CN111522315A - Optimized scheduling method for lithium battery lamination processing process - Google Patents

Abstract

The invention discloses an optimized scheduling method for a lithium battery lamination processing process, and belongs to the field of intelligent optimized scheduling of production workshops. The method comprises the steps of determining a scheduling model and an optimization target of a lithium battery lamination processing process in a factory, and optimizing the target by using an optimized scheduling method based on an improved bat algorithm; the dispatching model is established according to the maximum completion time of the lithium battery laminations processed on each processing device, and meanwhile, the optimization target is the minimum maximum completion time. The invention can obtain the approximate optimal solution of the scheduling problem in the lithium battery lamination processing process in a short time, thereby reducing the production cost of a factory and improving the production efficiency of the factory.

Description

Optimized scheduling method for lithium battery lamination processing process

Technical Field

The invention relates to an optimized scheduling method for a lithium battery lamination processing process, and belongs to the field of intelligent optimized scheduling of production workshops.

Background

With the development of science and technology and the improvement of the living standard of people's material culture, people have more and more large demand on batteries and have higher and more high requirements on the performance of the batteries. Particularly, with the development of space technology and the demand of military equipment, the emergence of a large number of industrial, civil and medical portable electronic products caused by the rapid development of information and microelectronic industries, the development and development of electric automobiles and the enhancement of environmental protection consciousness, people have more urgent demands on batteries which are small in size, light in weight, high in energy, safe, reliable, pollution-free and capable of being repeatedly charged and used. Lithium ion batteries are new high-energy secondary batteries that have rapidly developed under this form.

The manufacturing process of the lithium ion battery core is divided into a lamination process and a winding process, and the two processes are mainly different in the assembling mode of the pole piece. The lamination process is a manufacturing process of the lithium ion battery cell, wherein the positive electrode and the negative electrode are cut into small pieces and are laminated with the isolating membrane into small battery cell monomers, and then the small battery cell monomers are stacked and connected in parallel to form a large battery cell. The lamination process comprises the following steps: stirring, coating, roll-to-roll, die-cutting, laminating, welding, top sealing, liquid injection, pre-melting, air-pumping and sealing, forming and testing.

However, at present, production planning and scheduling of various domestic lithium battery manufacturing enterprises are still manually completed by planners through experience. The dispatcher mainly adopts a distribution rule based on the minimum completion time to dispatch, namely, the dispatching is performed according to the completion time of each lithium battery processing batch in an ascending order and is used as a processing sequence. The method can reduce the completion time of the production plan to a certain extent, but cannot consider the coupling effect between lithium battery batch processing sequences, and the dispatching scheme is single, so that the requirements of sudden events and diversity of the production plan cannot be met. And because a large amount of manual coordination and resource balance exist in the planning process, limited manpower is difficult to ensure the accuracy of coordination and balance, and the production efficiency of an enterprise is greatly influenced by the production pause, the optimized scheduling of the lithium battery lamination processing process has great influence on the production cost and the economic benefit of the enterprise. The processing production sequence of the reasonable arrangement batch can greatly reduce the completion time of the whole lithium battery lamination processing process, simultaneously save a large amount of human resources to a great extent, and improve the production efficiency and the economic benefit of enterprises.

Disclosure of Invention

The invention provides an optimized scheduling method for a lithium battery lamination processing process, which is used for solving the scheduling problem of obtaining a good solution in the lithium battery lamination processing process in a short time.

The technical scheme of the invention is as follows: an optimized scheduling method for a lithium battery lamination processing process is characterized in that a scheduling model and an optimized target of the lithium battery lamination processing process in a factory are determined, and an optimized scheduling method based on an improved bat algorithm is usedOptimizing the target; the dispatching model is established according to the maximum completion time of the lithium battery laminations processed on each processing device, and the optimization target is the minimum maximum completion time C_max(π):

C_max(π)＝C_π(n),m

C_π(i),j＝max{C_π(i-1),j,C_π(i),j-1}+p_π(i),j，i＝2,3,…,n,j＝2,3,…,m；

C_π(1),j＝C_π(1),j-1+p_π(1),j，j＝2,3,…,m；

C_π(i),1＝C_π(i-1),1+p_π(i),1，i＝2,3,…,n；

C_π(1),1＝p_π(1),1

Where the number of workpieces is n, there are m machines operating and specifying that no interruption will be allowed after the operation has begun, one solution to the problem of optimizing scheduling is pi ═ { pi (1), pi (2), …, pi (i), …, pi (n) } which represents a machining sequence for a lithium battery cell stack machining process, pi (i) represents the workpiece at the ith position in the machining sequence, p (i) represents the workpiece at the ith position in the machining sequence, and p (i) represents the workpiece at the ith position in the machining sequence_π(i),jRepresenting the standard machining time, C, for a workpiece pi (i) operating on a machine j_π(i),jRepresenting the completion time, C, of the operation of the workpiece pi (i) on the machine j_max(pi) represents the maximum completion time of the lithium battery lamination processing process, and the scheduling objective is to find a pi in a workpiece sequencing set phi^*So that the maximum completion time C_max(π^*) And minimum.

The optimized scheduling method based on the improved bat algorithm specifically comprises the following steps:

step1, population initialization: generating an initialization population Initpop by adopting a random method until the number of initial solutions meets the requirement of population scale; wherein the population size is NP;

step2, location and speed update of bat colony:

wherein f is_ζThe frequency of the zeta-th bat sending out when searching for a prey at the time t,

is of (f)_min,f_max]β belongs to [0,1 ]]Is a random number that is a uniform fraction of,

and

respectively represents the position and the speed of the zeta-th bat at the time t, x^*Representing the position of the optimal individual in the bat group at the time t;

step3, bats population local disturbance: for the local search section, once a solution is selected from the current best solution, a new solution is generated for each bat using random walk, the perturbation equation is as follows:

x_new(ζ)＝x_old+A_ξ

wherein, after the global optimal solution is selected, the current solution x of each individual in the current population_oldParticipate in updating their locations to obtain new solutions

Is the average value of the batloudness of front zeta and is the interval [ -1,1]Random numbers composed of internal random numbers;

Step4, updating bat populations: evaluating the updated position of each bat if the new position is better than the previous position

The new location is replaced with the original location,

is the average value of the batloudness of front zeta, and rand is a random number which is uniformly distributed;

step5, the optimal individual executes local search: performing 'Swap' and 'Insert' operations on the optimal individuals, replacing the individuals obtained by local search if the individuals are better than the current individuals, and taking the current generation population as a new generation population; since loudness is usually reduced and pulse transmission rate is increased once bats find a prey, the loudness and frequency of pulse transmissions must be changed in an iterative process, so the loudness can be chosen as a_minAnd A_maxThe traversal value between, assume A_min0 means that the bat has just found a game and temporarily stops making any sounds, then:

wherein the content of the first and second substances,

represents the loudness of the batzeta at time t,

the maximum pulse frequency, which is the batζ maximum, is also the initial pulse frequency,

denotes the pulse frequency of the ζ th bat at time t +1, α and γ denote the pulse sound intensity attenuation coefficient and the pulse frequency increase, respectivelyThe coefficients, both constant, are usually in the [0,1 ] range]Internal value taking;

step6, sorting the fitness values of all bats, and finding out the current optimal solution and optimal value;

and Step7, repeating the steps of Step 2-Step 5 until the set optimal solution condition is met or the maximum iteration times is reached, and outputting a global optimal value and an optimal solution.

The invention has the beneficial effects that: the invention can obtain the approximate optimal solution of the scheduling problem in the lithium battery lamination processing process in a short time, thereby reducing the production cost of a factory, improving the production efficiency of the factory, enhancing the competitiveness of enterprises, and effectively solving the problems of factory cost waste and low economic benefit caused by improper processing sequencing in the lithium battery lamination processing process.

Drawings

FIG. 1 is a general design flow diagram of the present invention;

FIG. 2 is an overall algorithm flow diagram of the present invention;

FIG. 3 is a schematic representation of the problem solution of the present invention;

FIG. 4 is a schematic diagram of a basic "Insert" field variation of the present invention;

figure 5 is a schematic diagram of a basic "Swap" domain variation of the present invention.

Detailed Description

Example 1: as shown in fig. 1-5, an optimized scheduling method for a lithium battery lamination processing process, which determines a lithium battery lamination processing process scheduling model and an optimized target in a factory, and optimizes the target by using an optimized scheduling method based on an improved bat algorithm; the dispatching model is established according to the maximum completion time of the lithium battery laminations processed on each processing device, and the optimization target is the minimum maximum completion time C_max(π):

C_max(π)＝C_π(n),m

C_π(i),j＝max{C_π(i-1),j,C_π(i),j-1}+p_π(i),j，i＝2,3,…,n,j＝2,3,…,m；

C_π(1),j＝C_π(1),j-1+p_π(1),j，j＝2,3,…,m；

C_π(i),1＝C_π(i-1),1+p_π(i),1，i＝2,3,…,n；

C_π(1),1＝p_π(1),1

Where the number of workpieces is n, there are m machines operating and specifying that no interruption will be allowed after the operation has begun, one solution to the problem of optimizing scheduling is pi ═ { pi (1), pi (2), …, pi (i), …, pi (n) } which represents a machining sequence for a lithium battery cell stack machining process, pi (i) represents the workpiece at the ith position in the machining sequence, p (i) represents the workpiece at the ith position in the machining sequence, and p (i) represents the workpiece at the ith position in the machining sequence_π(i),jRepresenting the standard machining time, C, for a workpiece pi (i) operating on a machine j_π(i),jRepresenting the completion time, C, of the operation of the workpiece pi (i) on the machine j_max(pi) represents the maximum completion time of the lithium battery lamination processing process, and the scheduling objective is to find a pi in a workpiece sequencing set phi^*So that the maximum completion time C_max(π^*) And minimum.

Further, the optimized scheduling method based on the improved bat algorithm may be specifically set as follows:

step1, population initialization: generating an initialization population Initpop by adopting a random method until the number of initial solutions meets the requirement of population scale; wherein the population size is NP;

step2, location and speed update of bat colony:

f_ξ＝f_min+(f_max-f_min)*β

wherein f is_ζThe frequency of the zeta-th bat sending out when searching for a prey at the time t,

is of (f)_min,f_max]β belongs to [0,1 ]]Is a random number that is a uniform fraction of,

and

respectively represents the position and the speed of the zeta-th bat at the time t, x^*Representing the position of the optimal individual in the bat group at the time t;

step3, bats population local disturbance: for the local search section, once a solution is selected from the current best solution, a new solution is generated for each bat using random walk, the perturbation equation is as follows:

x_new(ζ)＝x_old+A_ξ

wherein, after the global optimal solution is selected, the current solution x of each individual in the current population_oldParticipate in updating their location to obtain a new solution x_new(ξ)，A_ξIs the average value of the batloudness of front zeta and is the interval [ -1,1]Random numbers composed of internal random numbers;

step4, updating bat population: evaluating the updated position of each bat if the new position is better than the previous position and rand < A_ξThen, the new position is replaced with the original position,

is the average value of the batloudness of front zeta, and rand is a random number which is uniformly distributed;

step5, the optimal individual executes local search: performing 'Swap' and 'Insert' operations on the optimal individuals, replacing the individuals obtained by local search if the individuals are better than the current individuals, and taking the current generation population as a new generation population; once bat is foundTo prey, loudness is usually reduced and pulse emission rate is increased, and loudness and frequency of pulse emission must be changed in an iterative process, so loudness can be chosen as a_minAnd A_maxThe traversal value between, assume A_min0 means that the bat has just found a game and temporarily stops making any sounds, then:

wherein the content of the first and second substances,

represents the loudness of the batzeta at time t,

the maximum pulse frequency, which is the batζ maximum, is also the initial pulse frequency,

denotes the pulse frequency of the ζ th bat at time t +1, α and γ denote a pulse sound intensity attenuation coefficient and a pulse frequency increase coefficient, respectively, both of which are constant, and are generally at [0,1 ]]Internal value taking;

step6, sorting the fitness values of all bats, and finding out the current optimal solution and optimal value;

and Step7, repeating the steps of Step 2-Step 5 until the set optimal solution condition is met or the maximum iteration number (such as 50 multiplied by n) is reached, and outputting a global optimal value and an optimal solution.

The population size NP was set to 50, the pulse sound intensity attenuation coefficient α was set to 0.95, the pulse frequency increase coefficient γ was set to 0.9, and the initial pulse frequency was set

Is set to [0,0.5 ]]Initial loudness

Is set as [1,2 ]]. Table 1 shows the objective function values obtained under different problem scales, and it can be seen from table 1 that the method can be effectively used for solving the optimization objective of the lithium battery lamination processing process in a factory.

TABLE 1 values of objective function obtained for different problem scales

n×m	20×5	30×5	30×10	50×5	50×10	50×20
							Cmax(π)	748	1041	1582	1701	2183	2885

While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.

Claims (2)

1. An optimized scheduling method for a lithium battery lamination processing process is characterized by comprising the following steps: the method comprises the steps of determining a scheduling model and an optimization target of a lithium battery lamination processing process in a factory, and optimizing the target by using an optimized scheduling method based on an improved bat algorithm; the dispatching model is established according to the maximum completion time of the lithium battery laminations processed on each processing device, and the optimization target is the minimum maximum completion time C_max(π):

C_max(π)＝C_π(n),m

C_π(i),j＝max{C_π(i-1),j,C_π(i),j-1}+p_π(i),j，i＝2,3,…,n,j＝2,3,…,m；

C_π(1),j＝C_π(1),j-1+p_π(1),j，j＝2,3,…,m；

C_π(i),1＝C_π(i-1),1+p_π(i),1，i＝2,3,…,n；

C_π(1),1＝p_π(1),1

π^*＝arg{C_max(π)}→min,

Where the number of workpieces is n, there are m machines operating and specifying that no interruption will be allowed after the operation has begun, one solution to the problem of optimizing scheduling is pi ═ { pi (1), pi (2), …, pi (i), …, pi (n) } which represents a machining sequence for a lithium battery cell stack machining process, pi (i) represents the workpiece at the ith position in the machining sequence, p (i) represents the workpiece at the ith position in the machining sequence, and p (i) represents the workpiece at the ith position in the machining sequence_π(i),jRepresenting the standard machining time, C, for a workpiece pi (i) operating on a machine j_π(i),jRepresenting the completion time, C, of the operation of the workpiece pi (i) on the machine j_max(pi) represents the maximum completion time of the lithium battery lamination processing processThe scheduling objective is to find a pi in the set phi of workpiece ordering^*So that the maximum completion time C_max(π^*) And minimum.

2. The optimized scheduling method for the lithium battery lamination processing process according to claim 1, wherein: the optimized scheduling method based on the improved bat algorithm specifically comprises the following steps:

step1, population initialization: generating an initialization population Initpop by adopting a random method until the number of initial solutions meets the requirement of population scale; wherein the population size is NP;

step2, location and speed update of bat colony:

f_ξ＝f_min+(f_max-f_min)*β

wherein f is_ζThe frequency of the zeta-th bat sending out when searching for prey at the time t, f_ξIs of (f)_min,f_max]β belongs to [0,1 ]]Is a random number that is a uniform fraction of,

and

respectively represents the position and the speed of the zeta-th bat at the time t, x^*Representing the position of the optimal individual in the bat group at the time t;

step3, bats population local disturbance: for the local search section, once a solution is selected from the current best solution, a new solution is generated for each bat using random walk, the perturbation equation is as follows:

x_new(ζ)＝x_old+A_ξ

wherein, after the global optimal solution is selected, the current solution x of each individual in the current population_oldParticipate in updating their location to obtain a new solution x_new(ξ)，A_ξIs the average value of the batloudness of front zeta and is the interval [ -1,1]Random numbers composed of internal random numbers;

step4, updating bat population: evaluating the updated position of each bat if the new position is better than the previous position and rand < A_ξThen replace the new location with the original location, A_ξIs the average value of the batloudness of front zeta, and rand is a random number which is uniformly distributed;

step5, the optimal individual executes local search: performing 'Swap' and 'Insert' operations on the optimal individuals, replacing the individuals obtained by local search if the individuals are better than the current individuals, and taking the current generation population as a new generation population; since loudness is usually reduced and pulse transmission rate is increased once bats find a prey, the loudness and frequency of pulse transmissions must be changed in an iterative process, so the loudness can be chosen as a_minAnd A_maxThe traversal value between, assume A_min0 means that the bat has just found a game and temporarily stops making any sounds, then:

wherein the content of the first and second substances,

represents the loudness of the batzeta at time t,

the maximum pulse frequency, which is the batζ maximum, is also the initial pulse frequency,

denotes the pulse frequency of the ζ th bat at time t +1, α and γ denote a pulse sound intensity attenuation coefficient and a pulse frequency increase coefficient, respectively, both of which are constant, and are generally at [0,1 ]]Internal value taking;

step6, sorting the fitness values of all bats, and finding out the current optimal solution and optimal value;

and Step7, repeating the steps of Step 2-Step 5 until the set optimal solution condition is met or the maximum iteration times is reached, and outputting a global optimal value and an optimal solution.

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