CN117594860B - New energy automobile process guidance method and system based on DLP - Google Patents

New energy automobile process guidance method and system based on DLP Download PDF

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CN117594860B
CN117594860B CN202410080340.3A CN202410080340A CN117594860B CN 117594860 B CN117594860 B CN 117594860B CN 202410080340 A CN202410080340 A CN 202410080340A CN 117594860 B CN117594860 B CN 117594860B
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battery
welding
voltage
coefficient
dlp
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CN117594860A (en
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毛霖
陈海军
齐佰剑
杨庆庆
黄德民
李鹏
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Xinlixun Technology Group Co ltd
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New Lixun Technology Co ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
    • B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
    • B23K9/00—Arc welding or cutting
    • B23K9/095—Monitoring or automatic control of welding parameters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/04—Construction or manufacture in general
    • H01M10/0404—Machines for assembling batteries

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  • Chemical Kinetics & Catalysis (AREA)
  • Electrochemistry (AREA)
  • General Chemical & Material Sciences (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The invention belongs to the technical field of new energy automobile battery manufacturing processes, and discloses a new energy automobile process guidance method and system based on DLP; acquiring monomer characteristic data and an assembly operation data set; training a machine learning model according to the assembly operation data set; obtaining a battery quality coefficient; inputting the real-time positioning coordinates into a machine learning model to acquire real-time welding parameters; acquiring an electrical coefficient of a battery and acquiring a real-time welding coefficient; acquiring characteristic coefficients of assembled batteries; presetting a judgment threshold L; if the characteristic coefficient of the assembled battery is greater than or equal to L, acquiring a battery risk coefficient; if the characteristic coefficient of the assembled battery is smaller than L, the electrical mismatch is sent to the DLP projection system; presetting a risk threshold k; the degree of automation, quality controllability and process safety of battery assembly are improved.

Description

New energy automobile process guidance method and system based on DLP
Technical Field
The invention relates to the technical field of new energy automobile battery manufacturing processes, in particular to a new energy automobile process guidance method and system based on DLP.
Background
Patent application publication No. CN115692630A discloses a preparation method of NCM ternary cathode material for reducing DCR. The method of the invention comprises the following steps: (1) Mixing a nickel source, a cobalt source, a manganese source and lithium carbonate, adding a doping agent, and then carrying out dry mixing, and carrying out primary sintering to obtain a single crystal primary sintering product; (2) And (3) carrying out dry coating on the single crystal primary sintered product and a coating agent with a specific design according to a certain proportion, carrying out double-firing coating, and processing to obtain the single crystal NCM ternary positive electrode material with lower DCR of the matrix. The invention adopts a conventional dry coating and a specific coating process, has the effect of reducing the DCR of the cathode material, has certain reference significance on modified single crystal NCM ternary cathode materials by analogy with some coating processes with the effect of reducing the DCR, has certain optimizing effect on other indexes, and provides directional guiding suggestions for carrying out coating process selection design on the cathode materials.
At present, the new energy automobile battery assembly process mainly depends on experience operation, but has large individual difference and poor repeatability and stability; the manual assembly process cannot monitor the electrical matching condition of the battery in real time, and certain potential safety hazards exist; for example, abnormal current distribution in the battery caused by improper welding can cause accident caused by aggregation of heating points; when assembly deviation occurs, subjective experience is difficult to quickly position the problem and adjust the scheme; the quality of the assembled finished product is large in dispersion, which is not beneficial to stably controlling the quality of the finished product; the assembly process lacks real-time visual quality prompt, which is not beneficial for workers to quickly correct assembly problems;
in view of the above, the present invention provides a new energy automobile process guidance method and system based on DLP to solve the above problems.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides the following technical scheme for achieving the purposes: the new energy automobile process guidance method based on DLP comprises the following steps:
s1, acquiring monomer characteristic data and an assembly operation data set; the assembly operation data set comprises positioning coordinates and a welding parameter set;
s2, constructing a positioning coordinate matching database according to the assembly operation data set;
s3, acquiring battery quality data of the new energy automobile; acquiring a battery quality coefficient according to the battery quality data;
S4, inputting the real-time positioning coordinates into a positioning coordinate matching database to acquire real-time welding parameters;
s5, acquiring an electrical coefficient of the battery according to the monomer characteristic data; acquiring real-time welding coefficients according to the real-time welding parameters; the ratio of the battery electrical coefficient to the real-time welding coefficient is used as the characteristic coefficient of the assembled battery;
s6, presetting a judgment threshold L; if the characteristic coefficient of the assembled battery is greater than or equal to L, acquiring a battery risk coefficient according to the characteristic coefficient of the assembled battery and the battery quality coefficient;
if the characteristic coefficient of the assembled battery is smaller than L, a risk prompt is sent to the DLP projection system to be electrically unmatched;
s7, presetting a risk threshold k; if the risk coefficient of the battery is smaller than the risk threshold k, transmitting the real-time welding parameters and the real-time positioning coordinates to a DLP projection system for display for reference by workers;
and if the battery risk coefficient is greater than or equal to the risk threshold k, sending a risk prompt to the DLP projection system as a quality defect.
Further, the monomer characteristic data includes a charge cutoff voltage, a discharge cutoff voltage, a maximum charge current, a maximum sustained discharge current, an ac internal resistance value, and a dc internal resistance value;
the charge cut-off voltage, the discharge cut-off voltage, the maximum charge current and the maximum continuous discharge current are all obtained through a battery charge-discharge experiment.
Further, the battery charge and discharge experiment includes:
in an experimental environment, a constant-current and constant-voltage charging method is used for charging a battery, and the voltage change in the charging process is recorded; when the voltage is kept unchanged, the voltage value at the moment is the charging cut-off voltage;
discharging by a constant current discharging method, and recording the voltage change of the battery in the discharging process; when the voltage is reduced to be unchanged, the voltage value at the moment is the discharge cut-off voltage;
charging the battery with different currents, and recording voltage and temperature changes in the charging process; the battery temperature is still in the preset battery temperature range under the highest current condition, and the current value of which the voltage variation is still in the preset voltage range is the maximum charging current;
discharging different currents on the battery, and recording voltage and temperature changes in the discharging process; the battery temperature is still in the preset battery temperature range under the highest current condition, and the current value of which the voltage variation is still in the preset voltage range is the maximum continuous discharge current;
the AC internal resistance value is calculated by applying small-amplitude AC voltage to the battery at a fixed frequency and testing AC impedance by using an AC internal resistance tester, wherein the small-amplitude AC voltage is not more than 1% of rated voltage of the battery;
The direct current internal resistance value is obtained by using a direct current internal resistance tester to input a tiny direct current excitation current to the battery, testing the battery voltage U and the passing current I, and calculating according to a formula R=U/I, wherein the tiny direct current excitation current is not more than one thousandth of the rated capacity of the battery.
Further, the positioning coordinates comprise clamping positioning coordinates and DLP positioning coordinates;
the clamping and positioning coordinates are obtained in the following manner:
an encoder is arranged on a movable shaft of the clamping machine and used for detecting linear displacement of the movable shaft of the clamping machine; defining the center position of a base of the clamping machine as an origin of a three-dimensional coordinate system; the actual position of the movable shaft is calculated by combining the pulse signals sent by the encoder with the mechanical structure parameters of the clamping machine; the coordinates of the actual position in the three-dimensional coordinate system are clamping and positioning coordinates;
the DLP positioning coordinates are obtained as follows:
the DLP positioning coordinates are obtained by setting a DLP sampling system; the DLP sampling system comprises a laser emission unit, a scanning mirror and a camera detector; the laser emission unit adopts a laser diode or an optical fiber laser; the laser light emitted by the laser emission unit is emitted by the scanning mirror and irradiates the battery; the scanning mirror comprises two mutually perpendicular mirror surfaces, one mirror surface is used for linearly scanning the battery, and the other mirror surface is used for rotationally scanning the battery; the camera detector adopts a CMOS or CCD image sensor to receive laser light reflected from the battery; the camera detector acquires coordinates of points on the surface of the battery in a three-dimensional coordinate system according to the reflected laser light; reserving coordinate points corresponding to welding spots on points on the surface of the battery; and the coordinate point corresponding to the welding spot is DLP positioning coordinate.
Further, the welding parameters include welding current, welding voltage, welding speed, welding angle, gap distance, and shielding gas flow rate;
the welding current is obtained by a current detection device arranged in the welding machine; the welding voltage is obtained by a built-in voltage detection circuit of the welding machine; the welding speed is obtained by installing an encoder on a welding gun;
the welding angle is obtained as follows:
3 sound wave sensors are symmetrically arranged on the head of the welding gun and respectively point to the directions A, B, C; 3 sound wave reflecting targets are correspondingly arranged on the surface of the battery; when the welding gun works, each sound wave sensor measures distances dA, dB and dC between the sound wave sensor and a sound wave reflection target; then the welding angle θ=arccos [ (dB) 2 +dC 2 -dA 2 )/（2dB×dC）]；
The gap distance is obtained by installing a laser range finder on the head of the welding gun;
the flow rate of the shielding gas is obtained by installing an air flow meter at the gas outlet.
Further, the construction mode of the positioning coordinate matching database comprises the following steps:
selecting a MySQL relational database as a framework for locating the coordinate matching database; according to the type of data to be stored, two data tables 1 and 2 are designed; the data table 1 is used for storing positioning coordinate data, and the data table 2 is used for storing welding parameter data;
The data table 1 is a coordinate table and comprises a field id primary key, an x coordinate, a y coordinate, a z coordinate and an external key corresponding to welding parameters;
the data table 2 is a welding parameter table comprising a field id primary key, a welding voltage, a welding current, a welding speed, a welding angle, a gap distance and a shielding gas flow rate;
setting a hold_id field in a positioning coordinate table, wherein the value of the hold_id field is the id value of a corresponding welding parameter table, and establishing data association of the two tables; and acquiring positioning coordinate data and welding parameter data in the assembly process by using a PLC or an industrial robot control system, and writing a program to realize automatic database storage.
Further, the battery quality data includes power density, duration, and battery heat dissipation rate;
the power density is obtained as follows:
a current sensor and a voltage sensor are arranged at the positive and negative electrode connecting terminals of the battery in series and used for detecting a terminal voltage value U and a through current value I during charging and discharging; multiplying the terminal voltage and the through current value to obtain instantaneous power P at a certain moment, namely P=U×I; setting a data sampling card, sampling an end voltage value U and a through current value I by the data sampling card at a fixed frequency, and calculating to obtain n instantaneous powers P; average power Pj is obtained by carrying out average calculation on n instantaneous powers P; the ratio of the average power Pj to the battery quality is the power density;
The endurance time is obtained as follows:
performing discharge test on the new energy automobile with the battery installed in an experimental environment; the discharging test is to test the discharging condition of the battery of the new energy automobile in the simulated driving process; recording the running time of the vehicle from the full charge state to the discharge exhaustion of the battery, namely, the endurance time;
the battery heat dissipation rate is obtained as follows:
1C charging is carried out on the battery, and charging time t is recorded; in the charging process, a fixed sampling frequency is set through a temperature sensor to acquire the surface temperature T of the battery; fitting a temperature curve T=f (T) in the charging process by using a computer, and obtaining a temperature rise rate dT/dT according to the temperature curve T=f (T);
collecting the heat capacity C, the heat consumption power X and the ambient temperature T0 of the battery;
wherein the heat capacity C of the battery is the product of the mass and the specific heat capacity of the battery;
the heat consumption power X is the charging power of the battery in the charging process, and the charging current I is measured from the charging power supply in the charging process of the battery 1 And a charging voltage U 1 The method comprises the steps of carrying out a first treatment on the surface of the Heat consumption power x=i 1 ×U 1 The method comprises the steps of carrying out a first treatment on the surface of the Charging current I 1 Acquiring through a ammeter; charging voltageU 1 Acquiring through a voltmeter; the ambient temperature T0 is obtained by a temperature sensor;
heat dissipation rate of battery=(dT/dt)/(X/(C×ΔT))；
Wherein, delta T is the temperature rise of the battery and is obtained through the temperature rise rate and the charging time.
Further, the battery quality factor；
In the method, in the process of the invention,is the power density; />The heat dissipation rate of the battery is; />Is the endurance time; />、/>And->Adjusting parameters for the quality coefficient;
coefficient of battery electrical properties；
In the method, in the process of the invention,for charging off voltage, ">For the discharge cut-off voltage>Is maximum filled withElectric current, < >>For maximum sustained discharge current, +.>Is AC internal resistance value->Is the internal resistance of direct current, < >>Is an electrical adjustment parameter;
real-time welding coefficient；
In the method, in the process of the invention,for welding current, +.>For welding voltage, +.>For the welding speed +.>For the welding angle +.>Is the gap distance>Is the flow rate of the shielding gas; />And->Adjusting parameters for welding;
assembled battery characteristic coefficient；
Battery risk factor；
In the method, in the process of the invention,is a risk adjustment parameter.
Further, the setting mode of the judging threshold L includes:
step 1, establishing an electric-thermal joint simulation model in a battery, and setting internal defect parameters of different degrees;
step 2, obtaining the electrical coefficient of the battery according to the defect parameter simulation; taking the battery electrical coefficient of a normal battery as a reference;
step 3, setting different judging thresholds L, performing abnormal judgment, and recording the missing detection rate and the misjudgment rate;
step 4, setting an objective function to minimize the sum of the omission factor and the misjudgment rate;
And 5.N times of iteration, adopting an unconstrained nonlinear optimization algorithm to find an optimal judgment threshold L for minimizing the objective function.
Further, the setting manner of the risk threshold k includes:
s601, establishing a multi-physical field joint simulation model in a battery, and setting internal defect parameters of different degrees;
s602, simulating to obtain battery health state feature vectors x under different electrical matching levels;
s603, using a main component analysis PCA dimension reduction method to obtain an evaluation index y of the battery health state;
s604, establishing a battery state of health evaluation function: y=f (x);
s605, presetting y_min corresponding to the minimum requirement of qualified battery product quality, wherein y_min is determined according to an industry quality standard; presetting acceptable battery product reject ratio as p%, and determining p% by combining with enterprise risk management strategies; determining a risk level y_max with the goal of meeting the qualification rate (100-p)%;
s606, presetting a risk threshold k, and meeting the condition that the k is not less than y_min and not more than y_max.
The new energy automobile process guidance system based on the DLP is realized based on the new energy automobile process guidance method based on the DLP, and comprises the following steps: the data acquisition module is used for acquiring monomer characteristic data and an assembly operation data set; the assembly operation data set comprises positioning coordinates and a welding parameter set;
The database construction module is used for constructing a positioning coordinate matching database according to the assembly operation data set;
the first coefficient acquisition module is used for acquiring battery quality data of the new energy automobile; acquiring a battery quality coefficient according to the battery quality data;
the real-time welding parameter acquisition module is used for inputting the real-time positioning coordinates into the positioning coordinate matching database to acquire real-time welding parameters;
the second coefficient acquisition module is used for acquiring the electrical coefficient of the battery according to the monomer characteristic data; acquiring real-time welding coefficients according to the real-time welding parameters; the ratio of the battery electrical coefficient to the real-time welding coefficient is used as the characteristic coefficient of the assembled battery;
the first judging module is used for presetting a judging threshold L; if the characteristic coefficient of the assembled battery is greater than or equal to L, acquiring a battery risk coefficient according to the characteristic coefficient of the assembled battery and the battery quality coefficient;
if the characteristic coefficient of the assembled battery is smaller than L, a risk prompt is sent to the DLP projection system to be electrically unmatched;
the second judging module is used for presetting a risk threshold k; if the risk coefficient of the battery is smaller than the risk threshold k, transmitting the real-time welding parameters and the real-time positioning coordinates to a DLP projection system for display for reference by workers;
And if the battery risk coefficient is greater than or equal to the risk threshold k, sending a risk prompt to the DLP projection system as a quality defect.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a new energy vehicle process guidance method based on DLP when executing the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements a DLP-based new energy vehicle process guidance method.
The new energy automobile process guidance method and system based on DLP has the technical effects and advantages that:
the prediction and control of assembly parameters are realized by constructing a data set and a machine learning model of the battery assembly process, so that workers are effectively guided to carry out standardized assembly operation; the method is more reliable and more accurate than the traditional manual experience guidance mode; the electrical matching coefficient and the quality risk coefficient are utilized to evaluate the assembly quality and the safety of the battery at multiple angles, so that the problems of electrical mismatching and internal quality defects possibly occurring in the assembly process of the battery can be captured in real time; the method provides a direct and effective technical means for the quality control of the battery; the DLP digital light processing technology is applied to carry out real-time visual projection prompt on assembly risks, so that workers can quickly see the reasons of the problems and adjust assembly parameters; compared with the traditional mode, the method has the advantages of high prompting speed, intuitionism and clarity, and can remarkably improve the response speed and quality stability of the assembly process; compared with the prior art, the system realizes the monitoring and control of the whole battery assembly process, organically combines machine learning, multi-physical model prediction and digital light processing technology, and improves the automation degree, quality controllability and process safety of battery assembly.
Drawings
FIG. 1 is a schematic diagram of a new energy automobile process guidance method based on DLP according to the present invention;
FIG. 2 is a schematic diagram of a new energy automobile process guidance system based on DLP according to the present invention;
FIG. 3 is a schematic diagram of an electronic device of the present invention;
fig. 4 is a schematic diagram of a storage medium of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the new energy automobile process guidance method based on DLP in this embodiment includes:
s1, acquiring monomer characteristic data and an assembly operation data set; the assembly operation data set comprises positioning coordinates and a welding parameter set;
s2, constructing a positioning coordinate matching database according to the assembly operation data set;
s3, acquiring battery quality data of the new energy automobile; acquiring a battery quality coefficient according to the battery quality data;
S4, inputting the real-time positioning coordinates into a positioning coordinate matching database to acquire real-time welding parameters;
s5, acquiring an electrical coefficient of the battery according to the monomer characteristic data; acquiring real-time welding coefficients according to the real-time welding parameters; the ratio of the battery electrical coefficient to the real-time welding coefficient is used as the characteristic coefficient of the assembled battery;
s6, presetting a judgment threshold L; if the characteristic coefficient of the assembled battery is greater than or equal to L, acquiring a battery risk coefficient according to the characteristic coefficient of the assembled battery and the battery quality coefficient;
if the characteristic coefficient of the assembled battery is smaller than L, a risk prompt is sent to the DLP projection system to be electrically unmatched;
s7, presetting a risk threshold k; if the risk coefficient of the battery is smaller than the risk threshold k, transmitting the real-time welding parameters and the real-time positioning coordinates to a DLP projection system for display for reference by workers;
if the battery risk coefficient is greater than or equal to the risk threshold k, a risk prompt is sent to the DLP projection system to be a quality defect;
further, the monomer characteristic data comprises a charge cut-off voltage, a discharge cut-off voltage, a maximum charge current, a maximum continuous discharge current, an alternating current internal resistance value and a direct current internal resistance value;
the charge cut-off voltage, the discharge cut-off voltage, the maximum charge current and the maximum continuous discharge current are all obtained through a battery charge-discharge experiment;
The battery charge and discharge experiment includes:
in an experimental environment, a constant-current and constant-voltage charging method is used for charging a battery, and the voltage change in the charging process is recorded; when the voltage is kept unchanged, the voltage value at the moment is the charging cut-off voltage;
discharging by a constant current discharging method, and recording the voltage change of the battery in the discharging process; when the voltage is reduced to be unchanged, the voltage value at the moment is the discharge cut-off voltage;
charging the battery with different currents, and recording voltage and temperature changes in the charging process; the battery temperature is still in the preset battery temperature range under the highest current condition, and the current value of which the voltage variation is still in the preset voltage range is the maximum charging current;
discharging different currents on the battery, and recording voltage and temperature changes in the discharging process; the battery temperature is still in the preset battery temperature range under the highest current condition, and the current value of which the voltage variation is still in the preset voltage range is the maximum continuous discharge current;
it should be noted that, the current value still within the allowable range may look at the temperature range and the charge-discharge voltage range specified by the battery specification, and generally may give a numerical limitation, for example, the charge temperature range is 0-45 ℃, and the charge voltage is 4.2-4.3V; when a charge-discharge experiment is carried out, the temperature and the voltage of the battery are required to be monitored simultaneously and compared with the range given by the specification; if the temperature or the voltage exceeds the standard range under a certain current condition, the allowable range is considered to be exceeded;
The AC internal resistance value is calculated by using an AC internal resistance tester, applying small-amplitude AC voltage to the battery at a fixed frequency (such as 1 kHz) and testing the AC impedance, wherein the small-amplitude AC voltage is not more than 1% of the rated voltage of the battery;
the direct current internal resistance value is obtained by using a direct current internal resistance tester to input a tiny direct current excitation current to the battery, testing the battery voltage U and the passing current I, and calculating according to a formula R=U/I, wherein the tiny direct current excitation current is not more than one thousandth of the rated capacity of the battery;
the positioning coordinates comprise clamping positioning coordinates and DLP positioning coordinates;
the clamping and positioning coordinates are obtained in the following manner:
an encoder is arranged on a movable shaft of the clamping machine and used for detecting linear displacement of the movable shaft of the clamping machine; defining the center position of a base of the clamping machine as an origin of a three-dimensional coordinate system; the actual position of the moving shaft is obtained by combining the pulse signal sent by the encoder with the mechanical structure parameter of the clamping machine (clamping positioning coordinates are obtained by the encoder, which is the prior art, so the application is not repeated here); the coordinates of the actual position in the three-dimensional coordinate system are clamping and positioning coordinates;
The method is characterized in that a clamping machine controller reads pulse counts of a photoelectric encoder in real time and converts the pulse counts into actual displacement through parameter setting; according to mechanical structure parameters of the clamping machine, such as joint connecting rod length, pitch lead and the like, the actual positions of the joint angles and the end shafts in a three-dimensional coordinate system can be calculated; in a preferred embodiment, the Z axis of a chuck is driven by a screw to move up and down, and the main mechanical parameters are:
screw lead p=10 mm, screw pitch s=2 mm; the torque of the motor is 1Nm, the motor and the screw rod are directly connected to a motor shaft, an incremental rotary encoder is arranged on the motor shaft, and the resolution is 360pulse/rev; the course of motion can be described by the following equation:
encoder output pulse number n=motor rotation angle β×360 (pulse/rev)
Motor rotation angle β=screw travel distance z/pitch s (since the motor links are screws, their rotation angles correspond);
screw travel distance z = encoder pulse number N x pitch s/360; when N counts to 360, i.e. the motor rotates one revolution, z=10mm, i.e. the lead of the screw rod;
according to the pulse output of the encoder, the moving distance of the Z axis of the clamping machine is calculated in real time, and the accurate positioning coordinate of the clamping machine in the three-dimensional space can be obtained by combining the integral mechanism parameters of the clamping machine and the coordinate system conversion;
The DLP positioning coordinates are obtained as follows:
the DLP positioning coordinates are obtained by setting a DLP sampling system; the DLP sampling system comprises a laser emission unit, a scanning mirror and a camera detector; the laser emission unit adopts a laser diode or an optical fiber laser; the laser light emitted by the laser emission unit is emitted by the scanning mirror and irradiates the battery; the scanning mirror comprises two mutually perpendicular mirror surfaces, one mirror surface is used for linearly scanning the battery, and the other mirror surface is used for rotationally scanning the battery; the camera detector adopts a CMOS or CCD image sensor to receive laser light reflected from the battery; the camera detector acquires coordinates of points on the surface of the battery in a three-dimensional coordinate system according to the reflected laser light; reserving coordinate points corresponding to welding spots on points on the surface of the battery; coordinate points corresponding to the welding spots are DLP positioning coordinates;
the welding parameters include welding current, welding voltage, welding speed, welding angle, gap distance and shielding gas flow rate;
the welding current is obtained by a current detection device arranged in the welding machine; the welding voltage is obtained by a built-in voltage detection circuit of the welding machine; the welding speed is obtained by installing an encoder on a welding gun;
the welding angle is obtained as follows:
3 sound wave sensors are symmetrically arranged on the head of the welding gun and respectively point to the directions A, B, C; 3 sound wave reflecting targets are correspondingly arranged on the surface of the battery; when the welding gun works, each sound wave sensor measures distances dA, dB and dC between the sound wave sensor and a sound wave reflection target; then the welding angle θ=arccos [ (dB) 2 +dC 2 -dA 2 )/（2dB×dC）]；
The gap distance is obtained by installing a laser range finder on the head of the welding gun;
the flow rate of the shielding gas is obtained by installing an airflow meter at a gas outlet;
it should be noted that, the welding parameters are in one-to-one correspondence according to the positioning coordinates, in other words, when the fixed positioning coordinates are detected, the welding operation is performed by adopting the fixed welding parameters; the welding current, the welding voltage, the welding speed, the welding angle, the gap distance and the flow rate of the shielding gas are all fixed; welding parameters for a set of positioning coordinates can be considered an operational set;
further, the construction method of the positioning coordinate matching database comprises the following steps:
selecting a MySQL relational database as a framework for locating the coordinate matching database; according to the type of data to be stored, two data tables 1 and 2 are designed; the data table 1 is used for storing positioning coordinate data, and the data table 2 is used for storing welding parameter data;
The data table 1 is a coordinate table and comprises a field id primary key, an x coordinate, a y coordinate, a z coordinate and an external key corresponding to welding parameters;
the data table 2 is a welding parameter table comprising a field id primary key, a welding voltage, a welding current, a welding speed, a welding angle, a gap distance and a shielding gas flow rate;
setting a hold_id field in a positioning coordinate table, wherein the value of the hold_id field is the id value of a corresponding welding parameter table, and establishing data association of the two tables; acquiring positioning coordinate data and welding parameter data in the assembly process by using a PLC or an industrial robot control system, and writing a program to realize automatic database storage;
it should be noted that, through the MySQL relational database, history data can be stored efficiently, and the SQL query statement is utilized to search and match data quickly, so as to provide support for parameter prediction;
further, the step S3 includes:
the battery quality data includes power density, duration, and battery heat dissipation rate;
the power density is obtained as follows:
a current sensor and a voltage sensor are arranged at the positive and negative electrode connecting terminals of the battery in series and used for detecting a terminal voltage value U and a through current value I during charging and discharging; multiplying the terminal voltage and the through current value to obtain instantaneous power P at a certain moment, namely P=U×I; setting a data sampling card, sampling an end voltage value U and a through current value I by the data sampling card at a fixed frequency, and calculating to obtain n instantaneous powers P; average power Pj is obtained by carrying out average calculation on n instantaneous powers P; the ratio of the average power Pj to the battery quality is the power density;
It should be noted that, the power density is one of the key indicators for evaluating the performance of the battery; it directly reflects the energy storage and release capabilities of the battery;
the endurance time is obtained as follows:
performing discharge test on the new energy automobile with the battery installed in an experimental environment; the discharging test is to test the discharging condition of the battery of the new energy automobile in the simulated driving process; recording the running time of the vehicle from the full charge state to the discharge exhaustion of the battery, namely, the endurance time;
the battery heat dissipation rate is obtained as follows:
under an experimental environment, 1C charging is carried out on the battery, and charging time t is recorded; in the charging process, a fixed sampling frequency is set through a temperature sensor to acquire the surface temperature T of the battery; fitting a temperature curve T=f (T) in the charging process by using a computer, and obtaining a temperature rise rate dT/dT according to the temperature curve T=f (T);
collecting the heat capacity C, the heat consumption power X and the ambient temperature T0 of the battery;
the heat capacity C of the battery is the product of the mass and the specific heat capacity of the battery, and the mass and the specific heat capacity of the battery are recorded in a battery product instruction book and provided by a battery manufacturer;
the heat consumption power X is the charging power of the battery in the charging process, and the charging current I is measured from the charging power supply in the charging process of the battery 1 And a charging voltage U 1 The method comprises the steps of carrying out a first treatment on the surface of the Heat consumption power x=i 1 ×U 1 The method comprises the steps of carrying out a first treatment on the surface of the Charging current I 1 Acquiring through a ammeter; charging voltage U 1 Acquiring through a voltmeter; the ambient temperature T0 is obtained by a temperature sensor;
heat dissipation rate of battery=(dT/dt)/(X/(C×ΔT))；Wherein, delta T is the temperature rise of the battery and is obtained through the temperature rise rate and the charging time;
it should be noted that the heat dissipation rate reflects the balance condition of the heat productivity and the heat dissipation capacity of the battery, and directly affects the temperature rise rate and the highest temperature of the battery; the duration of the battery reflects the length of time the battery can actually provide power for the automobile; the heat dissipation rate and the endurance time are closely related to the quality factors such as the internal structural design and the manufacturing process of the battery; by measuring the two parameters, the effect of the thermal management system of the battery can be evaluated, and whether the internal structure has defects of increasing heating value or reducing heat dissipation value or not;
battery quality coefficient；
In the method, in the process of the invention,is the power density; />The heat dissipation rate of the battery is; />Is the endurance time; />、/>And->Adjusting parameters for the quality coefficient;
the quality coefficient adjustment parameter is as follows、/>And->The setting mode of (2) is as follows:
collecting a plurality of actual test data for the battery at a known quality level; determining the contribution degree of three parameters to the quality evaluation result through multiple regression analysis; setting an objective function as a mean square error of a minimum evaluation result and an actual quality level; iteratively solving the parameter combination by using a numerical optimization algorithm to minimize an objective function;
Further, the step S5 includes:
coefficient of battery electrical properties；
In the method, in the process of the invention,for charging off voltage, ">For the discharge cut-off voltage>For maximum charging current +.>For maximum sustained discharge current, +.>Is AC internal resistance value->Is the internal resistance of direct current, < >>Is an electrical adjustment parameter;
collecting the standard range data of the electrical performance of the same batch of batteries; establishing an electrical performance evaluation model and setting different electrical adjustment parametersThe method comprises the steps of carrying out a first treatment on the surface of the The objective function is optimized by grid search, climbing algorithm, etc. to maximize the matching degree of the evaluation result and the standard range>A value;
real-time welding coefficient；
In the method, in the process of the invention,for welding current, +.>For welding voltage, +.>For the welding speed +.>For the welding angle +.>Is the gap distance>Is the flow rate of the shielding gas; />And->Adjusting parameters for welding;
it should be noted that the number of the substrates,and->The setting mode of (2) is as follows:
collecting different welding parameters and welding quality data through a designed experiment; establishing a welding quality evaluation model, and setting different types of welding quality evaluation modelsAnd->Is a combination of (a); analyzing the influence of different parameters on the evaluation effect by using a visualization technology;
determining a combination of parameters that best optimizes the result;
assembled battery characteristic coefficient；
It should be noted that the electrical coefficient reflects the electrical performance of the battery itself; the welding coefficient reflects the process level in the assembly process; the ratio of the two can more comprehensively reflect the assembly characteristics of the battery; the judgment threshold L is set to be used for checking the electrical matching condition of the battery; the electrical matching directly affects the safety and performance of the battery; if the electrical match is not good, indicating a problem between the internal structure of the battery and the external assembly, such a problem may lead to failure of the battery in use; the high-risk battery with unmatched electrical property can be rapidly positioned through the first round of judgment; the part of the battery is alarmed in advance, and the battery is a first defense line for preventing the quality of the battery;
The setting mode of the judging threshold L comprises the following steps:
step 1, establishing an electric-thermal joint simulation model in a battery, and setting internal defect parameters of different degrees;
wherein, the electric-thermal joint simulation model in the battery is composed of an electrochemical model and a thermal model; the electrochemical model is a Newman model, and a porous electrode structure comprising a positive electrode, a negative electrode and a diaphragm is established; setting electrode material parameters including conductivity and continuous phase charge transfer coefficient; establishing a Butler-Volmer formula for ion transfer between electrodes; for calculating a current density distribution of the electrode surface; the thermal model sets a geometric grid inside the battery according to the battery construction; setting thermal parameters of materials inside the battery, including density and heat capacity, for calculating temperature distribution inside the battery; the electric-thermal joint simulation model in the battery takes the current density distribution calculated by the electrochemical model as an input heat source item of the thermal model; iterative calculation of an electrochemical-thermal joint simulation model until the voltage-temperature field is converged;
setting degradation parameters of electric or thermal conductivity reduction in the electrode material block, and simulating internal cracks; setting degradation degrees according to the defect positions and the range sizes to obtain defect parameters of different degrees;
Step 2, obtaining the electrical coefficient of the battery according to the defect parameter simulation; taking the battery electrical coefficient of a normal battery as a reference;
step 3, setting different judging thresholds L, performing abnormal judgment, and recording the missing detection rate and the misjudgment rate;
step 4, setting an objective function to minimize the sum of the omission factor and the misjudgment rate;
step 5.N iterations, adopting an unconstrained nonlinear optimization algorithm to find an optimal judgment threshold L for minimizing an objective function;
battery risk factor；
In the method, in the process of the invention,is a risk adjustment parameter;
further, the step S6 includes:
the setting mode of the risk threshold k comprises the following steps:
s601, establishing a multi-physical field joint simulation model in a battery, and setting internal defect parameters of different degrees;
s602, simulating to obtain battery health state feature vectors x under different electrical matching levels;
s603, obtaining an evaluation index y of the battery health state by using a dimension reduction method of Principal Component Analysis (PCA);
s604, establishing a battery state of health evaluation function: y=f (x);
s605, referring to industry quality standards, determining y_min corresponding to the minimum requirement of qualified battery product quality; combining with an enterprise risk management strategy, determining that the acceptable defective rate of the battery product is p%; determining a risk level y_max with the goal of meeting the qualification rate (100-p)%;
S606, presetting a risk threshold k, wherein the k is less than or equal to y_min and less than or equal to y_max;
it should be noted that p% in the percent of pass (100-p)% is a preset acceptable product reject rate; for example, according to enterprise policy, an acceptable reject ratio of 2%, i.e., p=2%, is determined; then yield (100-p)% = (100% -2%) = 98%; that is, the quality control objective is to ensure that the product yield reaches 98%; in the simulation test, the estimated reject ratio corresponding to different risk levels y can be obtained; then a maximum risk level y_max is determined, at which the estimated reject ratio of the product is just 2%;
the multi-physical-field joint simulation model inside the battery comprises an electrochemical model, a thermal model, a structural mechanical model and a multi-physical-field coupling process;
the DLP projection system is a digital light processing projection system; carrying out high-speed projection of the digital image by utilizing a micro-mirror array of the DLP chip; DLP projection systems have the characteristics of high brightness, real-time display of risk cues and quick response.
The battery risk coefficient comprehensively considers two aspects of the assembly characteristics and the self quality of the battery, and can reflect the health condition and the fault risk of the battery more comprehensively; the preset threshold k is an acceptable risk upper limit determined based on factors such as application environment of the battery, industry standard and the like; comparing the risk coefficient with a threshold k, and judging whether the actual fault risk of the battery is in a controllable range or not; when the risk coefficient exceeds k, a certain quality or assembly problem of the battery is indicated, and risk prompt is needed to prevent the problem battery from flowing into a subsequent use link;
According to the embodiment, the prediction and control of the assembly parameters are realized by constructing the data set and the machine learning model of the battery assembly process, so that workers are effectively guided to carry out standardized assembly operation; the method is more reliable and more accurate than the traditional manual experience guidance mode; the electrical matching coefficient and the quality risk coefficient are utilized to evaluate the assembly quality and the safety of the battery at multiple angles, so that the problems of electrical mismatching and internal quality defects possibly occurring in the assembly process of the battery can be captured in real time; the method provides a direct and effective technical means for the quality control of the battery; the DLP digital light processing technology is applied to carry out real-time visual projection prompt on assembly risks, so that workers can quickly see the reasons of the problems and adjust assembly parameters; compared with the traditional mode, the method has the advantages of high prompting speed, intuitionism and clarity, and can remarkably improve the response speed and quality stability of the assembly process; compared with the prior art, the system realizes the monitoring and control of the whole battery assembly process, organically combines machine learning, multi-physical model prediction and digital light processing technology, and improves the automation degree, quality controllability and process safety of battery assembly.
Example 2
Referring to fig. 2, the detailed description of the embodiment is not shown in the description of embodiment 1, and a new energy automobile process guidance system based on DLP is provided; comprising the following steps: the data acquisition module is used for acquiring monomer characteristic data and an assembly operation data set; the assembly operation data set comprises positioning coordinates and a welding parameter set;
the database construction module is used for constructing a positioning coordinate matching database according to the assembly operation data set;
the first coefficient acquisition module is used for acquiring battery quality data of the new energy automobile; acquiring a battery quality coefficient according to the battery quality data;
the real-time welding parameter acquisition module is used for inputting the real-time positioning coordinates into the positioning coordinate matching database to acquire real-time welding parameters;
the second coefficient acquisition module is used for acquiring the electrical coefficient of the battery according to the monomer characteristic data; acquiring real-time welding coefficients according to the real-time welding parameters; the ratio of the battery electrical coefficient to the real-time welding coefficient is used as the characteristic coefficient of the assembled battery;
the first judging module is used for presetting a judging threshold L; if the characteristic coefficient of the assembled battery is greater than or equal to L, acquiring a battery risk coefficient according to the characteristic coefficient of the assembled battery and the battery quality coefficient;
If the characteristic coefficient of the assembled battery is smaller than L, a risk prompt is sent to the DLP projection system to be electrically unmatched;
the second judging module is used for presetting a risk threshold k; if the risk coefficient of the battery is smaller than the risk threshold k, transmitting the real-time welding parameters and the real-time positioning coordinates to a DLP projection system for reference of workers;
if the battery risk coefficient is greater than or equal to the risk threshold k, a risk prompt is sent to the DLP projection system to be a quality defect; all the modules are connected in a wired and/or wireless mode, so that data transmission among the modules is realized.
Example 3
Referring to fig. 3, an electronic device 500 is also provided according to yet another aspect of the present application. The electronic device 500 may include one or more processors and one or more memories. Wherein the memory has stored therein computer readable code which, when executed by the one or more processors, may perform the new energy vehicle process guidance method based on DLP as described above.
The method or system according to embodiments of the present application may also be implemented by means of the architecture of the electronic device shown in fig. 3. As shown in fig. 3, the electronic device 500 may include a bus 501, one or more CPUs 502, a Read Only Memory (ROM) 503, a Random Access Memory (RAM) 504, a communication port 505 connected to a network, an input/output 506, a hard disk 507, and the like. A storage device in the electronic device 500, such as the ROM503 or the hard disk 507, may store a new energy automobile process guidance method based on DLP provided in the present application. Further, the electronic device 500 may also include a user interface 508. Of course, the architecture shown in fig. 3 is merely exemplary, and one or more components of the electronic device shown in fig. 3 may be omitted as may be practical in implementing different devices.
Example 4
Referring to FIG. 4, a computer readable storage medium 600 according to one embodiment of the present application is shown. Computer readable storage medium 600 has stored thereon computer readable instructions. The DLP-based new energy vehicle process guidance method according to the embodiments of the present application described with reference to the above drawings may be performed when the computer readable instructions are executed by the processor. Storage medium 600 includes, but is not limited to, for example, volatile memory and/or nonvolatile memory. Volatile memory can include, for example, random Access Memory (RAM), cache memory (cache), and the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, and the like.
In addition, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, the present application provides a non-transitory machine-readable storage medium storing machine-readable instructions executable by a processor to perform instructions corresponding to the method steps provided herein, a DLP-based new energy vehicle process guidance method. The above-described functions defined in the method of the present application are performed when the computer program is executed by a Central Processing Unit (CPU).
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center over a wired network or a wireless network. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely one, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (13)

1. The new energy automobile process guidance method based on the DLP is characterized by comprising the following steps of:
s1, acquiring monomer characteristic data and an assembly operation data set; the assembly operation data set comprises positioning coordinates and a welding parameter set;
s2, constructing a positioning coordinate matching database according to the assembly operation data set;
s3, acquiring battery quality data of the new energy automobile; acquiring a battery quality coefficient according to the battery quality data;
s4, inputting the real-time positioning coordinates into a positioning coordinate matching database to acquire real-time welding parameters;
s5, acquiring an electrical coefficient of the battery according to the monomer characteristic data; acquiring real-time welding coefficients according to the real-time welding parameters; the ratio of the battery electrical coefficient to the real-time welding coefficient is used as the characteristic coefficient of the assembled battery;
s6, presetting a judgment threshold L; if the characteristic coefficient of the assembled battery is greater than or equal to L, acquiring a battery risk coefficient according to the characteristic coefficient of the assembled battery and the battery quality coefficient;
if the characteristic coefficient of the assembled battery is smaller than L, a risk prompt is sent to the DLP projection system to be electrically unmatched;
s7, presetting a risk threshold k; if the risk coefficient of the battery is smaller than the risk threshold k, transmitting the real-time welding parameters and the real-time positioning coordinates to a DLP projection system for reference of workers;
And if the battery risk coefficient is greater than or equal to the risk threshold k, sending a risk prompt to the DLP projection system as a quality defect.
2. The DLP-based new energy automobile process guidance method according to claim 1, wherein the monomer characteristic data includes a charge cut-off voltage, a discharge cut-off voltage, a maximum charge current, a maximum sustained discharge current, an ac internal resistance value, and a dc internal resistance value;
the charge cut-off voltage, the discharge cut-off voltage, the maximum charge current and the maximum continuous discharge current are all obtained through a battery charge-discharge experiment.
3. The DLP-based new energy automobile process guidance method of claim 2, wherein the battery charge-discharge experiment comprises:
charging the battery by using a constant-current and constant-voltage charging method, and recording the voltage change in the charging process; when the voltage is kept unchanged, the voltage value at the moment is the charging cut-off voltage;
discharging by a constant current discharging method, and recording the voltage change of the battery in the discharging process; when the voltage is reduced to be unchanged, the voltage value at the moment is the discharge cut-off voltage;
charging the battery with different currents, and recording voltage and temperature changes in the charging process; the battery temperature is still in the preset battery temperature range under the highest current condition, and the current value of which the voltage variation is still in the preset voltage range is the maximum charging current;
Discharging different currents on the battery, and recording voltage and temperature changes in the discharging process; the battery temperature is still in the preset battery temperature range under the highest current condition, and the current value of which the voltage variation is still in the preset voltage range is the maximum continuous discharge current;
the AC internal resistance value is calculated by applying small-amplitude AC voltage to the battery at a fixed frequency and testing AC impedance by using an AC internal resistance tester, wherein the small-amplitude AC voltage is not more than 1% of rated voltage of the battery;
the direct current internal resistance value is obtained by using a direct current internal resistance tester to input a tiny direct current excitation current to the battery, testing the battery voltage U and the passing current I, and calculating according to a formula R=U/I, wherein the tiny direct current excitation current is not more than one thousandth of the rated capacity of the battery.
4. The new energy automobile process guidance method based on DLP according to claim 3, wherein the positioning coordinates comprise clamping positioning coordinates and DLP positioning coordinates;
the clamping and positioning coordinates are obtained in the following manner:
an encoder is arranged on a movable shaft of the clamping machine and used for detecting linear displacement of the movable shaft of the clamping machine; defining the center position of a base of the clamping machine as an origin of a three-dimensional coordinate system; calculating the actual position of the moving shaft through a pulse signal sent by the encoder; the coordinates of the actual position in the three-dimensional coordinate system are clamping and positioning coordinates;
The DLP positioning coordinates are obtained as follows:
the DLP positioning coordinates are obtained by setting a DLP sampling system; the DLP sampling system comprises a laser emission unit, a scanning mirror and a camera detector; the laser emission unit adopts a laser diode or an optical fiber laser; the laser light emitted by the laser emission unit is emitted by the scanning mirror and irradiates the battery; the scanning mirror comprises two mutually perpendicular mirror surfaces, one mirror surface is used for linearly scanning the battery, and the other mirror surface is used for rotationally scanning the battery; the camera detector adopts a CMOS or CCD image sensor to receive laser light reflected from the battery; the camera detector acquires coordinates of points on the surface of the battery in a three-dimensional coordinate system according to the reflected laser light; reserving coordinate points corresponding to welding spots on points on the surface of the battery; and the coordinate point corresponding to the welding spot is DLP positioning coordinate.
5. The DLP-based new energy automobile process guidance method of claim 4, wherein the welding parameters include welding current, welding voltage, welding speed, welding angle, gap distance, and shielding gas flow rate;
the welding current is obtained by a current detection device arranged in the welding machine; the welding voltage is obtained by a built-in voltage detection circuit of the welding machine; the welding speed is obtained by installing an encoder on a welding gun;
The welding angle is obtained as follows:
3 sound wave sensors are symmetrically arranged on the head of the welding gun and respectively point to the directions A, B, C; 3 sound wave reflecting targets are correspondingly arranged on the surface of the battery; when the welding gun works, each sound wave sensor measures distances dA, dB and dC between the sound wave sensor and a sound wave reflection target; then the welding angle θ=arccos [ (dB) 2 +dC 2 -dA 2 )/（2dB×dC）]；
The gap distance is obtained by installing a laser range finder on the head of the welding gun;
the flow rate of the shielding gas is obtained by installing an air flow meter at the gas outlet.
6. The DLP-based new energy automobile process guidance method according to claim 5, wherein the construction method of the positioning coordinate matching database comprises the following steps:
selecting a MySQL relational database as a framework for locating the coordinate matching database; according to the type of data to be stored, two data tables 1 and 2 are designed; the data table 1 is used for storing positioning coordinate data, and the data table 2 is used for storing welding parameter data;
the data table 1 is a coordinate table and comprises a field id primary key, an x coordinate, a y coordinate, a z coordinate and an external key corresponding to welding parameters;
the data table 2 is a welding parameter table comprising a field id primary key, a welding voltage, a welding current, a welding speed, a welding angle, a gap distance and a shielding gas flow rate;
Setting a hold_id field in a positioning coordinate table, wherein the value of the hold_id field is the id value of a corresponding welding parameter table, and establishing data association of the two tables; and acquiring positioning coordinate data and welding parameter data in the assembly process by using a PLC or an industrial robot control system, and writing a program to realize automatic database storage.
7. The DLP-based new energy automobile process guidance method of claim 6, wherein the battery quality data includes power density, duration and battery heat dissipation rate;
the power density is obtained as follows:
a current sensor and a voltage sensor are arranged at the positive and negative electrode connecting terminals of the battery in series and used for detecting a terminal voltage value U and a through current value I during charging and discharging; multiplying the terminal voltage and the through current value to obtain instantaneous power P at a certain moment, namely P=U×I; setting a data sampling card, sampling an end voltage value U and a through current value I by the data sampling card at a fixed frequency, and calculating to obtain n instantaneous powers P; average power Pj is obtained by carrying out average calculation on n instantaneous powers P; the ratio of the average power Pj to the battery quality is the power density;
the endurance time is obtained as follows:
Performing discharge test on the new energy automobile with the installed battery; the discharging test is to test the discharging condition of the battery of the new energy automobile in the simulated driving process; recording the running time of the vehicle from the full charge state to the discharge exhaustion of the battery, namely, the endurance time;
the battery heat dissipation rate is obtained as follows:
1C charging is carried out on the battery, and charging time t is recorded; in the charging process, a fixed sampling frequency is set through a temperature sensor to acquire the surface temperature T of the battery; fitting a temperature curve T=f (T) in the charging process by using a computer, and obtaining a temperature rise rate dT/dT according to the temperature curve T=f (T);
collecting the heat capacity C, the heat consumption power X and the ambient temperature T0 of the battery;
wherein the heat capacity C of the battery is the product of the mass and the specific heat capacity of the battery;
the heat consumption power X is the charge of the batteryCharging power during charging of a battery, a charging current I is measured from a charging source 1 And a charging voltage U 1 The method comprises the steps of carrying out a first treatment on the surface of the Heat consumption power x=i 1 ×U 1 The method comprises the steps of carrying out a first treatment on the surface of the Charging current I 1 Acquiring through a ammeter; charging voltage U 1 Acquiring through a voltmeter; the ambient temperature T0 is obtained by a temperature sensor;
heat dissipation rate of battery=(dT/dt)/(X/(C×ΔT))；
Wherein, delta T is the temperature rise of the battery and is obtained through the temperature rise rate and the charging time.
8. The DLP-based new energy automobile process guidance method of claim 7, wherein the battery quality factor is；
In the method, in the process of the invention,is the power density; />The heat dissipation rate of the battery is; />Is the endurance time; />、/>And->Adjusting parameters for the quality coefficient;
coefficient of battery electrical properties；
In the method, in the process of the invention,for charging off voltage, ">For the discharge cut-off voltage>For maximum charging current +.>For maximum sustained discharge current, +.>Is AC internal resistance value->Is the internal resistance of direct current, < >>Is an electrical adjustment parameter;
real-time welding coefficient；
In the method, in the process of the invention,for welding current, +.>For welding voltage, +.>For the welding speed +.>For the welding angle +.>Is the gap distance>Is the flow rate of the shielding gas; />And->Adjusting parameters for welding;
assembled battery characteristic coefficient；
Battery risk factor；
In the method, in the process of the invention,is a risk adjustment parameter.
9. The DLP-based new energy automobile process guidance method according to claim 8, wherein the determining the setting mode of the threshold L includes:
step 1, establishing an electric-thermal joint simulation model in a battery, and setting internal defect parameters of different degrees;
step 2, obtaining the electrical coefficient of the battery according to the defect parameter simulation; taking the battery electrical coefficient of a normal battery as a reference;
Step 3, setting different judging thresholds L, performing abnormal judgment, and recording the missing detection rate and the misjudgment rate;
step 4, setting an objective function to minimize the sum of the omission factor and the misjudgment rate;
and 5.N times of iteration, adopting an unconstrained nonlinear optimization algorithm to find an optimal judgment threshold L for minimizing the objective function.
10. The DLP-based new energy automobile process guidance method according to claim 9, wherein the setting manner of the risk threshold k comprises:
s601, establishing a multi-physical field joint simulation model in a battery, and setting internal defect parameters of different degrees;
s602, simulating to obtain battery health state feature vectors x under different electrical matching levels;
s603, using a main component analysis PCA dimension reduction method to obtain an evaluation index y of the battery health state;
s604, establishing a battery state of health evaluation function: y=f (x);
s605, presetting y_min corresponding to the minimum requirement of qualified battery product quality; presetting acceptable defective rate of battery products as p%; determining a risk level y_max with the goal of meeting the qualification rate (100-p)%;
s606, presetting a risk threshold k, and meeting the condition that the k is not less than y_min and not more than y_max.
11. A new energy automobile process guidance system based on DLP, which is realized based on the new energy automobile process guidance method based on DLP according to any one of claims 1 to 10, characterized by comprising: the data acquisition module is used for acquiring monomer characteristic data and an assembly operation data set; the assembly operation data set comprises positioning coordinates and a welding parameter set;
The database construction module is used for constructing a positioning coordinate matching database according to the assembly operation data set;
the first coefficient acquisition module is used for acquiring battery quality data of the new energy automobile; acquiring a battery quality coefficient according to the battery quality data;
the real-time welding parameter acquisition module is used for inputting the real-time positioning coordinates into the positioning coordinate matching database to acquire real-time welding parameters;
the second coefficient acquisition module is used for acquiring the electrical coefficient of the battery according to the monomer characteristic data; acquiring real-time welding coefficients according to the real-time welding parameters; the ratio of the battery electrical coefficient to the real-time welding coefficient is used as the characteristic coefficient of the assembled battery;
the first judging module is used for presetting a judging threshold L; if the characteristic coefficient of the assembled battery is greater than or equal to L, acquiring a battery risk coefficient according to the characteristic coefficient of the assembled battery and the battery quality coefficient;
if the characteristic coefficient of the assembled battery is smaller than L, a risk prompt is sent to the DLP projection system to be electrically unmatched;
the second judging module is used for presetting a risk threshold k; if the risk coefficient of the battery is smaller than the risk threshold k, transmitting the real-time welding parameters and the real-time positioning coordinates to a DLP projection system for reference of workers;
and if the battery risk coefficient is greater than or equal to the risk threshold k, sending a risk prompt to the DLP projection system as a quality defect.
12. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the DLP-based new energy automobile process guidance method of any one of claims 1-10 when the computer program is executed.
13. A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the DLP-based new energy vehicle process guidance method of any of claims 1-10.
CN202410080340.3A 2024-01-19 2024-01-19 New energy automobile process guidance method and system based on DLP Active CN117594860B (en)

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