the processing equipment in real time. The working status includes: normal operation status and non-working status. The non-working status includes fault status, cleaning status and maintenance status. The equipment status sensing module can be a sensor.

[0181] In the distributed control point network, each cell process control point 3 is electrically connected to the corresponding processing equipment 2. When a processing equipment is not operating, its nearest associated control point 3 automatically switches to blocking mode, preventing the flow of pallets to that processing equipment. When the processing equipment resumes operation, the nearest associated control point 3 simultaneously releases blocking mode. This blocking mode includes logical blocking, such as when a control point 3 moves a pallet via an elevator.

[0182] The above-mentioned terminal decision controller is set at the end of each conveyor line and is configured to: calculate the current equipment processing time T = f (core type, equipment parameters) when triggered; determine the destination of the next process according to the battery cell process logic diagram.

[0183] The pallet circulation system consists of a pallet conveyor line and a pallet return line. The pallet conveyor line carries pallets loaded with coils in a forward flow, while the pallet return line returns empty pallets to the starting station. A single conveyor line handles only one type of dedicated pallet, and the pallet return time is ≤1.5 times the forward conveyor time.

[0184] The equipment processing connection control module is used to respond to the core processing completion event and execute: updating the product status identification; triggering the new product transfer instruction according to the battery cell process logic diagram.

[0185] Specifically, the pallet conveyor line can be an upper conveyor line, and the pallet return line can be a lower return line. Of course, according to actual conditions, the pallet conveyor line can be a lower return line, and the pallet return line can be an upper conveyor line. This embodiment only takes the example that the pallet conveyor line can be an upper conveyor line and the pallet return line can be a lower return line.

[0186] When the processing equipment is in an inoperative state, such as a malfunction or being cleaned, the control point 3 closest to the equipment end is also in an inoperative state. The pallet will not flow to the next process when it reaches this control point 3. It will only flow to the next process after the processing equipment starts operating. When the pallet flows on the pallet conveyor line, it triggers the controller when it reaches the end, which determines the equipment's processing time for the core and the destination of the pallet to flow to the next process. When the core processing is completed and no new core enters the pallet conveyor line, the equipment is in an idle state. After the core pallet delivers the core to the equipment on a section of the pallet conveyor line, the pallet will flow back from the pallet return line to the pallet conveyor line, returning to the previous process to place the core into the pallet. Each section of the pallet conveyor line has a pallet, and the core will complete all battery cell processing steps within the entire processing equipment.

[0187] When the core roll pallet completes the processing of processing equipment 2 in the pallet conveyor line, the equipment processing connection control module inside processing equipment 2 will also be triggered. The core roll product will become the input condition of the new product type, and at this time, the new location for product transfer input will be determined according to the battery cell production process logic diagram.

[0188] The above solution can not only ensure that the logistics flow is automatically blocked when the processing equipment fails to prevent pallet accumulation, but also realize dynamic optimization of the processing time of the battery cell (±5% deviation control), intelligent path selection (accuracy ≥99.5%) and pallet recycling utilization rate ≥95%, while also achieving fast fault response speed (≤3) and high equipment utilization rate (≥85%).

[0189] Specific application examples

[0190] Modeling the battery cell production process to obtain a modeled battery cell production process model;

[0191] Based on the modeled cell production process model and actual factory data, a logistics simulation model is constructed, and logistics equipment parameters are set according to the logistics simulation model. For example, the logistics simulation model is organized, and the 3D model is imported into the factory simulation according to the actual factory dimensions, and the entire factory CAD layout is imported into the logistics simulation model. After the logistics simulation model is constructed, the ProcessFlow language in PlantSimulation is used to program the various actions and scheduling strategy control points of the logistics simulation model, as well as the equipment functions. At the same time, the logistics path of the cell assembly line logistics simulation optimization system is established. Programming for equipment functions includes programming for active equipment shutdowns due to cleaning, programming for equipment failure time, programming for the number of cells produced per minute, and programming for the cell yield rate.

[0192] The logistics simulation model is optimized to select the lowest-cost solution while meeting the factory's battery production capacity requirements. Taking battery cell production as an example, as shown in Table 1 below, there are several options to choose from. These options are combined to select the optimal optimization solution:

[0193] Table 1 Layout plan

[0194]

[0195]

[0196] The simulation schemes include: ACE, ACF, ADE, ADF, BCE, BCF, BDE, and BDF. By simulating these eight schemes, the lowest cost scheme is selected while meeting the battery cell production capacity.

[0197] Application Example 1 Select Solution A in Table 1

[0198] Reference Figure 7 and Figure 8 As shown, Figure 7 It is a planar schematic diagram of the conveyor line corresponding to the material simulation model of the battery cell production and coiling equipment in Application Example 1 provided by the present invention; Figure 8 Schematic diagram of the pallet transfer process of the conveyor line corresponding to the material simulation model of the battery cell production and cutting coil equipment in the application example 1 provided by the present invention; Scheme A is a material simulation model of the battery cell production and cutting coil equipment, and the pallet transfer process of the conveyor line corresponding to the material simulation model of the battery cell production and cutting coil equipment is as follows Figure 8As shown, the simulation model includes main roller conveyor lines (such as the first main roller conveyor line 1, the second main roller conveyor line 2, the third main roller conveyor line 3 and the fourth main roller conveyor line 4), side roller conveyor lines (the first side roller conveyor line 5 and the second side roller conveyor line 6), processing equipment (such as the first processing equipment 8, the second processing equipment 9, the third processing equipment 10 and the fourth processing equipment 11), sensors (such as the first sensor 12 and the second sensor 13), the size of the main roller conveyor line is modeled according to the actual size, the processing position of each processing equipment is placed according to the real position, and the battery cell conveying and assembly simulation scheduling system is responsible for the transportation of pallets and has the function of intelligent pallet calling;

[0199] According to the battery cell production process, the positive electrode cut coils are transported to the battery processing equipment 8 for slitting and winding to form the first core (such as the positive electrode core); at the same time, the negative electrode cut coils are transported to the battery processing equipment 10 for slitting and winding to form the second core (such as the negative electrode core). The battery cell conveying and assembly simulation scheduling system detects whether the first sensor 12 and the second sensor 13 have empty pallets. If there are no empty pallets, the first processing equipment 8 and the third processing equipment 10 are in a waiting state until an empty pallet is found. The core is placed in the empty pallet. The full pallet flows from the side roller conveyor line to the main roller conveyor line and then flows into the next process. Since the processing time of the first processing equipment 8 is less than that of the third processing equipment 10, the algorithm written by the first sensor 12 and the second sensor 13 is mutually associated with the battery cell production process. For example, the first sensor 12 corresponds to the first processing equipment 8 and the third processing equipment 10. After the first processing equipment 8 completes processing the first core, when the first core enters the main roller conveyor line (such as the third main roller conveyor line 3), the count of the first sensor 12 is increased by one. After the third processing equipment 10 completes processing the second core, when the second core enters the main roller conveyor line (such as the third main roller conveyor line 3), the count of the first sensor 12 is increased by one again; when the count of the first sensor 12 reaches 2 (an even number), the first core and the second core are allowed to move forward synchronously, and the count is reset to 0; if the first processing equipment 8 is completed first, its first core is temporarily stored on the main roller conveyor line and waits until the second core of the third processing equipment 10 arrives at the main roller conveyor line.

[0200] The second sensor 13 is used to detect the number of empty pallets on the side roller conveyor line (such as less than 6). If the empty pallets flow from the fourth main roller conveyor line 4 to the second side roller conveyor line 6, if the number of empty pallets on the second side roller conveyor line 6 is less than 6, the fourth main roller conveyor line 4 will fill the 6 empty pallets on the second side roller conveyor line 6, and one empty pallet will be transferred to the first side roller conveyor line 5 through the jacking transfer machine, waiting for the core processed by the first processing equipment 8 to be placed in the empty pallet. After the empty pallet is placed in the core, it will flow from the first side roller conveyor line 5 to the third main roller conveyor line 3, and the full pallet will flow into the next process. If the next process is blocked, the full pallet will be transferred by the elevator to the first main roller conveyor line 1, and then from the first main roller conveyor line 1 by the elevator to the third main roller conveyor line 3, and flow into the next process again. If the full pallet flows into the next process without blockage, the next process will take away the core, and the empty pallet will be lowered by the elevator to the fourth main roller conveyor line 4. If the second side roller conveyor line 6 is full of 6 empty pallets, the empty pallet will be transferred by the elevator to the second main roller conveyor line 2, flow to the right and then be lowered by the elevator to the fourth main roller conveyor line 4. It is then determined whether the number of empty pallets on the second side roller conveyor line 6 is less than 6. If it is less than 6, it will flow into the second side roller conveyor line 6. If it is not less than 6, it will continue to circulate on the main roller conveyor line.

[0201] Application Example 2: Select Solution C in Table 1

[0202] Reference Figure 9-10 As shown, Figure 9 A plan view of a conveyor line corresponding to a coil core transport model between a coil cutting device and a processing device in Application Example 2 provided by the present invention; Figure 10 This is a schematic diagram of the pallet transfer process of the conveyor line corresponding to the coil core transportation model between the coil cutting equipment and the processing equipment in the application example 2 provided by the present invention; Scheme C is a coil core transportation model between the coil cutting equipment and the processing equipment, and the pallet transfer process of the conveyor line corresponding to the coil core transportation model between the coil cutting equipment and the processing equipment is as follows Figure 10The transport model includes roller conveyor lines (first roller conveyor line s1, second roller conveyor line s2, third roller conveyor line s3, and fourth roller conveyor line s4), first core pallet turntable s5, second core pallet turntable s6, and lifting and transferring machines (first lifting and transferring machine s7, second lifting and transferring machine s8, third lifting and transferring machine s9, and fourth lifting and transferring machine s10). According to the pallet transfer process of the conveyor line corresponding to the core transport model between the cutting equipment and the processing equipment, the first core pallet is transported by the first roller conveyor line s1 to the first lifting and transferring machine s7, or is transported from the second roller conveyor line s2 through the first core pallet turntable s5 to the third roller conveyor line s3, and transported to the fourth lifting and transferring machine s10, and the second core pallet is transported by the fourth roller conveyor line s4 to the third lifting and transferring machine s9, or is transported from the third roller conveyor line s3 through the second core pallet turntable s6 to the second roller conveyor line s2 and transported to the second lifting and transferring machine s8.

[0203] When the core pallet is transported from the roller conveyor line to the lift transfer machine, it stops and triggers a sensor, causing the robot to grab the core and move it to the next process. The empty pallet then flows back down the roller conveyor line to the previous process. When the first core pallet is transported by the first roller conveyor line s1, the sensor is triggered, and the next first core pallet is transported to the second roller conveyor line s2. The first and second roller conveyor lines s1 and s2 transport the first core pallet carrying the first core back and forth. When the second core pallet is transported by the third roller conveyor line s3, the sensor is triggered, and the next second core pallet is transported to the fourth roller conveyor line s4. The third and fourth roller conveyor lines s3 and s4 transport the second core pallet carrying the second core back and forth.

[0204] If a material blockage occurs on the first roller conveyor line s1 or the second roller conveyor line s2, the sensor is triggered, and the first core tray carrying the first core stops its reciprocating circulation and is transported to a non-blocked route. If a material blockage occurs on both the first roller conveyor line s1 and the second roller conveyor line s2, the first core stops being transported to the first roller conveyor line s1 and the second roller conveyor line s2 and flows back to the first main roller conveyor line 1 of the coil cutting section of Solution A in Application Example 1. If a material blockage occurs on the third roller conveyor line s3 or the fourth roller conveyor line s4, the sensor is triggered, and the second core tray carrying the second core stops its reciprocating circulation and is transported to a non-blocked route. If a material blockage occurs on both the third roller conveyor line s3 and the fourth roller conveyor line s4, the second core tray carrying the second core stops being transported to the third roller conveyor line s3 and the fourth roller conveyor line s4 and flows back to the first main roller conveyor line 1 of the coil cutting section of Solution A in Application Example 1.

[0205] Application Example 3: Select Solution E in Table 1

[0206] See also Figure 11-12 As shown, Figure 11 This is a planar schematic diagram of the conveyor line corresponding to the real simulation model of battery cell assembly in Application Example 3 provided by the present invention; Figure 12 The schematic diagram of the cell processing and transportation of the conveyor line corresponding to the real simulation model of the cell assembly in the application example 3 provided by the present invention; the E scheme is a real simulation model of the cell assembly, and the schematic diagram of the cell processing and transportation of the conveyor line corresponding to the real simulation model of the cell assembly is as follows Figure 12 As shown, the logistics simulation model includes roller conveyor lines (p1 to p8), 9 battery cell processing equipment (e1 to e9), and battery cell pick-up and discharge points (a1 to a17). According to the battery cell production process, the pallet starts to be transported from the roller conveyor lines p1 and p2 to the battery cell pick-up and discharge points a1 and a2. Each pallet starts to put two first cores and two second cores. The battery cell processing equipment e1 uses a robot to take the first core of the pallet at the battery cell pick-up and discharge point a1. and the second core, and put them into the battery cell taking and unloading point a3 after the processing is completed; the battery cell processing equipment e2 uses a robot to take out the first core and the second core from the tray at the battery cell taking and unloading point a2, and put them into the battery cell taking and unloading point a4 after the processing is completed; the battery cell processing equipment e3 uses a robot to take out the first core and the second core from the tray at the battery cell taking and unloading point a5, and merge the first core and the second core into one battery cell, and put them into the battery cell taking and unloading point a7 after the processing is completed; the battery cell processing equipment e4 uses a robot to take out the first core and the second core from the tray at the battery cell taking and unloading point a6, and merge the first core and the second core into one battery cell, and put them into the battery cell taking and unloading point a8 after the processing is completed; the battery cell processing equipment e5 uses a robot to take out the battery cell from the tray at the battery cell taking and unloading point a9, and put it into the battery cell taking and unloading point a11 after the processing is completed; the battery cell processing equipment e6 uses a robot to take out the battery cell from the tray at the battery cell taking and unloading point a10, and put it into the battery cell taking and unloading point The battery cell processing equipment e7 uses a robot to take out the battery cell from the tray at the battery cell taking and placing point a13, and puts it into the battery cell taking and placing point a15 after the processing is completed; the battery cell processing equipment e8 uses a robot to take out the battery cell from the tray at the battery cell taking and placing point a14, and puts it into the battery cell taking and placing point a16 after the processing is completed; the battery cell processing equipment e9 takes out the battery cell flowing into the battery cell taking and placing point a16, and puts it into the battery cell taking and placing point a17 after the processing is completed, and then flows into the next process.

[0207] Although some specific embodiments of the present invention have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It should be understood by those skilled in the art that modifications may be made to the above embodiments without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A battery assembly line logistics simulation optimization method, characterized in that: The following steps are involved: Modeling the battery cell production process to obtain a modeled battery cell production process model; Constructing a logistics simulation model based on the modeled battery cell production process model and actual factory data, and setting logistics equipment parameters according to the logistics simulation model; Based on the logistics equipment parameters, a dynamic scheduling and logistics collaborative control strategy for battery cells is generated through factory simulation. The dynamic scheduling and logistics collaborative control strategy for battery cells includes control point configuration, core pairing rules, pallet reorganization logic, and beat control. According to the battery cell dynamic scheduling and logistics collaborative control strategy, simulation experiments on factors affecting logistics design are performed and the layout is optimized.

2. The battery assembly line logistics simulation optimization method according to claim 1, characterized in that: The battery cell production process includes a pre-processing process and an assembly process, wherein the pre-processing process includes a rolling process and a coiling process, and the assembly process includes a preheating process, a hot pressing process, a pole piece and thickness detection process, a tab welding process, a connecting piece welding process, a shell packaging process, a top cover welding process and a sealing detection process in sequence.

3. The battery assembly line logistics simulation optimization method according to claim 2, characterized in that: The logistics equipment parameters include conveyor lines, the conveyor lines include first conveyor lines and second conveyor lines, the battery cells include first winding cores and second winding cores; the pallets include first winding core pallets, second winding core pallets and mixed pallets; The battery cell dynamic scheduling and logistics coordinated control strategy includes: The first conveyor line and the second conveyor line are independently configured in front of a single processing equipment, the first conveyor line is used to transport the first core pallet, and the second conveyor line is used to transport the second core pallet; The robot arm of the preheating process synchronously grabs the first core of the first core tray and the second core of the second core tray; When the pole piece and thickness detection process is offline, the first winding core and the second winding core are reassembled to obtain the mixed tray, and each mixed tray stores the first winding core and the second winding core at the same time; The manipulator of the tab welding process synchronously grabs the first winding core and the second winding core, and completes the grabbing of each mixed pallet in batches; The robot arm in the connecting piece welding process grabs the battery cells and empties one of the mixed trays at a time; The robot arm of the sealing detection process grabs all the battery cells at one time.

4. The battery cell assembly line logistics simulation optimization method according to claim 3, characterized in that: The performing of simulation experiments on logistics design influencing factors and optimizing layout according to the battery cell dynamic scheduling and logistics coordinated control strategy includes: Setting logistics equipment simulation parameters, including the number of conveyor lines, the number of conveyor line layers, the battery cell scheduling method, the battery cell placement strategy, and the number of processing equipment enabled; Based on the logistics equipment simulation parameters, the logistics transportation logic of the battery cells from the reeling process to the sealing test process is constructed, and the flow of the first and second core trays, as well as the processing sequence of the manipulators and processing equipment in each process are simulated; Key indicators are collected and the logistics equipment simulation parameters are adjusted according to the key indicators until the logistics simulation model runs without congestion and meets the target production capacity.

5. The battery cell assembly line logistics simulation optimization method according to claim 4, characterized in that: The cell placement strategy includes single-line fixed placement or double-line alternating placement. When the cell placement strategy is double-line alternating placement, the cells produced during the tab welding process are alternately allocated to two conveyor lines.

6. The battery assembly line logistics simulation optimization method according to claim 4, characterized in that: The number of processing equipment activated is 1 or 2. When the number of processing equipment activated is 2, the two processing equipment share the same group of conveyor lines, and the logistics simulation model detects the task conflict probability of the processing equipment.

7. A battery assembly line logistics simulation optimization system, characterized in that: include: A process modeling module is used to model the battery cell production process and obtain a modeled battery cell production process model; a simulation model construction module, coupled to the process modeling module, configured to receive the modeled cell production process model, construct a logistics simulation model based on the modeled cell production process model and actual factory data, and set logistics equipment parameters based on the logistics simulation model; a control logic generation module, coupled to the simulation model construction module, configured to receive the logistics equipment parameters and generate a cell dynamic scheduling and logistics collaborative control strategy based on the logistics equipment parameters through factory simulation, wherein the cell dynamic scheduling and logistics collaborative control strategy includes control point configuration, core pairing rules, pallet reorganization logic, and beat control; A simulation optimization module is coupled to the control logic generation module, and is used to receive the battery cell dynamic scheduling and logistics collaborative control strategy, perform simulation experiments on logistics design influencing factors according to the battery cell dynamic scheduling and logistics collaborative control strategy, and optimize the layout.

8. A battery cell transport assembly simulation scheduling system, characterized in that: The battery cell assembly line logistics simulation optimization method according to any one of claims 1 to 6 is applied to a battery cell conveying assembly simulation scheduling system, which comprises: A dynamic assembly model, including an assembly logistics channel used to connect roller conveyor lines of adjacent processing equipment, is configured with a battery cell dynamic scheduling and logistics collaborative control strategy, an equipment processing connection control module, and battery cell production process logic data; A control point system comprising at least three control points, wherein the at least three control points are respectively arranged at the head, tail and side ends of the roller conveyor line; The equipment processing connection control module is linked to the processing time of the processing equipment. When the first core tray and the second core tray arrive at the head, tail and side ends of the control point, an action is triggered according to the battery cell dynamic scheduling and logistics collaborative control strategy and the preset battery cell production process logic data; The simulation construction module constructs the assembly logistics channel through the roller line module of the simulation software and configures the control point logic.

9. The battery cell transport assembly simulation scheduling system according to claim 8, characterized in that: Each of the control points has a unique number, wherein the control points at the head and tail ends of the roller conveyor line are used to detect the status of the equipment ahead in real time and stop the flow of the battery cell trays when a blockage is detected; The side end control point of the roller conveyor line is used to load an empty pallet when the dynamic assembly model is initialized.

10. The battery cell transport assembly simulation scheduling system according to claim 8, characterized in that: Also includes: An equipment status sensing module is used to detect the working status of the processing equipment in real time, wherein the working status includes a normal operating state and a non-working state; A distributed control point network, wherein the control point of each cell process is electrically connected to the corresponding processing equipment, wherein: when the processing equipment is in the non-operating state, its nearest associated control point automatically switches to a blocking mode to prevent the cell tray from flowing to the processing equipment; when the equipment resumes operation, the associated control point synchronously releases the blocking mode; The terminal decision controller is set at the end of each conveyor line and is configured to: calculate the current equipment processing time when triggered, and determine the destination of the next process according to the logical data of the battery cell production process; The pallet circulation system includes a pallet conveying line and a pallet return line, wherein the pallet conveying line is used to forwardly flow the first core pallet and the second core pallet; the pallet return line is used to reversely return the empty pallet to the starting station.

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