A production scheduling optimization method and system for battery cells used in mobile power supplies

Abstract

The present invention relates to the field of intelligent battery cell production technology, and specifically to a method for optimizing the production scheduling of battery cells for mobile power supplies, the method comprising: S100. Utilizing historical order data and market trends to predict the market demand for battery cells within a certain unit time; S200. Evaluating the production capacity of battery cells and conducting bottleneck analysis; S300. Establishing a multi-objective optimization model, and generating a production scheduling plan based on the model; S400. For each production scheduling plan, using a simulation model to evaluate its performance. S500. Establishing a decision support system, the user selects a production scheduling plan based on the performance of each production scheduling plan; S600. After the user selects a production scheduling plan, the actual performance of the production scheduling plan is monitored and evaluated. The purpose of the present invention is to provide a method for optimizing the production scheduling of battery cells for mobile power supplies, and the present application can specifically solve the problem that the existing battery cell production scheduling plans are single and have limitations.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent battery cell production, and in particular to a production scheduling optimization method and system for battery cells used in mobile power supplies. Background Art

[0002] As mobile power supplies are increasingly used in life and industry, for example, mobile power supplies equipped with automotive-grade batteries have been widely used. In addition, mobile power supplies are increasingly used in the field of energy storage, etc. Mobile power supplies can be regarded as a collection of one or more batteries. In related technologies, battery cell production plans are mostly strictly produced according to the required quantity of orders. This setting limits the feasibility of the production plan and cannot meet the uncertainty and diversity of demand for mobile power business. Summary of the invention

[0003] In order to overcome the shortcomings and deficiencies in the prior art, the purpose of the present invention is to provide a method and system for optimizing the production scheduling of battery cells for mobile power supplies. The present application can specifically solve the problem that the existing battery cell production scheduling scheme is single and has limitations.

[0004] The present invention is achieved through the following technical solutions:

[0005] In a first aspect, the present invention discloses a method for optimizing the production of a battery cell for a mobile power source, comprising the following steps:

[0006] S100. Use historical order data and market trends to predict the market demand for battery cells within a certain unit of time;

[0007] S200. Evaluate the production capacity of battery cells and conduct bottleneck analysis;

[0008] S300. Establish a multi-objective optimization model and generate a production scheduling plan based on the model;

[0009] S400. For each production scheduling plan, use the simulation model to evaluate its performance;

[0010] S500. Establish a decision support system, and the user selects the production scheduling plan according to the performance of each production scheduling plan;

[0011] S600. After the user selects a production scheduling plan, the actual performance of the production scheduling plan is monitored and evaluated.

[0012] In combination with the first aspect, further, in step S100, the calculation formula of the predicted value of the battery cell production capacity is:

[0013]

[0014] Among them, Y tis the predicted market demand value within time t; c is a constant term, representing the intercept in the model; ∈ t is the random error term within time t; φ i is the autoregression coefficient; θ i is the moving average coefficient; p is the order of the autoregressive term; q is the order of the moving average term.

[0015] In combination with the first aspect, further, in step S200, the production capacity of the battery cell is evaluated by linear programming, and the linear programming equation is:

[0016] maximize z=c T x

[0017] subject to Ax≤b

[0018] x≥0

[0019] Among them, z is the objective function value; x is the production decision variable vector, including the production quantity of each battery cell on the production line; c T is the profit vector, which represents the profit of each decision variable in unit time T; A represents the coefficient matrix of the constraint conditions; b represents the constant term vector of the constraint conditions, which contains the production capacity and demand forecast results Y t .

[0020] In combination with the first aspect, further, in step S300, the calculation formula of the multi-objective optimization model is:

[0021] minimize f1(x), f2(x), f3(x)

[0022] subject to g i (x)≤b i , i=1,...,m

[0023] h i (x) = c j , i=1,...,k

[0024] x∈X

[0025] Among them, f1(x), f2(x), f3(x) are different optimization objective functions, representing satisfaction rate, delivery timeliness and total cost respectively; g i (x) is the inequality constraint function, b i is the constant term of the inequality constraint function; h i (x) is the equality constraint function, c j is the constant term of the equality constraint function; X is the feasible domain of the decision variable.

[0026] In combination with the first aspect, further, the calculation formula of the satisfaction rate is:

[0027]

[0028] Among them, x i is the production decision variable, a i is the coefficient related to production capacity, and e is the base of a natural number;

[0029] The calculation formula for the delivery timeliness is:

[0030]

[0031] Among them, b j is the weight coefficient related to the urgency of order j, c ij is the production decision variable x i The impact coefficient on the delivery time of order j;

[0032] The total cost is calculated as follows:

[0033]

[0034] Among them, d i is the production decision variable x i The unit cost coefficient, e ij is the variable x i and z j The interaction coefficient between .

[0035] In combination with the first aspect, further, in step S400, the change of the system state is described by the following formula:

[0036] S(t+1)=F(S(t),E(t))

[0037] Among them, S(t) is the system state at time t, E(t) is the event at that time point, F is the state transition function, and S(t), E(t) can contain the decision variable x of the multi-objective optimization model.

[0038] In a second aspect, the present invention further discloses a battery production scheduling system, which includes:

[0039] Demand forecasting module, which uses historical order data and market trends to predict the market demand for battery cells within a certain unit of time;

[0040] Production assessment module, used to evaluate the production capacity of battery cells and perform bottleneck analysis;

[0041] Optimization module, used to generate production scheduling plans based on the model;

[0042] Decision support module, used to help users select production scheduling plans based on the performance of each production scheduling plan;

[0043] The monitoring and evaluation module is used to monitor and evaluate the actual performance of the production scheduling plan after the user selects the production scheduling plan.

[0044] In a third aspect, the present invention further discloses an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor runs the computer program to implement the battery cell production optimization method as described in any one of claims 1 to 6.

[0045] In a fourth aspect, the present invention further discloses a computer-readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the battery cell production scheduling optimization method as described in any one of claims 1-6.

[0046] Beneficial effects of the present invention:

[0047] The present invention provides a production scheduling optimization method, system, electronic device and storage medium for battery cells of mobile power supplies. After obtaining a target production scheduling plan, the present invention can specifically solve the problem that existing battery cell production scheduling plans are single and have limitations, which helps users decide which target production scheduling plan to select as the final implementation plan, thereby meeting the uncertainty and diversity of demand for mobile power business. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0049] Figure 1 A flowchart of the steps of the production scheduling optimization method provided in an embodiment of the present invention.

[0050] Figure 2 Schematic diagram of the structure of a battery cell scheduling system according to an embodiment of the present invention.

[0051] Figure 3 The present invention is a schematic structural diagram of an electronic device provided by an embodiment of the present invention.

[0052] Figure 4 A schematic diagram of a storage medium provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0054] As mobile power supplies are increasingly used in life and industry, for example, mobile power supplies equipped with automotive-grade batteries have been widely used. In addition, mobile power supplies are increasingly used in the field of energy storage, etc. Mobile power supplies can be regarded as a collection of one or more batteries. In related technologies, battery cell production plans are mostly strictly produced according to the required quantity of orders. This setting limits the feasibility of the production plan and cannot meet the uncertainty and diversity of demand for mobile power business.

[0055] In order to solve the above problem, this embodiment discloses a production scheduling optimization method executed in the above system, which includes the following steps:

[0056] S100. Use historical order data and market trends to predict the market demand for battery cells within a certain unit of time;

[0057] S200. Evaluate the production capacity of battery cells and conduct bottleneck analysis;

[0058] S300. Establish a multi-objective optimization model and generate a production scheduling plan based on the model;

[0059] S400. For each production scheduling plan, use the simulation model to evaluate its performance;

[0060] S500. Establish a decision support system, and the user selects the production scheduling plan according to the performance of each production scheduling plan;

[0061] S600. After the user selects a production scheduling plan, the actual performance of the production scheduling plan is monitored and evaluated.

[0062] In combination with the first aspect, further, in step S100, the calculation formula of the predicted value of the battery cell production capacity is:

[0063]

[0064] Among them, Y t is the predicted market demand value within time t; c is a constant term, representing the intercept in the model; ∈ t is the random error term within time t; φ i is the autoregression coefficient; θ iis the moving average coefficient; p is the order of the autoregressive term; q is the order of the moving average term.

[0065] In combination with the first aspect, further, in step S200, the production capacity of the battery cell is evaluated by linear programming, and the linear programming equation is:

[0066] maximize z=c T x

[0067] subject to Ax≤b

[0068] x≥0

[0069] Among them, z is the objective function value; x is the production decision variable vector, including the production quantity of each battery cell on the production line; c T is the profit vector, which represents the profit of each decision variable in unit time T; A represents the coefficient matrix of the constraint conditions; b represents the constant term vector of the constraint conditions, which contains the production capacity and demand forecast results Y t .

[0070] In combination with the first aspect, further, in step S300, the calculation formula of the multi-objective optimization model is:

[0071] minimize f1(x), f2(x), f3(x)

[0072] subject to g i (x)≤b i , i=1,...,m

[0073] h i (x) = c j , i=1,...,k

[0074] x∈X

[0075] Among them, f1(x), f2(x), f3(x) are different optimization objective functions, representing satisfaction rate, delivery timeliness and total cost respectively; g i (x) is the inequality constraint function, b i is the constant term of the inequality constraint function; h i (x) is the equality constraint function, c j is the constant term of the equality constraint function; X is the feasible domain of the decision variable.

[0076] In combination with the first aspect, further, the calculation formula of the satisfaction rate is:

[0077]

[0078] Among them, x iis the production decision variable, a i is the coefficient related to production capacity, and e is the base of a natural number;

[0079] The calculation formula for the delivery timeliness is:

[0080]

[0081] Among them, b j is the weight coefficient related to the urgency of order j, c ij is the production decision variable x i The impact coefficient on the delivery time of order j;

[0082] The total cost is calculated as follows:

[0083]

[0084] Among them, d i is the production decision variable x i The unit cost coefficient, e ij is the variable x i and x j The interaction coefficient between .

[0085] Furthermore, in step S400, the change of the system state is described by the following formula:

[0086] S(t+1)=F(S(t),E(t))

[0087] Among them, S(t) is the system state at time t, E(t) is the event at that time point, F is the state transition function, and S(t), E(t) can contain the decision variable x of the multi-objective optimization model.

[0088] Please refer to Figure 2 , which shows a battery production scheduling system 20 provided in some embodiments of the present application, which can be used to execute the method described in the previous embodiment. Figure 2 As shown, it includes:

[0089] The demand forecasting module 201 is used to forecast the market demand of battery cells within a certain unit time by using historical order data and market trends;

[0090] A production evaluation module 202 is used to evaluate the production capacity of the battery cells and perform bottleneck analysis;

[0091] The optimization module 203 is used to generate a production scheduling plan according to the model;

[0092] A decision support module 204, for providing users with the ability to select a production scheduling plan based on the performance of each production scheduling plan;

[0093] The monitoring and evaluation module 205 is used to monitor and evaluate the actual performance of the production scheduling plan after the user selects the production scheduling plan.

[0094] The battery scheduling device provided in the above-mentioned embodiment of the present application and the battery scheduling method provided in the embodiment of the present application are based on the same application concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0095] Please refer to Figure 3 , which shows a schematic diagram of the structure of an electronic device provided by some embodiments of the present application. Figure 3 As shown, the electronic device 30 includes: a processor 300, a memory 301, a bus 302 and a communication interface 303, and the processor 300, the communication interface 303 and the memory 301 are connected via the bus 302; the memory 301 stores a computer program that can be run on the processor 300, and the processor 300 executes the method provided in any of the aforementioned embodiments of the present application when running the computer program.

[0096] The memory 301 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 303 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0097] The bus 302 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store programs, and the processor 300 executes the programs after receiving execution instructions. The battery production scheduling method disclosed in any implementation of the aforementioned embodiment of the present application may be applied to the processor 300, or implemented by the processor 300.

[0098] The processor 300 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 300. The above processor 300 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 301, and the processor 300 reads the information in the memory 301 and completes the steps of the above method in combination with its hardware.

[0099] The electronic device provided in the embodiment of the present application and the battery production method provided in the embodiment of the present application are based on the same application concept and have the same beneficial effects as the methods adopted, operated or implemented therein.

[0100] The present application also provides a computer-readable storage medium corresponding to the battery production scheduling method provided in the above embodiment. Figure 4 The computer-readable storage medium shown is a CD, on which a computer program (ie, program product 40) is stored. When the computer program is run by a processor, the power scheduling method provided in any of the aforementioned embodiments will be executed.

[0101] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0102] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the battery scheduling method provided in the embodiments of the present application are based on the same application concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0103] It should be noted that: in the above text, the term "comprises", "includes" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the method and device in the embodiment of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0104] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0105] The embodiments of the present application are described above in conjunction with the accompanying drawings, which are only specific implementation modes of the present application. However, the present application is not limited to the above-mentioned specific implementation modes. The above-mentioned specific implementation modes are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A method for optimizing the production of battery cells for mobile power supplies, characterized in that: The following steps are involved: S100. Use historical order data and market trends to predict the market demand for battery cells within a certain unit of time; S200. Evaluate the production capacity of battery cells and conduct bottleneck analysis; S300. Establish a multi-objective optimization model and generate a production scheduling plan based on the model; S400. For each production scheduling scheme, evaluate its performance using a simulation model; S500. Establish a decision support system, where users select production scheduling plans based on the performance of each production scheduling plan; S600. After the user selects a production scheduling plan, monitor and evaluate the actual performance of the production scheduling plan; In step S300, the calculation formula of the multi-objective optimization model is: in, are different optimization objective functions, representing satisfaction rate, delivery timeliness and total cost respectively; is the inequality constraint function, is the constant term of the inequality constraint function; is the equality constraint function, is the constant term of the equality constraint function; is the feasible domain of decision variables; The calculation formula of the satisfaction rate is: in, is the production decision variable, is the coefficient related to production capacity, is the base of natural numbers; The calculation formula for the delivery timeliness is: in, Is with order The weight coefficient related to the urgency of is the production decision variable For orders The impact factor of delivery time; The total cost is calculated as follows: in, is the production decision variable The unit cost coefficient, is a variable and The interaction coefficient between .

2. The intelligent allocation method for battery cells of a mobile power source according to claim 1, characterized in that: In step S100, the calculation formula of the predicted value of the battery cell production capacity is: in, For time Demand forecast for the domestic market; is a constant term, representing the intercept in the model; For time Random error term within ; is the autoregression coefficient; is the moving average coefficient; is the order of the autoregressive term; is the order of the moving average term.

3. The intelligent allocation method for battery cells of a mobile power source according to claim 2, characterized in that: In step S200, the production capacity of the battery cell is evaluated using linear programming, and the linear programming equation is: in, is the objective function value; is the production decision variable vector, including the production quantity of each battery cell on the production line; is the profit vector, which represents the time per unit for each decision variable Income within The coefficient matrix representing the constraints; A constant vector representing constraints, including production capacity and demand forecast results .

4. The intelligent allocation method for battery cells of a mobile power source according to claim 1, characterized in that: In step S400, the change of the system state is described by the following formula: in, It's time The system status, is the event at that point in time, is the state transition function, Can include decision variables for multi-objective optimization models .

5. A battery production scheduling system, characterized in that: include: Demand forecasting module, which uses historical order data and market trends to predict the market demand for battery cells within a certain unit of time; Production assessment module, used to evaluate the production capacity of battery cells and perform bottleneck analysis; Optimization module, used to generate production scheduling plans based on the model; Decision support module, used to help users select production scheduling plans based on the performance of each production scheduling plan; The monitoring and evaluation module is used to monitor and evaluate the actual performance of the production scheduling plan after the user selects the production scheduling plan; The system is applied to the battery cell production scheduling optimization method as described in any one of claims 1-4.

6. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for optimizing battery cell production scheduling as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the battery cell production scheduling optimization method as described in any one of claims 1 to 4.

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