CN117077984A - Intelligent assembly method and system for passenger car battery module - Google Patents

Intelligent assembly method and system for passenger car battery module Download PDF

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Publication number
CN117077984A
CN117077984A CN202311330869.8A CN202311330869A CN117077984A CN 117077984 A CN117077984 A CN 117077984A CN 202311330869 A CN202311330869 A CN 202311330869A CN 117077984 A CN117077984 A CN 117077984A
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assembly
station
less
time
supply
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CN117077984B (en
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肖鸿鸿
邓爱玲
袁坤鹏
涂宏
谢宇航
彭林
赖龙飞
邓海燕
陶遂
钟辉波
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Jingma Motor Co ltd
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Jingma Motor Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing

Abstract

The invention relates to the technical field of intelligent assembly of buses, in particular to an intelligent assembly method and system of a bus battery module, comprising the following steps: step S1: selecting a target assembly station; step S2: setting the maximum residence time, and calculating the maximum assembly period; step S3: obtaining the maximum ex-warehouse quantity and feeding time of a single batch; step S4: obtaining average residence time of each target assembly station; step S5: calculating the supply quantity Q of the next batch; step S6: after the assembly task is completed, calculating an actual assembly period; step S7: judging whether the actual assembly period meets constraint conditions or not; step S8: when T' -delta T is less than or equal to T s When T is less than or equal to T, the next batch of goods supply is not modified; step S9: when T is less than T s When the supply quantity Q of the next batch is less than or equal to 1.25T, the supply quantity Q=Q-1; step S10: repeating steps S4-S9, inQ c And when the supply quantity is less than or equal to T/T', acquiring the next batch of supply quantity Q and informing the warehouse of supply. The invention can dynamically balance assembly and supply.

Description

Intelligent assembly method and system for passenger car battery module
Technical Field
The invention relates to the technical field of intelligent assembly of buses, in particular to an intelligent assembly method and system of a bus battery module.
Background
Passenger car assembly is an important automotive manufacturing process involving the assembly of various automotive parts and components into a complete passenger car. This process generally includes: component preparation, assembly line setup, quality control, final assembly, quality inspection and testing, and the like. Passenger car assembly is a highly coordinated and sophisticated process aimed at ensuring that each passenger car meets high quality standards and customer expectations. This process requires elaborate technical and engineering knowledge, as well as advanced production equipment and tools to ensure quality and reliability of the passenger car. A passenger car battery module refers to one component in a battery system for an electric or hybrid passenger car. This module typically includes a set of battery cells (typically lithium ion batteries or other types of battery cells) and an electronic control system associated therewith. Multiple cells are typically connected together to form a battery pack and the voltage, temperature, state of charge and state of discharge of the battery are monitored and managed by an electronic control system and ensure that the battery is operating within a safe operating range.
In the process of assembling the battery modules of the electric or hybrid passenger car on a formal line, when the feeding speed of the battery modules is higher than the assembling speed, the redundant battery modules which are already stored are possibly stacked on the production line; however, because the battery module is high in cost and has a certain potential safety hazard and the like, the battery module is stacked in a production workshop wantonly, and accidents such as theft or fire and the like can be caused; and for redundant battery modules, a re-warehouse entry mode is adopted, so that cooperation of warehouse management personnel, assembly personnel and transportation personnel is needed, and working time can be occupied, so that the flow is troublesome.
Disclosure of Invention
The invention aims to provide an intelligent assembly method and system for a passenger car battery module, which solve the technical problems.
The aim of the invention can be achieved by the following technical scheme:
an intelligent assembly method and system for a passenger car battery module comprises the following steps:
step S1: acquiring station distribution information of an assembly production line, and selecting a target assembly station, wherein the target assembly station represents a station for assembling a battery module;
step S2: setting a maximum residence time T of each target assembly station i Calculate the maximum fitting period t= Σt i Wherein T is i Representing the maximum residence time of an ith target assembly station, wherein the maximum residence time represents the maximum time for which a passenger car to be assembled stays in the station in the assembly process;
step S3: obtaining the maximum ex-warehouse quantity N of single batches of the battery modules and feeding time t, wherein the feeding time represents the time spent by the battery modules from warehouse to assembly line;
step S4: obtaining the average residence time of each target assembly station, wherein:
，t i indicating the average residence time of the ith target assembly station,/->Representing the sum of the residence time of the passenger car to be assembled in the ith target assembly station when the past N times of assembly are carried out in the historical data, and calculating the average assembly period T' = Σt i ；
Step S5: calculate the next lot supply q= (t) s -t _over ）/T'-Q c Wherein t is s Representing the current time node, t _over Represents the time to work, Q c Representing the remaining unassembled quantity of the assembly line, and rounding the supply quantity Q;
step S6: after the assembly task is completed, the residence time T of the passenger car to be assembled in the station in the assembly process of the target assembly station is obtained si Calculating an actual assembly period T s =∑T si ；
Step S7: judging the actual assembly period T s Whether the constraint condition is satisfied: t' -DeltaT is less than or equal to T s T is less than or equal to; when T is s Enter step S8, T when meeting the constraint condition s Step S9 is carried out when the constraint condition is not satisfied;
step S8: when T' -delta T is less than or equal to T s When T is less than or equal to T, the next batch of goods supply quantity Q is not modified, and the delta T is a preset time value;
step S9: when T is less than T s When the supply quantity Q of the next batch is less than or equal to 1.25T, the supply quantity Q=Q-1;
step S10: repeating steps S4-S9 until the assembly line has a remaining unassembled quantity Q c And when the supply quantity is less than or equal to T/T', acquiring the next batch of supply quantity Q and informing the warehouse of supply.
As a further scheme of the invention: in the step S4, when the next batch of battery modules arrives on the assembly line, data of past N times of assembly are acquired with the current time node as a standard, and the average assembly period T' is recalculated.
As a further scheme of the invention: in said step S9, when T s When T' - Δt is smaller, the next lot supply q=q+1.
As a further scheme of the invention: in the step S10, when the next lot supply Q > N, q=n is supplied.
As a further aspect of the inventionThe scheme is as follows: in said step S10, when said actual assembly cycle T s If the current assembly is more than 1.25T, the step S5 is repeated to recalculate the next batch of goods supply quantity Q, and the steps S6-S9 are circulated after the current assembly task is finished until the step S10 is entered.
As a further scheme of the invention: in the step S5, when there are other tasks occupying normal assembly in the assembly process, the next batch of the supplies is according to the following formula: q= (t s -t _over -t z ）/T'-Q c Wherein t is z Other occupied times outside the assembly task are represented, including information time, meeting time, and training time.
As a further scheme of the invention: in said step S5, when said assembly line remains unassembled by an amount Q c When > N, steps S5-S10 are not performed and are performed at Q c When N, the process proceeds to steps S5 to S10.
An intelligent assembly system for a passenger car battery module, comprising:
and a data acquisition module: acquiring station distribution information of an assembly production line, and selecting a target assembly station, wherein the target assembly station represents a station for assembling a battery module; obtaining the maximum ex-warehouse quantity N of single batches of the battery modules and feeding time t, wherein the feeding time represents the time spent by the battery modules from warehouse to assembly line;
an initialization module: setting a maximum residence time T of each target assembly station i Calculate the maximum fitting period t= Σt i Wherein T is i Representing the maximum residence time of an ith target assembly station, wherein the maximum residence time represents the maximum time for which a passenger car to be assembled stays in the station in the assembly process;
and a pretreatment module: obtaining the average residence time of each target assembly station, wherein:
，t i indicating the average residence time of the ith target assembly station,/->Representing the sum of the residence time of the passenger car to be assembled in the ith target assembly station when the past N times of assembly are carried out in the historical data, and calculating the average assembly period T' = Σt i The method comprises the steps of carrying out a first treatment on the surface of the Calculate the next lot supply q= (t) s -t _over ）/T'-Q c Wherein t is s Representing the current time node, t _over Represents the time to work, Q c Representing the remaining unassembled quantity of the assembly line, and rounding the supply quantity Q;
and a correction module: after the assembly task is completed, the residence time T of the passenger car to be assembled in the station in the assembly process of the target assembly station is obtained si Calculating an actual assembly period T s =∑T si The method comprises the steps of carrying out a first treatment on the surface of the Judging the actual assembly period T s Whether the constraint condition is satisfied: t' -DeltaT is less than or equal to T s T is less than or equal to; when T' -delta T is less than or equal to T s When T is less than or equal to T, the next batch of goods supply quantity Q is not modified, and the delta T is a preset time value; when T is less than T s When the supply quantity Q of the next batch is less than or equal to 1.25T, the supply quantity Q=Q-1; when T is s When the quantity is less than T' -delta T, the next batch of the supply quantity Q=Q+1;
and a distribution module: repeating calculation of the next batch supply in the correction module until the number Q of unassembled parts of the assembly line remains c And when the supply quantity is less than or equal to T/T', acquiring the next batch of supply quantity Q and informing the warehouse of supply.
The invention has the beneficial effects that: as can be seen from daily assembly production, during the assembly production process, a module or assembly often needs a plurality of stations to be assembled in a mutually matched manner, the stations may be in a line or a plurality of lines, for different situations, the assembly stations for the battery modules are defined as target assembly stations, the sum of the time of each completion of each target assembly station is taken as the assembly period of one battery module, and the assembly period is referenced by grabbing the average value of the past N times; calculating the average assembly period through the data of the past N times to calculate the total number of battery modules which can be used next in the assembly production line, namely the next batch of supply quantity Q calculated in the step S5 in the scheme, and correcting the next batch of supply quantity Q through the actual assembly for a period of time next, so that the assembly and the supply are dynamically balanced as much as possible; therefore, after the battery module is taken off duty, too many battery modules cannot be left on line to be installed, and the production requirement of assembly can be met.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of an intelligent assembly method of a passenger car battery module.
Description of the embodiments
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.
Referring to fig. 1, the invention provides an intelligent assembly method of a passenger car battery module, which comprises the following steps:
step S1: acquiring station distribution information of an assembly production line, and selecting a target assembly station, wherein the target assembly station represents a station for assembling a battery module;
step S2: setting a maximum residence time T of each target assembly station i Calculate the maximum fitting period t= Σt i Wherein T is i Representing the maximum residence time of an ith target assembly station, wherein the maximum residence time represents the maximum time for which a passenger car to be assembled stays in the station in the assembly process;
step S3: obtaining the maximum ex-warehouse quantity N of single batches of the battery modules and feeding time t, wherein the feeding time represents the time spent by the battery modules from warehouse to assembly line;
step S4: obtaining the average residence time of each target assembly station, wherein:
，t i indicating the average residence time of the ith target assembly station,/->Representing the sum of the residence time of the passenger car to be assembled in the ith target assembly station when the past N times of assembly are carried out in the historical data, and calculating the average assembly period T' = Σt i ；
Step S5: calculate the next lot supply q= (t) s -t _over ）/T'-Q c Wherein t is s Representing the current time node, t _over Represents the time to work, Q c Representing the remaining unassembled quantity of the assembly line, and rounding the supply quantity Q;
step S6: after the assembly task is completed, the residence time T of the passenger car to be assembled in the station in the assembly process of the target assembly station is obtained si Calculating an actual assembly period T s =∑T si ；
Step S7: judging the actual assembly period T s Whether the constraint condition is satisfied: t' -DeltaT is less than or equal to T s T is less than or equal to; when T is s Enter step S8, T when meeting the constraint condition s Step S9 is carried out when the constraint condition is not satisfied;
step S8: when T' -delta T is less than or equal to T s When T is less than or equal to T, the next batch of goods supply quantity Q is not modified, and the delta T is a preset time value;
step S9: when T is less than T s When the supply quantity Q of the next batch is less than or equal to 1.25T, the supply quantity Q=Q-1;
step S10: repeating steps S4-S9 until the assembly line has a remaining unassembled quantity Q c And when the supply quantity is less than or equal to T/T', acquiring the next batch of supply quantity Q and informing the warehouse of supply.
As can be seen from daily assembly production, during the assembly production process, a module or assembly often needs a plurality of stations to be assembled in a mutually matched manner, the stations may be in a line or a plurality of lines, for different situations, the assembly stations for the battery modules are defined as target assembly stations, the sum of the time of each completion of each target assembly station is taken as the assembly period of one battery module, and the assembly period is referenced by grabbing the average value of the past N times; calculating the average assembly period through the data of the past N times to calculate the total number of battery modules which can be used next in the assembly production line, namely the next batch of supply quantity Q calculated in the step S5 in the scheme, and correcting the next batch of supply quantity Q through the actual assembly for a period of time next, so that the assembly and the supply are dynamically balanced as much as possible; therefore, after the battery module is taken off duty, too many battery modules cannot be left on line to be installed, and the production requirement of assembly can be met.
In a preferred embodiment of the present invention, in the step S4, when the next batch of battery modules arrives on the assembly line, data of past N assemblies are acquired with the current time node as a standard, and the average assembly period T' is recalculated.
In a preferred embodiment of the present invention, in said step S9, when T s When T' - Δt is smaller, the next lot supply q=q+1.
In a preferred embodiment of the present invention, in the step S10, when the next lot supply Q > N, q=n is supplied.
In a preferred embodiment of the present invention, in said step S10, when said actual assembly cycle T s If the current assembly is more than 1.25T, the step S5 is repeated to recalculate the next batch of goods supply quantity Q, and the steps S6-S9 are circulated after the current assembly task is finished until the step S10 is entered.
In a preferred embodiment of the present invention, in said step S5, when there are other tasks occupying normal assembly during the assembly, the next batch of supplies is according to the following formula: q= (t s -t _over -t z ）/T'-Q c Wherein t is z Other occupied times outside the assembly task are represented, including information time, meeting time, and training time.
In a preferred embodiment of the inventionIn said step S5, when said assembly line remains unassembled by an amount Q c When > N, steps S5-S10 are not performed and are performed at Q c When N, the process proceeds to steps S5 to S10.
An intelligent assembly system for a passenger car battery module, comprising:
and a data acquisition module: acquiring station distribution information of an assembly production line, and selecting a target assembly station, wherein the target assembly station represents a station for assembling a battery module; obtaining the maximum ex-warehouse quantity N of single batches of the battery modules and feeding time t, wherein the feeding time represents the time spent by the battery modules from warehouse to assembly line;
an initialization module: setting a maximum residence time T of each target assembly station i Calculate the maximum fitting period t= Σt i Wherein T is i Representing the maximum residence time of an ith target assembly station, wherein the maximum residence time represents the maximum time for which a passenger car to be assembled stays in the station in the assembly process;
and a pretreatment module: obtaining the average residence time of each target assembly station, wherein:
，t i indicating the average residence time of the ith target assembly station,/->Representing the sum of the residence time of the passenger car to be assembled in the ith target assembly station when the past N times of assembly are carried out in the historical data, and calculating the average assembly period T' = Σt i The method comprises the steps of carrying out a first treatment on the surface of the Calculate the next lot supply q= (t) s -t _over ）/T'-Q c Wherein t is s Representing the current time node, t _over Represents the time to work, Q c Representing the remaining unassembled quantity of the assembly line, and rounding the supply quantity Q;
and a correction module: after the assembly task is completed, the residence time T of the passenger car to be assembled in the station in the assembly process of the target assembly station is obtained si Calculating an actual assembly period T s =∑T si The method comprises the steps of carrying out a first treatment on the surface of the Judging the actual assembly period T s Whether the constraint condition is satisfied: t' -DeltaT is less than or equal to T s T is less than or equal to; when T' -delta T is less than or equal to T s When T is less than or equal to T, the next batch of goods supply quantity Q is not modified, and the delta T is a preset time value; when T is less than T s When the supply quantity Q of the next batch is less than or equal to 1.25T, the supply quantity Q=Q-1; when T is s When the quantity is less than T' -delta T, the next batch of the supply quantity Q=Q+1;
and a distribution module: repeating calculation of the next batch supply in the correction module until the number Q of unassembled parts of the assembly line remains c And when the supply quantity is less than or equal to T/T', acquiring the next batch of supply quantity Q and informing the warehouse of supply.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.

Claims (8)

1. The intelligent assembly method of the passenger car battery module is characterized by comprising the following steps of:
step S1: acquiring station distribution information of an assembly production line, and selecting a target assembly station, wherein the target assembly station represents a station for assembling a battery module;
step S2: setting a maximum residence time T of each target assembly station i Calculate the maximum fitting period t= Σt i Wherein T is i Representing the maximum residence time of an ith target assembly station, wherein the maximum residence time represents the maximum time for which a passenger car to be assembled stays in the station in the assembly process;
step S3: obtaining the maximum ex-warehouse quantity N of single batches of the battery modules and feeding time t, wherein the feeding time represents the time spent by the battery modules from warehouse to assembly line;
step S4: obtaining the average residence time of each target assembly station, wherein:
，t i indicating the average residence time of the ith target assembly station,/->Representing the sum of the residence time of the passenger car to be assembled in the ith target assembly station when the past N times of assembly are carried out in the historical data, and calculating the average assembly period T' = Σt i ；
Step S5: calculate the next lot supply q= (t) s -t _over ）/T'-Q c Wherein t is s Representing the current time node, t _over Represents the time to work, Q c Representing the remaining unassembled quantity of the assembly line, and rounding the supply quantity Q;
step S6: after the assembly task is completed, the residence time T of the passenger car to be assembled in the station in the assembly process of the target assembly station is obtained si Calculating an actual assembly period T s =∑T si ；
Step S7: judging the actual assembly period T s Whether the constraint condition is satisfied: t' -DeltaT is less than or equal to T s T is less than or equal to; when T is s Enter step S8, T when meeting the constraint condition s Step S9 is carried out when the constraint condition is not satisfied;
step S8: when T' -delta T is less than or equal to T s When T is less than or equal to T, the next batch of goods supply quantity Q is not modified, and the delta T is a preset time value;
step S9: when T is less than T s When the supply quantity Q of the next batch is less than or equal to 1.25T, the supply quantity Q=Q-1;
step S10: repeating steps S4-S9 until the assembly line has a remaining unassembled quantity Q c And when the supply quantity is less than or equal to T/T', acquiring the next batch of supply quantity Q and informing the warehouse of supply.
2. The intelligent assembly method of a bus battery module according to claim 1, wherein in the step S4, when the battery module of the next batch arrives at the assembly line, the data of the past N assemblies are acquired based on the current time node, and the average assembly period T' is recalculated.
3. The intelligent assembly method for a bus battery module according to claim 1, wherein, in said step S9, when T s When T' - Δt is smaller, the next lot supply q=q+1.
4. The intelligent assembly method according to claim 1, wherein in the step S10, when the next lot of the supply Q > N, q=n is supplied.
5. The intelligent assembly method according to claim 1, wherein in said step S10, when said actual assembly cycle T is s If the current assembly is more than 1.25T, the step S5 is repeated to recalculate the next batch of goods supply quantity Q, and the steps S6-S9 are circulated after the current assembly task is finished until the step S10 is entered.
6. The intelligent assembly method of a bus battery module according to claim 1, wherein in the step S5, when there are other tasks occupying normal assembly during the assembly, the next batch of supply is according to the following formula: q= (t s -t _over -t z ）/T'-Q c Wherein t is z Other occupied times outside the assembly task are represented, including information time, meeting time, and training time.
7. The intelligent assembly method of a bus battery module according to claim 1, wherein in said step S5, when said assembly line remains unassembled Q c When > N, steps S5-S10 are not performed and are performed at Q c When N, the process proceeds to steps S5 to S10.
8. An intelligent assembly system for a passenger car battery module, comprising:
and a data acquisition module: acquiring station distribution information of an assembly production line, and selecting a target assembly station, wherein the target assembly station represents a station for assembling a battery module; obtaining the maximum ex-warehouse quantity N of single batches of the battery modules and feeding time t, wherein the feeding time represents the time spent by the battery modules from warehouse to assembly line;
an initialization module: setting a maximum residence time T of each target assembly station i Calculate the maximum fitting period t= Σt i Wherein T is i Representing the maximum residence time of an ith target assembly station, wherein the maximum residence time represents the maximum time for which a passenger car to be assembled stays in the station in the assembly process;
and a pretreatment module: obtaining the average residence time of each target assembly station, wherein:
，t i indicating the average residence time of the ith target assembly station,/->Representing the sum of the residence time of the passenger car to be assembled in the ith target assembly station when the past N times of assembly are carried out in the historical data, and calculating the average assembly period T' = Σt i The method comprises the steps of carrying out a first treatment on the surface of the Calculate the next lot supply q= (t) s -t _over ）/T'-Q c Wherein t is s Representing the current time node, t _over Represents the time to work, Q c Representing the remaining unassembled quantity of the assembly line, and rounding the supply quantity Q;
and a correction module: after the assembly task is completed, the residence time T of the passenger car to be assembled in the station in the assembly process of the target assembly station is obtained si Calculating an actual assembly period T s =∑T si The method comprises the steps of carrying out a first treatment on the surface of the Judging the actual assembly period T s Whether the constraint condition is satisfied: t' -DeltaT is less than or equal to T s T is less than or equal to; when T' -delta T is less than or equal to T s When T is less than or equal to T, the next batch of goods supply quantity Q is not modified, and the delta T is a preset time value; when T is less than T s When the temperature is less than or equal to 1.25T, the next batch is suppliedThe cargo quantity q=q-1; when T is s When the quantity is less than T' -delta T, the next batch of the supply quantity Q=Q+1;
and a distribution module: repeating calculation of the next batch supply in the correction module until the number Q of unassembled parts of the assembly line remains c And when the supply quantity is less than or equal to T/T', acquiring the next batch of supply quantity Q and informing the warehouse of supply.
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