CN117878455B - Discharging state optimization method and related device for retired battery - Google Patents
Discharging state optimization method and related device for retired battery Download PDFInfo
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- H—ELECTRICITY
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- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M10/00—Secondary cells; Manufacture thereof
- H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
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Abstract
The invention discloses a discharge state optimization method and a related device of retired batteries, and relates to the technical field of battery discharge, wherein the method comprises the following steps: predicting the charge state of the retired battery based on the mutual information value generated by the state parameter and the health state of the retired battery, and classifying the retired battery in a echelon manner; constructing a corresponding initial discharge strategy, and performing strategy optimization by utilizing a game tree to obtain an optimized discharge strategy; the battery discharging equipment performs discharging treatment based on an optimized discharging strategy; in the process of discharging treatment, judging whether the acquired temperature change value is greater than or equal to a preset change threshold value, if so, determining a first pressure relief amount; and correcting the first pressure relief amount based on the pressure-temperature time sequence correlation matrix to obtain a second pressure relief amount, determining the transmission rate of a heat dissipation medium, and continuously performing pressure relief heat dissipation treatment on the retired battery. The invention not only improves the discharging efficiency of the retired battery, but also can lead the discharging state of the retired battery to achieve ideal effect.
Description
Technical Field
The invention mainly relates to the technical field of battery discharge, in particular to a method and a related device for optimizing a discharge state of a retired battery.
Background
Along with the rapid development of new energy technology, the number of retired batteries is also rapidly increased, so that the retired batteries need to be reasonably and effectively recycled and disassembled, in order to ensure the safety of recycling the disassembled retired batteries, the retired batteries need to be subjected to discharging treatment, the conventional retired battery discharging method is often used for directly placing the retired batteries in discharging equipment for discharging without classifying the retired batteries in a ladder way under the condition that the difference of the charge states of the retired batteries is not considered, the battery discharging is insufficient, the conventional battery discharging method is usually based on a fixed discharging strategy, the battery discharging efficiency is not effectively improved, meanwhile, the fixed discharging strategy is also unfavorable for optimizing the state in the discharging process of the batteries, heat is generated when the battery discharging current flows, the temperature is required to be reduced, namely the pressure relief treatment is performed, but the temperature in the discharging process of the batteries is increased, if the temperature is increased to the preset pressure relief temperature, the temperature is released again, the temperature is required to be poor, a great time is required to influence the discharging efficiency, the discharging effect of the retired batteries cannot be accurately regulated until the whole discharging state is not influenced due to the fact that the preset discharging temperature is difficult to be regulated to the preset discharging temperature is greatly.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a method and a related device for optimizing the discharge state of a retired battery, which not only improve the discharge efficiency of the retired battery, but also enable the discharge state of the retired battery to achieve an ideal effect.
In order to solve the technical problems, the invention provides a method for optimizing the discharge state of a retired battery, which comprises the following steps:
Acquiring state parameters of the retired batteries, calculating mutual information values of the state parameters and the health states of the retired batteries, predicting the charge state of each retired battery based on the mutual information values, and classifying all the retired batteries in a echelon manner based on the charge states to obtain a plurality of retired batteries in each echelon category;
Constructing initial discharge strategies corresponding to a plurality of retired batteries of each echelon class, and performing strategy optimization on the initial discharge strategies by utilizing game trees to obtain optimized discharge strategies;
Placing a plurality of retired batteries of each echelon class in corresponding positions of battery discharging equipment, wherein the battery discharging equipment performs discharging treatment of the retired batteries based on the optimized discharging strategy;
During the discharging treatment of the retired battery, acquiring a temperature change value at a temperature mark point based on a plurality of monitoring sensors arranged at preset positions, judging whether the temperature change value is larger than or equal to a preset change threshold value, and determining a first pressure relief amount based on the temperature change value if the temperature change value is larger than or equal to the preset change threshold value;
Constructing a pressure-temperature time sequence incidence matrix, correcting the first pressure relief quantity based on the pressure-temperature time sequence incidence matrix to obtain a second pressure relief quantity, determining the heat dissipation medium incoming rate, and continuously performing pressure relief heat dissipation treatment on the retired battery based on the second pressure relief quantity and the heat dissipation medium incoming rate until the re-collected temperature change value is smaller than a preset change threshold.
Optionally, the calculating the mutual information value of the state parameter and the retired battery health state includes:
calculating a first edge probability distribution function of the state parameter and a second edge probability distribution function of the retired battery health state;
calculating a joint probability distribution function based on the first and second edge probability distribution functions;
And calculating based on the joint probability distribution function, the first edge probability distribution function and the second edge probability distribution function to obtain a mutual information value of the state parameter and the retired battery health state.
Optionally, predicting the state of charge of each retired battery based on the mutual information value, classifying all retired batteries in a echelon based on the state of charge, and obtaining a plurality of retired batteries in each echelon category, including:
taking the mutual information value as a model constraint condition, and constructing a generation model based on the model constraint condition;
Constructing a generated countermeasure network by utilizing a preset discrimination model and combining a Nash equilibrium strategy based on the generated model, and predicting the charge state of each retired battery based on the generated countermeasure network;
and acquiring a clustering center based on the state of charge of each retired battery by using an entropy method, and performing echelon classification on all retired batteries based on the clustering center to acquire a plurality of retired batteries in each echelon class.
Optionally, constructing an initial discharge strategy corresponding to the plurality of retired batteries of each echelon class, and performing strategy optimization on the initial discharge strategy by using a game tree to obtain an optimized discharge strategy, including:
acquiring historical discharge strategy data, and generating corresponding learning strategy data by using a reinforcement learning algorithm based on the echelon class corresponding to the retired battery;
updating the historical discharge strategy data based on the corresponding learning strategy data to obtain updated discharge strategy data, and performing iterative processing based on the updated discharge strategy data to obtain initial discharge strategies corresponding to a plurality of retired batteries of each echelon class;
Performing effect evaluation on the initial discharge strategy based on a preset evaluation rule to obtain effect evaluation data;
Generating a corresponding game tree based on the initial discharge strategy, and determining a corresponding optimized path based on the corresponding game tree;
and carrying out strategy optimization on the initial discharge strategy based on the effect evaluation data and the corresponding optimization path to obtain the optimized discharge strategy corresponding to a plurality of retired batteries of each echelon class.
Optionally, the placing the plurality of retired batteries of each echelon class in a corresponding position of a battery discharging device, where the battery discharging device performs discharging processing of the retired batteries based on the optimized discharging policy, includes:
Acquiring the positive electrode position and the negative electrode position of each retired battery, and placing a plurality of retired batteries corresponding to the echelon class in corresponding positions of battery discharging equipment based on the positive electrode position and the negative electrode position;
and acquiring a battery discharge instruction, and controlling the battery discharge equipment to perform discharge treatment of the retired battery by using the optimized discharge strategy based on the battery discharge instruction.
Optionally, if the temperature change value is greater than or equal to the preset change threshold, determining the first pressure relief amount based on the temperature change value includes:
calculating a temperature change rate based on the temperature change value, and calculating a temperature mutation line based on the temperature change rate;
Acquiring the occurrence proportion of a high-temperature point of a region based on a database, and determining a pressure relief radius by utilizing a temperature position diagram based on the occurrence proportion of the region high Wen Dianchu;
And setting a first pressure relief amount based on the pressure relief radius and the temperature abrupt change line.
Optionally, the constructing a pressure-temperature time sequence correlation matrix, correcting the first pressure relief amount based on the pressure-temperature time sequence correlation matrix to obtain a second pressure relief amount, and determining a heat dissipation medium incoming rate, including:
analyzing the temperature change trend based on the temperature change value of the temperature mark point to obtain a temperature change trend;
Determining a correlation coefficient related to the corrected pressure based on the pressure distribution of the battery discharging equipment at the temperature mark point, and constructing a pressure-temperature time sequence correlation matrix based on the temperature change trend and the correlation coefficient;
Performing parameter guidance based on the pressure-temperature time sequence correlation matrix to obtain a pressure state time sequence matrix;
Correcting the first pressure relief amount based on the pressure state time sequence matrix to obtain a second pressure relief amount;
And determining a heat dissipation medium acting range based on the second pressure relief amount and the preset cooling efficiency, and determining a heat dissipation medium incoming rate based on the heat dissipation medium acting range and the preset cooling efficiency.
In addition, the invention also provides a discharging state optimizing device of the retired battery, which comprises the following components:
The echelon classification module: the method comprises the steps of obtaining state parameters of retired batteries, calculating mutual information values of the state parameters and the health states of the retired batteries, predicting the charge state of each retired battery based on the mutual information values, and classifying all the retired batteries in a echelon mode based on the charge states to obtain a plurality of retired batteries in each echelon mode;
a discharge strategy generation module: the method comprises the steps of constructing initial discharge strategies corresponding to a plurality of retired batteries of each echelon class, and performing strategy optimization on the initial discharge strategies by utilizing game trees to obtain optimized discharge strategies;
and a discharge processing module: the battery discharging device is used for discharging the retired batteries of each echelon class in the corresponding position of the battery discharging device, and the battery discharging device performs discharging treatment of the retired batteries based on the optimized discharging strategy;
A first pressure relief amount determination module: the method comprises the steps that in the discharging process of a retired battery, a plurality of monitoring sensors arranged at preset positions are used for collecting temperature change values at temperature mark points, judging whether the temperature change values are larger than or equal to a preset change threshold value or not, and if the temperature change values are larger than or equal to the preset change threshold value, determining a first pressure relief amount based on the temperature change values;
Pressure relief heat dissipation module: the method comprises the steps of constructing a pressure-temperature time sequence incidence matrix, correcting the first pressure relief quantity based on the pressure-temperature time sequence incidence matrix, obtaining a second pressure relief quantity, determining a heat dissipation medium incoming rate, and continuously performing pressure relief heat dissipation treatment on the retired battery based on the second pressure relief quantity and the heat dissipation medium incoming rate until a re-collected temperature change value is smaller than a preset change threshold.
In addition, the invention also provides electronic equipment, which comprises a processor and a memory, wherein the memory is used for storing instructions, and the processor is used for calling the instructions in the memory so that the electronic equipment executes the method for optimizing the discharge state of the retired battery.
In addition, the invention also provides a computer readable storage medium, which stores computer instructions that, when executed on an electronic device, cause the electronic device to execute the above-mentioned method for optimizing the discharge state of the retired battery.
In the embodiment of the invention, the state of charge of each retired battery is predicted through the mutual information value calculated by the state parameters and the state of health of the retired battery, the retired batteries are classified in a gradient manner through the state of charge, the gradient classification can be more accurately divided, the retired batteries in different gradient classifications are prevented from being mixed together, the individual retired batteries are not fully discharged, a corresponding optimized discharging strategy is constructed for a plurality of retired batteries in each gradient classification, the discharging strategy can be better adapted to the actual condition of the retired batteries, the discharging efficiency of the retired batteries is improved, the state in the discharging process of the subsequent retired batteries is easier to control, the influence caused by the delay of the temperature rise is avoided through constructing a pressure-temperature time sequence association matrix, the first pressure relief quantity can be corrected more accurately, the second pressure relief quantity is more reliable, the medium is transmitted into the speed through the action range of a heat dissipation medium, the discharging efficiency of the retired batteries is improved, the discharging efficiency of the retired batteries is only reduced to the ideal discharging effect is achieved, and the discharging effect of the retired batteries is not improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings which are required in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for optimizing the discharge state of retired batteries in an embodiment of the invention;
FIG. 2 is a schematic structural diagram of a discharging state optimizing device of a retired battery in an embodiment of the present invention;
Fig. 3 is a schematic structural composition diagram of an electronic device in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for optimizing a discharge state of a retired battery according to an embodiment of the invention.
As shown in fig. 1, a method for optimizing a discharge state of a retired battery, the method comprising:
s11: acquiring state parameters of the retired batteries, calculating mutual information values of the state parameters and the health states of the retired batteries, predicting the charge state of each retired battery based on the mutual information values, and classifying all the retired batteries in a echelon manner based on the charge states to obtain a plurality of retired batteries in each echelon category;
In the implementation process of the invention, the calculating the mutual information value of the state parameter and the retired battery health state comprises the following steps: calculating a first edge probability distribution function of the state parameter and a second edge probability distribution function of the retired battery health state; calculating a joint probability distribution function based on the first and second edge probability distribution functions; and calculating based on the joint probability distribution function, the first edge probability distribution function and the second edge probability distribution function to obtain a mutual information value of the state parameter and the retired battery health state.
Further, predicting the state of charge of each retired battery based on the mutual information value, classifying all retired batteries in a echelon based on the state of charge, and obtaining a plurality of retired batteries in each echelon category, including: taking the mutual information value as a model constraint condition, and constructing a generation model based on the model constraint condition; constructing a generated countermeasure network by utilizing a preset discrimination model and combining a Nash equilibrium strategy based on the generated model, and predicting the charge state of each retired battery based on the generated countermeasure network; and acquiring a clustering center based on the state of charge of each retired battery by using an entropy method, and performing echelon classification on all retired batteries based on the clustering center to acquire a plurality of retired batteries in each echelon class.
Specifically, state parameters of the retired battery are obtained through a data sensor, the state parameters of the retired battery comprise voltage data and current data, the state parameters are respectively fitted with a plurality of continuous probability distribution functions to obtain a plurality of first fitting distribution functions, the health state of the retired battery is respectively fitted with the plurality of continuous probability distribution functions to obtain a plurality of second fitting distribution functions, the optimized first fitting distribution functions are determined in the plurality of first fitting distribution functions based on an information criterion method, the first edge probability distribution functions of the state parameters are generated according to the optimized first fitting distribution functions, the optimized second fitting distribution functions are determined in the plurality of second fitting distribution functions based on the information criterion method, and the second edge probability distribution functions of the health state of the retired battery are generated according to the optimized second fitting distribution functions; calculating a joint probability distribution function by Copulas functions in combination with the first edge probability distribution function and the second edge probability distribution function, wherein the Copulas functions are functions for describing the correlation between the probability distribution functions, and different edge probability distribution functions can be coupled through Copulas functions to obtain the joint probability distribution function; and calculating based on the joint probability distribution function, the first edge probability distribution function and the second edge probability distribution function to obtain a mutual information value of the state parameter and the retired battery health state, wherein the calculation expression of the mutual information value is as follows:
，
wherein X is a continuous random variable taking the state parameter of the retired battery as a continuous random variable taking the state of the retired battery as a continuous random variable, Is a mutual information value,/>As a joint probability distribution function,/>As a first edge probability distribution function,/>A second edge probability distribution function;
Taking the mutual information value as a model constraint condition, taking the mutual information value as a lower bound value of the generated model output, and constructing a generated model based on the model constraint condition; based on the generation model, a preset discrimination model is combined with a Nash equilibrium strategy to construct a generation countermeasure network, the generation countermeasure network comprises a generation model and a discrimination model, the generation model and the preset discrimination model are deep learning network models, and the capability of discriminating samples and lifting samples is improved by the generation model and the preset discrimination model which are mutually opposed by utilizing countermeasure attributes, and the Nash equilibrium strategy has the following concept: in a game process, regardless of the strategy selection of the opposite party, the party selects a certain strategy, the strategy is called a dominant strategy, if the strategy combination of the parties of two games respectively forms the respective dominant strategy, the combination is defined as Nash equilibrium, namely, when a generating model and a color number judging model form a self-learning network closed loop in the process of mutual antagonism, the Nash equilibrium is achieved, an objective function tends to converge, the Nash equilibrium is achieved, the state of charge of each retired battery is predicted based on the generating antagonism network, and the accurate prediction of the state of charge of the retired battery can be realized by alternately training the generating model and a preset judging model in the antagonism network to ensure that each model tends to converge; obtaining a clustering center based on the state of charge of each retired battery by utilizing an entropy method, carrying out initial clustering through the state of charge of each retired battery to obtain a plurality of clusters, calculating the retired battery with the highest score in each cluster through the entropy method by utilizing the state of charge of each retired battery, taking the state of charge corresponding to the retired battery with the highest score as the clustering center, carrying out gradient classification on all the retired batteries based on the clustering center, calculating the accuracy of the clustering center, if the calculated accuracy of the clustering center reaches a preset threshold value, completing gradient classification through the clustering center, if the calculated accuracy of the clustering center does not reach the preset threshold value, calculating the closest clustering center of the retired battery to be gradient classified based on a Mahalation distance method, classifying the retired battery to the closest clustering center until all the retired batteries are classified, obtaining a plurality of retired batteries with different gradient classes, wherein the states of charge corresponding to the retired batteries with different gradient are different gradient, and the states of charge are different gradient batteries, and the gradient of the rest battery and the rest battery are completely charged by the gradient parameters are not being managed, and the gradient is more difficult than the gradient of the gradient battery is managed by gradient classification.
S12: constructing initial discharge strategies corresponding to a plurality of retired batteries of each echelon class, and performing strategy optimization on the initial discharge strategies by utilizing game trees to obtain optimized discharge strategies;
In the specific implementation process of the invention, the construction of the initial discharge strategy corresponding to a plurality of retired batteries of each echelon class, and the strategy optimization of the initial discharge strategy by utilizing a game tree, the acquisition of the optimized discharge strategy comprises the following steps: acquiring historical discharge strategy data, and generating corresponding learning strategy data by using a reinforcement learning algorithm based on the echelon class corresponding to the retired battery; updating the historical discharge strategy data based on the corresponding learning strategy data to obtain updated discharge strategy data, and performing iterative processing based on the updated discharge strategy data to obtain initial discharge strategies corresponding to a plurality of retired batteries of each echelon class; performing effect evaluation on the initial discharge strategy based on a preset evaluation rule to obtain effect evaluation data; generating a corresponding game tree based on the initial discharge strategy, and determining a corresponding optimized path based on the corresponding game tree; and carrying out strategy optimization on the initial discharge strategy based on the effect evaluation data and the corresponding optimization path to obtain the optimized discharge strategy corresponding to a plurality of retired batteries of each echelon class.
Specifically, historical discharge strategy data is obtained through a local database, corresponding learning strategy data is generated by using a reinforcement learning algorithm based on echelon categories corresponding to retired batteries, the reinforcement learning algorithm modifies self strategy data by using the generated data and interacts with the environment to generate new data, the reinforcement learning algorithm with approximate functions is selected because the state space and the action space of the retired batteries corresponding to the echelon categories are larger, the corresponding learning strategy data is generated by using the reinforcement learning algorithm with approximate functions through the corresponding echelon categories, a Beeman equation is constructed according to the corresponding value function, the corresponding approximation function is calculated according to the optimality principle of the Beeman equation, iterative update is performed through the approximation function, and data interaction is performed through the approximation function after iterative update to obtain the corresponding learning strategy data; updating the historical discharge strategy data based on the corresponding learning strategy data, namely, interacting the historical discharge strategy data with the corresponding learning strategy data to obtain updated discharge strategy data, carrying out iterative processing based on the updated discharge strategy data, executing the current discharge strategy in a simulation environment through iteration of the updated discharge strategy data each time, acquiring feedback data in real time in the process of executing the current discharge strategy, and updating the discharge strategy data based on the feedback data until the iterative processing reaches the preset iteration times to obtain initial discharge strategies corresponding to a plurality of retired batteries of each echelon class; performing effect evaluation on the initial discharge strategy based on a preset evaluation rule, wherein the preset evaluation rule comprises set indexes and expert rules, and performing strategy effect evaluation on the initial discharge strategy in a simulation environment to obtain effect evaluation data, wherein the effect evaluation data can comprise the residual capacity of a battery, the discharge time, the discharge speed and the like; generating a corresponding game tree based on the initial discharging strategy, generating a plurality of child nodes corresponding to the root node by taking the initial discharging strategy as a root node, determining a target child node by taking the prior probability and the evaluation value of each child node, repeatedly generating a new child node by taking the target child node as a father node, determining the new target child node by taking the prior probability and the evaluation value of the new child node as well, carrying out connection by taking the new target child node and the root node to obtain a corresponding game path, calculating the importance value of the game path, carrying out iterative expansion by the importance value of the game path to obtain a corresponding game tree, determining a corresponding optimization path based on the corresponding game tree, calculating the importance value of the child node corresponding to the root node in the game tree, taking the child node with the largest importance value as an optimization child node, calculating the importance value of the child node corresponding to the optimization child node, taking the child node with the largest importance value as a new optimization child node, repeating the above processes until the leaf nodes reaching the game tree, and sequentially connecting the root node and all the optimization child nodes to obtain an optimization path; and carrying out strategy optimization on the initial discharge strategy based on the effect evaluation data and the corresponding optimization paths to obtain optimized discharge strategies corresponding to a plurality of retired batteries of each echelon class, wherein the optimized discharge strategies can be different in discharge rate of the retired batteries of different echelon classes and different in pressure applied to the retired batteries, so that the retired batteries of different echelon classes can achieve rapid discharge under safer conditions, the discharge is more sufficient, the optimized discharge strategies can better adapt to the actual conditions of the retired batteries, the discharge efficiency of the retired batteries is improved, and the state of the subsequent retired batteries in the discharge process is easier to control.
S13: placing a plurality of retired batteries of each echelon class in corresponding positions of battery discharging equipment, wherein the battery discharging equipment performs discharging treatment of the retired batteries based on the optimized discharging strategy;
In the implementation process of the invention, the placing of the plurality of retired batteries of each echelon class in the corresponding positions of the battery discharging equipment, the battery discharging equipment performs discharging treatment of the retired batteries based on the optimized discharging strategy, and the method comprises the following steps: acquiring the positive electrode position and the negative electrode position of each retired battery, and placing a plurality of retired batteries corresponding to the echelon class in corresponding positions of battery discharging equipment based on the positive electrode position and the negative electrode position; and acquiring a battery discharge instruction, and controlling the battery discharge equipment to perform discharge treatment of the retired battery by using the optimized discharge strategy based on the battery discharge instruction.
Specifically, acquiring a real-time image of each retired battery, acquiring an anode position and a cathode position of each retired battery through image feature recognition, and grabbing a plurality of retired batteries corresponding to echelon categories into corresponding positions of battery discharging equipment through a mechanical arm; the battery discharging equipment fixes the retired battery placed at the corresponding position, so that the anode and the cathode of the retired battery are tightly connected with the corresponding contact position, a battery discharging instruction is generated through the control terminal, the battery discharging equipment obtains the battery discharging instruction, the discharging treatment of the retired battery is carried out by optimizing the discharging strategy, different pressures are applied to the retired batteries of different echelon categories, and the discharging rate of the retired battery is controlled.
S14: during the discharging treatment of the retired battery, acquiring a temperature change value at a temperature mark point based on a plurality of monitoring sensors arranged at preset positions, judging whether the temperature change value is larger than or equal to a preset change threshold value, and determining a first pressure relief amount based on the temperature change value if the temperature change value is larger than or equal to the preset change threshold value;
In the implementation process of the present invention, if the temperature change value is greater than or equal to the preset change threshold, determining the first pressure relief amount based on the temperature change value includes: calculating a temperature change rate based on the temperature change value, and calculating a temperature mutation line based on the temperature change rate; acquiring the occurrence proportion of a high-temperature point of a region based on a database, and determining a pressure relief radius by utilizing a temperature position diagram based on the occurrence proportion of the region high Wen Dianchu; and setting a first pressure relief amount based on the pressure relief radius and the temperature abrupt change line.
Specifically, in the process of performing discharge processing of the retired battery, acquiring a temperature change value at a temperature mark point based on a plurality of monitoring sensors arranged at preset positions, wherein the temperature mark point is a preset set mark point, and because heat is generated in the battery discharge process, determining the temperature change condition of the mark point, determining whether the temperature change value at the temperature mark point is used as a judging standard for pressure relief at preset intervals, thereby avoiding the influence caused by the delay of temperature rise, determining whether the temperature change value is greater than or equal to a preset change threshold value, if the temperature change value is smaller than the preset change threshold value, no pressure relief is needed, if the temperature change value is greater than or equal to the preset change threshold value, calculating a temperature change rate based on the temperature change value, calculating a temperature change rate through preset intervals and the temperature change value, constructing a temperature change curve based on the temperature change rate, acquiring mutation rate data in the temperature change curve, acquiring a abnormal constant point sequence according to the mutation rate data, and constructing a temperature change line according to the abnormal constant point sequence; the method comprises the steps of obtaining the current proportion of the regional height Wen Dianchu in the discharging process of the past retired battery through a database, determining the pressure relief radius through the occurrence proportion of the regional high-temperature points by utilizing a temperature position diagram, wherein the temperature position diagram can reflect the region with temperature mutation, and setting a first pressure relief amount based on the pressure relief radius and a temperature mutation line.
S15: constructing a pressure-temperature time sequence incidence matrix, correcting the first pressure relief quantity based on the pressure-temperature time sequence incidence matrix to obtain a second pressure relief quantity, determining the heat dissipation medium incoming rate, and continuously performing pressure relief heat dissipation treatment on the retired battery based on the second pressure relief quantity and the heat dissipation medium incoming rate until the re-collected temperature change value is smaller than a preset change threshold.
In the implementation process of the present invention, the construction of the pressure-temperature time sequence correlation matrix, the correction of the first pressure relief amount based on the pressure-temperature time sequence correlation matrix, the obtaining of the second pressure relief amount, and the determination of the heat dissipation medium transfer rate include: analyzing the temperature change trend based on the temperature change value of the temperature mark point to obtain a temperature change trend; determining a correlation coefficient related to the corrected pressure based on the pressure distribution of the battery discharging equipment at the temperature mark point, and constructing a pressure-temperature time sequence correlation matrix based on the temperature change trend and the correlation coefficient; performing parameter guidance based on the pressure-temperature time sequence correlation matrix to obtain a pressure state time sequence matrix; correcting the first pressure relief amount based on the pressure state time sequence matrix to obtain a second pressure relief amount; and determining a heat dissipation medium acting range based on the second pressure relief amount and the preset cooling efficiency, and determining a heat dissipation medium incoming rate based on the heat dissipation medium acting range and the preset cooling efficiency.
Specifically, the first pressure relief amount is only set by the temperature abrupt change line and the current proportion of Wen Dianchu, but the relation between the change trend and the pressure and the temperature time sequence is not considered, so that the first pressure relief amount needs to be corrected to improve the heat dissipation effect, the temperature change trend analysis is performed based on the temperature change value of the temperature mark point, and the temperature change trend can be known through the rate change of the temperature change value in each time period in the preset interval time; determining a correlation coefficient related to the correction pressure based on the pressure distribution of the battery discharging equipment at the temperature mark point, determining the correlation coefficient related to the correction pressure according to the rule between the temperature change condition of the temperature mark point and the pressure value applied by the battery discharging equipment after the pressure is applied by the battery discharging equipment, constructing a pressure-temperature time sequence correlation matrix based on the temperature change trend and the correlation coefficient, performing time sequence correlation processing according to the temperature change trend and the correlation coefficient, performing correlation encoding on the temperature change trend and the correlation coefficient by adopting a cross correlation function, obtaining a first time sequence feature matrix vector corresponding to the temperature change trend and a second time sequence feature matrix vector corresponding to the correlation coefficient, calculating a position distance matrix between the first time sequence feature matrix vector and the second time sequence feature matrix vector, and constructing a pressure-temperature time sequence correlation matrix by the position distance matrix, the first time sequence feature matrix vector and the second time sequence feature matrix vector, wherein the pressure-temperature time sequence correlation matrix is as follows:
，
Wherein FT is a pressure-temperature time sequence correlation matrix, For the first time sequence characteristic matrix vector,/>For the second time sequence characteristic matrix vector,/>Is super-parameter,/>As the pressure value and the temperature have a cooperative relationship in the time dimension, parameter guidance is performed on the basis of the pressure-temperature time sequence correlation matrix, namely separation parameter guidance is performed on the pressure-temperature time sequence correlation matrix, a pressure state time sequence matrix is obtained, whether the current pressure relief amount needs to be increased or decreased can be determined through the pressure state time sequence matrix, and the first pressure relief amount is corrected through the pressure state time sequence matrix, so that a second pressure relief amount is obtained; the method comprises the steps of determining a heat dissipation medium acting range based on the second pressure relief quantity and preset cooling efficiency, obtaining the contact area of the retired battery and the heat dissipation medium through the second pressure relief quantity, determining the heat dissipation medium acting range through the contact area and the preset cooling efficiency, determining the heat dissipation medium incoming rate based on the heat dissipation medium acting range and the preset cooling efficiency, and correcting the first pressure relief quantity by constructing a pressure-temperature time sequence correlation matrix, so that the pressure relief quantity can be corrected more accurately, the second pressure relief quantity is more reliable, the heat dissipation medium incoming rate is determined through the heat dissipation medium acting range, and then pressure relief heat dissipation is carried out.
In the embodiment of the invention, the state of charge of each retired battery is predicted through the mutual information value calculated by the state parameters and the state of health of the retired battery, the retired batteries are classified in a gradient manner through the state of charge, the gradient classification can be more accurately divided, the retired batteries in different gradient classifications are prevented from being mixed together, the individual retired batteries are not fully discharged, a corresponding optimized discharging strategy is constructed for a plurality of retired batteries in each gradient classification, the discharging strategy can be better adapted to the actual condition of the retired batteries, the discharging efficiency of the retired batteries is improved, the state in the discharging process of the subsequent retired batteries is easier to control, the influence caused by the delay of the temperature rise is avoided through constructing a pressure-temperature time sequence association matrix, the first pressure relief quantity can be corrected more accurately, the second pressure relief quantity is more reliable, the medium is transmitted into the speed through the action range of a heat dissipation medium, the discharging efficiency of the retired batteries is improved, the discharging efficiency of the retired batteries is only reduced to the ideal discharging effect is achieved, and the discharging effect of the retired batteries is not improved.
Example two
Referring to fig. 2, fig. 2 is a schematic structural diagram of a discharging state optimizing device for retired battery according to an embodiment of the invention.
As shown in fig. 2, a discharge state optimizing apparatus for retired batteries, the apparatus comprising:
The echelon classification module 21: the method comprises the steps of obtaining state parameters of retired batteries, calculating mutual information values of the state parameters and the health states of the retired batteries, predicting the charge state of each retired battery based on the mutual information values, and classifying all the retired batteries in a echelon mode based on the charge states to obtain a plurality of retired batteries in each echelon mode;
In the implementation process of the invention, the calculating the mutual information value of the state parameter and the retired battery health state comprises the following steps: calculating a first edge probability distribution function of the state parameter and a second edge probability distribution function of the retired battery health state; calculating a joint probability distribution function based on the first and second edge probability distribution functions; and calculating based on the joint probability distribution function, the first edge probability distribution function and the second edge probability distribution function to obtain a mutual information value of the state parameter and the retired battery health state.
Further, predicting the state of charge of each retired battery based on the mutual information value, classifying all retired batteries in a echelon based on the state of charge, and obtaining a plurality of retired batteries in each echelon category, including: taking the mutual information value as a model constraint condition, and constructing a generation model based on the model constraint condition; constructing a generated countermeasure network by utilizing a preset discrimination model and combining a Nash equilibrium strategy based on the generated model, and predicting the charge state of each retired battery based on the generated countermeasure network; and acquiring a clustering center based on the state of charge of each retired battery by using an entropy method, and performing echelon classification on all retired batteries based on the clustering center to acquire a plurality of retired batteries in each echelon class.
Specifically, state parameters of the retired battery are obtained through a data sensor, the state parameters of the retired battery comprise voltage data and current data, the state parameters are respectively fitted with a plurality of continuous probability distribution functions to obtain a plurality of first fitting distribution functions, the health state of the retired battery is respectively fitted with the plurality of continuous probability distribution functions to obtain a plurality of second fitting distribution functions, the optimized first fitting distribution functions are determined in the plurality of first fitting distribution functions based on an information criterion method, the first edge probability distribution functions of the state parameters are generated according to the optimized first fitting distribution functions, the optimized second fitting distribution functions are determined in the plurality of second fitting distribution functions based on the information criterion method, and the second edge probability distribution functions of the health state of the retired battery are generated according to the optimized second fitting distribution functions; calculating a joint probability distribution function by Copulas functions in combination with the first edge probability distribution function and the second edge probability distribution function, wherein the Copulas functions are functions for describing the correlation between the probability distribution functions, and different edge probability distribution functions can be coupled through Copulas functions to obtain the joint probability distribution function; and calculating based on the joint probability distribution function, the first edge probability distribution function and the second edge probability distribution function to obtain a mutual information value of the state parameter and the retired battery health state, wherein the calculation expression of the mutual information value is as follows:
，
wherein X is a continuous random variable taking the state parameter of the retired battery as a continuous random variable taking the state of the retired battery as a continuous random variable, Is a mutual information value,/>As a joint probability distribution function,/>As a first edge probability distribution function,/>A second edge probability distribution function;
Taking the mutual information value as a model constraint condition, taking the mutual information value as a lower bound value of the generated model output, and constructing a generated model based on the model constraint condition; based on the generation model, a preset discrimination model is combined with a Nash equilibrium strategy to construct a generation countermeasure network, the generation countermeasure network comprises a generation model and a discrimination model, the generation model and the preset discrimination model are deep learning network models, and the capability of discriminating samples and lifting samples is improved by the generation model and the preset discrimination model which are mutually opposed by utilizing countermeasure attributes, and the Nash equilibrium strategy has the following concept: in a game process, regardless of the strategy selection of the opposite party, the party selects a certain strategy, the strategy is called a dominant strategy, if the strategy combination of the parties of two games respectively forms the respective dominant strategy, the combination is defined as Nash equilibrium, namely, when a generating model and a color number judging model form a self-learning network closed loop in the process of mutual antagonism, the Nash equilibrium is achieved, an objective function tends to converge, the Nash equilibrium is achieved, the state of charge of each retired battery is predicted based on the generating antagonism network, and the accurate prediction of the state of charge of the retired battery can be realized by alternately training the generating model and a preset judging model in the antagonism network to ensure that each model tends to converge; obtaining a clustering center based on the state of charge of each retired battery by utilizing an entropy method, carrying out initial clustering through the state of charge of each retired battery to obtain a plurality of clusters, calculating the retired battery with the highest score in each cluster through the entropy method by utilizing the state of charge of each retired battery, taking the state of charge corresponding to the retired battery with the highest score as the clustering center, carrying out gradient classification on all the retired batteries based on the clustering center, calculating the accuracy of the clustering center, if the calculated accuracy of the clustering center reaches a preset threshold value, completing gradient classification through the clustering center, if the calculated accuracy of the clustering center does not reach the preset threshold value, calculating the closest clustering center of the retired battery to be gradient classified based on a Mahalation distance method, classifying the retired battery to the closest clustering center until all the retired batteries are classified, obtaining a plurality of retired batteries with different gradient classes, wherein the states of charge corresponding to the retired batteries with different gradient are different gradient, and the states of charge are different gradient batteries, and the gradient of the rest battery and the rest battery are completely charged by the gradient parameters are not being managed, and the gradient is more difficult than the gradient of the gradient battery is managed by gradient classification.
The discharge strategy generation module 22: the method comprises the steps of constructing initial discharge strategies corresponding to a plurality of retired batteries of each echelon class, and performing strategy optimization on the initial discharge strategies by utilizing game trees to obtain optimized discharge strategies;
In the specific implementation process of the invention, the construction of the initial discharge strategy corresponding to a plurality of retired batteries of each echelon class, and the strategy optimization of the initial discharge strategy by utilizing a game tree, the acquisition of the optimized discharge strategy comprises the following steps: acquiring historical discharge strategy data, and generating corresponding learning strategy data by using a reinforcement learning algorithm based on the echelon class corresponding to the retired battery; updating the historical discharge strategy data based on the corresponding learning strategy data to obtain updated discharge strategy data, and performing iterative processing based on the updated discharge strategy data to obtain initial discharge strategies corresponding to a plurality of retired batteries of each echelon class; performing effect evaluation on the initial discharge strategy based on a preset evaluation rule to obtain effect evaluation data; generating a corresponding game tree based on the initial discharge strategy, and determining a corresponding optimized path based on the corresponding game tree; and carrying out strategy optimization on the initial discharge strategy based on the effect evaluation data and the corresponding optimization path to obtain the optimized discharge strategy corresponding to a plurality of retired batteries of each echelon class.
Specifically, historical discharge strategy data is obtained through a local database, corresponding learning strategy data is generated by using a reinforcement learning algorithm based on echelon categories corresponding to retired batteries, the reinforcement learning algorithm modifies self strategy data by using the generated data and interacts with the environment to generate new data, the reinforcement learning algorithm with approximate functions is selected because the state space and the action space of the retired batteries corresponding to the echelon categories are larger, the corresponding learning strategy data is generated by using the reinforcement learning algorithm with approximate functions through the corresponding echelon categories, a Beeman equation is constructed according to the corresponding value function, the corresponding approximation function is calculated according to the optimality principle of the Beeman equation, iterative update is performed through the approximation function, and data interaction is performed through the approximation function after iterative update to obtain the corresponding learning strategy data; updating the historical discharge strategy data based on the corresponding learning strategy data, namely, interacting the historical discharge strategy data with the corresponding learning strategy data to obtain updated discharge strategy data, carrying out iterative processing based on the updated discharge strategy data, executing the current discharge strategy in a simulation environment through iteration of the updated discharge strategy data each time, acquiring feedback data in real time in the process of executing the current discharge strategy, and updating the discharge strategy data based on the feedback data until the iterative processing reaches the preset iteration times to obtain initial discharge strategies corresponding to a plurality of retired batteries of each echelon class; performing effect evaluation on the initial discharge strategy based on a preset evaluation rule, wherein the preset evaluation rule comprises set indexes and expert rules, and performing strategy effect evaluation on the initial discharge strategy in a simulation environment to obtain effect evaluation data, wherein the effect evaluation data can comprise the residual capacity of a battery, the discharge time, the discharge speed and the like; generating a corresponding game tree based on the initial discharging strategy, generating a plurality of child nodes corresponding to the root node by taking the initial discharging strategy as a root node, determining a target child node by taking the prior probability and the evaluation value of each child node, repeatedly generating a new child node by taking the target child node as a father node, determining the new target child node by taking the prior probability and the evaluation value of the new child node as well, carrying out connection by taking the new target child node and the root node to obtain a corresponding game path, calculating the importance value of the game path, carrying out iterative expansion by the importance value of the game path to obtain a corresponding game tree, determining a corresponding optimization path based on the corresponding game tree, calculating the importance value of the child node corresponding to the root node in the game tree, taking the child node with the largest importance value as an optimization child node, calculating the importance value of the child node corresponding to the optimization child node, taking the child node with the largest importance value as a new optimization child node, repeating the above processes until the leaf nodes reaching the game tree, and sequentially connecting the root node and all the optimization child nodes to obtain an optimization path; and carrying out strategy optimization on the initial discharge strategy based on the effect evaluation data and the corresponding optimization paths to obtain optimized discharge strategies corresponding to a plurality of retired batteries of each echelon class, wherein the optimized discharge strategies can be different in discharge rate of the retired batteries of different echelon classes and different in pressure applied to the retired batteries, so that the retired batteries of different echelon classes can achieve rapid discharge under safer conditions, the discharge is more sufficient, the optimized discharge strategies can better adapt to the actual conditions of the retired batteries, the discharge efficiency of the retired batteries is improved, and the state of the subsequent retired batteries in the discharge process is easier to control.
Discharge processing module 23: the battery discharging device is used for discharging the retired batteries of each echelon class in the corresponding position of the battery discharging device, and the battery discharging device performs discharging treatment of the retired batteries based on the optimized discharging strategy;
In the implementation process of the invention, the placing of the plurality of retired batteries of each echelon class in the corresponding positions of the battery discharging equipment, the battery discharging equipment performs discharging treatment of the retired batteries based on the optimized discharging strategy, and the method comprises the following steps: acquiring the positive electrode position and the negative electrode position of each retired battery, and placing a plurality of retired batteries corresponding to the echelon class in corresponding positions of battery discharging equipment based on the positive electrode position and the negative electrode position; and acquiring a battery discharge instruction, and controlling the battery discharge equipment to perform discharge treatment of the retired battery by using the optimized discharge strategy based on the battery discharge instruction.
Specifically, acquiring a real-time image of each retired battery, acquiring an anode position and a cathode position of each retired battery through image feature recognition, and grabbing a plurality of retired batteries corresponding to echelon categories into corresponding positions of battery discharging equipment through a mechanical arm; the battery discharging equipment fixes the retired battery placed at the corresponding position, so that the anode and the cathode of the retired battery are tightly connected with the corresponding contact position, a battery discharging instruction is generated through the control terminal, the battery discharging equipment obtains the battery discharging instruction, the discharging treatment of the retired battery is carried out by optimizing the discharging strategy, different pressures are applied to the retired batteries of different echelon categories, and the discharging rate of the retired battery is controlled.
The first pressure relief amount determination module 24: the method comprises the steps that in the discharging process of a retired battery, a plurality of monitoring sensors arranged at preset positions are used for collecting temperature change values at temperature mark points, judging whether the temperature change values are larger than or equal to a preset change threshold value or not, and if the temperature change values are larger than or equal to the preset change threshold value, determining a first pressure relief amount based on the temperature change values;
In the implementation process of the present invention, if the temperature change value is greater than or equal to the preset change threshold, determining the first pressure relief amount based on the temperature change value includes: calculating a temperature change rate based on the temperature change value, and calculating a temperature mutation line based on the temperature change rate; acquiring the occurrence proportion of a high-temperature point of a region based on a database, and determining a pressure relief radius by utilizing a temperature position diagram based on the occurrence proportion of the region high Wen Dianchu; and setting a first pressure relief amount based on the pressure relief radius and the temperature abrupt change line.
Specifically, in the process of performing discharge processing of the retired battery, acquiring a temperature change value at a temperature mark point based on a plurality of monitoring sensors arranged at preset positions, wherein the temperature mark point is a preset set mark point, and because heat is generated in the battery discharge process, determining the temperature change condition of the mark point, determining whether the temperature change value at the temperature mark point is used as a judging standard for pressure relief at preset intervals, thereby avoiding the influence caused by the delay of temperature rise, determining whether the temperature change value is greater than or equal to a preset change threshold value, if the temperature change value is smaller than the preset change threshold value, no pressure relief is needed, if the temperature change value is greater than or equal to the preset change threshold value, calculating a temperature change rate based on the temperature change value, calculating a temperature change rate through preset intervals and the temperature change value, constructing a temperature change curve based on the temperature change rate, acquiring mutation rate data in the temperature change curve, acquiring a abnormal constant point sequence according to the mutation rate data, and constructing a temperature change line according to the abnormal constant point sequence; the method comprises the steps of obtaining the current proportion of the regional height Wen Dianchu in the discharging process of the past retired battery through a database, determining the pressure relief radius through the occurrence proportion of the regional high-temperature points by utilizing a temperature position diagram, wherein the temperature position diagram can reflect the region with temperature mutation, and setting a first pressure relief amount based on the pressure relief radius and a temperature mutation line.
Pressure relief heat dissipation module 25: the method comprises the steps of constructing a pressure-temperature time sequence incidence matrix, correcting the first pressure relief quantity based on the pressure-temperature time sequence incidence matrix, obtaining a second pressure relief quantity, determining a heat dissipation medium incoming rate, and continuously performing pressure relief heat dissipation treatment on the retired battery based on the second pressure relief quantity and the heat dissipation medium incoming rate until a re-collected temperature change value is smaller than a preset change threshold.
In the implementation process of the present invention, the construction of the pressure-temperature time sequence correlation matrix, the correction of the first pressure relief amount based on the pressure-temperature time sequence correlation matrix, the obtaining of the second pressure relief amount, and the determination of the heat dissipation medium transfer rate include: analyzing the temperature change trend based on the temperature change value of the temperature mark point to obtain a temperature change trend; determining a correlation coefficient related to the corrected pressure based on the pressure distribution of the battery discharging equipment at the temperature mark point, and constructing a pressure-temperature time sequence correlation matrix based on the temperature change trend and the correlation coefficient; performing parameter guidance based on the pressure-temperature time sequence correlation matrix to obtain a pressure state time sequence matrix; correcting the first pressure relief amount based on the pressure state time sequence matrix to obtain a second pressure relief amount; and determining a heat dissipation medium acting range based on the second pressure relief amount and the preset cooling efficiency, and determining a heat dissipation medium incoming rate based on the heat dissipation medium acting range and the preset cooling efficiency.
Specifically, the first pressure relief amount is only set by the temperature abrupt change line and the current proportion of Wen Dianchu, but the relation between the change trend and the pressure and the temperature time sequence is not considered, so that the first pressure relief amount needs to be corrected to improve the heat dissipation effect, the temperature change trend analysis is performed based on the temperature change value of the temperature mark point, and the temperature change trend can be known through the rate change of the temperature change value in each time period in the preset interval time; determining a correlation coefficient related to the correction pressure based on the pressure distribution of the battery discharging equipment at the temperature mark point, determining the correlation coefficient related to the correction pressure according to the rule between the temperature change condition of the temperature mark point and the pressure value applied by the battery discharging equipment after the pressure is applied by the battery discharging equipment, constructing a pressure-temperature time sequence correlation matrix based on the temperature change trend and the correlation coefficient, performing time sequence correlation processing according to the temperature change trend and the correlation coefficient, performing correlation encoding on the temperature change trend and the correlation coefficient by adopting a cross correlation function, obtaining a first time sequence feature matrix vector corresponding to the temperature change trend and a second time sequence feature matrix vector corresponding to the correlation coefficient, calculating a position distance matrix between the first time sequence feature matrix vector and the second time sequence feature matrix vector, and constructing a pressure-temperature time sequence correlation matrix by the position distance matrix, the first time sequence feature matrix vector and the second time sequence feature matrix vector, wherein the pressure-temperature time sequence correlation matrix is as follows:
，
Wherein FT is a pressure-temperature time sequence correlation matrix, For the first time sequence characteristic matrix vector,/>For the second time sequence characteristic matrix vector,/>Is super-parameter,/>As the pressure value and the temperature have a cooperative relationship in the time dimension, parameter guidance is performed on the basis of the pressure-temperature time sequence correlation matrix, namely separation parameter guidance is performed on the pressure-temperature time sequence correlation matrix, a pressure state time sequence matrix is obtained, whether the current pressure relief amount needs to be increased or decreased can be determined through the pressure state time sequence matrix, and the first pressure relief amount is corrected through the pressure state time sequence matrix, so that a second pressure relief amount is obtained; the method comprises the steps of determining a heat dissipation medium acting range based on the second pressure relief quantity and preset cooling efficiency, obtaining the contact area of the retired battery and the heat dissipation medium through the second pressure relief quantity, determining the heat dissipation medium acting range through the contact area and the preset cooling efficiency, determining the heat dissipation medium incoming rate based on the heat dissipation medium acting range and the preset cooling efficiency, and correcting the first pressure relief quantity by constructing a pressure-temperature time sequence correlation matrix, so that the pressure relief quantity can be corrected more accurately, the second pressure relief quantity is more reliable, the heat dissipation medium incoming rate is determined through the heat dissipation medium acting range, and then pressure relief heat dissipation is carried out.
In the embodiment of the invention, the state of charge of each retired battery is predicted through the mutual information value calculated by the state parameters and the state of health of the retired battery, the retired batteries are classified in a gradient manner through the state of charge, the gradient classification can be more accurately divided, the retired batteries in different gradient classifications are prevented from being mixed together, the individual retired batteries are not fully discharged, a corresponding optimized discharging strategy is constructed for a plurality of retired batteries in each gradient classification, the discharging strategy can be better adapted to the actual condition of the retired batteries, the discharging efficiency of the retired batteries is improved, the state in the discharging process of the subsequent retired batteries is easier to control, the influence caused by the delay of the temperature rise is avoided through constructing a pressure-temperature time sequence association matrix, the first pressure relief quantity can be corrected more accurately, the second pressure relief quantity is more reliable, the medium is transmitted into the speed through the action range of a heat dissipation medium, the discharging efficiency of the retired batteries is improved, the discharging efficiency of the retired batteries is only reduced to the ideal discharging effect is achieved, and the discharging effect of the retired batteries is not improved.
An embodiment of the present invention provides a computer readable storage medium, where a computer program is stored, where the program when executed by a processor implements the method for optimizing a discharge state of a retired battery according to any one of the embodiments. The computer readable storage medium includes, but is not limited to, any type of disk including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks, ROMs (Read-Only memories), RAMs (Random AcceSS Memory, random access memories), EPROMs (EraSable Programmable Read-Only memories), EEPROMs (ELECTRICALLY ERASABLE PROGRAMMABLEREAD-Only memories), flash memories, magnetic cards, or optical cards. That is, a storage device includes any medium that stores or transmits information in a form readable by a device (e.g., computer, cell phone), and may be read-only memory, magnetic or optical disk, etc.
Example III
Referring to fig. 3, fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the invention.
The embodiment of the invention also provides an electronic device comprising a memory 31, a processor 33 and a computer program 32 stored in the memory 31 and executable on the processor 33, as shown in fig. 3. Those skilled in the art will appreciate that the electronic device shown in fig. 3 does not constitute a limitation of all devices, and may include more or fewer components than shown, or may combine certain components. The memory 31 may be used to store a computer program 32 and functional modules, and the processor 33 runs the computer program 32 stored in the memory 31 to perform various functional applications of the device and data processing. The memory may be internal memory or external memory, or include both internal memory and external memory. The internal memory may include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), flash memory, or random access memory. The external memory may include a hard disk, floppy disk, ZIP disk, U-disk, tape, etc. The Processor 33 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor, a single-chip microcomputer or the processor 33 may be any conventional processor or the like. The processors and memories disclosed herein include, but are not limited to, these types of processors and memories. The processors and memories disclosed herein are by way of example only and not by way of limitation.
As one embodiment, the electronic device includes: the system includes one or more processors 33, a memory 31, and one or more computer programs 32, wherein the one or more computer programs 32 are stored in the memory 31 and configured to be executed by the one or more processors 33, and the one or more computer programs 32 are configured to execute the method for optimizing the discharge state of the retired battery in any of the foregoing embodiments, and the specific implementation procedure is referred to the foregoing embodiments and is not repeated herein.
In the embodiment of the invention, the state of charge of each retired battery is predicted through the mutual information value calculated by the state parameters and the state of health of the retired battery, the retired batteries are classified in a gradient manner through the state of charge, the gradient classification can be more accurately divided, the retired batteries in different gradient classifications are prevented from being mixed together, the individual retired batteries are not fully discharged, a corresponding optimized discharging strategy is constructed for a plurality of retired batteries in each gradient classification, the discharging strategy can be better adapted to the actual condition of the retired batteries, the discharging efficiency of the retired batteries is improved, the state in the discharging process of the subsequent retired batteries is easier to control, the influence caused by the delay of the temperature rise is avoided through constructing a pressure-temperature time sequence association matrix, the first pressure relief quantity can be corrected more accurately, the second pressure relief quantity is more reliable, the medium is transmitted into the speed through the action range of a heat dissipation medium, the discharging efficiency of the retired batteries is improved, the discharging efficiency of the retired batteries is only reduced to the ideal discharging effect is achieved, and the discharging effect of the retired batteries is not improved.
In addition, the above description has been made in detail of a method for optimizing a discharge state of a retired battery and a related device according to the embodiments of the present invention, and specific examples should be adopted to illustrate the principles and embodiments of the present invention, where the description of the above examples is only for helping to understand the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (8)
1. A method for optimizing the discharge state of a retired battery, the method comprising:
Acquiring state parameters of the retired batteries, calculating mutual information values of the state parameters and the health states of the retired batteries, predicting the charge state of each retired battery based on the mutual information values, and classifying all the retired batteries in a echelon manner based on the charge states to obtain a plurality of retired batteries in each echelon category;
Constructing initial discharge strategies corresponding to a plurality of retired batteries of each echelon class, and performing strategy optimization on the initial discharge strategies by utilizing game trees to obtain optimized discharge strategies;
Placing a plurality of retired batteries of each echelon class in corresponding positions of battery discharging equipment, wherein the battery discharging equipment performs discharging treatment of the retired batteries based on the optimized discharging strategy;
During the discharging treatment of the retired battery, acquiring a temperature change value at a temperature mark point based on a plurality of monitoring sensors arranged at preset positions, judging whether the temperature change value is larger than or equal to a preset change threshold value, and determining a first pressure relief amount based on the temperature change value if the temperature change value is larger than or equal to the preset change threshold value;
Constructing a pressure-temperature time sequence incidence matrix, correcting the first pressure relief quantity based on the pressure-temperature time sequence incidence matrix to obtain a second pressure relief quantity, determining a heat dissipation medium transmission rate, and continuously performing pressure relief heat dissipation treatment on the retired battery based on the second pressure relief quantity and the heat dissipation medium transmission rate until a re-collected temperature change value is smaller than a preset change threshold;
The method for constructing the initial discharge strategy corresponding to the plurality of retired batteries of each echelon category, and carrying out strategy optimization on the initial discharge strategy by utilizing a game tree to obtain an optimized discharge strategy comprises the following steps: acquiring historical discharge strategy data, and generating corresponding learning strategy data by using a reinforcement learning algorithm based on the echelon class corresponding to the retired battery; updating the historical discharge strategy data based on the corresponding learning strategy data to obtain updated discharge strategy data, and performing iterative processing based on the updated discharge strategy data to obtain initial discharge strategies corresponding to a plurality of retired batteries of each echelon class; performing effect evaluation on the initial discharge strategy based on a preset evaluation rule to obtain effect evaluation data; generating a corresponding game tree based on the initial discharge strategy, and determining a corresponding optimized path based on the corresponding game tree; performing strategy optimization on the initial discharge strategy based on the effect evaluation data and the corresponding optimization path to obtain an optimized discharge strategy corresponding to a plurality of retired batteries of each echelon class;
The constructing a pressure-temperature time sequence correlation matrix, correcting the first pressure relief amount based on the pressure-temperature time sequence correlation matrix to obtain a second pressure relief amount, and determining the heat dissipation medium incoming rate, including: analyzing the temperature change trend based on the temperature change value of the temperature mark point to obtain a temperature change trend; determining a correlation coefficient related to the corrected pressure based on the pressure distribution of the battery discharging equipment at the temperature mark point, and constructing a pressure-temperature time sequence correlation matrix based on the temperature change trend and the correlation coefficient; performing parameter guidance based on the pressure-temperature time sequence correlation matrix to obtain a pressure state time sequence matrix; correcting the first pressure relief amount based on the pressure state time sequence matrix to obtain a second pressure relief amount; and determining a heat dissipation medium acting range based on the second pressure relief amount and the preset cooling efficiency, and determining a heat dissipation medium incoming rate based on the heat dissipation medium acting range and the preset cooling efficiency.
2. The method for optimizing the discharge state of a retired battery according to claim 1, wherein the calculating the mutual information value between the state parameter and the retired battery state of health includes:
calculating a first edge probability distribution function of the state parameter and a second edge probability distribution function of the retired battery health state;
calculating a joint probability distribution function based on the first and second edge probability distribution functions;
And calculating based on the joint probability distribution function, the first edge probability distribution function and the second edge probability distribution function to obtain a mutual information value of the state parameter and the retired battery health state.
3. The method for optimizing the discharge state of retired batteries according to claim 1, wherein predicting the state of charge of each retired battery based on the mutual information value, classifying all retired batteries in a ladder based on the state of charge, and obtaining a plurality of retired batteries in each ladder category comprises:
taking the mutual information value as a model constraint condition, and constructing a generation model based on the model constraint condition;
Constructing a generated countermeasure network by utilizing a preset discrimination model and combining a Nash equilibrium strategy based on the generated model, and predicting the charge state of each retired battery based on the generated countermeasure network;
and acquiring a clustering center based on the state of charge of each retired battery by using an entropy method, and performing echelon classification on all retired batteries based on the clustering center to acquire a plurality of retired batteries in each echelon class.
4. The method for optimizing a discharge state of a retired battery according to claim 1, wherein the placing of a plurality of retired batteries of each echelon class in corresponding positions of a battery discharging device, which performs a discharge process of retired batteries based on the optimized discharge strategy, comprises:
Acquiring the positive electrode position and the negative electrode position of each retired battery, and placing a plurality of retired batteries corresponding to the echelon class in corresponding positions of battery discharging equipment based on the positive electrode position and the negative electrode position;
and acquiring a battery discharge instruction, and controlling the battery discharge equipment to perform discharge treatment of the retired battery by using the optimized discharge strategy based on the battery discharge instruction.
5. The method of optimizing a discharge state of a retired battery according to claim 1, wherein determining a first pressure relief amount based on the temperature change value if the temperature change value is greater than or equal to the preset change threshold value comprises:
calculating a temperature change rate based on the temperature change value, and calculating a temperature mutation line based on the temperature change rate;
Acquiring the occurrence proportion of a high-temperature point of a region based on a database, and determining a pressure relief radius by utilizing a temperature position diagram based on the occurrence proportion of the region high Wen Dianchu;
And setting a first pressure relief amount based on the pressure relief radius and the temperature abrupt change line.
6. A discharge state optimizing apparatus for retired batteries, the apparatus comprising:
The echelon classification module: the method comprises the steps of obtaining state parameters of retired batteries, calculating mutual information values of the state parameters and the health states of the retired batteries, predicting the charge state of each retired battery based on the mutual information values, and classifying all the retired batteries in a echelon mode based on the charge states to obtain a plurality of retired batteries in each echelon mode;
a discharge strategy generation module: the method comprises the steps of constructing initial discharge strategies corresponding to a plurality of retired batteries of each echelon class, and performing strategy optimization on the initial discharge strategies by utilizing game trees to obtain optimized discharge strategies;
and a discharge processing module: the battery discharging device is used for discharging the retired batteries of each echelon class in the corresponding position of the battery discharging device, and the battery discharging device performs discharging treatment of the retired batteries based on the optimized discharging strategy;
A first pressure relief amount determination module: the method comprises the steps that in the discharging process of a retired battery, a plurality of monitoring sensors arranged at preset positions are used for collecting temperature change values at temperature mark points, judging whether the temperature change values are larger than or equal to a preset change threshold value or not, and if the temperature change values are larger than or equal to the preset change threshold value, determining a first pressure relief amount based on the temperature change values;
Pressure relief heat dissipation module: the method comprises the steps of constructing a pressure-temperature time sequence incidence matrix, correcting the first pressure relief quantity based on the pressure-temperature time sequence incidence matrix, obtaining a second pressure relief quantity, determining a heat dissipation medium incoming rate, and continuously performing pressure relief heat dissipation treatment on the retired battery based on the second pressure relief quantity and the heat dissipation medium incoming rate until a re-collected temperature change value is smaller than a preset change threshold;
The method for constructing the initial discharge strategy corresponding to the plurality of retired batteries of each echelon category, and carrying out strategy optimization on the initial discharge strategy by utilizing a game tree to obtain an optimized discharge strategy comprises the following steps: acquiring historical discharge strategy data, and generating corresponding learning strategy data by using a reinforcement learning algorithm based on the echelon class corresponding to the retired battery; updating the historical discharge strategy data based on the corresponding learning strategy data to obtain updated discharge strategy data, and performing iterative processing based on the updated discharge strategy data to obtain initial discharge strategies corresponding to a plurality of retired batteries of each echelon class; performing effect evaluation on the initial discharge strategy based on a preset evaluation rule to obtain effect evaluation data; generating a corresponding game tree based on the initial discharge strategy, and determining a corresponding optimized path based on the corresponding game tree; performing strategy optimization on the initial discharge strategy based on the effect evaluation data and the corresponding optimization path to obtain an optimized discharge strategy corresponding to a plurality of retired batteries of each echelon class;
The constructing a pressure-temperature time sequence correlation matrix, correcting the first pressure relief amount based on the pressure-temperature time sequence correlation matrix to obtain a second pressure relief amount, and determining the heat dissipation medium incoming rate, including: analyzing the temperature change trend based on the temperature change value of the temperature mark point to obtain a temperature change trend; determining a correlation coefficient related to the corrected pressure based on the pressure distribution of the battery discharging equipment at the temperature mark point, and constructing a pressure-temperature time sequence correlation matrix based on the temperature change trend and the correlation coefficient; performing parameter guidance based on the pressure-temperature time sequence correlation matrix to obtain a pressure state time sequence matrix; correcting the first pressure relief amount based on the pressure state time sequence matrix to obtain a second pressure relief amount; and determining a heat dissipation medium acting range based on the second pressure relief amount and the preset cooling efficiency, and determining a heat dissipation medium incoming rate based on the heat dissipation medium acting range and the preset cooling efficiency.
7. An electronic device comprising a processor and a memory, wherein the memory is configured to store instructions, and wherein the processor is configured to invoke the instructions in the memory, such that the electronic device performs the method of optimizing the discharge state of retired batteries according to any one of claims 1-5.
8. A computer readable storage medium storing computer instructions that, when run on an electronic device, cause the electronic device to perform the retired battery discharge state optimization method of any one of claims 1-5.
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