A new energy vehicle battery disassembling method and system

Abstract

The application belongs to the technical field of battery disassembly, and particularly relates to a new energy automobile battery disassembly method and system, the method comprising: acquiring the battery quantity, state of charge and electrolyte residual quantity of the current batch of batteries; calculating initial environmental impact parameters according to initial input parameters, and obtaining a standardized environmental impact evaluation value through efficiency analysis and standardization processing; determining the disassembly operation parameters of the current batch according to the standardized environmental impact evaluation value, and obtaining optimal disassembly operation parameters through multi-dimensional mapping relationship between process parameters and environmental impact parameters for multi-objective optimization; and disassembling the new energy automobile batteries according to the optimal disassembly operation parameters. According to the state of charge and electrolyte residual quantity of the batch of batteries, the adjustable parameters of the disassembly and other processes are adaptively adjusted, and on the basis of synergistically controlling environmental impact factors, the effective disassembly of the new energy automobile batteries is realized.

Description

Technical Field

[0001] This invention belongs to the field of battery disassembly technology, specifically relating to a method and system for disassembling new energy vehicle batteries. Background Technology

[0002] With the continuous growth of new energy vehicle ownership, a large number of power lithium batteries are entering the retirement and dismantling stage. The dismantling and recycling of power lithium batteries involves processes such as discharge pretreatment, module dismantling, cell separation, and electrolyte collection. Discharge power and duration determine the energy release rate and short-circuit heat release intensity, while the dismantling rate determines the electrolyte leakage rate, thus affecting the amount of organic waste gas and fluorides generated. The four types of environmental impacts—discharge energy consumption, short-circuit heat release, organic waste gas generation, and fluoride generation—are all directly driven by adjustable process parameters.

[0003] However, the four types of environmental impacts exhibit an inherent competitive relationship in their response to the same process parameters. Increasing discharge power can reduce the risk of subsequent short-circuit heat release, but it also increases its own heat loss; reducing the dismantling rate can reduce electrolyte leakage, but it prolongs the electrolyte's exposure time to high temperatures. The strength of these competitive relationships varies depending on the batch's battery state of charge and electrolyte residue. Current dismantling operations uniformly apply fixed process parameters to all batches, failing to establish a quantitative mapping relationship between the four types of environmental impacts and process parameters, resulting in the ineffective dismantling of new energy vehicle batteries.

[0004] Therefore, there is an urgent need for a method and system that can dynamically adjust process parameters and collaboratively optimize various environmental impact factors based on the batch battery status, thereby achieving effective dismantling of new energy vehicle batteries. Summary of the Invention

[0005] (1) Technical problems to be solved The purpose of this invention is to provide a method and system for dismantling new energy vehicle batteries, in order to solve the problem that existing dismantling operations use fixed process parameters, which cannot be adaptively adjusted according to the state of batch batteries, making it difficult to coordinate and control various environmental factors during the dismantling process, thus restricting the effective dismantling of new energy vehicle batteries.

[0006] (2) Technical solution To achieve the above objectives, in one aspect, the present invention provides a method for disassembling a new energy vehicle battery, the method comprising: Obtain the initial input parameters for the current batch of new energy vehicle batteries to be disassembled. The initial input parameters include the number of batteries, state of charge, and residual electrolyte in the current batch.

[0007] The initial environmental impact parameters of the current batch in the dismantling and recycling process are calculated based on the initial input parameters; the initial environmental impact parameters are then processed through efficiency analysis and standardization to obtain standardized environmental impact assessment values.

[0008] The dismantling operation parameters for the current batch are determined based on the standardized environmental impact assessment values; the optimal dismantling operation parameters are obtained by multi-objective optimization through the multi-dimensional mapping relationship between process parameters and environmental impact parameters.

[0009] The optimal dismantling parameters are used to drive the equipment in each process to dismantle the new energy vehicle battery.

[0010] Furthermore, the method for calculating the initial environmental impact parameters of the current batch in the dismantling and recycling process based on the initial input parameters includes: The residual charge is obtained based on the state of charge, rated capacity, and rated voltage of the new energy vehicle battery; the discharge energy consumption is obtained based on the residual charge and discharge efficiency; the total discharge energy consumption of the discharge pretreatment process is obtained based on the discharge energy consumption and the number of batteries; the short-circuit release energy is obtained based on the state of charge, rated capacity, and rated voltage; the short-circuit heat release is obtained based on the short-circuit release energy and short-circuit thermal conversion coefficient; the total short-circuit heat release of the module dismantling process is obtained based on the short-circuit heat release and the number of batteries; the amount of organic waste gas and fluoride generated in the dismantling and recycling process is obtained based on the number of batteries and the amount of electrolyte residue; the initial environmental impact parameters of the current batch in the dismantling and recycling process are obtained based on the total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation.

[0011] Furthermore, the method for determining the amount of organic waste gas and fluoride generated during the dismantling and recycling process based on the number of batteries and the amount of residual electrolyte includes: Obtain the casing integrity index of the new energy vehicle battery, and combine it with the preset leakage ratio to obtain the electrolyte leakage ratio of the module disassembly process; obtain the electrolyte leakage ratio of the crushing process according to the mass conservation relationship of the electrolyte between the disassembly process and the crushing process; query the corresponding organic solvent mass fraction and fluorine electrolyte mass fraction from the preset battery model-electrolyte formula comparison database according to the battery model identifier of the current batch of batteries; if there is no matching model record in the database, the average value of the same battery system in the database is used as the fallback value; multiply the number of batteries and the residual amount of electrolyte by the electrolyte leakage ratio and organic solvent mass fraction of the disassembly process and the crushing process respectively to obtain the organic waste gas generation of each process, and summarize them to obtain the organic waste gas generation of the disassembly and recycling process; calculate the fluoride generation in the same way and summarize.

[0012] Furthermore, the method for obtaining standardized environmental impact assessment values ​​from the initial environmental impact parameters through efficiency analysis and standardization includes: After each batch is completed, the cumulative runtime, cumulative processing load, and measured environmental impact parameters for that batch are obtained; the ratio of the measured environmental impact parameters to the corresponding initial environmental impact parameters is used as the new observation value. ;in, For the first Each battery disassembly batch; the current batch state vector is constructed based on the cumulative runtime and cumulative processing load. Based on the forgetting factor and the current batch state vector Information matrix of the previous batch The gain vector is calculated. The gain vector The calculation formula is: ;in, Forgetting factor; based on the gain vector Further calculations yielded the updated information matrix. The update matrix The calculation formula is: ;in, The current estimated values ​​of the time decay coefficient and load decay coefficient corresponding to each component.

[0013] The state vector is based on the cumulative runtime and cumulative processing load of the current batch. Substitute into the attenuation coefficient estimation vector The parameterized efficiency decay function is used to calculate the current efficiency correction factor for each process equipment; the initial environmental impact parameters corresponding to each process are corrected according to the current efficiency correction factor to obtain the final environmental impact parameters; the final environmental impact parameters and the current batch number are appended to the online batch accumulation record; the final environmental impact parameters are standardized according to the online batch accumulation record to obtain the standardized environmental impact assessment value.

[0014] Furthermore, the method for standardizing the final environmental impact parameters based on the online batch cumulative records to obtain the standardized environmental impact assessment value includes: The total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation are used as four components. If there is no historical batch data in the online batch cumulative record, the final environmental impact parameter of the current batch is used as the initial value of the historical maximum value of each component; otherwise, the historical maximum value of each component is taken from the historical batch record. The ratio of each component of the final environmental impact parameter of the current batch to the corresponding historical maximum value is used as the normalized value of each component. If the sum of the four normalized values ​​is not zero, each normalized value is divided by its sum to obtain the weight coefficient of the corresponding dimension; if the sum is zero, the weight coefficient of each dimension is taken as one-quarter. The standardized environmental impact assessment value is obtained by arranging the weight coefficients of the four dimensions in order.

[0015] Furthermore, the method for determining the dismantling operation parameters for the current batch based on the standardized environmental impact assessment values ​​includes: The standardized environmental impact assessment value is used as the environmental impact risk vector. Measured records of adjustable parameters and corresponding environmental impact parameters for each process in historical batches are obtained. These adjustable parameters include the discharge power and duration of the discharge pretreatment process, the disassembly rate of the module disassembly process, the sealing intervention sequence of the electrolyte collection process, and the ventilation rate of the exhaust gas treatment process. For two adjacent historical batches, the ratio of the difference between the i-th adjustable parameter and the difference between the j-th environmental impact parameter is used as an element of the sensitivity matrix, and a recent weighted average is taken to obtain the environmental impact sensitivity matrix. The environmental impact risk vector is weighted against the sensitivity matrix to obtain a comprehensive priority adjustment coefficient vector, which serves as the adjustment direction for each adjustable parameter. The termination condition is that the change in each adjustable parameter between two adjacent iterations is lower than the convergence accuracy. In each iteration, the current parameter point is moved by a step size along the adjustment direction, and out-of-bounds components are truncated to the corresponding process operation constraint boundary. After iteration convergence, the optimal adjustment amount for each adjustable parameter is output. The optimal adjustment amount is superimposed on the operation reference benchmark for each process to obtain the disassembly operation parameter set for the current batch.

[0016] Furthermore, the method for obtaining the optimal dismantling operation parameters through multi-objective optimization using the multi-dimensional mapping relationship between process parameters and environmental impact parameters includes: Obtain records of adjustable parameter combinations for each process in historical batches, along with corresponding measured total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation. Based on the adjustable parameters of each process as independent variables, establish mapping functions with cross terms for total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation. The mapping functions are as follows: ;in, These correspond to total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation, respectively. These are adjustable parameters; is the regression coefficient.

[0017] Minimizing total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation are four independent optimization objectives. A multi-objective optimization problem is constructed within the operational constraints of each process parameter. The Pareto front search method is used to search for the Pareto optimal solution set within the parameter feasible region. Standardized environmental impact assessment values ​​are used as weight vectors for the four optimization objectives. The weighted distance d from each Pareto optimal solution to the positive ideal point is calculated based on the weight vectors. + The weighted distance d to the negative ideal point - Positive ideal points are formed by the optimal values ​​of each objective in the Pareto optimal solution set, while negative ideal points are formed by the worst values ​​of each objective; according to d+ and d - Calculate the overall relative proximity of each Pareto optimal solution, and select the Pareto optimal solution with the highest proximity; output the process parameter values ​​of the Pareto optimal solution as the optimal dismantling operation parameters.

[0018] Furthermore, the method for obtaining the Pareto optimal solution set by employing the Pareto front search method within the parameter feasible region includes: Using the actual execution parameters of each process in the current batch as initial references, the feasible domain of parameters is obtained by determining the boundary values ​​of each adjustable parameter based on the upper and lower limits of safe discharge in the discharge pretreatment process, the production line rate range of the module disassembly process, the sealing intervention timing window of the electrolyte collection process, and the ventilation volume configuration range of the exhaust gas treatment process. Within the feasible domain of parameters, several sets of process parameter combinations are randomly generated as the initial population. Each set of parameter combinations is substituted into the mapping function to calculate the corresponding total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation.

[0019] Within the population, the dominance relationships of each parameter combination across the four target dimensions are compared. Non-dominated individuals that are not surpassed by any other individual across all target dimensions are retained, while dominated individuals are eliminated. For each non-dominated individual, the difference in target values ​​between adjacent individuals in each target dimension is summed to obtain the distribution interval of each non-dominated individual. Winning individuals are selected based on the distribution interval. Crossover and perturbation mutations are performed on the winning individuals within the parameter feasible region to generate the next generation population. Using a preset reference point formed by the acceptable upper bound of each target dimension as a benchmark, the hypervolume covered by the current generation and the previous generation's non-dominated frontier are calculated respectively, and the hypervolume increment is obtained based on the difference between the two. When the hypervolume increment is lower than the convergence accuracy requirement, the iteration is terminated, and all parameter combinations on the non-dominated frontier are output as the Pareto optimal solution set.

[0020] Based on the same inventive concept, the present invention also provides a new energy vehicle battery dismantling system, the system comprising: The data acquisition module is used to obtain the initial input parameters of the current batch of new energy vehicle batteries to be disassembled. The initial input parameters include the number of batteries, state of charge, and residual electrolyte of the current batch of batteries.

[0021] The environmental impact analysis module is used to calculate the initial environmental impact parameters of the current batch in the dismantling and recycling process based on the initial input parameters, and to obtain the standardized environmental impact assessment value by efficiency analysis and standardization processing of the initial environmental impact parameters.

[0022] The dismantling operation parameter calculation module is used to determine the dismantling operation parameters for the current batch based on the standardized environmental impact assessment values; and to obtain the optimal dismantling operation parameters by performing multi-objective optimization through the multi-dimensional mapping relationship between process parameters and environmental impact parameters.

[0023] The disassembly execution module is used to drive the equipment in each process to disassemble the new energy vehicle battery according to the optimal disassembly operation parameters.

[0024] (3) Beneficial effects Compared with the prior art, the beneficial effects of the present invention are: 1. By establishing a mapping relationship between process parameters and environmental impact quantities, and combining standardized environmental impact assessment values ​​for multi-objective optimization, the optimal dismantling operation parameters can be dynamically generated based on input parameters such as the state of charge and electrolyte residue of different batches of batteries. This allows for coordinated control of discharge energy consumption, short-circuit heat release, organic waste gas generation, and fluoride generation, thereby achieving effective dismantling of new energy vehicle batteries.

[0025] 2. An efficiency decay correction mechanism is introduced, which updates the equipment efficiency decay coefficient in real time through online batch data, so that the environmental impact assessment value can continuously track the actual operating status of the equipment and ensure the optimization accuracy and reliability of cross-batch dismantling operations. Attached Figure Description

[0026] Figure 1 This is a flowchart of a new energy vehicle battery disassembly method according to Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the module composition of a new energy vehicle battery dismantling system according to Embodiment 2 of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0028] Before providing examples, it is necessary to describe the application scenarios of this invention. It is applicable to new energy vehicle battery dismantling and recycling production lines, which typically include processes such as discharge pretreatment, module dismantling, electrolyte collection, and waste gas treatment.

[0029] Example 1: As Figure 1 As shown in the figure, this embodiment provides a method for disassembling a new energy vehicle battery, the method including: S1, obtain the initial input parameters of the current batch of new energy vehicle batteries to be disassembled. The initial input parameters include the number of batteries, state of charge, and residual electrolyte of the current batch of batteries.

[0030] For example, the following parameters are collected for the new energy vehicle batteries to be disassembled, with the current batch containing 200 batteries; the sampled batteries are tested online using a constant current discharge tester, and the state of charge of this batch is recorded as 35%; the average residual electrolyte content of a single battery in this batch is recorded as 85 grams, based on the electrolyte volume traceability file and the factory verification report.

[0031] S2, calculate the initial environmental impact parameters of the current batch in the dismantling and recycling process based on the initial input parameters; obtain the standardized environmental impact assessment value by efficiency analysis and standardization of the initial environmental impact parameters.

[0032] S3, determine the dismantling operation parameters for the current batch based on the standardized environmental impact assessment values; and perform multi-objective optimization on the dismantling operation parameters through the multi-dimensional mapping relationship between process parameters and environmental impact parameters to obtain the optimal dismantling operation parameters.

[0033] S4, drive the equipment of each process to disassemble the new energy vehicle battery according to the optimal disassembly operation parameters.

[0034] The method for calculating the initial environmental impact parameters of the current batch in the dismantling and recycling process based on the initial input parameters includes: The residual charge is obtained based on the state of charge, rated capacity, and rated voltage of the new energy vehicle battery; the discharge energy consumption is obtained based on the residual charge and discharge efficiency; the total discharge energy consumption of the discharge pretreatment process is obtained based on the discharge energy consumption and the number of batteries; the short-circuit release energy is obtained based on the state of charge, rated capacity, and rated voltage; the short-circuit heat release is obtained based on the short-circuit release energy and short-circuit thermal conversion coefficient; the total short-circuit heat release of the module dismantling process is obtained based on the short-circuit heat release and the number of batteries; the amount of organic waste gas and fluoride generated in the dismantling and recycling process is obtained based on the number of batteries and the amount of electrolyte residue; the initial environmental impact parameters of the current batch in the dismantling and recycling process are obtained based on the total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation.

[0035] For example, the residual charge of a single battery is obtained by multiplying its state of charge, rated capacity, and rated voltage: 35% multiplied by 60 amp-hours and then by 3.7 volts, resulting in 77.7 watt-hours. Considering the discharge efficiency of this process is 92%, the discharge energy consumption of a single battery is approximately 84.5 watt-hours (77.7 divided by 92%). The total discharge energy consumption of 200 batteries is 16,900 watt-hours (84.5 multiplied by 200). The short-circuit release energy is physically equal to the battery's current residual energy storage—when a battery short-circuits, all the residual energy stored inside is released instantaneously as heat. Therefore, the short-circuit release energy and the residual charge are numerically the same, both being 77.7 watt-hours per battery. The short-circuit thermal conversion coefficient is taken as 0.85 (calibrated after conducting short-circuit calorimetry tests on NMC523-60 model batteries, characterizing the proportion of electrical energy actually converted into heat energy during the short circuit to the total electrical energy released during the short circuit); the short-circuit thermal release of a single battery is 77.7 multiplied by 0.85, which equals 66.0 Wh, and converted to kilojoules, it is approximately 237.7 kilojoules multiplied by 3.6; the total short-circuit thermal release of 200 batteries is 237.7 multiplied by 200, which equals 47540 kilojoules. It should be noted that the discharge efficiency of 92% is the factory calibration value of the existing discharge pretreatment equipment in this embodiment, reflecting the actual ratio of the residual battery charge to external circuit electrical energy under rated operating conditions; the short-circuit thermal conversion coefficient of 0.85 is obtained after calibrating 5 NMC523-60 model batteries after conducting short-circuit calorimetry tests. Both are equipment and battery characteristic parameters entered during the initialization phase. The values ​​may vary depending on the company, equipment model, and battery system. The values ​​should be substituted according to the calibration results of each company.

[0036] The method for determining the amount of organic waste gas and fluoride generated during the dismantling and recycling process based on the number of batteries and the amount of residual electrolyte includes: Obtain the casing integrity index of the new energy vehicle battery, and combine it with the preset leakage ratio to obtain the electrolyte leakage ratio of the module disassembly process; obtain the electrolyte leakage ratio of the crushing process according to the mass conservation relationship of the electrolyte between the disassembly process and the crushing process; query the corresponding organic solvent mass fraction and fluorine electrolyte mass fraction from the preset battery model-electrolyte formula comparison database according to the battery model identifier of the current batch of batteries; if there is no matching model record in the database, the average value of the same battery system in the database is used as the fallback value; multiply the number of batteries and the residual amount of electrolyte by the electrolyte leakage ratio and organic solvent mass fraction of the disassembly process and the crushing process respectively to obtain the organic waste gas generation of each process, and summarize them to obtain the organic waste gas generation of the disassembly and recycling process; calculate the fluoride generation in the same way and summarize.

[0037] For example, the incoming casing integrity test records of 200 batteries in this batch were retrieved. After reviewing the casing dents, cracks, and airtightness test results for each battery, the average casing integrity index for this batch was 0.92 (out of 1.0). According to the preset leakage ratio conversion rules, a casing integrity index of 0.92 corresponds to an 8% electrolyte leakage rate during the module disassembly process, meaning approximately 8% of the electrolyte overflows due to mechanical impact during disassembly. Based on the electrolyte mass conservation relationship between the disassembly and crushing processes, the preset maximum allowable leakage rate for both processes is 20% (the remaining 80% is recovered and disposed of by the electrolyte collection process). The disassembly process has already consumed 8% of the electrolyte. According to the mass conservation principle, the upper limit of the remaining leakage that can be allocated to the crushing process is 20% minus 8%, which equals 12%. The actual measured electrolyte leakage rate during the crushing process in this batch is 12%. Searching the preset battery model-electrolyte formula comparison database using the battery model NMC523-60 of this batch, it was found that the organic solvent mass fraction corresponding to the model is 82% and the fluorinated electrolyte mass fraction is 12% (the database entry has been verified by the factory safety data sheet of the battery model; the main components of the organic solvent are a mixture of ethylene carbonate and dimethyl carbonate, and the fluorinated electrolyte is lithium hexafluorophosphate).

[0038] The organic waste gas generation for each process is calculated as follows: For the module disassembly process, 200 units multiplied by 85 grams per unit, multiplied by 8%, and then multiplied by 82% yields 1115.2 grams; for the crushing process, 200 units multiplied by 85 grams per unit, multiplied by 12%, and then multiplied by 82% yields 1672.8 grams. The total organic waste gas generation for both processes is 1115.2 + 1672.8 = 2788 grams. The fluoride generation for each process is calculated using the same method: For the module disassembly process, 200 units multiplied by 85 grams, multiplied by 8%, and then multiplied by 12% yields 163.2 grams; for the crushing process, 200 units multiplied by 85 grams, multiplied by 12%, and then multiplied by 12% yields 244.8 grams. The total fluoride generation for both processes is 163.2 + 244.8 = 408 grams. All four initial environmental impact parameters for this batch have been determined: total discharge energy consumption 16.9 kWh, total short-circuit heat release 47,540 kJ, organic waste gas generation 2,788 g, and fluoride generation 408 g. It should be noted that the conversion relationship between the shell integrity index and the leakage ratio in this embodiment is a segmented mapping rule derived from the shell inspection records and corresponding measured leakage amounts of the previous 14 batches. This represents the specific manifestation of the preset leakage ratio in practical application, rather than a fixed parameter of the invention method. Different treatment companies can establish their own corresponding conversion rules based on their equipment conditions and historical experience, and apply them accordingly.

[0039] The method for obtaining standardized environmental impact assessment values ​​from the initial environmental impact parameters through efficiency analysis and standardization includes: After each batch is completed, the cumulative runtime, cumulative processing load, and measured environmental impact parameters for that batch are obtained; the ratio of the measured environmental impact parameters to the corresponding initial environmental impact parameters is used as the new observation value. ;in, For the first Each battery disassembly batch; the current batch state vector is constructed based on the cumulative runtime and cumulative processing load. Based on the forgetting factor and the current batch state vector Information matrix of the previous batch The gain vector is calculated. The gain vector The calculation formula is: ;in, Forgetting factor; based on the gain vector and new observations The attenuation coefficient estimation vector is calculated. The formula for calculating the attenuation coefficient estimation vector is as follows: According to the gain vector Further calculations yielded the updated information matrix. The update matrix The calculation formula is: ;in, The current estimated values ​​of the time decay coefficient and load decay coefficient corresponding to each component.

[0040] The state vector is based on the cumulative runtime and cumulative processing load of the current batch. Substitute into the attenuation coefficient estimation vector The parameterized efficiency decay function is used to calculate the current efficiency correction factor for each process equipment; the initial environmental impact parameters corresponding to each process are corrected according to the current efficiency correction factor to obtain the final environmental impact parameters; the final environmental impact parameters and the current batch number are appended to the online batch accumulation record; the final environmental impact parameters are standardized according to the online batch accumulation record to obtain the standardized environmental impact assessment value.

[0041] For example, the 14th batch was completed before the start of this batch. According to the operation record of the batch, the following data was read: the cumulative operating time of the equipment was 312 hours and the cumulative battery load processed was 2850 cells. At the same time, the ratios of the four measured environmental impact parameters of the 14th batch to the corresponding initial parameters were read: the ratio of total discharge energy consumption was 1.09, the ratio of total short-circuit heat release was 1.07, the ratio of organic waste gas generation was 1.13, and the ratio of fluoride generation was 1.10. The average of the above four ratios was taken to obtain the new observation value of 1.097.

[0042] The state vector for the current batch is constructed based on the cumulative runtime of 312 hours and the cumulative processing load of 2850 units in the 14th batch. Using a forgetting factor of 0.95, the gain vector is calculated with two components: 0.000285 and 0.0000162, based on the current state vector and the information matrix stored in the 13th batch. The estimated decay coefficients stored after the 13th batch are: a time decay coefficient of 0.000178 per hour and a load decay coefficient of 0.0000122 per unit. Substituting these values ​​into the efficiency decay function, the predicted value is calculated as 1 + 0.000178 × 312 + 0.0000122 × 2850 = 1 + 0.05554 + 0.03477, totaling 1.090. The difference between the new observation value of 1.097 and the predicted value of 1.090 is the residual of 0.007. The estimated value of the time decay coefficient after the update is obtained by multiplying the gain vector by the residual. The estimated value of the load decay coefficient is 0.000180 per hour and the estimated value of the load decay coefficient is 0.0000123 per unit. The information matrix is ​​updated synchronously and stored in the 14th batch of records.

[0043] By the time the 15th batch was entered, the equipment had accumulated 320 hours of runtime and processed a total of 3050 units (after adding the 200 units from the 14th batch). The efficiency decay function used in this embodiment is a linear superposition form, i.e., the correction factor equals 1 plus the time decay coefficient multiplied by the accumulated runtime plus the load decay coefficient multiplied by the accumulated processing load. Substituting the updated parameters: the correction factor equals 1 plus 0.000180 multiplied by 320 plus 0.0000123 multiplied by 3050, i.e., 1 plus 0.0576 plus 0.03752, totaling 1.09512, approximately 1.095.

[0044] The final environmental impact parameters were obtained by correcting each initial environmental impact parameter with a correction factor of 1.095. These parameters are as follows: total discharge energy consumption is approximately 18.50 kWh (16.9 x 1.095); total short-circuit heat release is approximately 52060 kJ (47540 x 1.095); organic waste gas generation is approximately 3053 g (2788 x 1.095); and fluoride generation is approximately 447 g (408 x 1.095). These four final parameters, along with batch number 15, were added to the online batch accumulation record. It should be noted that for the first batch after the enterprise is put into operation, since there is no historical batch information matrix, the system uses a 1000-fold identity matrix as the initial value of the information matrix. The initial value of the attenuation coefficient estimation vector is preset according to the experience of similar dismantling equipment, with a time attenuation coefficient of 0.000175 per hour and a load attenuation coefficient of 0.0000120 per unit. As the batches accumulate, it will automatically converge to the measured data, which will not affect the normal execution of the method in the initial batches.

[0045] The method for standardizing the final environmental impact parameters based on the online batch cumulative records to obtain standardized environmental impact assessment values ​​includes: The total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation are used as four components. If there is no historical batch data in the online batch cumulative record, the final environmental impact parameter of the current batch is used as the initial value of the historical maximum value of each component; otherwise, the historical maximum value of each component is taken from the historical batch record. The ratio of each component of the final environmental impact parameter of the current batch to the corresponding historical maximum value is used as the normalized value of each component. If the sum of the four normalized values ​​is not zero, each normalized value is divided by its sum to obtain the weight coefficient of the corresponding dimension; if the sum is zero, the weight coefficient of each dimension is taken as one-quarter. The standardized environmental impact assessment value is obtained by arranging the weight coefficients of the four dimensions in order.

[0046] For example, the historical maximum values ​​of four components were extracted from the online cumulative records of the first 14 batches. Upon investigation, the highest total discharge energy consumption in the historical batches occurred in batch 11 (a batch of 220 batteries with a state of charge of 40%, this batch had high residual battery capacity and a large batch size), with a final value of 22.9 kWh; the highest total short-circuit heat release also occurred in batch 11, at 64,600 kJ; the highest organic waste gas generation occurred in batch 5 (a batch of 206 batteries, several batteries had low casing integrity scores in the early stages of equipment operation, and the combined leakage rate of the disassembly and crushing processes reached 24%, this batch's efficiency correction factor was close to 1.0), with a final value of 3,440 grams; the highest fluoride generation occurred in batch 14 (a batch of 220 batteries, a large batch size, with a combined leakage rate of 21%, slightly higher than this batch's 20%), with a final value of 510 grams.

[0047] The normalized values ​​of the four final environmental impact parameters for this batch were obtained by dividing each parameter by its corresponding historical maximum value: the normalized value of total discharge energy consumption was 18.50 divided by 22.9, which is approximately 0.808; the normalized value of total short-circuit heat release was 52060 divided by 64600, which is approximately 0.806; the normalized value of organic waste gas generation was 3053 divided by 3440, which is approximately 0.887; and the normalized value of fluoride generation was 447 divided by 510, which is approximately 0.876.

[0048] The sum of the four normalized values ​​is 0.808 + 0.806 + 0.887 + 0.876, totaling 3.377. The weighting coefficients for each component are calculated by dividing the corresponding normalized value by the sum. The weight for the discharge energy consumption dimension is approximately 0.239 (0.808 divided by 3.377); the weight for the short-circuit heat release dimension is approximately 0.239 (0.806 divided by 3.377); the weight for the organic waste gas dimension is approximately 0.263 (0.887 divided by 3.377); and the weight for the fluoride dimension is approximately 0.259 (0.876 divided by 3.377). Arranged in order, the standardized environmental impact assessment values ​​for this batch are (0.239, 0.239, 0.263, 0.259). The weights for organic waste gas and fluoride dimensions are slightly higher than those for discharge energy consumption and short-circuit heat release, reflecting that the waste gas and fluoride pressures in this batch are more prominent compared to historical peak values. Subsequent parameter optimization will focus on reducing leakage and volatilization.

[0049] The method for determining the dismantling operation parameters for the current batch based on the standardized environmental impact assessment values ​​includes: The standardized environmental impact assessment value is used as the environmental impact risk vector. Measured records of adjustable parameters and corresponding environmental impact parameters for each process in historical batches are obtained. These adjustable parameters include the discharge power and duration of the discharge pretreatment process, the disassembly rate of the module disassembly process, the sealing intervention sequence of the electrolyte collection process, and the ventilation rate of the exhaust gas treatment process. For two adjacent historical batches, the ratio of the difference between the i-th adjustable parameter and the difference between the j-th environmental impact parameter is used as an element of the sensitivity matrix, and a recent weighted average is taken to obtain the environmental impact sensitivity matrix. The environmental impact risk vector is weighted against the sensitivity matrix to obtain a comprehensive priority adjustment coefficient vector, which serves as the adjustment direction for each adjustable parameter. The termination condition is that the change in each adjustable parameter between two adjacent iterations is lower than the convergence accuracy. In each iteration, the current parameter point is moved by a step size along the adjustment direction, and out-of-bounds components are truncated to the corresponding process operation constraint boundary. After iteration convergence, the optimal adjustment amount for each adjustable parameter is output. The optimal adjustment amount is superimposed on the operation reference benchmark for each process to obtain the disassembly operation parameter set for the current batch.

[0050] For example, the standardized environmental impact assessment values ​​(0.239, 0.239, 0.263, 0.259) are used as the environmental impact risk vector and incorporated into the dismantling operation parameter calculation process. Adjustable parameters for each process (discharge power, discharge duration, dismantling rate, sealing intervention sequence, ventilation volume) and corresponding environmental impact parameters are extracted from the measured records of 14 historical batches. A sensitivity matrix is ​​constructed by weighting the difference between the i-th adjustable parameter and the j-th environmental impact parameter for the five most recent batches over two adjacent batches. Taking typical sensitivity data as an example: for every 1 kW increase in discharge power, the total discharge energy consumption increases by about 0.82 kWh, and the total short-circuit heat release decreases by about 180 kJ; for every 0.1 m / s decrease in disassembly rate, organic waste gas decreases by about 52 grams, and fluoride decreases by about 9 grams; for every 30 seconds advance in the sealing intervention sequence, organic waste gas decreases by about 68 grams; for every 500 cubic meters per hour increase in ventilation, fluoride decreases by about 13 grams.

[0051] The sensitivity matrix is ​​weighted according to the risk vector to obtain the comprehensive priority adjustment coefficient vector. Since the weights of the two dimensions of organic waste gas (0.263) and fluoride (0.259) are higher than those of the dimensions of discharge energy consumption and short-circuit heat release (0.239 each), the coefficients of the sensitivity matrix related to dismantling rate, sealing intervention time, and ventilation volume are larger after weighting. The adjustment directions of each adjustable parameter are as follows: positive adjustment of discharge power, positive adjustment of discharge duration, negative adjustment of dismantling rate (reducing speed to reduce electrolyte leakage caused by mechanical impact), negative adjustment of sealing intervention time (advancing to shorten the electrolyte volatilization window), and positive adjustment of ventilation volume.

[0052] The convergence accuracy is defined as the change in each adjustable parameter being less than 0.01 between adjacent iterations. The current parameter point is moved iteratively by step size, and out-of-bounds components are truncated according to the operational constraints of each process. After 4 iterations, convergence is achieved, and the optimal adjustment values ​​for each adjustable parameter are output: discharge power is increased by 2.0 kW, discharge duration is increased by 6 minutes, dismantling rate is decreased by 0.09 m / s, sealing intervention timing is advanced by 48 seconds, and ventilation volume is increased by 850 cubic meters per hour.

[0053] The above adjustments are then added to the operational reference standards for each process in this processing company, resulting in the following set of dismantling operation parameters for this batch:

[0054] The method for obtaining the optimal dismantling operation parameters through multi-objective optimization using the multi-dimensional mapping relationship between process parameters and environmental impact parameters includes: Obtain records of adjustable parameter combinations for each process in historical batches, along with corresponding measured total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation. Based on the adjustable parameters of each process as independent variables, establish mapping functions with cross terms for total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation. The mapping functions are as follows: ;in, These correspond to total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation, respectively. These are adjustable parameters; is the regression coefficient.

[0055] Minimizing total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation are four independent optimization objectives. A multi-objective optimization problem is constructed within the operational constraints of each process parameter. The Pareto front search method is used to search for the Pareto optimal solution set within the parameter feasible region. Standardized environmental impact assessment values ​​are used as weight vectors for the four optimization objectives. The weighted distance d from each Pareto optimal solution to the positive ideal point is calculated based on the weight vectors. + The weighted distance d to the negative ideal point - Positive ideal points are formed by the optimal values ​​of each objective in the Pareto optimal solution set, while negative ideal points are formed by the worst values ​​of each objective; according to d + and d - Calculate the overall relative proximity of each Pareto optimal solution, and select the Pareto optimal solution with the highest proximity; output the process parameter values ​​of the Pareto optimal solution as the optimal dismantling operation parameters.

[0056] For example, adjustable parameter combinations for each process and corresponding measured total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation were extracted from 14 historical batches of records. Using five adjustable parameters—discharge power, discharge duration, dismantling rate, sealing intervention sequence, and ventilation rate—as independent variables, a mapping function containing cross terms was established for each of the four environmental impact quantities, resulting in a total of fifteen regression coefficients. These coefficients were obtained by minimizing the sum of squared prediction residuals of each function on the 14 batches of historical data. Taking the actual fitting results as an example, in the mapping function of total discharge energy consumption, the first-order term of discharge power has the largest coefficient (positive drive), and the cross term of the product of discharge power and discharge duration contributes approximately 12% of the variance explained; in the mapping function of organic waste gas generation, the first-order term of dismantling rate is the strongest positive drive term, and the coefficient of the cross term of the product of dismantling rate and sealing intervention time is negative, reflecting the synergistic reduction effect of deceleration and early sealing on organic waste gas generation. It should be noted that... The intercept term represents the value of the first parameter when all adjustable parameters are zero. The baseline forecast values ​​for each environmental impact quantity are used to correct for systematic biases; , ∈{1,2,3,4,5}, which correspond to the numbers of the five adjustable parameters.

[0057] A multi-objective optimization problem is constructed within the operational constraints of each process, with the minimization of four environmental impact quantities as four independent optimization objectives. The constraint boundaries are taken from the safety operating procedures of each process. The Pareto front optimization method is used to search within the feasible region of the parameters to obtain the Pareto optimal solution set. Based on the standardized environmental impact assessment values ​​(0.239, 0.239, 0.263, 0.259) as the weight vectors for the four optimization objectives, the weighted distances are calculated for each solution in the Pareto optimal solution set. Positive ideal points are formed by the optimal values ​​of each of the four objectives in the Pareto optimal solution set, while negative ideal points are formed by the worst values ​​of each objective. The weighted distances from each solution to the positive ideal points and to the negative ideal points are calculated to obtain the comprehensive relative proximity.

[0058] The group with the highest overall relative proximity is as follows: weighted distance to the positive ideal point is 0.135, weighted distance to the negative ideal point is 0.408, and the overall relative proximity is 0.408 divided by (0.135 plus 0.408) is approximately 0.751. The corresponding process parameters are: discharge power 17.0 kW, discharge duration 51 minutes, dismantling rate 0.76 m / s, sealing intervention time 72 seconds after dismantling starts, and ventilation volume 3850 cubic meters per hour. These parameters will be output as the optimal dismantling operation parameters for the current batch.

[0059] The method of using the Pareto front search to search for the Pareto optimal solution set within the parameter feasible region includes: Using the actual execution parameters of each process in the current batch as initial references, the feasible domain of parameters is obtained by determining the boundary values ​​of each adjustable parameter based on the upper and lower limits of safe discharge in the discharge pretreatment process, the production line rate range of the module disassembly process, the sealing intervention timing window of the electrolyte collection process, and the ventilation volume configuration range of the exhaust gas treatment process. Within the feasible domain of parameters, several sets of process parameter combinations are randomly generated as the initial population. Each set of parameter combinations is substituted into the mapping function to calculate the corresponding total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation.

[0060] Within the population, the dominance relationships of each parameter combination across the four target dimensions are compared. Non-dominated individuals that are not surpassed by any other individual across all target dimensions are retained, while dominated individuals are eliminated. For each non-dominated individual, the difference in target values ​​between adjacent individuals in each target dimension is summed to obtain the distribution interval of each non-dominated individual. Winning individuals are selected based on the distribution interval. Crossover and perturbation mutations are performed on the winning individuals within the parameter feasible region to generate the next generation population. Using a preset reference point formed by the acceptable upper bound of each target dimension as a benchmark, the hypervolume covered by the current generation and the previous generation's non-dominated frontier are calculated respectively, and the hypervolume increment is obtained based on the difference between the two. When the hypervolume increment is lower than the convergence accuracy requirement, the iteration is terminated, and all parameter combinations on the non-dominated frontier are output as the Pareto optimal solution set.

[0061] For example, the feasible domain of parameters is defined according to the safety operating procedures of each process, namely, the upper and lower limits of discharge power are 10 kW and 22 kW, respectively; the discharge duration range is 30 minutes to 90 minutes; the disassembly rate range is 0.40 m / s to 1.20 m / s; the sealing intervention timing window is 30 seconds to 180 seconds after disassembly starts; and the ventilation rate configuration range is 1500 cubic meters per hour to 6000 cubic meters per hour. The value boundaries of the above five-dimensional parameters together constitute the feasible domain of parameters in this optimization.

[0062] Eighty sets of process parameter combinations are randomly generated within the feasible region of parameters as the initial population. Each set of parameters is substituted into a mapping function to calculate the predicted values ​​of the four corresponding environmental impact quantities. Dominance relationships are compared among the 80 initial individuals. If an individual is not inferior to another individual in all four target dimensions and is superior in at least one dimension, the latter is dominated and eliminated. After the first generation selection, 23 non-dominated individuals are retained. For these 23 non-dominated individuals, the difference in target values ​​between adjacent individuals in each target dimension is summed to obtain the distribution interval of each non-dominated individual. From these, 12 superior individuals with larger distribution intervals are selected. Within the feasible region of parameters, crossover and recombination (randomly swapping components between two superior individuals) and perturbation mutation (adding or subtracting random values ​​to each component based on its current value) are performed on these individuals to generate the next generation of 80 population members.

[0063] Pre-defined reference points are established using acceptable upper bounds for each target dimension: total discharge energy consumption upper bound 25 kWh, total short-circuit heat release upper bound 65,000 kJ, organic waste gas upper bound 4,000 g, and fluoride upper bound 650 g (set comprehensively based on industry emission control indicators and the internal control requirements of this treatment company). The hypervolume covered by the non-dominated frontier in the four-dimensional target space is calculated generation by generation. The hypervolume and increment of each generation in actual iterations are recorded as follows:

[0064] At the end of the 18th iteration, the hypervolume increment dropped to 0.31% of the previous generation's hypervolume, below the set convergence accuracy of 0.5%, thus terminating the iteration. All 47 parameter combinations on the 18th generation non-dominated front were output as the Pareto optimal solution set. It should be noted that the four acceptable upper bounds for the preset reference points were set comprehensively based on industry emission control indicators and the internal control requirements of the treatment company, rather than the theoretical extreme values ​​of each indicator. The reasonable setting of the reference points ensures that the hypervolume indicators can truly reflect the degree of improvement of the Pareto front relative to the production acceptable boundary, thereby guaranteeing that the convergence judgment has practical significance.

[0065] Example 2: Based on the same inventive concept, such as Figure 2 As shown, this embodiment also provides a new energy vehicle battery dismantling system, including: The data acquisition module is used to obtain the initial input parameters of the current batch of new energy vehicle batteries to be disassembled. The initial input parameters include the number of batteries, state of charge, and residual electrolyte of the current batch of batteries.

[0066] The environmental impact analysis module is used to calculate the initial environmental impact parameters of the current batch in the dismantling and recycling process based on the initial input parameters, and to obtain the standardized environmental impact assessment value by efficiency analysis and standardization processing of the initial environmental impact parameters.

[0067] The dismantling operation parameter calculation module is used to determine the dismantling operation parameters for the current batch based on the standardized environmental impact assessment values; and to obtain the optimal dismantling operation parameters by performing multi-objective optimization through the multi-dimensional mapping relationship between process parameters and environmental impact parameters.

[0068] The disassembly execution module is used to drive the equipment in each process to disassemble the new energy vehicle battery according to the optimal disassembly operation parameters.

[0069] It should be noted that the specific ways in which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0070] Finally, it should be noted that although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for disassembling batteries for new energy vehicles, characterized in that, The method includes: Obtain the initial input parameters for the current batch of new energy vehicle batteries to be disassembled. The initial input parameters include the number of batteries, state of charge, and residual electrolyte in the current batch. Calculate the initial environmental impact parameters of the current batch in the dismantling and recycling process based on the initial input parameters; obtain the standardized environmental impact assessment value by efficiency analysis and standardization of the initial environmental impact parameters; The dismantling operation parameters for the current batch are determined based on the standardized environmental impact assessment values; the optimal dismantling operation parameters are obtained by multi-objective optimization through the multi-dimensional mapping relationship between process parameters and environmental impact parameters. The optimal dismantling parameters are used to drive the equipment in each process to dismantle the new energy vehicle battery.

2. The method for dismantling a new energy vehicle battery according to claim 1, characterized in that, The method for calculating the initial environmental impact parameters of the current batch in the dismantling and recycling process based on the initial input parameters includes: The residual charge is obtained based on the state of charge, rated capacity, and rated voltage of the new energy vehicle battery; the discharge energy consumption is obtained based on the residual charge and discharge efficiency; the total discharge energy consumption of the discharge pretreatment process is obtained based on the discharge energy consumption and the number of batteries; the short-circuit release energy is obtained based on the state of charge, rated capacity, and rated voltage; the short-circuit heat release is obtained based on the short-circuit release energy and short-circuit thermal conversion coefficient; the total short-circuit heat release of the module dismantling process is obtained based on the short-circuit heat release and the number of batteries; the amount of organic waste gas and fluoride generated in the dismantling and recycling process is obtained based on the number of batteries and the amount of electrolyte residue; the initial environmental impact parameters of the current batch in the dismantling and recycling process are obtained based on the total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation.

3. The method for disassembling a new energy vehicle battery according to claim 2, characterized in that, The method for determining the amount of organic waste gas and fluoride generated during the dismantling and recycling process based on the number of batteries and the amount of residual electrolyte includes: Obtain the casing integrity index of the new energy vehicle battery, and combine it with the preset leakage ratio to obtain the electrolyte leakage ratio of the module disassembly process; obtain the electrolyte leakage ratio of the crushing process according to the mass conservation relationship of the electrolyte between the disassembly process and the crushing process; query the corresponding organic solvent mass fraction and fluorine electrolyte mass fraction from the preset battery model-electrolyte formula comparison database according to the battery model identifier of the current batch of batteries; if there is no matching model record in the database, the average value of the same battery system in the database is used as the fallback value; multiply the number of batteries and the residual amount of electrolyte by the electrolyte leakage ratio and organic solvent mass fraction of the disassembly process and the crushing process respectively to obtain the organic waste gas generation of each process, and summarize them to obtain the organic waste gas generation of the disassembly and recycling process; calculate the fluoride generation in the same way and summarize.

4. The method for disassembling a new energy vehicle battery according to claim 3, characterized in that, The method for obtaining standardized environmental impact assessment values ​​from the initial environmental impact parameters through efficiency analysis and standardization includes: After each batch is completed, the cumulative runtime, cumulative processing load, and measured environmental impact parameters for that batch are obtained; the ratio of the measured environmental impact parameters to the corresponding initial environmental impact parameters is used as the new observation value. ;in, For the first Each battery disassembly batch; the current batch state vector is constructed based on the cumulative runtime and cumulative processing load. Based on the forgetting factor and the current batch state vector Information matrix of the previous batch The gain vector is calculated. The gain vector The calculation formula is: ;in, Forgetting factor; based on the gain vector and new observations The attenuation coefficient estimation vector is calculated. The formula for calculating the attenuation coefficient estimation vector is as follows: According to the gain vector Further calculations yielded the updated information matrix. The update matrix The calculation formula is: ;in, The current estimated values ​​of the time decay coefficient and load decay coefficient corresponding to each component; The state vector is based on the cumulative runtime and cumulative processing load of the current batch. Substitute into the attenuation coefficient estimation vector The parameterized efficiency decay function is used to calculate the current efficiency correction factor for each process equipment; the initial environmental impact parameters corresponding to each process are corrected according to the current efficiency correction factor to obtain the final environmental impact parameters; the final environmental impact parameters and the current batch number are appended to the online batch accumulation record; the final environmental impact parameters are standardized according to the online batch accumulation record to obtain the standardized environmental impact assessment value.

5. A method for disassembling a new energy vehicle battery according to claim 4, characterized in that, The method for standardizing the final environmental impact parameters based on the online batch cumulative records to obtain standardized environmental impact assessment values ​​includes: The total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation are used as four components. If there is no historical batch data in the online batch cumulative record, the final environmental impact parameter of the current batch is used as the initial value of the historical maximum value of each component; otherwise, the historical maximum value of each component is taken from the historical batch record. The ratio of each component of the final environmental impact parameter of the current batch to the corresponding historical maximum value is used as the normalized value of each component. If the sum of the four normalized values ​​is not zero, each normalized value is divided by its sum to obtain the weight coefficient of the corresponding dimension; if the sum is zero, the weight coefficient of each dimension is taken as one-quarter. The standardized environmental impact assessment value is obtained by arranging the weight coefficients of the four dimensions in order.

6. A method for disassembling a new energy vehicle battery according to claim 5, characterized in that, The method for determining the dismantling operation parameters for the current batch based on the standardized environmental impact assessment values ​​includes: The standardized environmental impact assessment value is used as the environmental impact risk vector. Measured records of adjustable parameters and corresponding environmental impact parameters for each process in historical batches are obtained. These adjustable parameters include the discharge power and duration of the discharge pretreatment process, the disassembly rate of the module disassembly process, the sealing intervention sequence of the electrolyte collection process, and the ventilation rate of the exhaust gas treatment process. For two adjacent historical batches, the ratio of the difference between the i-th adjustable parameter and the difference between the j-th environmental impact parameter is used as an element of the sensitivity matrix, and a recent weighted average is taken to obtain the environmental impact sensitivity matrix. The environmental impact risk vector is weighted against the sensitivity matrix to obtain a comprehensive priority adjustment coefficient vector, which serves as the adjustment direction for each adjustable parameter. The termination condition is that the change in each adjustable parameter between two adjacent iterations is lower than the convergence accuracy. In each iteration, the current parameter point is moved by a step size along the adjustment direction, and out-of-bounds components are truncated to the corresponding process operation constraint boundary. After iteration convergence, the optimal adjustment amount for each adjustable parameter is output. The optimal adjustment amount is superimposed on the operation reference benchmark for each process to obtain the disassembly operation parameter set for the current batch.

7. A method for disassembling a new energy vehicle battery according to claim 6, characterized in that, The method for obtaining the optimal dismantling operation parameters through multi-objective optimization using the multi-dimensional mapping relationship between process parameters and environmental impact parameters includes: Obtain records of adjustable parameter combinations for each process in historical batches, along with corresponding measured total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation. Based on the adjustable parameters of each process as independent variables, establish mapping functions with cross terms for total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation. The mapping functions are as follows: ;in, Corresponding to total discharge energy consumption Total short-circuit heat release Organic waste gas generation and fluoride production ; These are adjustable parameters; These are the regression coefficients; Minimizing total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation are four independent optimization objectives. A multi-objective optimization problem is constructed within the operational constraints of each process parameter. The Pareto front search method is used to search for the Pareto optimal solution set within the parameter feasible region. Standardized environmental impact assessment values ​​are used as weight vectors for the four optimization objectives. The weighted distance d from each Pareto optimal solution to the positive ideal point is calculated based on the weight vectors. + The weighted distance d to the negative ideal point - Positive ideal points are formed by the optimal values ​​of each objective in the Pareto optimal solution set, while negative ideal points are formed by the worst values ​​of each objective; according to d + and d - Calculate the overall relative proximity of each Pareto optimal solution, and select the Pareto optimal solution with the highest proximity; output the process parameter values ​​of the Pareto optimal solution as the optimal dismantling operation parameters.

8. A method for disassembling a new energy vehicle battery according to claim 7, characterized in that, The method of using the Pareto front search to search for the Pareto optimal solution set within the parameter feasible region includes: Using the actual execution parameters of each process in the current batch as initial references, the feasible domain of parameters is obtained by determining the boundary values ​​of each adjustable parameter based on the safe discharge upper and lower limits of the discharge pretreatment process, the production line rate range of the module disassembly process, the sealing intervention timing window of the electrolyte collection process, and the ventilation volume configuration range of the exhaust gas treatment process. Within the feasible domain of parameters, several sets of process parameter combinations are randomly generated as the initial population. Each set of parameter combinations is substituted into the mapping function to calculate the corresponding total discharge energy consumption, total short-circuit heat release, organic waste gas generation, and fluoride generation. Within the population, the dominance relationships of each parameter combination across the four target dimensions are compared. Non-dominated individuals that are not surpassed by any other individual across all target dimensions are retained, while dominated individuals are eliminated. For each non-dominated individual, the difference in target values ​​between adjacent individuals in each target dimension is summed to obtain the distribution interval of each non-dominated individual. Winning individuals are selected based on the distribution interval. Crossover and perturbation mutations are performed on the winning individuals within the parameter feasible region to generate the next generation population. Using a preset reference point formed by the acceptable upper bound of each target dimension as a benchmark, the hypervolume covered by the current generation and the previous generation's non-dominated frontier are calculated respectively, and the hypervolume increment is obtained based on the difference between the two. When the hypervolume increment is lower than the convergence accuracy requirement, the iteration is terminated, and all parameter combinations on the non-dominated frontier are output as the Pareto optimal solution set.

9. A new energy vehicle battery dismantling system, used to perform the method according to any one of claims 1 to 8, characterized in that, The system includes: The data acquisition module is used to obtain the initial input parameters of the current batch of new energy vehicle batteries to be disassembled. The initial input parameters include the number of batteries, state of charge, and residual electrolyte of the current batch of batteries. The environmental impact analysis module is used to calculate the initial environmental impact parameters of the current batch in the dismantling and recycling process based on the initial input parameters, and to obtain the standardized environmental impact assessment value by efficiency analysis and standardization of the initial environmental impact parameters. The dismantling operation parameter calculation module is used to determine the dismantling operation parameters for the current batch based on the standardized environmental impact assessment values; and to obtain the optimal dismantling operation parameters by performing multi-objective optimization through the multi-dimensional mapping relationship between process parameters and environmental impact parameters. The disassembly execution module is used to drive the equipment in each process to disassemble the new energy vehicle battery according to the optimal disassembly operation parameters.

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