CN113405743B - New energy electric vehicle production and manufacturing test data analysis processing method and system based on cloud computing and storage medium - Google Patents
New energy electric vehicle production and manufacturing test data analysis processing method and system based on cloud computing and storage medium Download PDFInfo
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- CN113405743B CN113405743B CN202110664569.8A CN202110664569A CN113405743B CN 113405743 B CN113405743 B CN 113405743B CN 202110664569 A CN202110664569 A CN 202110664569A CN 113405743 B CN113405743 B CN 113405743B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M3/00—Investigating fluid-tightness of structures
- G01M3/02—Investigating fluid-tightness of structures by using fluid or vacuum
- G01M3/26—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors
- G01M3/32—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for containers, e.g. radiators
- G01M3/3236—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for containers, e.g. radiators by monitoring the interior space of the containers
- G01M3/3263—Investigating fluid-tightness of structures by using fluid or vacuum by measuring rate of loss or gain of fluid, e.g. by pressure-responsive devices, by flow detectors for containers, e.g. radiators by monitoring the interior space of the containers using a differential pressure detector
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- 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
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Abstract
The invention discloses a new energy electric vehicle production and manufacturing test data analysis processing method, a system and a storage medium based on cloud computing, wherein the method comprises the steps of counting each automobile battery pack in the new energy electric vehicle production and manufacturing, respectively detecting the pressure at an explosion-proof valve when each automobile battery pack is charged with gas of each unit volume, comparing and analyzing the pressure difference at the explosion-proof valve when each automobile battery pack is charged with gas of each unit volume, simultaneously obtaining the pressure comparison difference value at the explosion-proof valve in each time period after each automobile battery pack is charged with gas of each unit volume, calculating the tightness influence coefficient of each automobile battery pack, respectively detecting the battery temperature and the battery discharge power of each automobile battery pack in each normal working time period, calculating the comprehensive test performance coefficient of each automobile battery pack, screening and counting each automobile battery pack with qualified test performance, thereby realizing the accurate analysis of the test performance of the automobile battery packs, the operation safety of the new energy electric automobile is improved.
Description
Technical Field
The invention relates to the field of analysis of automobile production test data, in particular to a new energy electric automobile production manufacturing test data analysis processing method and system based on cloud computing and a storage medium.
Background
With the support of the continuous new energy policy of China, the key technology of new energy automobiles in China is remarkably improved, related products are developed and applied more and more, and the battery pack as a key component enters a large-scale production stage. However, the existing automobile battery pack production and manufacturing tests still have a plurality of problems:
1. the existing sealing performance testing equipment in the production and manufacturing of the automobile battery pack is not perfect in function, and the integration level and the automation degree of the testing equipment are not high, so that the sealing performance of the automobile battery pack cannot be accurately tested, the function of testing the sealing performance of the automobile battery pack cannot be realized, the running potential safety hazard of a new energy electric automobile is increased, and a huge driving potential safety hazard is brought to a new energy electric automobile owner;
2. the existing automobile battery pack production and manufacturing test only tests the battery discharge power in the normal working time period of the battery pack, and the influence of the working temperature of the automobile battery pack on the discharge power in the normal working time period is not considered, so that the accuracy and reliability of new energy electric automobile production and manufacturing test data are reduced, and the test performance of the new energy electric automobile battery pack cannot be accurately analyzed;
in order to solve the problems, a new energy electric vehicle production and manufacturing test data analysis and processing method, system and storage medium based on cloud computing are designed.
Disclosure of Invention
The invention aims to provide a new energy electric vehicle production and manufacturing test data analysis processing method, a system and a storage medium based on cloud computing, wherein the method comprises the steps of counting each automobile battery pack in the new energy electric vehicle production and manufacturing, respectively detecting the pressure at an explosion-proof valve when each automobile battery pack is filled with gas of each unit volume, comparing and analyzing the pressure difference at the explosion-proof valve when each automobile battery pack is filled with gas of each unit volume, simultaneously obtaining the pressure comparison difference value at the explosion-proof valve in each time period after each automobile battery pack is filled with gas of each unit volume, calculating the sealing influence coefficient of each automobile battery pack, respectively detecting the battery temperature and the battery discharge power of each automobile battery pack in each normal working time period, calculating the comprehensive test performance coefficient of each automobile battery pack, screening each automobile battery pack with qualified statistical test performance, and putting the automobile battery packs into production and use of the new energy electric vehicle, the problems existing in the background technology are solved.
The purpose of the invention can be realized by the following technical scheme:
in a first aspect, the invention provides a new energy electric vehicle production and manufacturing test data analysis and processing method based on cloud computing, which comprises the following steps:
s1, counting the automobile battery pack: respectively counting each automobile battery pack in the production and manufacturing of the new energy electric automobile through a battery pack counting module, and numbering each automobile battery pack;
s2, detecting the pressure of the explosion-proof valve: respectively guiding the gas into each automobile battery pack in the production and manufacturing of the new energy electric automobile through a battery pack tightness tester, and respectively detecting the pressure of each automobile battery pack at an explosion-proof valve when each unit volume of gas is charged into each automobile battery pack through a pressure detection module;
s3, pressure analysis of the explosion-proof valve: extracting standard pressure at the explosion-proof valve when the battery pack stored in the storage database is charged with each unit volume of gas through a pressure analysis module, and comparing the pressure at the explosion-proof valve when each automobile battery pack is charged with each unit volume of gas with the standard pressure at the explosion-proof valve when the corresponding unit volume of gas is charged to obtain pressure difference at the explosion-proof valve when each automobile battery pack is charged with each unit volume of gas;
s4, pressure comparison difference statistics: respectively counting the pressure at the anti-explosion valve in each time period after each unit volume of gas is charged into each automobile battery pack through a pressure comparison difference value counting module, and comparing the pressure at the anti-explosion valve in each time period after each unit volume of gas is charged into each automobile battery pack with the pressure at the anti-explosion valve in the corresponding time period before the corresponding time period after the corresponding unit volume of gas is charged into the corresponding automobile battery pack to obtain the pressure comparison difference value at the anti-explosion valve in each time period after each unit volume of gas is charged into each automobile battery pack;
s5, analyzing the influence coefficient of the sealing performance: extracting a sealing influence proportional coefficient of the pressure of the explosion-proof valve stored in a storage database and a correction coefficient of a pressure comparison difference value at the explosion-proof valve by an analysis server, and calculating the sealing influence coefficient of each automobile battery pack;
s6, acquiring a battery temperature difference value: respectively detecting the battery temperature of each automobile battery pack in each normal working time period through a temperature detection module, extracting the standard temperature of each automobile battery pack in each normal working time period, and comparing the battery temperature of each automobile battery pack in each normal working time period with the standard temperature in the corresponding normal working time period to obtain the battery temperature difference value of each automobile battery pack in each normal working time period;
s7, battery discharge power statistics: the discharging power detection module is used for respectively detecting the discharging power of each automobile battery pack in each normal time period, and counting the discharging power of each automobile battery pack in each normal time period;
s8, analyzing comprehensive test performance coefficients: the method comprises the steps of extracting performance influence coefficients corresponding to battery temperature and battery discharge power stored in a storage database through an analysis server, calculating comprehensive test performance coefficients of all automobile battery packs, comparing the comprehensive test performance coefficients of all the automobile battery packs with a set battery pack test performance coefficient threshold, and putting all the automobile battery packs with qualified test performance into production and use of the new energy electric automobile if the comprehensive test performance coefficient of a certain automobile battery pack is larger than or equal to the set battery pack test performance coefficient threshold, so that the test performance of the automobile battery pack is qualified.
In a possible design of the first aspect, the step S2 includes counting pressures at the anti-explosion valve of each car battery pack during charging of each unit volume of gas, and forming a pressure set p at the anti-explosion valve of each car battery pack during charging of each unit volume of gas i V(p i V 1 ,p i V 2 ,...,p i V j ,...,p i V m )，p i V j Expressed as the pressure at the explosion-proof valve when the ith car battery pack is charged with j unit volumes of gas.
In a possible design of the first aspect, the step S3 includes configuring a set p 'of pressure differences at an explosion-proof valve when each unit volume of gas is charged into each automobile battery pack' i V(p′ i V 1 ,p′ i V 2 ,...,p′ i V j ,...,p′ i V m )，p′ i V j Expressed as the pressure difference at the explosion-proof valve when the ith automobile battery pack is charged with j unit volumes of gas.
In a possible design of the first aspect, the step S4 includes configuring a set of pressure comparison differences at the explosion-proof valve in each time period after each unit volume of gas is filled in each automobile battery pack
Expressed as the ith automobile battery pack is charged into j unitsAnd comparing the pressure at the explosion-proof valve in the nth time period after the volume of the gas with the difference value.
In one possible design of the first aspect, the calculation formula of the sealing influence coefficient of each automobile battery pack is
ξ i Expressed as the sealability influence coefficient of the ith automobile battery pack, mu expressed as the sealability influence proportionality coefficient of the explosion-proof valve pressure, p Sign board V j Expressed as the standard pressure at the explosion-proof valve when the battery pack is filled with j unit volumes of gas, lambda is expressed as the correction coefficient of the pressure at the explosion-proof valve compared with the difference value,
expressed as the pressure at the explosion-proof valve in the r time period after the ith automobile battery pack is charged with j unit volumes of gas.
In a possible design of the first aspect, the step S6 includes configuring a battery temperature difference set Δ T of each vehicle battery pack in each normal operation time period i K(ΔT i K 1 ,ΔT i K 2 ,...,ΔT i K x ,...,ΔT i K y )，ΔT i K x Is expressed as the battery temperature difference of the ith automobile battery pack in the xth normal working time period.
In a possible design of the first aspect, the step S7 includes configuring a battery discharging power set T of each car battery pack in each normal operation time period i W(T i W 1 ,T i W 2 ,...,T i W x ,...,T i W y ),T i W x The battery discharge power of the ith automobile battery pack in the xth normal working time period is shown.
In a possible design of the first aspect, the calculation formula of the comprehensive test performance coefficient of each automobile battery pack is
ψ i Is shown asComprehensive test performance coefficient, xi of i automobile battery packs i The sealing performance influence coefficient of the ith automobile battery pack is shown, the alpha and the beta are respectively shown as the performance influence coefficients corresponding to the battery temperature and the battery discharge power, and the T is x K Sign board Expressed as the standard temperature, W, of the automobile battery pack in the x-th normal working period Sign board The standard discharge power is shown for the vehicle battery pack during normal operation.
In a second aspect, the invention further provides a new energy electric vehicle production and manufacturing test data analysis and processing system based on cloud computing, wherein the battery pack statistics module is connected with the pressure detection module, the pressure analysis module is respectively connected with the pressure detection module, the storage database and the analysis server, the temperature detection module is respectively connected with the storage database and the analysis server, and the analysis server is respectively connected with the pressure comparison difference value statistics module, the discharge power detection module and the storage database.
In a third aspect, the present invention further provides a storage medium, where a computer program is burned in the storage medium, and when the computer program runs in a memory of a server, the method of the present invention is implemented.
Has the advantages that:
(1) the invention provides a new energy electric vehicle production and manufacturing test data analysis processing method, a system and a storage medium based on cloud computing, which lay a foundation for later detection of the use performance of each vehicle battery pack by counting each vehicle battery pack in the new energy electric vehicle production and manufacturing, respectively detect the pressure at an explosion-proof valve when each vehicle battery pack is filled with each unit volume of gas, compare and analyze the pressure difference at the explosion-proof valve when each vehicle battery pack is filled with each unit volume of gas, provide reliable reference data for later calculation of the sealing influence coefficient of each vehicle battery pack, simultaneously obtain the pressure comparison difference at the explosion-proof valve in each time period after each vehicle battery pack is filled with each unit volume of gas, calculate the sealing influence coefficient of each vehicle battery pack, thereby accurately testing the sealing performance of the vehicle battery packs and realizing the function of testing the sealing performance of the vehicle battery packs, and further, the running safety of the new energy electric automobile is improved, and the running safety guarantee is brought to a new energy electric automobile owner.
(2) According to the invention, the battery temperature and the battery discharge power of each automobile battery pack in each normal working time period are respectively detected, and the comprehensive test performance coefficient of each automobile battery pack is calculated, so that the accuracy and the reliability of the new energy electric automobile production and manufacturing test data are improved, the test performance of the new energy electric automobile battery pack can be accurately analyzed, each automobile battery pack with qualified test performance is screened and counted, and is put into the production and use of the new energy electric automobile, so that a reliable reference basis is provided for the later analysis of the qualified test performance of the automobile battery pack.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the method steps of the present invention;
fig. 2 is a schematic view of a module connection structure according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a first aspect of the present invention provides a new energy electric vehicle production manufacturing test data analysis and processing method based on cloud computing, including the following steps:
s1, counting the automobile battery pack: and respectively counting each automobile battery pack in the production and manufacturing of the new energy electric automobile through a battery pack counting module, and numbering each automobile battery pack.
In this embodiment, in step S1, the automobile battery packs are numbered sequentially according to the manufacturing time sequence, so as to form an automobile battery pack number set a (a) in the new energy electric automobile production and manufacturing process 1 ,a 2 ,...,a i ,...,a n )，a i The number of the ith automobile battery pack in the new energy electric automobile production and manufacturing is expressed, and a foundation is laid for detecting the service performance of each automobile battery pack in the later period.
S2, detecting the pressure of the explosion-proof valve: gas is respectively guided into each automobile battery pack in the production and manufacturing of the new energy electric automobile through a battery pack tightness tester, the pressure at the explosion-proof valve when each automobile battery pack is charged with gas in each unit volume is respectively detected through a pressure detection module, the pressure at the explosion-proof valve when each automobile battery pack is charged with gas in each unit volume is counted, and a pressure set p at the explosion-proof valve when each automobile battery pack is charged with gas in each unit volume is formed i V(p i V 1 ,p i V 2 ,...,p i V j ,...,p i V m )，p i V j Expressed as the pressure at the explosion-proof valve when the ith car battery pack is charged with j unit volumes of gas.
S3, pressure analysis of the explosion-proof valve: the standard pressure at the explosion-proof valve when the battery pack stored in the storage database is charged with each unit volume of gas is extracted through the pressure analysis module, the pressure at the explosion-proof valve when each automobile battery pack is charged with each unit volume of gas is compared with the standard pressure at the explosion-proof valve when the corresponding unit volume of gas is charged, the pressure difference at the explosion-proof valve when each automobile battery pack is charged with each unit volume of gas is obtained, and a pressure difference set p 'at the explosion-proof valve when each automobile battery pack is charged with each unit volume of gas is formed' i V(p′ i V 1 ,p′ i V 2 ,...,p′ i V j ,...,p′ i V m )，p′ i V j The pressure difference at the explosion-proof valve when j unit volumes of gas are charged into the ith automobile battery pack is represented, and reliable reference data are provided for calculating the sealing influence coefficient of each automobile battery pack in the later period.
S4, pressure comparison difference statistics: the pressure comparison difference value counting module is used for respectively counting the pressure of the anti-explosion valve in each time period after each unit volume of gas is filled into each automobile battery pack, the pressure of the anti-explosion valve in each time period after each unit volume of gas is filled into each automobile battery pack is compared with the pressure of the anti-explosion valve in the corresponding time period before the corresponding time period after the corresponding unit volume of gas is filled into the corresponding automobile battery pack, and the pressure comparison difference value of the anti-explosion valve in each time period after each unit volume of gas is filled into each automobile battery pack is obtained.
In this embodiment, the step S4 includes configuring a set of pressure comparison differences at the anti-explosion valve in each time period after each unit volume of gas is filled into each automobile battery pack
And the pressure at the anti-explosion valve in the ith time period after j unit volumes of gas are filled into the ith automobile battery pack is compared with the difference value.
S5, analyzing the influence coefficient of the sealing performance: and (3) extracting the sealing influence proportional coefficient of the pressure of the explosion-proof valve stored in the storage database and the correction coefficient of the pressure comparison difference value at the explosion-proof valve by the analysis server, and calculating the sealing influence coefficient of each automobile battery pack.
In this embodiment, the calculation formula of the sealing effect coefficient of each automobile battery pack is
ξ i The sealing performance influence coefficient of the ith automobile battery pack is expressed, mu is expressed as the sealing performance influence proportionality coefficient of the pressure of the explosion-proof valve, p Sign board V j Expressed as the standard pressure at the explosion-proof valve when the battery pack is filled with j unit volumes of gas, lambda is expressed as the correction coefficient of the pressure at the explosion-proof valve compared with the difference value,
expressed as the ith automobile battery pack is charged into jPressure at the explosion-proof valve in the r time period after the unit volume of gas.
Specifically, the pressure at the explosion-proof valve when each unit volume of gas is filled into each automobile battery pack is detected, the pressure difference at the explosion-proof valve when each unit volume of gas is filled into each automobile battery pack is contrastively analyzed, meanwhile, the pressure contrast difference value at the explosion-proof valve in each time period after each unit volume of gas is filled into each automobile battery pack is obtained, and the influence coefficient of the sealing performance of each automobile battery pack is calculated, so that the sealing performance of the automobile battery pack can be accurately tested, the function of testing the sealing performance of the automobile battery pack is realized, the running safety of the new-energy electric automobile is improved, and the running safety guarantee is brought to a new-energy electric automobile owner.
S6, acquiring a battery temperature difference value: the battery temperature of each automobile battery pack in each normal working time period is detected through a temperature detection module, the standard temperature of each automobile battery pack in each normal working time period is extracted, the battery temperature of each automobile battery pack in each normal working time period is compared with the standard temperature in the corresponding normal working time period, the battery temperature difference value of each automobile battery pack in each normal working time period is obtained, and a battery temperature difference value set delta T of each automobile battery pack in each normal working time period is formed i K(ΔT i K 1 ,ΔT i K 2 ,...,ΔT i K x ,...,ΔT i K y )，ΔT i K x Is expressed as the battery temperature difference of the ith automobile battery pack in the xth normal working time period.
S7, battery discharge power statistics: the discharging power detection module is used for respectively detecting the discharging power of each automobile battery pack in each normal time period, and the discharging power of each automobile battery pack in each normal time period is counted to form a battery discharging power set T of each automobile battery pack in each normal working time period i W(T i W 1 ,T i W 2 ,...,T i W x ,...,T i W y ),T i W x The battery discharge power of the ith automobile battery pack in the xth normal working time period is shown.
S8, analyzing comprehensive test performance coefficients: the method comprises the steps of extracting performance influence coefficients corresponding to battery temperature and battery discharge power stored in a storage database through an analysis server, calculating comprehensive test performance coefficients of all automobile battery packs, comparing the comprehensive test performance coefficients of all the automobile battery packs with a set battery pack test performance coefficient threshold, if the comprehensive test performance coefficient of a certain automobile battery pack is smaller than the set battery pack test performance coefficient threshold, indicating that the test performance of the automobile battery pack is unqualified, and if the comprehensive test performance coefficient of a certain automobile battery pack is larger than or equal to the set battery pack test performance coefficient threshold, indicating that the test performance of the automobile battery pack is qualified, putting all the automobile battery packs with qualified test performance into production and use of the new energy electric automobile.
In this embodiment, the calculation formula of the comprehensive test performance coefficient of each automobile battery pack is
ψ i Expressed as the comprehensive test performance coefficient, xi, of the ith automobile battery pack i The sealing performance influence coefficient of the ith automobile battery pack is shown, the alpha and the beta are respectively shown as the performance influence coefficients corresponding to the battery temperature and the battery discharge power, and the T is x K Sign board Expressed as the standard temperature, W, of the automobile battery pack in the x-th normal working period Sign board The standard discharge power is shown for the vehicle battery pack during normal operation.
Specifically, the battery temperature and the battery discharge power of each automobile battery pack in each normal working time period are respectively detected, and the comprehensive test performance coefficient of each automobile battery pack is calculated, so that the accuracy and the reliability of the new energy electric automobile production and manufacturing test data are improved, the test performance of the new energy electric automobile battery pack can be accurately analyzed, each automobile battery pack with qualified test performance is screened and counted, and is put into production and use of the new energy electric automobile, and a reliable reference basis is provided for the later analysis of the qualified test performance of the automobile battery pack.
In a second aspect, the invention further provides a new energy electric vehicle production and manufacturing test data analysis and processing system based on cloud computing, wherein the battery pack statistics module is connected with the pressure detection module, the pressure analysis module is respectively connected with the pressure detection module, the storage database and the analysis server, the temperature detection module is respectively connected with the storage database and the analysis server, and the analysis server is respectively connected with the pressure comparison difference value statistics module, the discharge power detection module and the storage database.
In a third aspect, the present invention further provides a storage medium, where a computer program is burned in the storage medium, and when the computer program runs in a memory of a server, the method of the present invention is implemented.
The foregoing is merely exemplary and illustrative of the principles of the present invention and various modifications, additions and substitutions of the specific embodiments described herein may be made by those skilled in the art without departing from the principles of the present invention or exceeding the scope of the claims set forth herein.
Claims (8)
1. A new energy electric automobile production manufacturing test data analysis processing method based on cloud computing is characterized in that: the method comprises the following steps:
s1, counting the automobile battery pack: respectively counting each automobile battery pack in the production and manufacturing of the new energy electric automobile through a battery pack counting module, and numbering each automobile battery pack;
s2, detecting the pressure of the explosion-proof valve: respectively guiding the gas into each automobile battery pack in the production and manufacturing of the new energy electric automobile through a battery pack tightness tester, and respectively detecting the pressure of each automobile battery pack at an explosion-proof valve when each unit volume of gas is charged into each automobile battery pack through a pressure detection module;
s3, pressure analysis of the explosion-proof valve: extracting standard pressure at the explosion-proof valve when the battery pack stored in the storage database is charged with each unit volume of gas through a pressure analysis module, and comparing the pressure at the explosion-proof valve when each automobile battery pack is charged with each unit volume of gas with the standard pressure at the explosion-proof valve when the corresponding unit volume of gas is charged to obtain the pressure difference at the explosion-proof valve when each automobile battery pack is charged with each unit volume of gas;
s4, pressure comparison difference statistics: respectively counting the pressure at the anti-explosion valve in each time period after each unit volume of gas is charged into each automobile battery pack through a pressure comparison difference value counting module, and comparing the pressure at the anti-explosion valve in each time period after each unit volume of gas is charged into each automobile battery pack with the pressure at the anti-explosion valve in the corresponding time period before the corresponding time period after the corresponding unit volume of gas is charged into the corresponding automobile battery pack to obtain the pressure comparison difference value at the anti-explosion valve in each time period after each unit volume of gas is charged into each automobile battery pack;
s5, analyzing the sealing performance influence coefficient: extracting a sealing influence proportional coefficient of the pressure of the explosion-proof valve stored in a storage database and a correction coefficient of a pressure comparison difference value at the explosion-proof valve by an analysis server, and calculating the sealing influence coefficient of each automobile battery pack; the calculation formula of the sealing influence coefficient of each automobile battery pack is
ξ i Expressed as the sealability influence coefficient of the ith automobile battery pack, mu expressed as the sealability influence proportionality coefficient of the explosion-proof valve pressure, p Sign board V j Expressed as the standard pressure at the explosion-proof valve when the battery pack is filled with j unit volumes of gas, lambda is expressed as the correction coefficient of the pressure at the explosion-proof valve compared with the difference value,
the pressure at an explosion-proof valve in the ith time period after j unit volumes of gas are filled in the ith automobile battery pack is expressed, wherein j is 1, 2, m is the mth unit volume of gas, and r is 1, 2, f is the fth time period;
s6, acquiring a battery temperature difference value: respectively detecting the battery temperature of each automobile battery pack in each normal working time period through a temperature detection module, extracting the standard temperature of each automobile battery pack in each normal working time period, and comparing the battery temperature of each automobile battery pack in each normal working time period with the standard temperature in the corresponding normal working time period to obtain the battery temperature difference value of each automobile battery pack in each normal working time period;
s7, battery discharge power statistics: the discharging power detection module is used for respectively detecting the discharging power of each automobile battery pack in each normal time period, and counting the discharging power of each automobile battery pack in each normal time period;
s8, analyzing comprehensive test performance coefficients: extracting performance influence coefficients corresponding to battery temperature and battery discharge power stored in a storage database through an analysis server, calculating comprehensive test performance coefficients of all automobile battery packs, comparing the comprehensive test performance coefficients of all the automobile battery packs with a set battery pack test performance coefficient threshold, and putting all the automobile battery packs with qualified test performance into production and use of a new energy electric automobile if the comprehensive test performance coefficient of a certain automobile battery pack is greater than or equal to the set battery pack test performance coefficient threshold, which indicates that the test performance of the automobile battery pack is qualified;
the comprehensive test performance coefficient calculation formula of each automobile battery pack is
ψ i Expressed as the comprehensive test performance coefficient, xi, of the ith automobile battery pack i The sealing performance influence coefficient of the ith automobile battery pack is shown, the alpha and the beta are respectively shown as the performance influence coefficients corresponding to the battery temperature and the battery discharge power, and the T is x K Sign board Expressed as the standard temperature, W, of the automobile battery pack in the x-th normal working period Sign board The standard discharge power of the automobile battery pack during normal operation is shown, and x is 1, 2.
2. The cloud-computing-based new energy electric vehicle production and manufacturing test data analysis and processing method according to claim 1, characterized in that: the step S2 includes counting the pressure at the anti-explosion valve when each car battery pack is charged with each unit volume of gas to form a pressure set p at the anti-explosion valve when each car battery pack is charged with each unit volume of gas i V(p i V 1 ,p i V 2 ,...,p i V j ,...,p i V m )，p i V j Expressed as the pressure at the explosion-proof valve when the ith car battery pack is charged with j unit volumes of gas.
3. The cloud-computing-based new energy electric vehicle production and manufacturing test data analysis and processing method according to claim 1, characterized in that: the step S3 includes constructing a set p 'of pressure differences at the explosion-proof valve when each unit volume of gas is filled in each automobile battery pack' i V(p′ i V 1 ,p′ i V 2 ,...,p′ i V j ,...,p′ i V m )，p′ i V j Expressed as the pressure difference at the explosion-proof valve when the ith automobile battery pack is charged with j unit volumes of gas.
4. The cloud-computing-based new energy electric vehicle production and manufacturing test data analysis and processing method according to claim 1, characterized in that: the step S4 includes forming a pressure comparison difference set at the anti-explosion valve in each time period after each unit volume of gas is filled into each automobile battery pack
And the pressure at the anti-explosion valve in the ith time period after j unit volumes of gas are filled into the ith automobile battery pack is compared with the difference value.
5. The cloud-computing-based new energy electric vehicle production and manufacturing test data analysis and processing method according to claim 1, characterized in that: the step S6 includes configuring a battery temperature difference set Δ T of each vehicle battery pack in each normal operating time period i K(ΔT i K 1 ,ΔT i K 2 ,...,ΔT i K x ,...,ΔT i K y )，ΔT i K x Is expressed as the battery temperature difference of the ith automobile battery pack in the xth normal working time period.
6. The cloud-computing-based new energy electric vehicle production and manufacturing test data analysis and processing method according to claim 1, characterized in that: the step S7 includes forming a battery discharge power set T of each automobile battery pack in each normal operating time period i W(T i W 1 ,T i W 2 ,...,T i W x ,...,T i W y ),T i W x The discharge power of the battery of the ith automobile battery pack in the x normal working time period is shown.
7. The new energy electric vehicle production and manufacturing test data analysis and processing method based on cloud computing according to any one of claims 1 to 6, characterized in that: the processing method comprises the steps of relating to a new energy electric vehicle production manufacturing test data analysis processing system based on cloud computing, wherein the battery pack counting module is connected with the pressure detection module, the pressure analysis module is respectively connected with the pressure detection module, the storage database and the analysis server, the temperature detection module is respectively connected with the storage database and the analysis server, and the analysis server is respectively connected with the pressure comparison difference value counting module, the discharge power detection module and the storage database.
8. A storage medium, characterized by: the storage medium is burned with a computer program, which when run in the memory of the server implements the method of any of the above claims 1-7.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10104325A (en) * | 1996-09-30 | 1998-04-24 | Nissan Motor Co Ltd | Maximum charge/discharge power operation method of battery |
| CN208723006U (en) * | 2018-07-27 | 2019-04-09 | 东莞氢宇新能源科技有限公司 | A kind of comprehensive performance test device of hydrogen fuel cell |
| JP2020076588A (en) * | 2018-11-06 | 2020-05-21 | トヨタ自動車株式会社 | Gas leak checking device |
| CN111707957A (en) * | 2020-04-23 | 2020-09-25 | 北京邮电大学 | Method and device for estimating residual value of battery of electric vehicle |
| CN112026523A (en) * | 2020-08-31 | 2020-12-04 | 蜂巢能源科技有限公司 | Battery pack sealing performance detection system and electric vehicle |
| CN112098853A (en) * | 2020-08-21 | 2020-12-18 | 北京车和家信息技术有限公司 | Capacity attenuation battery discharge power determination method and device |
| CN112611523A (en) * | 2020-12-22 | 2021-04-06 | 芜湖奇达动力电池***有限公司 | New energy automobile battery pack sealing performance testing method |
| CN112629770A (en) * | 2020-11-24 | 2021-04-09 | 芜湖奇达动力电池***有限公司 | New energy automobile power battery pack liquid cooling system test method |
-
2021
- 2021-06-16 CN CN202110664569.8A patent/CN113405743B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH10104325A (en) * | 1996-09-30 | 1998-04-24 | Nissan Motor Co Ltd | Maximum charge/discharge power operation method of battery |
| CN208723006U (en) * | 2018-07-27 | 2019-04-09 | 东莞氢宇新能源科技有限公司 | A kind of comprehensive performance test device of hydrogen fuel cell |
| JP2020076588A (en) * | 2018-11-06 | 2020-05-21 | トヨタ自動車株式会社 | Gas leak checking device |
| CN111707957A (en) * | 2020-04-23 | 2020-09-25 | 北京邮电大学 | Method and device for estimating residual value of battery of electric vehicle |
| CN112098853A (en) * | 2020-08-21 | 2020-12-18 | 北京车和家信息技术有限公司 | Capacity attenuation battery discharge power determination method and device |
| CN112026523A (en) * | 2020-08-31 | 2020-12-04 | 蜂巢能源科技有限公司 | Battery pack sealing performance detection system and electric vehicle |
| CN112629770A (en) * | 2020-11-24 | 2021-04-09 | 芜湖奇达动力电池***有限公司 | New energy automobile power battery pack liquid cooling system test method |
| CN112611523A (en) * | 2020-12-22 | 2021-04-06 | 芜湖奇达动力电池***有限公司 | New energy automobile battery pack sealing performance testing method |
Non-Patent Citations (1)
| Title |
|---|
| 电动汽车动力锂电池包结构设计及其液冷散热性能研究;杨明飞;《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅱ辑》;20191115(第11期);第C035-337页 * |
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