CN116579121B - Man-machine cooperation disassembly line balancing method for recycling power battery module - Google Patents
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Abstract
The invention provides a man-machine cooperation disassembly line balancing method for recycling a power battery module, and belongs to the technical field of disassembly line balancing. The method comprises the following steps: acquiring the disassembly time, the part attribute and the disassembly priority relation of any part in the battery module; establishing a multi-objective function of the disassembly line, and simultaneously establishing constraint conditions of the model; based on a real number coding mode, decoding the real number coding mode, and then generating an initial solution according to a pareto elite strategy; and optimizing and updating the initial solution through INSGA-II algorithm, and introducing pareto elite retention strategy to obtain the optimal scheme and the optimal solution. In the disassembly line balancing scheme finally obtained based on the method, fewer workers and workstations are needed, the cost is low, the safety coefficient of the workers is high, and meanwhile, the disassembly time distribution of each worker and each workstation is more balanced.
Description
Technical Field
The invention relates to the technical field of disassembly line balancing, in particular to a man-machine cooperation disassembly line balancing method for recycling a power battery module.
Background
The rapid growth of electric automobiles on the global scale will generate a large number of scrapped power batteries in the future, the batteries contain a large amount of electronic garbage, and the direct landfill treatment mode can cause serious pollution to air, soil and water resources on which human beings depend. The disposal of such discarded electronic and electrical devices can then present a great challenge in terms of disassembly and recycling of the power cells for reuse in other stationary settings, providing opportunities for research.
Currently, research on power cell recovery is mainly focused on recovery of valuable metals such as cobalt, lithium, nickel, molybdenum, etc. from waste cathode materials by pyrometallurgical and hydrometallurgical processes. In the future, in consideration of the good energy storage characteristics of the waste power batteries, students are proposing a recycling method, and the secondary use of the power batteries has wider economical and environmental feasibility by applying a hierarchical cascading mode.
Disassembly is the first step in the secondary use link of the power battery module. It is noted that the conventional remanufacturing process for recovering the product performance by replacing part of the parts has to be completely disassembled because the product architecture and reliability characteristics of the battery of the electric vehicle are not feasible.
The problem of balancing the disassembly lines is used for optimizing the disassembly process of the waste products, generally, the disassembly tasks are distributed to the workstations in a disassembly sequence in a manner of optimizing one or more indexes, and the distribution process is required to meet a series of constraints, such as task priority relationship, beat time and the like. The most common optimization criteria in the line balancing problem are the number of workstations, idle time, part requirements, and based thereon many different line types have been developed, such as straight, U-shaped, parallel lines. It is worth noting that in the modeling of the disassembly line balance problem, the disassembly objects of the main research at present are waste refrigerators, televisions, mobile phones, computers and the like, and the battery is not widely paid attention to as a case research.
Unlike traditional disassembly objects such as waste refrigerator, television, mobile phone, computer and other products, the battery pack has large quality, complex structure and high voltage, and disassembly personnel and special tools with certain skills are needed during disassembly, and untrained technicians can risk life when disassembling the power battery, so that the scheme of the traditional disassembly object cannot be simply modified to serve as a disassembly line balancing scheme of the power battery.
Disclosure of Invention
In order to solve at least one of the problems, the invention provides a man-machine cooperation disassembly line balancing method for recycling a power battery module.
In order to achieve the above object, the technical scheme of the present invention is as follows: a man-machine cooperation disassembly line balancing method for recycling a power battery module, the method comprising the steps of:
s1, acquiring the disassembly time, the part attribute and the disassembly priority relation of any part in a battery module;
S2, establishing a multi-objective function of the disassembly line, wherein the multi-objective function comprises the steps of minimizing the number of starting workstations, balancing idle time indexes, minimizing disassembly cost and minimizing worker risks; simultaneously establishing constraint conditions of the model, including disassembly task allocation constraint, priority relation constraint, time constraint, position constraint and workstation configuration constraint;
S3, decoding the real number based on the real number coding mode, and then generating an initial solution according to the pareto elite strategy;
And S4, optimizing and updating the initial solution through INSGA-II algorithm, and introducing a pareto elite retention strategy to obtain an optimal scheme and an optimal solution.
The beneficial effects are that:
The invention designs a man-machine cooperation disassembly method for recovering a power battery module, and a disassembly process model from the power battery module to a single battery core is established for the first time by considering the actual condition of power battery disassembly, and mainly comprises disassembly task allocation constraint, priority relation constraint, time constraint, position constraint and workstation allocation constraint. Meanwhile, an improved part priority relation diagram (IPPD) is designed by considering the AND/OR relation among all parts of the battery module, so that in a disassembly line balancing scheme finally obtained, compared with a scheme obtained by a conventional method, fewer workers AND work stations are needed, the cost is lower, the safety coefficient of the workers is higher, meanwhile, the disassembly time distribution of each worker AND work station is more balanced, AND finally, a more reasonable disassembly line scheme is obtained.
Drawings
FIG. 1 is a task priority diagram;
FIG. 2 is a schematic diagram of a crossover operation;
FIG. 3 is a schematic diagram of a variation operation;
Fig. 4 is a diagram showing a priority relationship based on TeslaModel S battery modules according to an embodiment of the present invention;
Fig. 5 is a disassembly scheme of the battery module according to the embodiment of the invention based on TeslaModel S;
Fig. 6 is a schematic diagram showing another disassembly scheme of a TeslaModel S-based battery module according to an embodiment of the present invention.
Detailed Description
The following detailed description of the invention will be clearly and fully described in connection with the examples which are set forth to illustrate, but are not necessarily all embodiments of the invention.
The invention is further described below with reference to examples:
in the following examples, unless otherwise specified, the operations described are conventional in the art.
A man-machine cooperation disassembly line balancing method for recycling a power battery module, the method comprising the steps of:
s1, acquiring the disassembly time, the part attribute and the disassembly priority relation of any part in a battery module;
the disassembly time comprises disassembly time when a worker is used for disassembly and disassembly time when a robot is used for disassembly;
The part attributes comprise a dangerous part, a complex part and a common part, wherein the dangerous part represents a part with a certain potential safety hazard in the disassembly process, and the dangerous part is required to be preferentially allocated to a robot for disassembly; the complex parts represent parts which have complex structures and are difficult to disassemble by robots, and are usually distributed to workers for disassembly; common parts represent more conventional parts that can be assigned to a robot or worker for disassembly.
The disassembly priority relationship refers to that for some parts, the parts can be disassembled after other parts are disassembled, so that other parts are called the task of just before the parts; meanwhile, for the power battery, because the structure is complex, the description of the power battery is difficult to be clear by adopting only the task immediately before, AND therefore, in the embodiment of the invention, the constraint of the AND/OR priority relation is also adopted: wherein the AND priority constraint means that task i has at least two immediately preceding tasks, AND the disassembly of task i can be performed only after each immediately preceding task is disassembled; the OR priority constraint means that task i has at least two immediately preceding tasks, and that the disassembly of task i can be performed as long as one of the immediately preceding tasks is disassembled.
S2, establishing a multi-objective function of the disassembly line, wherein the multi-objective function comprises the steps of minimizing the number of starting workstations, balancing idle time indexes, minimizing disassembly cost and minimizing worker risks; meanwhile, constraint conditions of the model are established, including disassembly task allocation constraint, priority relation constraint, time constraint, position constraint and workstation configuration constraint, so that a man-machine cooperation disassembly model of the power battery module is established.
The objective function is as follows:
F1＝min[f1,f2,f3,f4]
(1)
In the mathematical model of battery module disassembly, we have established four goals. The first goal, f 1, is given by equation (2), which represents minimizing the number of workstations to reduce cost. The second goal, f 2, is given by equation (3) to improve load smoothness between workstations, reducing workstation idle time to increase disassembly efficiency. A third objective f 3 is given by equation (4) to minimize the number of manual work stations to reduce the risk of workers removing the battery. A fourth objective f 4 is given by equation (5) for measuring the disassembly cost, where the disassembly cost includes four parts: the workstation opening cost, the robot input cost, the worker disassembly cost, and the robot disassembly cost.
Wherein for the above objective function it is a better solution to the combination optimization problem. However, it is known to those skilled in the art that for the combinatorial optimization problem, the objectives often cannot be improved at the same time, and optimizing one objective may result in a decrease in performance of the other objective.
Meanwhile, the relevant constraints of the model are expressed as follows:
Constraint (6) represents an allocation constraint, meaning that all tasks are allocated to a workstation.
Constraints (7) - (10) represent AND/OR priority constraint. Constraint (7) limits the AND of task i to the immediately preceding task to the preceding workstation or to the same workstation as task i. Constraint (8) indicates that at least one OR immediately preceding task of task i is assigned to a preceding workstation OR to the same workstation as task i. Constraint (9) requires that if task i AND its AND immediately preceding task are assigned to the same workstation, task i starts later in time than any AND immediately preceding task ends in the same workstation. Constraint (10) indicates that task i starts at least one OR in the same workstation immediately after the end time of the preceding task if task i and its OR immediately preceding task are assigned to the same workstation.
Constraints (11) - (15) represent time-dependent constraints. Constraints (11) and (12) indicate that all tasks assigned to the same workstation must be performed one by one; that is, the last task must be performed before the next task can be performed. Constraints (13) limit the completion time of each task not to exceed the cycle time of its assigned workstation. Constraints (14) limit the total task time of a workstation to no more than the cycle time. Constraint (15) represents a start time constraint for each task.
Constraints (16) and (17) represent location-dependent constraints, and if task i and task j are assigned to the same workstation, task i and task j cannot occur at other workstations.
Constraints (18) - (22) represent workstation configuration constraints. The constraint (18) gives an upper and lower limit on the total number of workstations. Likewise, constraints (19) and (20) give a lower limit on the number of manual and robotic workstations, respectively. Constraint (21) indicates that the robotic workstation and the manual workstation cannot be opened simultaneously; i.e. the workstation that is opened at a time can only be one type of workstation. Constraints (22) limit the sequential opening of the workstations.
Constraint (23) represents a defined binary variable.
After the above model is built, the model needs to be solved, and the model solving includes the following steps:
s3, decoding the real number based on the real number coding mode, and then generating an initial solution according to the pareto elite strategy.
To elaborate on the coding scheme herein, a 10-scale case is used for illustration. As shown in FIG. 1, which is a modified 10-scale TPD (task priority relationship graph), the A node AND the O node are virtual nodes representing the AND immediately preceding relationship AND the OR immediately preceding relationship, respectively, of the disassembly task. Wherein, only after the tasks 1 AND 2 are all disassembled, the disassembly of the tasks 3,4, 5 AND 6 can be performed, so the tasks 1 AND 2 are the tasks just before the AND of the 3 (4, 5 AND 6); meanwhile, after one of the tasks 3,4, 5 and 6 is disassembled, the disassembly limits of the tasks 7 and 9 can be performed, so that the tasks 3,4, 5 and 6 are regarded as the tasks immediately before the OR of the tasks 7 and 9.
After determining its priority, it is set as a finite relation matrix, AND the priority relation matrix supported by the priority relation diagram shown in fig. 1 is shown below, where task numbers with column vector elements of 1 constitute an AND immediately preceding task set, AND task numbers with column vector elements of-1 constitute an OR immediately preceding task set. The column vector element of the task number without the precedence relationship constraint is 0.
Obtaining an initial solution according to the TP matrix: first, the tasks without any immediate relation constraint are found, namely, all elements of the column vector corresponding to the task number in the TP matrix are 0, and the tasks are randomly ordered. When the task i epsilon P AND (i), i is allocated, the i row elements of the TP matrix are all set to zero to release the limit of the task i to the immediately following task j. When the task i epsilon P OR (i), the i is distributed, and then the i row element and the j column element of the TP matrix are all set to zero, so that the limitation of the task i on the immediately following task j is relieved. Finally, repeating the operation to obtain a group of feasible disassembly sequences with the array length equal to the task scale.
After the feasible disassembly sequence is obtained, all disassembly tasks corresponding to the feasible disassembly sequence are firstly subjected to attribute identification, and tasks with complex attributes and hazard attributes are required to be respectively distributed to a manual workstation and a robot workstation according to model constraint, namely the type of the workstation is determined by the task attributes. If a workstation is first assigned a task without any attributes, the workstation type is temporarily undefined until after the assigned task with jeopardy or complex attributes, the workstation type is defined according to the task attributes. When the workstation type is determined, a new workstation must be started for tasks of a different nature. Notably, the allocation of these disassembly tasks must satisfy the constraints of the workstation cycle operating time. A set of initial solutions is obtained.
S4, calculating by adopting INSGA-II algorithm, wherein the specific steps are as follows:
Firstly, setting INSGA-II algorithm problem parameters and algorithm parameters, wherein the problem parameters comprise preset takt time CT, manual operation cost, robot operation cost, workstation hardware cost and robot hardware cost; the algorithm parameters comprise population scale pop_num, iteration termination condition GEN and number of external files N;
secondly, based on a pareto elite strategy, a genetic algorithm is adopted, a better individual in an initial solution is screened out, and an external archive WD is established; and when the optimal solution is screened, judging according to the objective function value.
And secondly, randomly taking an individual from the external archive WD, carrying out two-point mapping on the random probability and all the individuals in the primary population, changing the mapping by a two-point exchange method, and merging the new individuals with the previous population. The specific operation is as follows:
referring to fig. 2, two parent individuals are arbitrarily selected, and two crossing points of the parent individuals are determined according to a preset crossing probability. The sequence outside the two crossing points is kept unchanged, and a new sequence fragment is obtained after the sequence between the two crossing points is mapped with another parent individual, so that a new child individual is generated. In addition, in order to ensure that the crossing result still meets the priority constraint, each child generation individual is judged, the error sequence is deleted, and the original sequence is reserved.
Randomly selecting crossed offspring individuals, carrying out single-point mapping on the offspring individuals with random probability and all individuals in the primary population, changing the disassembly sequences of all the individuals in the primary population through a single-point directional insertion method after mapping to obtain new individuals, and combining the new individuals with the previous population. Referring specifically to fig. 3, fig. 3 is an exemplary diagram of an individual mutation operation, which is as follows:
In a feasible sequence, a task is selected from the individual by the mutation probability as a mutation point, a randomly mutated position interval is determined according to a task set immediately before and a task set immediately after the task, in the feasible sequence, a task is selected from the individual by the mutation probability as a mutation point, a randomly mutated position interval is determined according to a task set immediately before and a task set immediately after the task, and the task is inserted into any position of the position interval.
Thirdly, screening elite individuals in the combined population according to the pareto elite strategy and the crowding distance, and updating an external file WD by utilizing the pareto elite strategy: in order to avoid that the excessive number of Pareto non-inferior solutions stored in the external file in the later stage of the algorithm influences the operation efficiency and the quality of the solution, the crowding distance is utilized to sort all Pareto non-inferior solutions obtained by each iteration of the algorithm, and the equal number of non-inferior solutions are selected in a descending order according to the allowable storage number of the external file WD, so that the update of the external file WD is realized.
Finally, sequentially taking elite individuals in the external file WD, repeating the operations in the step INSGA-II algorithm to update the external file WD until the iterative calculation times meet the termination condition, and screening out non-inferior solutions in the external file WD of the objective function by using the crowding distance evaluation standard as final output.
In order to further explain the effects of the present invention, specific examples will be described below.
The following is a specific program test environment in this embodiment:
The simulated computing environment is the Intel (R) Core (TM) i9-10900K [email protected],32GB RAM Windows 10Pro operating system and is programmed and run using MATLAB R2020 b.
The Tesla Model S the battery module mainly comprises five subsystems of a battery pack, a structural system, a thermal management system, a wire harness system and an electrical system. The battery pack comprises 444 lithium batteries 18650, and is divided into 7 groups of battery cells, wherein the number of single lithium batteries contained in each group of battery cells is 62, 64, 64, 65, 65, 63 and 61 respectively, and the 7 groups of battery cells are connected in series through a bus plate so as to have high-voltage release capability; the total of 37 parts except the battery pack is 37, and the 37 parts are all protection measures made by manufacturers in terms of physical impact, high temperature, electric leakage and the like in order to ensure the normal operation of the battery pack. Due to the presence of these 37 parts for protection, the cells are left in an enclosed physical state and can only be removed last.
However, a TeslaModel S battery module measures 481 parts in total, and for a 481-scale case, calculation using the prior art is not practical. Because of the characteristics of the battery, the disassembling mode of each group of battery cells is identical, so that each group of battery cells is regarded as a disassembling task, and finally the scale of each group of battery cells is reduced to 44, and the specific situation of 44 disassembling tasks is as shown in table 1.
TABLE 1 statistics of part conditions
For disassembly tasks with hazard attributes, assigned to robot disassembly, the table 1 corresponds to a column "Y" for robot disassembly; for a disassembly task with complex attributes, assigned to manual disassembly, the column corresponding to manual disassembly in table 1 is "Y"; the other tasks are common tasks and can be distributed to the robot or manually detached.
For the simplified disassembly task, a priority relationship diagram is drawn according to the disassembly priority relationship, see fig. 4. It can be seen that the AND/OR relationship between the parts is represented by virtual nodes, the complete part disassembly priority relationship is represented by arrows, AND compared with PPD, AOG AND DT, the drawing difficulty is greatly reduced, AND the display effect is clearer. It should be noted that, the battery core and the insulating diaphragm do not have a disassembly constraint relationship, but in order to enable the battery core to be intensively disassembled, when the part priority diagram is drawn, the insulating diaphragm is used as a task immediately before the battery core, so that the battery core can be intensively disassembled as much as possible in the disassembly optimization process, and the potential harm to the disassembly environment can be reduced.
According to the objective function and the constraint condition, the problem is solved by utilizing INSGA-II algorithm, wherein f 1、f2、f3、f4 respectively represents the number of workstations, the balance index, the number of workers and the disassembly cost, and through a large number of experimental tests, the optimal parameters are selected as follows: population size pop_num=200, iteration number inter=300, coefficient of variation pm=0.06, algorithm run 10 times, taking the two better results.
Hazard attribute tasks (11, 12, 13, 14, 15, 16, 17, 38, 39, 40, 41, 42, 43, 44) and complex attribute tasks (7, 8,9, 10) are assigned to specific workstations, and other non-attribute tasks are assigned randomly. In order to enable the battery cells to be continuously disassembled, an insulating diaphragm is arranged in the disassembly priority relation diagram as an immediately preceding disassembly task of the battery cells to influence the allocation scheme. The two sets of schemes finally calculated are shown in fig. 5 and 6. The objective function values for these two sets of protocols are shown in table 2, with disassembly protocol 1 shown in fig. 5 and disassembly protocol 2 shown in fig. 6.
Table 2 objective function values for disassembly scheme
| Sequence number | f1 | f2 | f3 | f4 |
| 1 | 14 | 2614 | 3 | 1187000 |
| 2 | 14 | 2614 | 2 | 1187000 |
From fig. 5 and 6, the removal tasks of the cells are assigned to successive robotic workstations (numbered 8,9, 10, 11, 12, 13, 14) in both schemes, demonstrating that it is possible to influence the final optimization scheme by the removal sequence planning. Comparing the drawn priority diagram (fig. 4), the disassembly sequences of both schemes satisfy the part disassembly sequence.
It should be noted that the idle times of the two schemes are identical, and the total task time of three workstations in both schemes occupies the workstation cycle time. Notably, in scheme 1, the 7 th workstation is defined as a manual workstation because it includes the complex attribute task 8 (temperature sensor), thereby reducing the number of robot workstations, reducing the fixed cost and running cost of the robot, and therefore, the disassembly cost of scheme 1 is lower than that of scheme 2. But scheme 2 has a smaller number of people.
We found that when a business in the city of adults is used as a practical case study, the two goals of the labor number f 1 and the disassembly cost f 4 are non-contradictory, i.e. the lower the labor number is, the lower the disassembly cost is. From the modeling level, the two objectives of the manual quantity f 3 and the disassembly cost f 4 are respectively substituted into the fitness function from the two aspects of worker risk and enterprise cost to evaluate the results, whether they are opposite to be determined by the national or regional economic condition, and for developed countries, the reduction of the manual labor contributes to the cost reduction, but for developing countries is not. Overall, the model presented by the present invention gives the decision maker a choice, since reducing the number of people reduces the risk of worker life.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the technical scope of the present invention disclosed in the embodiments of the present invention should be covered by the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (5)
1. The man-machine cooperation disassembly line balancing method for recycling the power battery module is characterized by comprising the following steps of:
s1, acquiring the disassembly time, the part attribute and the disassembly priority relation of any part in a battery module;
s2, establishing a multi-objective function of the disassembly line, wherein the multi-objective function comprises the steps of minimizing the number of starting workstations, balancing idle time indexes, minimizing disassembly cost and minimizing worker risks; simultaneously establishing constraint conditions of the model, including disassembly task allocation constraint, priority relation constraint, time constraint, position constraint and workstation configuration constraint;
S3, decoding the real number based on the real number coding mode, and then generating an initial solution according to the pareto elite strategy;
s4, optimizing and updating the initial solution through INSGA-II algorithm, and introducing a pareto elite retention strategy to obtain an optimal scheme and an optimal solution;
The objective function is:
Minimizing the number of open workstations:
minimizing idle time balance index:
minimizing disassembly costs:
minimizing worker risk:
wherein, S k is a workstation turn-on variable, if the kth workstation is turned on, S k =1, otherwise S k =0; k is the workstation number and m is the total number of workstations available; CT is beat time; n is the total number of disassembly tasks, I, j is the disassembly task number, I is the set of disassembly task numbers, I e {1,2,., n }, X ik is a task allocation variable, if the ith task is allocated to the kth workstation, x ik =1, otherwise x ik＝0;ti represents the disassembly time for disassembling task i; p k represents a worker workstation turn-on variable, if the kth workstation is a manual workstation, P k =1, otherwise P k＝0;Cw is the cost required to turn on one workstation; c r is the cost required to use one robot; r k represents a robot workstation turn-on variable, if the kth workstation is a robot workstation, R k = 1, otherwise R k＝0;Cpt is cost per unit time of worker disassembly; c rt is the cost per unit time of robot disassembly;
the constraint conditions are as follows:
Allocation constraints: Where x ik is a task allocation variable, if the ith task is allocated to the kth workstation, x ik = 1 otherwise x ik = 0,I e {1, 2..n };
Priority relation constraint: Wherein I is a disassembly task number set; m is the number set of the task station; x il is also a task allocation variable, if the ith task is allocated to the ith workstation, x il =1 otherwise x il＝0,I∈{1,2,...,n},M∈{1,2,...,n};xjk、xsk is the same; p AND(i) is the set of immediately preceding task of task i satisfying the AND relationship; p OR(i) is the set of immediately preceding task of task i satisfying the OR relationship; ST i denotes the start time of task i; ST j denotes the start time of task j;
Time constraint:
In the formula, CT is beat time; k is the workstation number;
Position constraint: In the method, in the process of the invention,
Workstation configuration constraints:
Wherein v is the disassembly task number of the complex part, u is the disassembly task number of the hazard part, and B c represents the disassembly task set of the complex part; b h represents a disassembly task set of the hazard parts;
Binary variable: xik = {0,1}, zijk = {0,1}, sk = {0,1}, rk = {0,1},
2. The method of claim 1, wherein the part attributes include a hazard part, a complex part, and a common part, wherein the hazard part is required to be preferentially allocated to the robot disassembly, the complex part is allocated to the worker disassembly, and the common part is allocated to the robot or the worker disassembly.
3. The method according to claim 1, wherein the encoding and decoding operations are specifically:
s1, converting an improved task relation priority diagram into a task priority relation matrix, and obtaining a group of feasible disassembly sequences according to the priority relation matrix;
S2, carrying out attribute identification on each disassembly task, and respectively distributing tasks with complex attributes and harm attributes to a manual workstation and a robot workstation according to model constraint; when the workstation type is determined, a new workstation is started for tasks of different attributes.
4. The method according to claim 1, wherein the optimizing update specifically comprises: after decoding is finished, a genetic algorithm is enabled to screen out individuals with better solution feasibility based on the pareto elite strategy, and an external archive WD is established; then, cross mutation operation is carried out to update the population, and elite individuals after population updating are screened by adopting a multi-target processing method; and finally, continuously repeating the steps until the iteration times meet the termination condition, and screening out non-inferior solutions in the external file WD of the objective function by using the crowding distance evaluation standard as final output.
5. The method of claim 1, wherein the cells in the power battery module are considered as 1 disassembly task.
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