WO2024077587A1 - Procédé de prédiction de performance de batterie et procédé de prédiction de distribution de performance de batterie - Google Patents

Procédé de prédiction de performance de batterie et procédé de prédiction de distribution de performance de batterie Download PDF

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WO2024077587A1
WO2024077587A1 PCT/CN2022/125298 CN2022125298W WO2024077587A1 WO 2024077587 A1 WO2024077587 A1 WO 2024077587A1 CN 2022125298 W CN2022125298 W CN 2022125298W WO 2024077587 A1 WO2024077587 A1 WO 2024077587A1
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battery
battery performance
process parameters
manufacturing
performance
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PCT/CN2022/125298
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Chinese (zh)
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李晓彤
吴兴远
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宁德时代新能源科技股份有限公司
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Publication of WO2024077587A1 publication Critical patent/WO2024077587A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • 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

Definitions

  • the present application relates to the field of batteries, and specifically to a battery performance prediction method, device, computer equipment, storage medium and computer program product; it also relates to a battery performance distribution prediction method, device, computer equipment, storage medium and computer program product.
  • the present application provides a battery performance prediction method, apparatus, computer equipment, storage medium and computer program product to achieve accurate prediction of battery performance; and provides a battery performance distribution prediction method, apparatus, computer equipment, storage medium and computer program product to achieve accurate prediction of battery performance distribution.
  • the present application provides a battery performance prediction method, the method comprising:
  • the battery performance of the battery is obtained.
  • the process parameters of battery manufacturing are first obtained, and then the correction value of the influence of the manufacturing process corresponding to the process parameters on the battery performance of the battery is obtained, and finally the battery performance of the battery is obtained according to the correction value.
  • the influence of the manufacturing process corresponding to the process parameters on the battery performance is considered, and the battery performance of the battery can be accurately predicted.
  • obtaining a correction value of the effect of the manufacturing process corresponding to the process parameter on the battery performance of the battery includes:
  • the trained battery correction value prediction model is used to process the process parameters to obtain the correction value of the impact of the manufacturing process corresponding to the process parameters on the battery performance of the battery.
  • a trained battery correction value prediction model is used to accurately obtain the impact correction value corresponding to the process parameters.
  • the model fully explores the influence of different process parameters on battery performance, and can obtain the accurate correction value of the impact of the manufacturing process corresponding to the process parameters on the battery performance of the battery. Therefore, the battery performance of the battery can be accurately predicted in the end.
  • the method of obtaining the battery correction value prediction model includes:
  • the battery performance prediction value prediction model is trained based on the correspondence between the process parameters of battery manufacturing and the battery performance prediction value.
  • the battery performance prediction value prediction model is trained based on the correspondence between the process parameters of battery manufacturing and the battery performance prediction values, so that the battery performance prediction value prediction model can accurately characterize the influence of the process parameters on the battery performance. Therefore, the battery performance prediction value prediction model finally trained can support the accurate prediction of the correction value of the influence of the process parameters on the battery performance.
  • the method of obtaining the battery correction value prediction model includes:
  • the battery performance of several sample batteries is obtained, and the corresponding relationship between the process parameters and the battery performance is generated;
  • the machine learning model is trained according to the correspondence between process parameters and battery performance to obtain a battery correction value prediction model.
  • sample process parameters of several sample batteries are first obtained, and the corresponding relationship between the battery performance of the sample batteries is obtained according to the sample process parameters, and the corresponding relationship between the process parameters and the battery performance of the sample batteries is constructed.
  • the machine learning model is trained with the constructed corresponding relationship to obtain a battery correction value prediction model.
  • machine learning training is carried out using sample data, and the battery correction value prediction model finally trained can accurately characterize the corresponding relationship between the process parameters and the battery performance, and thus can support the final accurate prediction of the battery performance of the battery.
  • the battery performance of several sample batteries is obtained, and the corresponding relationship between the process parameters and the battery performance is generated, including:
  • the P2D electrochemical-thermal coupling model was used to process several sample process parameters to obtain the battery performance of each sample battery;
  • the sample process parameters and battery performance are associated to obtain the corresponding relationship between the process parameters and battery performance of the sample battery.
  • the P2D electrochemical-thermal coupling model is used to process several sample process parameters respectively to obtain the battery performance of each sample battery, and then the battery performance of each sample battery is associated with the sample process parameters, that is, the corresponding relationship between the process parameters and the battery performance of the sample battery is obtained.
  • the battery performance of the sample battery can be quickly obtained through the P2D electrochemical-thermal coupling model, thereby improving the processing efficiency.
  • the P2D electrochemical-thermal coupling model is obtained by:
  • test process parameters are processed through the initial P2D electrochemical-thermal coupling model to obtain the initial battery performance prediction results corresponding to the test process parameters;
  • the initial P2D electrochemical-thermal coupling model was optimized according to the test battery performance and the initial battery performance prediction results to obtain the P2D electrochemical-thermal coupling model.
  • the initial P2D electrochemical-thermal coupling model is optimized based on the test cell performance corresponding to the measured test process parameters to obtain a more accurate P2D electrochemical-thermal coupling model that better meets the scenario requirements.
  • the initial P2D electrochemical-thermal coupling model is optimized according to the test battery performance and the initial battery performance prediction results to obtain the P2D electrochemical-thermal coupling model, including:
  • the initial P2D electrochemical-thermal coupling model is adjusted according to the correction factor to obtain the P2D electrochemical-thermal coupling model.
  • the test battery performance is compared with the initial battery performance prediction results to obtain battery performance difference data, identify the fluctuating process parameters corresponding to the battery performance difference, and obtain the correction factor corresponding to the indicator.
  • the initial P2D electrochemical-thermal coupling model is adjusted with the correction factor to obtain a more accurate P2D electrochemical-thermal coupling model.
  • obtaining a correction value of the effect of the manufacturing process corresponding to the process parameter on the battery performance of the battery includes:
  • the P2D electrochemical-thermal coupling model is used to process the process parameters to obtain correction values of the effects of the manufacturing process corresponding to the process parameters on the battery performance of the battery.
  • the P2D electrochemical-thermal coupling model is used to directly process the process parameters. Since the P2D electrochemical-thermal coupling model is a model with stable performance and accurate battery performance prediction, it can accurately obtain the correction value of the impact of the process parameters on the battery performance of the battery.
  • obtaining process parameters for battery manufacturing includes:
  • At least one process parameter of battery manufacturing including particle size, coating thickness, pole piece size, porosity, compaction density and surface density is obtained.
  • the process parameters include indicators of multiple dimensions such as particle size, coating thickness, pole piece size, porosity, compaction density and surface density, and the influence of different process parameters on battery performance is analyzed.
  • the present application provides a method for predicting battery performance distribution, the method comprising:
  • process parameter fluctuation data several groups of process parameters for battery manufacturing are generated.
  • the process parameter fluctuation data of battery manufacturing is first obtained, and several groups of process parameters are generated according to the process parameter fluctuation data of battery manufacturing. This can fully simulate the process parameters that may correspond in actual battery manufacturing, so that the subsequent battery performance distribution prediction results are more in line with the actual situation.
  • process parameter fluctuation data including:
  • process parameter fluctuation data several groups of process parameters of the manufacturing process are randomly generated.
  • a random generation method is adopted to generate several groups of process parameters.
  • the random generation method enriches the number of process parameters; on the other hand, the random generation method makes the generated process parameters closer to the actual situation of the corresponding process parameters in actual battery manufacturing, so that the subsequent battery performance distribution prediction results are more in line with the actual situation.
  • the present application further provides a battery performance prediction device, the device comprising:
  • a process parameter acquisition module is used to obtain process parameters for battery manufacturing
  • a processing module used to obtain a correction value of the effect of the manufacturing process corresponding to the process parameter on the battery performance of the battery;
  • the performance prediction module is used to obtain the battery performance of the battery according to the impact correction value.
  • the present application further provides a battery performance distribution prediction device, the device comprising:
  • a correction module used to obtain correction values of the effects of the manufacturing process on the battery performance of the battery corresponding to several groups of process parameters, and obtain several groups of influence correction values;
  • a battery performance acquisition module used to obtain battery performance of several groups of batteries according to several impact correction values
  • the battery performance distribution processing module is used to generate battery performance distribution prediction results based on the battery performance of several groups of batteries.
  • the present application further provides a computer device.
  • the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:
  • the battery performance of the battery is obtained.
  • the present application further provides a computer device.
  • the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:
  • the present application further provides a computer-readable storage medium.
  • the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:
  • the battery performance of the battery is obtained.
  • the present application further provides a computer-readable storage medium.
  • the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the following steps are implemented:
  • the present application further provides a computer program product.
  • the computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:
  • the battery performance of the battery is obtained.
  • the present application further provides a computer program product.
  • the computer program product includes a computer program, and when the computer program is executed by a processor, the following steps are implemented:
  • the process parameters of battery manufacturing are first obtained, and then the correction value of the influence of the manufacturing process corresponding to the process parameters on the battery performance of the battery is obtained, and finally the battery performance of the battery is obtained according to the correction value.
  • the influence of the manufacturing process corresponding to the process parameters on the battery performance is considered, and the battery performance of the battery can be accurately predicted.
  • the battery performance distribution prediction device, computer equipment, storage medium and computer program product obtain several groups of process parameters for battery manufacturing, obtain the corresponding manufacturing process's impact correction values on the battery performance of the battery for the several groups of process parameters, and then obtain the battery performance of several groups of batteries based on the several groups of impact correction values, analyze the battery performance of these batteries, and generate the battery performance distribution prediction results corresponding to the several groups of batteries.
  • the impact of the manufacturing process corresponding to the process parameters on the battery performance is considered respectively, and the battery performance of several groups of batteries can be accurately predicted, so that accurate battery performance distribution prediction can be achieved.
  • FIG1 is a diagram showing an application environment of a battery performance prediction method according to an embodiment
  • FIG2 is a schematic diagram of a flow chart of a battery performance prediction method in one embodiment
  • FIG3 is a schematic flow chart of a battery performance prediction method in another embodiment
  • FIG4 is a schematic flow chart of a battery performance prediction method in yet another embodiment
  • FIG5 is a schematic diagram of a flow chart of a method for predicting battery performance distribution in one embodiment
  • FIG6 is a schematic flow chart of a method for predicting battery performance distribution in another embodiment
  • FIG7 is a schematic diagram of the technical concept of a battery performance distribution prediction method in practical application in one embodiment
  • FIG8 is a structural block diagram of a battery performance prediction device in one embodiment
  • FIG9 is a structural block diagram of a battery performance distribution prediction device in one embodiment
  • FIG10 is a diagram showing the internal structure of a computer device in one embodiment
  • Power batteries are not only used in energy storage power systems such as hydropower, thermal power, wind power and solar power stations, but also widely used in electric vehicles such as electric bicycles, electric motorcycles, electric vehicles, as well as military equipment and aerospace and other fields.
  • electric vehicles such as electric bicycles, electric motorcycles, electric vehicles, as well as military equipment and aerospace and other fields.
  • the market demand is also constantly expanding.
  • people can get more data support and reference when designing batteries to design batteries with higher performance or more in line with the needs of actual applications.
  • some researchers have proposed to optimize battery design and conduct battery research and development by analyzing battery performance, so some scholars have proposed a battery performance test scheme. In traditional schemes, battery performance is tested by experimental testing.
  • the inventor of this application proposes a new battery performance prediction scheme. First, the process parameters of battery manufacturing are obtained, then the correction value of the impact of the manufacturing process corresponding to the process parameters on the battery performance of the battery is obtained, and finally the battery performance of the battery is obtained according to the correction value. In the whole scheme, the impact of the manufacturing process corresponding to the process parameters on the battery performance is considered, and the battery performance of the battery can be accurately predicted.
  • the present application provides a battery performance prediction method, which can be specifically applied to the scenario of Figure 1, where the terminal 102 sends a battery performance prediction request to the server 104, and the server 104 responds to the battery performance prediction request, extracts the process parameters of the battery manufacturing carried in the battery performance prediction request, and then obtains the correction value of the impact of the manufacturing process corresponding to the process parameters on the battery performance of the battery, and finally obtains the battery performance of the battery according to the correction value. Further, the server 104 can send the battery performance of the battery to the terminal 102, and the terminal 102 displays the battery performance of the battery.
  • the above-mentioned battery performance prediction scheme can be directly applied to the terminal, that is, the terminal can complete the performance prediction alone.
  • the user can operate on the terminal side, input the process parameters of battery manufacturing to the terminal to perform battery performance prediction, and the terminal responds to the user's battery performance prediction request, executes the above-mentioned battery performance prediction method, obtains the battery performance prediction result, and then pushes the battery performance prediction result to the user, for example, the battery performance of the battery can be displayed on the terminal display interface.
  • the specific processing process is similar to the above content and will not be repeated here.
  • the present application provides a battery performance prediction method, comprising:
  • Process parameters refer to parameters related to the battery manufacturing process, which may include but are not limited to particle size, coating thickness, pole piece size, porosity, compaction density and surface density, among which particle size refers to the diameter of the granular material in the incoming material; coating thickness refers to the coating thickness in the production of the battery cell; pole piece size refers to the size of the pole piece in the wound battery cell, which specifically includes the width of the pole piece, etc.; porosity refers to the percentage of the pore volume of the block material in the incoming material to the total volume of the material in the natural state; compaction density refers to the ratio of surface density to material thickness in battery production. In battery cell production, compaction density has a great influence on battery performance.
  • Compaction density is not only related to the size and density of the particles, but also to the gradation of the particles. Generally, particles with large compaction density have a good normal distribution. It can be considered that under certain process conditions, the greater the compaction density, the higher the battery capacity; surface density refers to the mass per unit area of a material with a certain thickness in the field of engineering materials.
  • the basic data of process parameters can be obtained through the relevant fluctuations of incoming materials in the battery manufacturing process, the incoming material detection system within the battery manufacturing company, or the incoming material fluctuation data provided by the supplier of incoming materials (raw materials) for battery manufacturing. Since there may be fluctuations in the manufacturing process during the actual manufacturing of the battery, and these fluctuations in the process are likely to affect the final battery performance, it is necessary to first obtain the process parameters for battery manufacturing.
  • Different process parameters will have different corresponding manufacturing processes.
  • the manufacturing process changes (fluctuates), it will affect the battery performance of the final battery produced. That is, in actual battery manufacturing, the battery performance of the battery is not only affected by the battery design parameters (the main influencing factor), but also by the battery manufacturing process.
  • the impact of the manufacturing process corresponding to the process parameters on the battery performance is considered, and the impact correction value of the manufacturing process corresponding to the process parameters on the battery performance of the battery is obtained.
  • the corresponding relationship between the process parameters and the impact correction values of the battery performance can be constructed based on historical data. When it is necessary to obtain the impact correction values of the battery performance, the impact correction values are directly obtained based on the obtained process parameters and the above-mentioned corresponding relationship.
  • the corresponding relationship between the process parameters and the impact correction values of the battery performance can be a simple corresponding relationship table, or it can be a machine learning model obtained by specific training.
  • the corresponding relationship between the process parameters and the impact correction values of the battery performance is fully explored and characterized by the machine learning model.
  • S400 obtains the correction value of the influence of the manufacturing process corresponding to the process parameters on the battery performance of the battery.
  • the battery performance of the battery can be obtained, that is, the battery performance of the battery is obtained by comprehensively considering the benchmark battery performance and the correction value.
  • the benchmark battery performance is obtained based on the battery design parameters, which specifically refers to the battery performance corresponding to the battery design parameters under an ideal battery manufacturing environment.
  • Battery design parameters refer to the relevant parameters specified by the battery designer when it is designed. Generally speaking, different types of batteries correspond to different battery design parameters. When designers design and develop a new battery, they will give the specific design parameters corresponding to the new battery.
  • the benchmark battery performance can be obtained based on the battery design parameters.
  • the benchmark battery performance refers to the battery performance of the battery manufactured only under the conditions of the design parameters, or it can be simply understood as the benchmark battery performance of the battery under experimental conditions without considering the influence of the battery manufacturing process.
  • the influence of the process parameters in the battery manufacturing process on the battery performance is further considered, that is, the benchmark battery performance and the influence correction value are combined to obtain the corrected battery performance of the battery.
  • the above-mentioned battery performance prediction method first obtains the process parameters of battery manufacturing, uses the battery performance prediction value prediction model to process the process parameters, obtains the processing results, and then considers the impact of the manufacturing process on the battery performance based on the benchmark battery performance corresponding to the battery design parameters to obtain the battery performance of the battery.
  • the impact of the manufacturing process on the battery performance is considered; on the other hand, the impact of the process parameters on the battery performance is accurately analyzed by using the prediction model based on the battery performance prediction value, and the impact of different process parameters on the battery performance is fully explored through the model; therefore, the whole scheme can obtain accurate battery performance prediction results.
  • S400 includes: using the trained battery correction value prediction model to process the process parameters to obtain a correction value of the impact of the manufacturing process corresponding to the process parameters on the battery performance of the battery.
  • the battery correction value prediction model is a model for obtaining the correction value of the impact of the manufacturing process on the battery performance of the battery according to the process parameters.
  • the battery correction value prediction model characterizes the corresponding relationship between the process parameters and the correction value of the impact of the battery performance.
  • the correction value of the impact of the battery performance corresponding to the current process parameters can be obtained based on the trained battery correction value prediction model and process parameters to fully consider the impact of the battery manufacturing process on the battery performance of the battery.
  • the battery correction value prediction model is a pre-trained model, which can be specifically trained based on historical data.
  • the corresponding relationship between the process parameter and the correction value of the impact of the battery performance can be obtained based on the analysis of historical data, and the model can be trained based on the corresponding relationship to obtain the trained battery correction value prediction model. Furthermore, the corresponding relationship between the process parameter and the correction value of the impact of the battery performance can be used to train the machine learning model, give full play to the learning and analysis capabilities of the machine learning model, deeply explore the impact of different process parameters on the final battery performance, and obtain accurate correction values for the impact of the battery performance.
  • a trained battery correction value prediction model is used to accurately obtain the impact correction value corresponding to the process parameters.
  • the model fully explores the influence of different process parameters on battery performance, and can obtain the accurate correction value of the impact of the manufacturing process corresponding to the process parameters on the battery performance of the battery. Therefore, the battery performance of the battery can be accurately predicted in the end.
  • the method of obtaining the battery correction value prediction model includes:
  • the battery performance prediction value prediction model is trained based on the correspondence between the process parameters of battery manufacturing and the battery performance prediction value.
  • the battery performance prediction value prediction model is trained based on the correspondence between the process parameters of battery manufacturing and the battery performance prediction values, so that the battery performance prediction value prediction model can accurately characterize the influence of the process parameters on the battery performance. Therefore, the battery performance prediction value prediction model finally trained can support the accurate prediction of the correction value of the influence of the process parameters on the battery performance.
  • the method of obtaining the battery correction value prediction model includes:
  • Sample batteries refer to batteries used to train battery performance prediction models. Generally, a small number of batteries are selected as sample batteries, for example, 100 batteries are randomly selected as sample batteries. The process parameters of these sample batteries in the actual manufacturing process are obtained, that is, the sample process parameters of the sample batteries are obtained. During the manufacturing process of batteries, for batteries of the same model (same design parameters), their process parameters fluctuate within a certain range of values. Specifically, multiple process parameters are randomly selected/composed within the range of process parameter values that can fluctuate as sample process parameters.
  • S340 Obtain battery performance of a plurality of sample batteries according to the sample process parameters, and generate a corresponding relationship between the process parameters and the battery performance.
  • the currently available battery performance prediction method can be used to obtain the corresponding battery performance, and then generate the corresponding relationship between the process parameters and the battery performance.
  • the sample process parameters can be used to design a battery, and then the designed battery can be tested by experimental measurement to obtain the battery performance of the sample battery.
  • a virtual battery can be simulated according to the sample process parameters, and then the battery performance corresponding to the simulated battery can be obtained.
  • the sample process parameters can also be input into the electrochemical model to obtain the battery performance of the sample battery.
  • the correspondence between the process parameters and the battery performance of the sample battery is generated by associating the sample process parameters and the battery performance of the sample battery.
  • the correspondence between the process parameters and the battery performance of the sample battery can be a table of the correspondence between the process parameters and the battery performance.
  • any one of the process parameters can be used as a variable, and the other process parameters can be used as invariants to draw the corresponding battery performance change relationship curve, that is, to obtain a battery performance fluctuation curve with different single process parameters as variables.
  • the correspondence between the process parameters and the battery performance of the sample battery can be obtained by calculation. A set of accurate data sets are used, and this data set is used as the data source for the next step of machine learning model training.
  • the coating thickness fluctuation as an example: when only the coating thickness fluctuation is considered, the corresponding battery performance under the influence of different coating thicknesses is obtained.
  • the different battery performance can be characterized by battery performance distribution, and the battery performance distribution can specifically include capacity distribution, power distribution, or temperature distribution.
  • Machine learning is a multi-disciplinary interdisciplinary subject involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. It specializes in studying how computers simulate or implement human learning behavior to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their own performance.
  • association rules use rules to describe the relationship between two or more variables, and are a method that objectively reflects the nature of the data itself. It is a large category of machine learning tasks, which can be divided into two stages. First, high-frequency project groups are found from the data set, and then their association rules are studied. The analysis results obtained are a summary of the laws between variables. Here, the correspondence between the process parameters and battery performance of the sample battery is used as training data to train the machine learning model to obtain a battery correction value prediction model. Through machine learning, the correlation between process parameter fluctuations and battery performance fluctuations is fully explored.
  • sample process parameters of several sample batteries are first obtained, and the corresponding relationship between the battery performance of the sample batteries is obtained according to the sample process parameters, and the corresponding relationship between the process parameters and the battery performance of the sample batteries is constructed.
  • the machine learning model is trained with the constructed corresponding relationship to obtain a battery correction value prediction model.
  • machine learning training is carried out using sample data, and the battery correction value prediction model finally trained can accurately characterize the corresponding relationship between the process parameters and the battery performance, and thus can support the final accurate prediction of the battery performance of the battery.
  • S340 includes:
  • S344 Correlate the sample process parameters and the battery performance to obtain a corresponding relationship between the process parameters and the battery performance of the sample battery.
  • the P2D electrochemical-thermal coupling model is a model obtained by coupling the P2D electrochemical model with the thermal model. Since the battery charging and discharging process generates heat, the battery temperature rises, and the increase in battery temperature will change the battery performance. If this phenomenon is ignored, it will inevitably lead to inaccurate prediction of battery performance. Therefore, it is necessary to couple the P2D electrochemical model with the battery thermal model to more accurately predict the battery performance.
  • the P2D electrochemical model is based on the theory of concentrated solution and porous electrode theory.
  • the grid (a discrete method) is divided according to the finite element idea, and then the partial differential equations in the electrochemical process are solved to obtain electrochemical properties such as electrode potential, electrolyte potential, and electrolyte concentration; the heat calculated by the electrochemical model is coupled into the thermal model as a heat source, causing the temperature change in the 3D thermal model, and the temperature change is fed back to the electrochemical model, causing the temperature-related parameters in the electrochemical model to change.
  • the changes in these parameters further trigger the changes in the heat source, thereby realizing the coupling of the electrochemical model and the thermal model, and ultimately affecting the electrothermal performance of the battery.
  • the sample process parameters are input into the P2D electrochemical-thermal coupling model to obtain the battery performance of the sample battery. It is precisely because the P2D electrochemical-thermal coupling model has the above-mentioned performance advantages and functions that the battery performance can be obtained after the sample process parameters are input into the P2D electrochemical-thermal coupling model.
  • the P2D electrochemical-thermal coupling model can obtain the battery performance of the sample battery based on the input sample process parameters, on the one hand, the processing speed of the P2D electrochemical-thermal coupling model is still relatively slow, and it takes a certain amount of time to process the output battery performance, which is acceptable for processing a small number of sample battery data, but it can no longer achieve the best performance for batch and large-scale battery performance prediction; on the other hand, the P2D electrochemical-thermal coupling model cannot deeply analyze and explore the relationship between fluctuations in different process parameters and fluctuations in battery performance, and it does not have a learning function.
  • the battery performance parameters directly obtained by the P2D electrochemical-thermal coupling model have limited guiding effect on subsequent battery design and development.
  • the P2D electrochemical-thermal coupling model is first used to process the sample process parameters of a small number of sample batteries, and the corresponding relationship between the sample process parameters of the sample batteries and the battery performance is established.
  • the corresponding relationship between the sample process parameters of the sample batteries and the battery performance is then used as training data to train the machine learning model, and the relationship between the fluctuations of different process parameters and the fluctuations of battery performance is mined and analyzed through the machine learning model.
  • Associating sample process parameters and battery performance refers to associating and recording the corresponding battery performance under different sample process parameters.
  • a data table can be set to record the battery performance corresponding to different sample process parameters, or a graphical curve can be used to record the battery performance corresponding to different sample process parameters.
  • the P2D electrochemical-thermal coupling model is used to process several sample process parameters respectively to obtain the battery performance of each sample battery, and then the battery performance of each sample battery is associated with the sample process parameters, that is, the corresponding relationship between the process parameters and the battery performance of the sample battery is obtained.
  • the battery performance of the sample battery can be quickly obtained through the P2D electrochemical-thermal coupling model, thereby improving the processing efficiency.
  • the P2D electrochemical-thermal coupling model is obtained by:
  • test battery performance corresponding to the measured test process parameters is obtained; the test process parameters are processed by an initial P2D electrochemical-thermal coupling model to obtain initial battery performance prediction results corresponding to the test process parameters; the initial P2D electrochemical-thermal coupling model is optimized according to the test battery performance and the initial battery performance prediction results to obtain a P2D electrochemical-thermal coupling model.
  • the initial P2D electrochemical-thermal coupling model refers to the conventional and general P2D electrochemical-thermal coupling model.
  • the test process parameters refer to the process parameters of the battery corresponding to the battery performance tested in the experimental test state. For example, here we choose to use the experimental test method to obtain a very small amount of battery process parameters and battery performance corresponding data. This very small number of batteries can be understood as test batteries.
  • the process parameters corresponding to this part of the test batteries are the test process parameters.
  • a complete and rigorous experimental test method is used to test the corresponding test battery performance.
  • the test battery performance has a very high accuracy, and the error between it and the battery's true battery performance is basically negligible, which can accurately characterize the true performance of the test battery.
  • the test process parameters are input into the initial P2D electrochemical-thermal coupling model to obtain the initial battery performance prediction result, which is the result of performance prediction of the test battery and belongs to the predicted value; the test battery performance and the initial battery performance prediction result are compared, and the difference between the two is analyzed. According to the difference between the actual value and the predicted value, the initial P2D electrochemical-thermal coupling model is optimized and corrected to obtain the P2D electrochemical-thermal coupling model, so as to obtain a more accurate P2D electrochemical-thermal coupling model.
  • the initial P2D electrochemical-thermal coupling model is optimized based on the test cell performance corresponding to the measured test process parameters to obtain a more accurate P2D electrochemical-thermal coupling model that better meets the scenario requirements.
  • the initial P2D electrochemical-thermal coupling model is optimized according to the test battery performance and the initial battery performance prediction results to obtain the P2D electrochemical-thermal coupling model, including:
  • Some process parameters have a high influence on battery performance, while some process parameters have a low influence on battery performance.
  • Different weight values or correlation coefficients have been assigned to different process parameters in the entire model.
  • the weights/coefficients or constant values related to these process parameters in the P2D electrochemical-thermal coupling model need to be corrected. Specifically, the correction factors are directly used.
  • the correction factors corresponding to different process parameters can be obtained based on historical data analysis, or they may be obtained through feedback adjustment, that is, by selecting different correction factors, the P2D electrochemical-thermal coupling model is updated so that the final predicted value is infinitely close to the actual measured value, so as to obtain a more accurate P2D electrochemical-thermal coupling model.
  • the test battery can be obtained specifically in the following two ways: one is to artificially manufacture batteries of different designs, and specifically test performance such as capacity/DCR, etc., and simulate the performance of different designs respectively, compare them with actual measurements, and analyze the accuracy of the influence of each parameter of the model on the results; the other is to directly select from the batteries in the upper and lower warehouses of the production line. Specifically, by taking the incoming material/design/process data of the battery that has been manufactured and tested in the lower warehouse, n groups of design input models are generated according to the fluctuation data for calculation, and the calculated battery cell performance is compared with the performance of this batch of batteries after actual storage, and the accuracy of the influence of each parameter of the model on the results and the influence of the model are analyzed.
  • the test battery performance is compared with the initial battery performance prediction results to obtain battery performance difference data, identify the fluctuating process parameters corresponding to the battery performance difference, and obtain the correction factor corresponding to the indicator.
  • the initial P2D electrochemical-thermal coupling model is adjusted with the correction factor to obtain a more accurate P2D electrochemical-thermal coupling model.
  • training a machine learning model based on the corresponding relationship between the process parameters of a sample battery and the battery performance to obtain a battery correction value prediction model includes:
  • An initial Gaussian process regression model is obtained; the initial Gaussian process regression model is trained using the corresponding relationship between the process parameters of the sample battery and the battery performance to obtain a battery correction value prediction model.
  • GPR Gaussian Process Regression
  • GP Gaussian Process
  • the model assumptions of GPR include noise (regression residual) and Gaussian process priors, and its solution is based on Bayesian inference. If the form of the kernel function is not restricted, GPR is theoretically a universal approximator for any continuous function in a compact space. Based on the convenient properties of Gaussian processes and their kernel functions, GPR has been applied to problems in the fields of time series analysis, image processing, and automatic control. GPR is an algorithm with high computational overhead and is usually used for regression problems with low dimensions and small samples, but there are also extended algorithms suitable for large samples and high dimensions.
  • the initial Gaussian process regression model is trained based on the correspondence between the process parameters and battery performance of the sample battery.
  • part of the data can be used as a training set, and the other part of the data can be used as a test set.
  • the initial Gaussian process regression model is first trained with the training set to obtain the trained model, and then the trained model is tested with the test set to verify whether the trained model is qualified. If it is unqualified, the iterative training and optimization of the model are continued until it is finally qualified.
  • the training set can be composed of more data, and the test set can be composed of relatively less data.
  • the data volume ratio of the training set and the test set can be 8:2 to achieve a balance between training and testing.
  • the initial Gaussian process regression model can specifically be the Gaussian process regression model of ARD.
  • the Gaussian process regression model based on association rules can further accurately explore the correlation between process parameters and battery performance, that is, it can ultimately obtain a more accurate battery performance distribution result.
  • a Gaussian process regression model is used as the basic model, and the initial Gaussian process regression model is trained using the correspondence between the process parameters of the sample battery and the battery performance to obtain a battery correction value prediction model. Since the Gaussian process regression model can realize non-parametric regression of a stationary random field, the battery correction value prediction model finally obtained can support stable and accurate battery performance prediction.
  • obtaining a correction value of the effect of the manufacturing process corresponding to the process parameter on the battery performance of the battery includes:
  • the P2D electrochemical-thermal coupling model is used to process the process parameters to obtain correction values of the effects of the manufacturing process corresponding to the process parameters on the battery performance of the battery.
  • the P2D electrochemical-thermal coupling model is used to directly process the process parameters. Since the P2D electrochemical-thermal coupling model is a model with stable performance and accurate battery performance prediction, it can accurately obtain the correction value of the impact of the process parameters on the battery performance of the battery.
  • obtaining process parameters for battery manufacturing includes:
  • At least one process parameter of battery manufacturing including particle size, coating thickness, pole piece size, porosity, compaction density and surface density is obtained.
  • Particle size refers to the diameter of the particulate matter in the incoming material
  • coating thickness refers to the thickness of the coating in the production of battery cells
  • pole piece size refers to the size of the pole piece in the wound battery cell, which specifically includes the width of the pole piece, etc.
  • porosity refers to the percentage of the pore volume of the block material in the incoming material to the total volume of the material in the natural state
  • compaction density refers to the ratio of surface density to material thickness in battery production. In battery cell production, compaction density has a great influence on battery performance. Compaction density is not only related to the size and density of the particles, but also to the grading of the particles. Generally, particles with large compaction density have a good normal distribution. It can be considered that under certain process conditions, the greater the compaction density, the higher the capacity of the battery; surface density refers to the mass per unit area of a material of a certain thickness in the field of engineering materials.
  • the process parameters include indicators of multiple dimensions such as particle size, coating thickness, pole piece size, porosity, compaction density and surface density, and the influence of different process parameters on battery performance is analyzed.
  • the inventor of the present application further realized that the performance prediction can be performed on batch batteries to obtain the performance prediction results of batch batteries, and then the performance prediction results of batch batteries can be further distributed analyzed to finally obtain the distribution results of battery performance.
  • Designers can intuitively understand the performance distribution of batch batteries based on the distribution analysis results of battery performance, which is helpful for designers to design batteries and shorten the battery research and development cycle.
  • the direct measurement method for batch battery performance research will also have the defect of inaccuracy.
  • the battery performance distribution prediction scheme several groups of process parameters for battery manufacturing are obtained, and the above-mentioned battery performance prediction method is used for processing to obtain the corrected battery performance of several groups of batteries, and the battery performance distribution results are analyzed with the corrected battery performance of several groups of batteries.
  • the above-mentioned battery performance prediction method is used for battery performance prediction, it is possible to obtain accurate corrected battery performance of several groups of batteries, and therefore, the battery performance distribution results of the battery can be accurately predicted in the end.
  • the battery performance distribution prediction scheme provided by the present application can also be applied to the application scenario shown in FIG. 1.
  • the terminal 102 sends a battery performance distribution prediction request to the server 104.
  • the server 104 responds to the battery performance distribution prediction request and obtains several groups of process parameters for battery manufacturing; obtains several groups of process parameters corresponding to the manufacturing process and correction values of the battery performance of the battery, and obtains several groups of correction values; obtains the battery performance of several groups of batteries according to the correction values; and generates the battery performance distribution prediction results based on the battery performance of several groups of batteries.
  • the server 104 sends the battery performance distribution results to the terminal 102, and the terminal 102 displays the battery performance distribution results, so that the R&D personnel can intuitively understand the battery performance of the batch battery and find the correlation between the battery performance and the electrochemical parameters, which is conducive to shortening the battery R&D cycle.
  • the above-mentioned battery performance distribution prediction solution can be directly applied to the terminal, that is, the terminal completes the performance distribution prediction alone, and its specific processing process is similar to the above-mentioned content, which will not be repeated here.
  • the present application provides a method for predicting battery performance distribution, the method comprising:
  • S820 Acquire several groups of process parameters for battery manufacturing.
  • S880 Generate a battery performance distribution prediction result based on the battery performance of several groups of batteries.
  • S820 includes:
  • S824 Generate several groups of process parameters for battery manufacturing according to the process parameter fluctuation data.
  • the process fluctuation data of battery manufacturing refers to the corresponding process fluctuation data in the battery manufacturing process, which can be obtained through the relevant fluctuations of incoming materials in the battery manufacturing process, the incoming material detection system within the battery manufacturing company, or the incoming material fluctuation data provided by the battery manufacturing incoming material (raw material) supplier.
  • the process parameter fluctuation data here can be understood as a fluctuation range value. Taking the coating thickness as an example, it can fluctuate within the range of (a, b).
  • the process parameter fluctuation data several groups of process parameters of the manufacturing process are combined and generated. For example, several groups of process parameters of the manufacturing process can be generated by random extraction and combination to simulate the diversified process parameter changes in real battery manufacturing.
  • the process parameter fluctuation data of battery manufacturing is first obtained, and several groups of process parameters are generated according to the process parameter fluctuation data of battery manufacturing. This can fully simulate the process parameters that may correspond in actual battery manufacturing, so that the subsequent battery performance distribution prediction results are more in line with the actual situation.
  • process parameter fluctuation data including:
  • process parameter fluctuation data several groups of process parameters of the manufacturing process are randomly generated.
  • a random generation algorithm can be used to generate several groups of process parameters of the manufacturing process, virtual batteries can be generated based on these process parameters, and then the battery performance corresponding to these virtual batteries can be obtained by the above-mentioned battery performance prediction method to generate battery performance distribution results.
  • the battery performance distribution result can be a normal distribution of battery performance, which can accurately characterize the distribution of battery performance corresponding to the fluctuation of process parameters.
  • random generation algorithms include Monte Carlo algorithms. Monte Carlo method, also known as statistical simulation method, is an important numerical calculation method in the mid-1940s.
  • the random generation algorithm uses the Monte Carlo algorithm, which can realize accurate data random generation processing and support the subsequent accurate battery performance distribution.
  • a random generation method is adopted to generate several groups of process parameters.
  • the random generation method enriches the number of process parameters; on the other hand, the random generation method makes the generated process parameters closer to the actual situation of the corresponding process parameters in actual battery manufacturing, so that the subsequent battery performance distribution prediction results are more in line with the actual situation.
  • the present application also provides a battery performance prediction device, the device comprising:
  • a process parameter acquisition module 200 is used to acquire process parameters for battery manufacturing
  • the processing module 400 is used to obtain a correction value of the effect of the manufacturing process corresponding to the process parameter on the battery performance of the battery;
  • the performance prediction module 600 is used to obtain the battery performance of the battery according to the impact correction value.
  • the battery performance prediction device of the present application first obtains the process parameters of battery manufacturing, uses the battery performance prediction value prediction model to process the process parameters, obtains the processing results, and then considers the impact of the manufacturing process on the battery performance based on the benchmark battery performance corresponding to the battery design parameters to obtain the battery performance of the battery.
  • the impact of the manufacturing process on the battery performance is considered; on the other hand, the impact of the process parameters on the battery performance is accurately analyzed by using the prediction model based on the battery performance prediction value, and the impact of different process parameters on the battery performance is fully explored through the model; therefore, the whole scheme can obtain accurate battery performance prediction results.
  • the processing module 400 is further used to process the process parameters using the trained battery correction value prediction model to obtain the correction value of the impact of the manufacturing process corresponding to the process parameter on the battery performance of the battery.
  • the battery performance prediction value prediction model is trained based on the correspondence between the process parameters of battery manufacturing and the battery performance prediction values.
  • the processing module 400 is also used to obtain sample process parameters of several sample batteries; based on the sample process parameters, obtain the battery performance of several sample batteries, and generate a correspondence between the process parameters and the battery performance; train a machine learning model based on the correspondence between the process parameters and the battery performance to obtain a battery correction value prediction model.
  • the processing module 400 is also used to use the P2D electrochemical-thermal coupling model to process several sample process parameters respectively to obtain the battery performance of each sample battery; associate the sample process parameters and battery performance to obtain the corresponding relationship between the process parameters and battery performance of the sample battery.
  • the processing module 400 is also used to obtain the test battery performance corresponding to the measured test process parameters; process the test process parameters through the initial P2D electrochemical-thermal coupling model to obtain the initial battery performance prediction results corresponding to the test process parameters; optimize the initial P2D electrochemical-thermal coupling model according to the test battery performance and the initial battery performance prediction results to obtain the P2D electrochemical-thermal coupling model.
  • the processing module 400 is also used to compare the test battery performance with the initial battery performance prediction results to obtain battery performance difference data; identify the fluctuating process parameters corresponding to the battery performance difference data; obtain the correction factors corresponding to the fluctuating process parameters; adjust the initial P2D electrochemical-thermal coupling model according to the correction factors to obtain the P2D electrochemical-thermal coupling model.
  • the processing module 400 is further used to process the process parameters using a P2D electrochemical-thermal coupling model to obtain a correction value of the impact of the manufacturing process corresponding to the process parameters on the battery performance of the battery.
  • the process parameter acquisition module 200 is also used to obtain at least one process parameter of the battery manufacturing process including particle size, coating thickness, pole piece size, porosity, compaction density and surface density.
  • the present application also provides a battery performance distribution prediction device, the device comprising:
  • a correction module 840 is used to obtain correction values of the effects of the manufacturing process on the battery performance of the battery corresponding to several groups of process parameters, and obtain several groups of effect correction values;
  • a battery performance acquisition module 860 for obtaining battery performance of a plurality of battery groups according to a plurality of impact correction values
  • the battery performance distribution processing module 880 is used to generate a battery performance distribution prediction result based on the battery performance of several groups of batteries.
  • the battery performance distribution prediction device obtains several groups of process parameters for battery manufacturing, processes them using the battery performance prediction method, obtains the corrected battery performance of several groups of batteries, and analyzes the battery performance distribution results using the corrected battery performance of several groups of batteries.
  • the battery performance prediction method since the battery performance prediction method is used to predict the battery performance, it can obtain accurate corrected battery performance of several groups of batteries, and therefore, it can finally accurately predict the battery performance distribution results of the battery.
  • the several groups of process parameter acquisition modules 820 are also used to acquire process parameter fluctuation data for battery manufacturing; and generate several groups of process parameters for battery manufacturing according to the process parameter fluctuation data.
  • the several groups of process parameter acquisition modules 820 are further used to randomly generate several groups of process parameters of the manufacturing process according to the process parameter fluctuation data.
  • Each module in the above-mentioned battery performance prediction device can be implemented in whole or in part by software, hardware and a combination thereof.
  • the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.
  • a computer device which may be a server, and its internal structure diagram may be as shown in FIG10.
  • the computer device includes a processor, a memory, and a network interface connected via a system bus.
  • the processor of the computer device is used to provide computing and control capabilities.
  • the memory of the computer device includes a non-volatile storage medium and an internal memory.
  • the non-volatile storage medium stores an operating system, a computer program, and a database.
  • the internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium.
  • the database of the computer device is used to store data related to the trained model.
  • the network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a battery performance prediction method is implemented.
  • FIG. 10 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.
  • the specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.
  • a computer device including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:
  • the battery performance of the battery is obtained.
  • the processor when the processor executes the computer program, the processor further implements the following steps:
  • the trained battery correction value prediction model is used to process the process parameters to obtain the correction value of the impact of the manufacturing process corresponding to the process parameters on the battery performance of the battery.
  • the processor when the processor executes the computer program, the processor further implements the following steps:
  • the battery performance prediction value prediction model is trained based on the correspondence between the process parameters of battery manufacturing and the battery performance prediction value.
  • the P2D electrochemical-thermal coupling model is used to process several sample process parameters respectively to obtain the battery performance of each sample battery; the sample process parameters and battery performance are correlated to obtain the corresponding relationship between the process parameters and battery performance of the sample battery.
  • the processor when the processor executes the computer program, the processor further implements the following steps:
  • test battery performance corresponding to the measured test process parameters is obtained; the test process parameters are processed by an initial P2D electrochemical-thermal coupling model to obtain initial battery performance prediction results corresponding to the test process parameters; the initial P2D electrochemical-thermal coupling model is optimized according to the test battery performance and the initial battery performance prediction results to obtain a P2D electrochemical-thermal coupling model.
  • the processor when the processor executes the computer program, the processor further implements the following steps:
  • the processor when the processor executes the computer program, the processor further implements the following steps:
  • the P2D electrochemical-thermal coupling model is used to process the process parameters to obtain correction values of the effects of the manufacturing process corresponding to the process parameters on the battery performance of the battery.
  • the processor when the processor executes the computer program, the processor further implements the following steps:
  • At least one process parameter of battery manufacturing including particle size, coating thickness, pole piece size, porosity, compaction density and surface density is obtained.
  • a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:
  • the battery performance of the battery is obtained.
  • the trained battery correction value prediction model is used to process the process parameters to obtain the correction value of the impact of the manufacturing process corresponding to the process parameters on the battery performance of the battery.
  • the battery performance prediction value prediction model is trained based on the correspondence between the process parameters of battery manufacturing and the battery performance prediction value.
  • the P2D electrochemical-thermal coupling model is used to process several sample process parameters respectively to obtain the battery performance of each sample battery; the sample process parameters and battery performance are correlated to obtain the corresponding relationship between the process parameters and battery performance of the sample battery.
  • test battery performance corresponding to the measured test process parameters is obtained; the test process parameters are processed by an initial P2D electrochemical-thermal coupling model to obtain initial battery performance prediction results corresponding to the test process parameters; the initial P2D electrochemical-thermal coupling model is optimized according to the test battery performance and the initial battery performance prediction results to obtain a P2D electrochemical-thermal coupling model.
  • the P2D electrochemical-thermal coupling model is used to process the process parameters to obtain correction values of the effects of the manufacturing process corresponding to the process parameters on the battery performance of the battery.
  • At least one process parameter of battery manufacturing including particle size, coating thickness, pole piece size, porosity, compaction density and surface density is obtained.
  • a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:
  • the battery performance of the battery is obtained.
  • the trained battery correction value prediction model is used to process the process parameters to obtain the correction value of the impact of the manufacturing process corresponding to the process parameters on the battery performance of the battery.
  • the battery performance prediction value prediction model is trained based on the correspondence between the process parameters of battery manufacturing and the battery performance prediction value.
  • the P2D electrochemical-thermal coupling model is used to process several sample process parameters respectively to obtain the battery performance of each sample battery; the sample process parameters and battery performance are correlated to obtain the corresponding relationship between the process parameters and battery performance of the sample battery.
  • test battery performance corresponding to the measured test process parameters is obtained; the test process parameters are processed by an initial P2D electrochemical-thermal coupling model to obtain initial battery performance prediction results corresponding to the test process parameters; the initial P2D electrochemical-thermal coupling model is optimized according to the test battery performance and the initial battery performance prediction results to obtain a P2D electrochemical-thermal coupling model.
  • the P2D electrochemical-thermal coupling model is used to process the process parameters to obtain correction values of the effects of the manufacturing process corresponding to the process parameters on the battery performance of the battery.
  • At least one process parameter of battery manufacturing including particle size, coating thickness, pole piece size, porosity, compaction density and surface density is obtained.
  • a computer device including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:
  • the processor when the processor executes the computer program, the processor further implements the following steps:
  • the processor when the processor executes the computer program, the processor further implements the following steps:
  • process parameter fluctuation data several groups of process parameters of the manufacturing process are randomly generated.
  • a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:
  • process parameter fluctuation data several groups of process parameters of the manufacturing process are randomly generated.
  • a computer program product comprising a computer program, which, when executed by a processor, implements the following steps:
  • process parameter fluctuation data several groups of process parameters of the manufacturing process are randomly generated.
  • Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

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Abstract

Procédé et appareil de prédiction de performance de batterie, dispositif informatique, support de stockage et produit de programme informatique. Le procédé de prédiction de performance de batterie consiste à : tout d'abord acquérir un paramètre de processus de fabrication de batterie, puis acquérir une valeur de correction d'un impact d'un processus de fabrication correspondant au paramètre de processus sur les performances de batterie d'une batterie, et enfin obtenir les performances de batterie de la batterie en fonction de la valeur de correction de l'impact. Toute la solution prend en considération l'impact du processus de fabrication correspondant au paramètre de processus sur les performances de batterie, de telle sorte que les performances de batterie de la batterie peuvent être prédites avec précision. Le procédé et l'appareil de prédiction de distribution de performance de batterie, le dispositif informatique, le support de stockage et le produit de programme informatique peuvent réaliser une prédiction précise de la distribution de performance de batterie.
PCT/CN2022/125298 2022-10-14 2022-10-14 Procédé de prédiction de performance de batterie et procédé de prédiction de distribution de performance de batterie WO2024077587A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108009397A (zh) * 2017-12-01 2018-05-08 中南大学 预测锂离子电池材料电化学性能的仿真方法、装置及设备
CN113102516A (zh) * 2021-03-05 2021-07-13 东北大学 融合轧制机理和深度学习的热连轧带钢头部宽度预测方法
CN113687250A (zh) * 2021-08-18 2021-11-23 蜂巢能源科技有限公司 电芯容量预测方法、装置、电子设备及介质
CN114298091A (zh) * 2021-12-13 2022-04-08 国网湖北省电力有限公司电力科学研究院 Sf6气体流量计量值修正方法、装置、设备及存储介质
EP4011834A1 (fr) * 2020-12-10 2022-06-15 Basf Se Procédé de fabrication d'un matériau actif d'électrode

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108009397A (zh) * 2017-12-01 2018-05-08 中南大学 预测锂离子电池材料电化学性能的仿真方法、装置及设备
EP4011834A1 (fr) * 2020-12-10 2022-06-15 Basf Se Procédé de fabrication d'un matériau actif d'électrode
CN113102516A (zh) * 2021-03-05 2021-07-13 东北大学 融合轧制机理和深度学习的热连轧带钢头部宽度预测方法
CN113687250A (zh) * 2021-08-18 2021-11-23 蜂巢能源科技有限公司 电芯容量预测方法、装置、电子设备及介质
CN114298091A (zh) * 2021-12-13 2022-04-08 国网湖北省电力有限公司电力科学研究院 Sf6气体流量计量值修正方法、装置、设备及存储介质

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