A method for optimizing the production sequence of raw materials for battery products based on customized production scheduling

Abstract

The present invention discloses a method for optimizing the production sequence of raw materials for battery products based on customized production scheduling, which belongs to the technical field of power battery production planning. The method analyzes the production process of the battery to obtain an objective function with the minimum battery cost as the premise and the minimum processing time as the purpose, and obtains the shortest completion time and sorting method based on the engineering depth indication algorithm, thereby improving the production efficiency of the battery and reducing the production cost.

Description

Technical Field

[0001] The present invention relates to a method for optimizing the production sequence of raw materials for battery products based on customized production scheduling, and belongs to the technical field of power battery production planning. Background Art

[0002] Against the backdrop of the rapid development of the new energy market, the battery industry has risen rapidly, with strong market demand for products, requiring large-scale production. Under the large-scale production model, the battery qualification rate will vary due to the different production processes and corresponding raw materials of various types of batteries. For example, a battery manufacturer can produce three types of batteries: A, B, and C. The battery capacity, voltage and other parameters of the three types of batteries are the same, but the corresponding production raw materials of the three types of batteries are different, and the production processes are also different. Therefore, the total production costs are also different. Ultimately, the production qualification rates of these three types of batteries are also different under the large-scale production model.

[0003] When receiving orders from customers, battery manufacturers need to consider how to reduce their own production costs as much as possible while meeting customer requirements for order quantity, delivery time and total order qualification rate. For example, customers require a total order qualification rate of 85%, and among the three types of batteries A, B and C that battery manufacturers can produce, the production qualification rate of Class A batteries under the large-scale production mode can reach 96%, but relatively speaking, its production cost is also relatively high. Among them, the production qualification rate of Class C batteries under the large-scale production mode is only 60%, but correspondingly, its production cost is also relatively low. When battery manufacturers carry out actual battery production, they need to comprehensively consider meeting customer needs and minimizing their own production costs.

[0004] On the other hand, different types of batteries have different production costs and corresponding production times. Since the entire battery production process includes multiple steps and there is a strict production sequence between the steps, the production equipment corresponding to each step needs to operate at maximum load, that is, the equipment corresponding to the subsequent step cannot be idle due to the failure of the previous step. Therefore, it is necessary to optimize the production sequence of the production raw materials corresponding to each type of battery so as to shorten the production time of the order as much as possible while minimizing its own production cost. Summary of the invention

[0005] In order to minimize the production cost and production time of battery manufacturers while meeting customer needs, the present invention provides a method for optimizing the production sequence of raw materials for battery products based on customized scheduling, which improves the production efficiency of batteries and reduces production costs by decomposing customer orders and reformulating the production sequence.

[0006] A method for optimizing the production sequence of raw materials for battery products based on customized production scheduling, the method comprising:

[0007] Step S1, decomposing the customer order based on the battery qualification rate required by the customer order and the qualification rate of each of n types of batteries that the battery manufacturer can produce in a large-scale production mode;

[0008] Step S2, establishing a time-minimizing production model based on the demand quantity of each type of battery decomposed in step S1;

[0009] Step S3: Based on the time minimization production model established in step S2, the production order of each type of battery is determined based on the rule that the battery with the longest total production time among the n types of batteries enters production first;

[0010] Step S4: Based on the number of machines and equipment, determine the production order of each raw material.

[0011] Optionally, the step S1 includes:

[0012] Assume that the total number of batteries required by the customer order is μ, the order requires a delivery time of D days, and the number of machine hours available for processing per day for the kth machine is w k , the total processing time of the i-th type of battery on the k-th machine is l ik , let the cost of the i-th type battery be c i , find μ that satisfies the following constraints i :

[0013]

[0014]

[0015]

[0016] Among them, M% is the battery qualification rate required by the customer order, M i % is the qualified rate of the i-th type of battery among the n types of batteries that the battery manufacturer can produce in the large-scale production mode.

[0017] μ i Represents the number of batteries of category i contained in the customer order after decomposition, satisfying:

[0018]

[0019] Optionally, step S2 includes:

[0020] Assuming that there are m types of raw materials required for the n types of batteries that a battery manufacturer can produce, the total quantity required for each type of raw material is:

[0021]

[0022] Among them, Aj represents the total quantity required for the jth type of material, a ij It indicates the quantity of the jth type of raw materials required to produce a unit of the ith type of battery;

[0023] The total time required to produce each type of battery is:

[0024] T i =f(μ i a ij ,b jk )t jk

[0025] Among them, T i represents the total time required to produce the i-th type of battery, b jk It means that the jth type of raw material is used in the kth machine equipment E k The maximum input amount per time, t jk Indicates the kth machine device E k The processing time required to invest one type of raw material j; f(μ i a ij ,b jk ) is defined as:

[0026]

[0027]

[0028] Where N + represents a positive integer; the total time T required to produce each type of battery is obtained i After that, the total time required to get the customer order is:

[0029]

[0030] Where T represents the total time required to produce all types of batteries in a customer order.

[0031] Optionally, step S3 includes:

[0032] Calculate the proportion of the total time required for each type of battery to the total time required for a customer order using the following formula:

[0033] P i =T i / T

[0034] Further calculation of P i The logarithmic entropy value of is:

[0035] H i =P i ln(1+P i )

[0036] For Hi Sort by the production order of each type of battery from large to small, where 0 <Pi<1。

[0037] Optionally, step S4 includes:

[0038] Calculate the intermediate parameter Q j , and according to the Q corresponding to each raw material j Determine the production sequence:

[0039] Q j =-q j *ln(q j )

[0040] q j =a j / gcd(a j ),j=1,2....g

[0041] Among them, g means that the machine equipment E needs to be used at the same time k The number of types of raw materials, g<m; the quantity corresponding to each type of raw material is a j , each a j The greatest common divisor is gcd(a j ), take each a j The greatest common divisor gcd(a j ) as each investment in machinery and equipment E k The jth type of raw material requires q j It can be fully processed in times;

[0042] According to the intermediate parameter Q j The size of each raw material determines the machine equipment E k The order of production.

[0043] Optionally, the intermediate parameter Q j The size of each raw material determines the machine equipment E k The production sequence includes:

[0044] Step 1: Select Q from all the raw materials to be put into production j The raw materials with the largest value are invested first;

[0045] Step 2: Recalculate the intermediate parameter Q corresponding to the remaining raw materials after production starts. j ;

[0046] Step 3, based on the recalculated intermediate parameter Q j Determine the raw materials for the next production;

[0047] Step 4, repeat Step 2-Step 3 until all raw materials Q jThe value is 0.

[0048] Optionally, the intermediate parameter Q j The size of each raw material determines the machine equipment E k When the production sequence is as follows, if the intermediate parameters Q corresponding to several raw materials are j If they are equal and both are minimum, the raw material with the longest time interval from the last production will be selected for production.

[0049] Optionally, the method is applied to the battery cell production stage in the battery production process.

[0050] The present application also provides application of the above method in battery production management.

[0051] The beneficial effects of the present invention are:

[0052] By breaking down customer orders and rescheduling production, we have improved battery production efficiency and reduced production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 It is a basic process diagram of the battery production of the present invention.

[0055] Figure 2 It is a flow chart of a method for optimizing the production sequence of raw materials for battery products based on customized production scheduling disclosed in one embodiment of the present invention.

[0056] Figure 3 It is the corresponding Gantt chart after optimizing the production sequence of the first process of battery production by using the method of the present application. DETAILED DESCRIPTION

[0057] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0058] like Figure 1 As shown, the production process of batteries is divided into three stages: cell production, module assembly and PACK assembly; the cell production stage involves film making process, assembly process and formation process; the film making process includes the following processes: batching process, coating process and positive and negative electrode slitting process; the assembly process includes the following processes: winding, lamination process, packaging, baking process and liquid injection, sealing process; the formation process includes formation process, capacity separation process and testing process;

[0059] Among the three stages, the battery cell production is the first stage of the entire battery production. Whether it is completed directly determines whether the subsequent processes can be started. Therefore, the subsequent embodiments of the present application take the first process involved in the film making process in the first stage, that is, the positive and negative electrode ingredients, as an example to optimize the production sequence of the production raw materials corresponding to the various types of batteries to be produced; in the positive and negative electrode ingredients process, the raw materials required for the positive and negative electrode ingredients of the battery are different, and the equipment used is also different.

[0060] In the actual production process of batteries, the method of this application can be used to optimize the production sequence for processes involving multiple production raw materials.

[0061] Embodiment 1:

[0062] This embodiment provides a method for optimizing the production sequence of raw materials for battery products based on customized production scheduling. Figure 2 , the method comprising:

[0063] Step S1, based on the battery qualification rate required by the customer order and the qualification rate of each of the n types of batteries that the battery manufacturer can produce in the mass production mode, the customer order is decomposed, specifically:

[0064] Assume that the total number of batteries required by the customer order is μ, the order requires a delivery time of D days, and the number of machine hours available for processing per day for the kth machine is w k , the total processing time of the i-th type of battery on the k-th machine is l ik , let the cost of the i-th type battery be c i , find μ that satisfies the following constraints i :

[0065]

[0066]

[0067]

[0068] Among them, M% is the battery qualification rate required by the customer order, M i % is the qualified rate of the i-th type of battery among the n types of batteries that the battery manufacturer can produce in the large-scale production mode.

[0069] μ i Represents the number of batteries of category i contained in the customer order after decomposition, satisfying:

[0070]

[0071] Step S2, establishing a time-minimizing production model based on the demand quantity of each type of battery decomposed in step S1;

[0072] Since the processing time of raw materials for each type of battery is different, assuming that there are m types of raw materials required for the n types of batteries that a battery manufacturer can produce, the total quantity of each type of raw material required is:

[0073]

[0074] Among them, A j represents the total quantity required for the jth type of material, a ij It represents the quantity of the jth type of raw material required to produce the i-th type of battery; assuming that the corresponding machine equipment for the production of the j-th type of raw material is recorded as E k .

[0075] Further considering the maximum input of each machine and equipment each time, the total time required to produce each type of battery is:

[0076] T i =f(μ i a ij ,b jk )t jk

[0077] Among them, T i represents the total time required to produce the i-th type of battery, b jk It means that the jth type of raw material is used in the kth machine equipment E k The maximum input amount per time, t jk Indicates the kth machine device E k The processing time required to invest one type of raw material j. i a ij ,b jk ) is defined as:

[0078]

[0079]

[0080] Where N + Represents a positive integer.

[0081] Get the total time T required to produce each type of battery i After that, the total time required to get the customer order is:

[0082]

[0083] Where T represents the total time required to produce all types of batteries in a customer order.

[0084] Production planning collaborative optimization model with engineering depth indication:

[0085] The production plan based on engineering depth indication regards the maximum daily production capacity and daily available working time of a certain component in the workshop as the limited production capacity constraints of the workshop. Based on this constraint, according to the daily available working time of the workshop and the actual measured standard operation processing time and flow time of each component, after considering the offline inventory of the process, the safety stock of each component in the corresponding process is obtained. This safety stock is the minimum production task of the workshop component production operation. The safety stock data of all processes will be summarized and divided by the daily working time of the workshop to obtain the production lead time of each component to achieve collaborative production.

[0086] Assume that a workshop can produce m types of parts, and the daily production capacity of the i-th type of parts is q i , i = 1, 2, ..., m, the daily working time of the workshop is h. The production process of this component includes n processing steps, and the processing time of the jth process is x ij , the flow time is y ij , j = 1, 2, 3, ..., n, if the next process is immediately processed after the process parts are off the line, the flow time y ij =0, the safety stock below the process line is w ij , the turnover batch is z ij .

[0087] The calculation process of the production plan synchronization algorithm based on engineering depth indication is as follows:

[0088] 1) Calculate the process safety stock time t ij =k ij *(x ij +y ij ). According to the process processing time x ij and the turnover time y ij , considering the safety stock factor k ij After that, the process safety stock time ts can be calculated. ij is the safety stock factor, k ij ≥1, its value depends on the buffer time between processes that the workshop can accept. Ideally, k ij =1, indicating that the process will be transferred to the next process immediately after completion. However, in the actual production process, it is difficult to achieve a flow under large-scale customized production conditions, considering factors such as production cost and logistics cost. Usually, there will be a certain waiting time after the process is completed. Therefore, k ij ≥1

[0089] 2) Calculate the process safety stock s ij =t ij *q i / h+wij [Press z ij Rounding], based on the process safety stock time ts, combined with the unit time output of the workshop, and considering the process offline safety stock w ij , the process safety stock s can be calculated ij . Due to the safety stock s ij The calculation result may be a decimal, so it is necessary to calculate the value based on the turnover batch of the parts. ij Adjust to its integer multiple so that the components are z ij Turnover batches are used for turnover to reduce production costs and manufacturing cycles.

[0090] 3) Calculate the safety stock of all processes Si = ∑s ij According to the first two steps, calculate the safety stock time and safety stock of all processes respectively. After summing up the safety stocks of all processes, it is the total safety stock of the component production process in the workshop.

[0091] 4) Calculate the safety lead time T'=Si / qi. Divide the safety stock of all processes of the component by the daily production capacity of the component to obtain the safety lead time for the production of the component.

[0092] Step S3: Based on the time minimization production model established in step S2, the production order of each type of battery is determined based on the rule that the battery with the longest total production time among the n types of batteries enters production first;

[0093] Calculate the proportion of the total time required for each type of battery to the total time required for a customer order using the following formula:

[0094] P i =T i / T

[0095] Further calculation of P i The logarithmic entropy value of is:

[0096] H i =P i ln(1+P i )

[0097] For H i Sort by the production order of each type of battery from large to small, where 0 <Pi<1。

[0098] Step S4: Based on machine device E k The quantity of raw materials is determined to determine the production sequence of each raw material;

[0099] Since the number of machines and equipment in each process of battery manufacturing enterprises is limited, it is impossible to process all kinds of raw materials together. Therefore, it is necessary to sort the production order of various raw materials. Under the premise of minimizing the total production time of the order, determine the production order of each raw material and the quantity of each production. Specifically:

[0100] Calculate the intermediate parameter Q j , and according to the Q corresponding to each raw material j Determine the production sequence:

[0101] Q j =-q j *ln(q j )

[0102] q j =a j / gcd(a j ),j=1,2....g

[0103] In the battery production process, when producing the i-th type of battery, the production machinery and equipment E required in a certain process k At the same time, there may be one or more raw materials that need to be processed. Suppose at the same time, there are g types (g < m) of raw materials that need to use machine equipment E k , the quantity corresponding to each type of raw material is a j , then take each a j The greatest common divisor gcd(a j ) as each investment in machinery and equipment E k The jth type of raw material requires q j It takes several times to complete all the processing.

[0104] Q j As a criterion for optimizing the order of raw material production, the greater the proportion of product output in the total output, the corresponding Q j The larger the value, the earlier the production will be started. The lowest common factor will be used as the production quantity each time. After production starts, the remaining order products will be recalculated and the next raw materials will be determined until the production order of all raw materials is determined. j The specific solution process is as follows:

[0105] A. Calculate the raw materials required for various types of batteries corresponding to the production ratio of the machine, and determine the number of products in the minimum production cycle unit manufacturing cycle;

[0106] B. Calculate Q j ;

[0107] C. Select Q from all the raw materials to be put into production line by line according to the calculation process j The raw material with the largest value is invested first. If there are several raw materials corresponding to Qj If they are equal and both are minimum, the raw materials with the longest time interval from the last production will be selected first, that is, the raw materials currently put into production should be different from the last time as much as possible to avoid repeated investment.

[0108] D. Update q j That is, the ratio of the lowest common factor and the total number of raw materials to be produced minus the number of production in the previous round is used as the new q j '. Substitute back and calculate. And so on, until the q of all raw materials is j 'Set to 1 (i.e. the remaining quantity is used for the last round of production). At this time, the corresponding Q j The value is 0.

[0109] After determining the production sequence of each raw material, the production scheduling plan and the corresponding processing time can be further given in combination with each production equipment, and the final pickup period of the customer order can be further determined and a Gantt chart can be given.

[0110] To verify the effectiveness of the method of this application, assume that a certain order requires the production of lithium cobalt oxide batteries, lithium manganese oxide batteries, and lithium iron phosphate batteries. The production time of lithium cobalt oxide batteries is 200 hours, the production time of lithium manganese oxide batteries is 150 hours, and the production time of lithium iron phosphate batteries is 220 hours, that is, T1 = 200, T2 = 150, T3 = 220, T = T1 + T2 + T3 = 570, as shown in Table 1 below. According to the given model, P is calculated. i =T i / T:

[0111] Table 1: Parameters of battery production order

[0112] Battery type (i) Lithium cobalt oxide battery Lithium manganese oxide battery Lithium iron phosphate battery Production time (Ti) / h 200 150 220 Total production time (T) 570 570 570 Production time percentage (Pi) 200 / 570 150 / 570 220 / 570 Production weight ratio (Hi) 0.1055277733 0.06147759242 0.1259776292

[0113] Press P i The logarithmic entropy value Hi determines that the production order of the decomposed battery orders is: lithium manganese oxide batteries, lithium cobalt oxide batteries, and lithium iron phosphate batteries.

[0114] A lithium battery cell and PACK system manufacturing company is planning and scheduling the production of cells and PACK systems. As the key components of the PACK system, the cells need to be put into production in advance according to the lead time calculation, and the PACK system performs large-scale customized assembly based on customer order requirements.

[0115] Assume that the mixed-flow assembly line of the PACK system plans to produce 210 tons of type A materials, 180 tons of type B materials, and 150 tons of type C materials. The daily production capacity of a certain type of battery cell in the battery workshop is 10,000, and the daily working hours of the workshop are 20 hours. The parameters such as the processing time, flow time, offline safety stock of each process, and turnover batch are shown in Table 2. The safety stock system is set to 1.

[0116] At the same time, it is assumed that one round of A needs to be processed by machine 1 for 2 hours, machine B for 3 hours, and machine C for 1 hour; one round of B needs to be processed by machine 1 for 4 hours, and machine 3 for 2 hours; one round of C needs to be processed by machine 1 for 1 hour, machine 2 for 1 hour, and machine 3 for 3 hours. As shown in Table 3.

[0117] Table 2: Operating parameters

[0118] Processing time Turnover time Offline safety stock Turnover batch Mixing 4h 0.2h 0 1 can Coating 0.8h 0.2h 0 1 volume Producer 0.8h 0.2h 0 1 volume Lamination 0.8h 0.2h 0 100pcs Encapsulation 0.8h 0.2h 0 100pcs bake 5h 0.2h 0 100pcs Injection 0.8h 0.2h 0 100pcs form 6h 30h 0 100pcs Capacity 10h 30h 0 100pcs Test packaging 0.5h 0 0 100pcs

[0119] Table 3: Simulation product model production table

[0120] Machine / time(h) A B C Machine 1 2 3 1 Machine 2 4 0 2 Machine 3 1 1 3

[0121] First, the greatest common divisor entropy weight method is applied to optimize the assembly and production sequence of lithium battery PACK system products. According to the assumption, the three types of materials ABC that need to be preprocessed are 210, 180, and 150 tons respectively (i.e., the a of each raw material j ), the PACK system uses a mixed-flow assembly line for assembly operations, and based on the inverse production ratio method, the assembly and production order of the production line order products is balanced and optimized. The production volume per time is determined to be 30 (tons per piece), and the final production order is: ABACBACBACCCBBBAAA.

[0122] The specific production sequence calculation process is shown in Table 4:

[0123] Table 4: Production sequence optimization process

[0124]

[0125]

[0126] After determining the product assembly and production sequence, in order to ensure that the PACK system assembly can be started smoothly according to the production plan, the battery cell components must be complete before production. Therefore, it is necessary to coordinate the production plan of the battery cell products according to the assembly and production plan of the PACK system. The battery cell production plan demand comes from the PACK system assembly plan. Based on the engineering depth indication strategy, the safety inventory and safety lead time of each process of battery cell production are determined, and the battery cell production date and production quantity are determined according to the period quantity policy and demand. The safety lead time is determined to be 4.55 days, and the production quantity is determined according to the product assembly plan demand quantity. The specific calculation process is shown in Table 5.

[0127] Table 5: Calculation process

[0128]

[0129] The Gantt chart of the battery production schedule produced according to the above arrangement is as follows: Figure 3As shown in Table 6, the time results obtained by comparing the improved algorithm with the actual empirical method production time are shown in Table 6:

[0130] Table 6: Comparison of sorting schemes

[0131]

[0132]

[0133] The time taken was 19h, saving 30% of the time.

[0134] The Gantt chart given based on the production quantity and production status is as follows Figure 3 As shown, Figure 3 In [x,y], x represents the type of material (i.e. 1-A, 2-B, 3-C), and y represents the corresponding production machine. In this example, there are 3 machines 1, 2, and 3. The vertical axis of the given Gantt chart represents a certain machine number corresponding to machine y, and the product produced on this machine belongs to the material of type x. The horizontal axis corresponds to the start and end time of the production of the material on this machine.

[0135] Some steps in the embodiments of the present invention may be implemented using software, and the corresponding software program may be stored in a readable storage medium, such as a CD or a hard disk.

[0136] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing the production sequence of raw materials for battery products based on customized production scheduling, characterized in that: The method comprises: Step S1, decomposing the customer order based on the battery qualification rate required by the customer order and the qualification rate of each of n types of batteries that the battery manufacturer can produce in a large-scale production mode; Step S2, establishing a time-minimizing production model based on the demand quantity of each type of battery decomposed in step S1; Step S3: Based on the time minimization production model established in step S2, the production order of each type of battery is determined based on the rule that the battery with the longest total production time among the n types of batteries enters production first; Step S4: Determine the production order of each raw material based on the number of machines and equipment; The step S4 comprises: Calculate the intermediate parameter Q j , and according to the Q corresponding to each raw material j Determine the production sequence: Q j =-q j *ln(q j ) q j =a j / gcd(a j ),j=1,2....g Among them, it is assumed that the raw materials required for the n types of batteries that a battery manufacturer can produce are m, and g represents the number of machines and equipment E that need to be used at the same time. k The number of types of raw materials, g<m; the quantity corresponding to each type of raw material is a j , each a j The greatest common divisor is gcd(a j ), take each a j The greatest common divisor gcd(a j ) as each investment in machinery and equipment E k The jth type of raw material requires q j It can be fully processed in times; According to the intermediate parameter Q j The size of each raw material determines the machine equipment E k The order of production.

2. The method according to claim 1, characterized in that The step S1 comprises: Assume that the total number of batteries required by the customer order is μ, the order requires a delivery time of D days, and the number of machine hours available for processing per day for the kth machine is w k , the total processing time of the i-th type of battery on the k-th machine is l ik , let the cost of the i-th type battery be c i , find μ that satisfies the following constraints i : Among them, M% is the battery qualification rate required by the customer order, M i % is the qualified rate of the i-th type of battery among the n types of batteries that the battery manufacturer can produce in the large-scale production mode, μ i It represents the number of batteries of category i contained in the customer order after decomposition.

3. The method according to claim 2, characterized in that The step S2 comprises: Assuming that there are m types of raw materials required for the n types of batteries that a battery manufacturer can produce, the total quantity required for each type of raw material is: Among them, A j represents the total quantity required for the jth type of material, a ij It indicates the quantity of the jth type of raw materials required to produce a unit of the ith type of battery; The total time required to produce each type of battery is: T i =f(μ i a ij ,b jk )t jk Among them, T i represents the total time required to produce the i-th type of battery, b jk It means that the jth type of raw material is used in the kth machine equipment E k The maximum input amount per time, t jk Indicates the kth machine device E k The processing time required to invest in one type of raw material j; f(μ i a ij ,b jk ) is defined as: Where N + represents a positive integer; the total time T required to produce each type of battery is obtained i After that, the total time required to get the customer order is: Where T represents the total time required to produce all types of batteries in a customer order.

4. The method according to claim 3, characterized in that The step S3 comprises: Calculate the proportion of the total time required for each type of battery to the total time required for a customer order using the following formula: P i =T i / T Further calculation of P i The logarithmic entropy value of is: A i =P i ln(1+P i ) For H i Sort by the production order of each type of battery from large to small, where 0 <P i <1.

5. The method according to claim 4, characterized in that According to the intermediate parameter Q j The size of each raw material determines the machine equipment E k The production sequence includes: Step 1: Select Q from all the raw materials to be put into production j The raw materials with the largest value are invested first; Step 2: Recalculate the intermediate parameter Q corresponding to the remaining raw materials after production starts. j ; Step 3, based on the recalculated intermediate parameter Q j Determine the raw materials for the next production; Step 4, repeat Step 2-Step 3 until all raw materials Q j The value is 0.

6. The method according to claim 5, characterized in that According to the intermediate parameter Q j The size of each raw material determines the machine equipment E k When the production sequence is as follows, if the intermediate parameters Q corresponding to several raw materials are j If they are equal and both are minimum, the raw material with a longer time interval from the last production will be selected for production.

7. The method according to claim 6, characterized in that The method is applied to the cell production stage in the battery production process.

8. Application of the method according to any one of claims 1 to 7 in battery production management.

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