CN-121541609-B - Battery pack production process regulation and control system and method based on PLC

Abstract

The invention relates to the technical field of intelligent manufacturing and industrial automation control of new energy batteries, in particular to a battery pack production process regulation and control system and method based on PLC, comprising the steps of mapping physical field data to a unified space-time coordinate system and constructing a physical state feature vector; the method comprises the steps of generating a comprehensive coupling drift index, generating a dynamic energy output instruction by utilizing a nonlinear self-adaptive compensation model based on the comprehensive coupling drift index, writing the dynamic energy output instruction into an executing mechanism to execute closed loop energy compensation, calculating a dynamic risk index based on a physical state characteristic vector, responding to the dynamic risk index being larger than a preset risk threshold value, triggering an emergency stop instruction and marking defective products, responding to the dynamic risk index being smaller than or equal to the preset risk threshold value, and maintaining execution of the dynamic energy output instruction.

Inventors

• GAO HUILING
• WANG JIANMIN
• ZHANG XIANG
• WANG CHANGWEI

Assignees

• 安徽易嘉益新能源科技有限公司

Dates

Publication Date

20260512

Application Date

20251208

Claims (2)

1. 1. The method for regulating and controlling the production process of the battery pack based on the PLC is characterized by comprising the following steps of: Acquiring physical field data by using a laser contour sensor, an infrared thermal imager and a servo pressure sensor; Step 1, mapping physical field data to a unified space-time coordinate system, and constructing a physical state feature vector; step 2, generating a comprehensive coupling drift index based on the physical state feature vector and a preset standard process reference; step 3, generating a dynamic energy output instruction by utilizing a nonlinear self-adaptive compensation model based on the comprehensive coupling drift index; Step 4, writing the dynamic energy output instruction into an executing mechanism to execute closed-loop energy compensation; step 5, calculating a dynamic risk index based on the physical state feature vector; Step 6, triggering an emergency stop instruction and marking defective products in response to the dynamic risk index being greater than a preset risk threshold; step 7, in response to the dynamic risk index being less than or equal to a preset risk threshold, maintaining execution of the dynamic energy output instruction; constructing a physical state feature vector, comprising: Obtaining a vertical gap between a busbar and a pole; Acquiring heat accumulation temperature rise of the current area relative to the ambient temperature; Acquiring a normal compression force; packaging the vertical gap, the heat accumulation temperature rise and the normal compression force into a physical state feature vector; The preset standard process reference comprises a standard gap, a standard pressure, an upper limit of equipment safety temperature and a single process standard working period; Generating a composite coupling drift index comprising: calculating the geometric deviation ratio of the vertical gap relative to the standard gap; Calculating thermodynamic dimension deviation ratio based on heat accumulation temperature rise, equipment safety temperature upper limit and single process standard working period; Calculating the mechanical deviation ratio of the normal pressing force relative to the standard pressure; algebraic weighted summation of geometric deviation ratio, thermodynamic dimension deviation ratio and mechanical deviation ratio to generate comprehensive coupling drift index ; Comprehensive coupling drift index The specific calculation formula is as follows: ; Wherein: Is a preset process weight coefficient and meets the following requirements ; At the current moment, a preset standard process temperature rise curve is adopted Theoretical temperature rise value of (2); Scanning a period sequence number; vertical clearance; standard clearance; heat accumulation and temperature rise; the upper limit of the safety temperature of the equipment; Normal compression force; standard pressure; generating a dynamic energy output instruction using a nonlinear adaptive compensation model, comprising: obtaining a reference energy value, a maximum allowable compensation scale factor and an adjustment sensitivity coefficient preset by a process formula; calculating the product of the comprehensive coupling drift index and the adjustment sensitivity coefficient; carrying out hyperbolic tangent function operation on the product to obtain a saturated gain intermediate value; calculating a gain coefficient based on the saturated gain intermediate value and the maximum allowable compensation scale factor; Correcting the reference energy value by using the gain coefficient to generate a dynamic energy output instruction ; Dynamic energy output instruction The specific mathematical model is as follows: ; Wherein, the Dynamic energy output instruction; a reference energy value; Maximum allowable compensation scale factor; adjusting a sensitivity coefficient; Calculating a normalized physical state fluctuation rate, comprising: Respectively calculating the variation of the vertical clearance, the heat accumulation temperature rise and the normal compression force in the adjacent PLC scanning period; Dividing the variation by corresponding preset standard process references to obtain dimensionless variation ratios; calculating the sum of squares of the dimensionless variation ratios; performing open square operation on the square sum to obtain a variable modulus; dividing the change modulus by the PLC scanning period to obtain a normalized physical state fluctuation rate; normalized physical state fluctuation rate The specific formula of (2) is as follows: ; Wherein, the The scanning period of the PLC is in seconds; vertical clearance of the previous scanning period; The heat accumulation temperature rise of the previous scanning period; normal compression force of the previous scanning period; Calculating a dynamic risk index comprising: Acquiring a physical state characteristic vector of a current scanning period and a physical state characteristic vector of a previous scanning period; Calculating a normalized physical state fluctuation rate based on the physical state feature vector of the current scanning period and the physical state feature vector of the previous scanning period; acquiring static drift weight and dynamic fluctuation weight; calculating the square of the product of the comprehensive coupling drift index and the static drift weight to obtain a static risk component; calculating the square of the product of the normalized physical state fluctuation rate and the dynamic fluctuation weight to obtain a dynamic risk component; Summing the static risk component and the dynamic risk component, and opening a root number to generate a dynamic risk index; dynamic risk index The calculation formula of (2) is as follows: ; in the case of the model of the present invention, Dynamic risk index; static drift weight; dynamic fluctuation weights; normalizing the fluctuation rate of the physical state.
2. 2. A PLC-based battery pack production process control system, applied to the PLC-based battery pack production process control method of claim 1, comprising: The data acquisition unit is used for acquiring physical field data by using the laser profile sensor, the infrared thermal imager and the servo pressure sensor; the state mapping unit is used for mapping the physical field data to a unified space-time coordinate system and constructing a physical state feature vector; The drift quantization unit is used for generating a comprehensive coupling drift index based on the physical state characteristic vector and a preset standard process reference; the self-adaptive control unit is used for generating a dynamic energy output instruction by utilizing a nonlinear self-adaptive compensation model based on the comprehensive coupling drift index; the closed loop execution unit is used for writing the dynamic energy output instruction into the execution mechanism and executing closed loop energy compensation; The risk assessment unit is used for calculating a dynamic risk index based on the physical state feature vector; And the blocking control unit is used for triggering the emergency stop instruction and marking defective products in response to the dynamic risk index being larger than a preset risk threshold value and maintaining the execution of the dynamic energy output instruction in response to the dynamic risk index being smaller than or equal to the preset risk threshold value.

Description

Battery pack production process regulation and control system and method based on PLC Technical Field The invention relates to the technical field of intelligent manufacturing and industrial automatic control of new energy batteries, in particular to a battery pack production process regulation and control system and method based on a PLC. Background In the automatic battery pack assembly production scene, key processes such as welding or gluing are obviously influenced by geometrical gaps, thermal field distribution, contact pressure and other physical field coupling factors, and the real-time states of the physical parameters are directly related to the processing quality and structural stability of products; The existing production regulation and control scheme generally depends on single-dimensional parameter monitoring or open-loop control based on a fixed formula, and is in face of discrete data acquired by heterogeneous equipment such as laser profile, infrared thermal image, pressure sensing and the like, and lacks an effective space-time coordinate unified alignment mechanism. Because a multi-dimensional data fusion evaluation system cannot be established, the traditional method is difficult to accurately quantify the comprehensive deviation degree of the complex processing environment relative to an ideal process reference. In addition, the fixed parameter control can not cope with the dynamic fluctuation caused by the accumulation tolerance or the heat accumulation effect, so that the system lacks self-adaptability in energy output, and is extremely easy to cause the cold welding, burning-through and equipment structural failure due to overload or insufficient compensation. Disclosure of Invention The invention aims to provide a battery pack production process regulation and control system and method based on PLC, which can realize space-time unified mapping and fusion calculation of heterogeneous physical field data, solve the problem that coupling drift is difficult to accurately quantify in a complex processing environment, and execute closed-loop energy compensation and dynamic risk blocking through a nonlinear self-adaptive model so as to avoid false welding, burning-through and equipment structural failure caused by accumulated tolerance or thermal effect, and remarkably improve process stability and product yield, and concretely, the technical scheme of the invention is as follows: A battery pack production process regulation and control method based on PLC includes: Acquiring physical field data by using a laser contour sensor, an infrared thermal imager and a servo pressure sensor; Step 1, mapping physical field data to a unified space-time coordinate system, and constructing a physical state feature vector; step 2, generating a comprehensive coupling drift index based on the physical state feature vector and a preset standard process reference; step 3, generating a dynamic energy output instruction by utilizing a nonlinear self-adaptive compensation model based on the comprehensive coupling drift index; Step 4, writing the dynamic energy output instruction into an executing mechanism to execute closed-loop energy compensation; step 5, calculating a dynamic risk index based on the physical state feature vector; Step 6, triggering an emergency stop instruction and marking defective products in response to the dynamic risk index being greater than a preset risk threshold; And 7, responding to the dynamic risk index being smaller than or equal to a preset risk threshold value, and maintaining the execution of the dynamic energy output instruction. Optionally, constructing the physical state feature vector includes: Obtaining a vertical gap between a busbar and a pole; Acquiring heat accumulation temperature rise of the current area relative to the ambient temperature; Acquiring a normal compression force; and packaging the vertical gap, the heat accumulation temperature rise and the normal compression force into a physical state characteristic vector. Optionally, the preset standard process reference comprises a standard gap, a standard pressure, an upper safety temperature limit of equipment and a single process standard working period; Generating a composite coupling drift index comprising: calculating the geometric deviation ratio of the vertical gap relative to the standard gap; Calculating thermodynamic dimension deviation ratio based on heat accumulation temperature rise, equipment safety temperature upper limit and single process standard working period; Calculating the mechanical deviation ratio of the normal pressing force relative to the standard pressure; Algebraic weighted summation is carried out on the geometric deviation ratio, the thermodynamic dimension deviation ratio and the mechanical deviation ratio to generate the comprehensive coupling drift index. Optionally, generating the dynamic energy output instruction using the nonlinear adaptive compensation model includes: ob