Method for measuring wettability of battery electrolyte by combining ultrasonic scanning and heat conductivity coefficient measurement
By combining ultrasonic scanning and thermal conductivity measurement, the infiltration model is trained to evaluate the electrolyte infiltration, which solves the problem of electrolyte detection under the closed structure of lithium-ion batteries, and achieves lossless and accurate electrolyte infiltration detection and battery status monitoring.
Patent Information
- Application Number
- CN202411544228.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-31
- Publication Date
- 2025-02-07
AI Technical Summary
Due to the closed structure of lithium-ion batteries, it is difficult to detect the infiltration of the electrolyte in the absence of damage, affecting battery status monitoring and fault diagnosis.
Using a method of combining ultrasonic scanning and thermal conductivity measurement, the ultrasonic transmission pattern, heat flow, temperature measurement data and slice element content of the battery are obtained, and the thermal conductivity distribution is calculated based on the thermal conductivity equation, and the infiltration model is trained to evaluate the electrolyte infiltration.
It accurately detects the electrolyte infiltration in lithium batteries in a lossless situation, provides an accurate estimate of the in-situ electrolyte content of the battery, and improves the efficiency of battery quality monitoring and safety evaluation.
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Figure CN119394852A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of lithium-ion batteries, and in particular to a method for measuring the wettability of battery electrolytes by combining ultrasonic scanning with thermal conductivity measurement. Background Art
[0002] Lithium-ion batteries, characterized by high specific energy, high specific power, long cycle life, and low self-discharge, have been widely used in new energy vehicles, grid energy storage systems, and portable electronic devices. However, electrolyte content is crucial to the basic performance and safety of batteries and ensures their high voltage and high specific energy. Excessive electrolyte content not only directly impacts the primary chemical effect but also leads to unimpregnated separators, reduced conductivity, and increased internal resistance due to inadequate electrode wetting, shortening battery life. A significant electrolyte deficiency can lead to serious safety concerns, such as a drop in battery capacity. Increasing electrolyte content facilitates full utilization of active materials. However, excessive electrolyte content can generate significant gas during battery charge and discharge, resulting in reduced cycle performance and increased internal pressure, which can lead to battery casing rupture and electrolyte leakage. Therefore, battery electrolyte content monitoring is crucial for monitoring battery status and diagnosing battery failures in advance. However, due to the closed structure of the battery, non-destructive detection of electrolyte infiltration in lithium batteries is difficult. Summary of the Invention
[0003] Based on this, it is necessary to provide a battery electrolyte infiltration measurement method, device, computer equipment, computer-readable storage medium and computer program product that combines ultrasonic scanning with thermal conductivity measurement to accurately detect the electrolyte infiltration in lithium batteries without damage in order to solve the above technical problems.
[0004] In a first aspect, the present application provides a method for measuring the wettability of a battery electrolyte by combining ultrasonic scanning with thermal conductivity measurement, comprising:
[0005] Obtaining an ultrasonic transmission map of a target battery, battery heat flow, temperature measurement data, element content of each battery slice, and a wettability model to be trained; the element content of the battery slice is obtained by scanning each slice area of the target battery;
[0006] Substituting the battery heat flow and the temperature measurement data as calculation parameters into the heat conduction equation of the target battery to obtain the thermal conductivity distribution of the target battery;
[0007] Using the ultrasonic transmission map, the thermal conductivity distribution, and the element content of each battery slice, the wettability model to be trained is trained to obtain a target wettability model;
[0008] The ultrasonic transmission map and thermal conductivity distribution of the battery to be measured are input into the target wettability model to obtain the electrolyte wettability information of the battery to be measured.
[0009] In a second aspect, the present application further provides a battery electrolyte wettability measurement device combining ultrasonic scanning with thermal conductivity measurement, comprising:
[0010] A battery data acquisition module is used to obtain the ultrasonic transmission map, battery heat flow, temperature measurement data, element content of each battery slice, and the wettability model to be trained of the target battery; the element content of the battery slice is obtained by scanning each slice area of the target battery;
[0011] a coefficient distribution obtaining module, configured to substitute the battery heat flow and the temperature measurement data as calculation parameters into the heat conduction equation of the target battery to obtain the thermal conductivity distribution of the target battery;
[0012] a wettability model training module, configured to train the wettability model to be trained using the ultrasonic transmission map, the thermal conductivity distribution, and the element content of each battery slice to obtain a target wettability model;
[0013] The wettability calculation module is used to input the ultrasonic transmission map and thermal conductivity distribution of the battery to be measured into the target wettability model to obtain the electrolyte wettability information of the battery to be measured.
[0014] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:
[0015] Obtaining an ultrasonic transmission map of a target battery, battery heat flow, temperature measurement data, element content of each battery slice, and a wettability model to be trained; the element content of the battery slice is obtained by scanning each slice area of the target battery;
[0016] Substituting the battery heat flow and the temperature measurement data as calculation parameters into the heat conduction equation of the target battery to obtain the thermal conductivity distribution of the target battery;
[0017] Using the ultrasonic transmission map, the thermal conductivity distribution, and the element content of each battery slice, the wettability model to be trained is trained to obtain a target wettability model;
[0018] The ultrasonic transmission map and thermal conductivity distribution of the battery to be measured are input into the target wettability model to obtain the electrolyte wettability information of the battery to be measured.
[0019] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:
[0020] Obtaining an ultrasonic transmission map of a target battery, battery heat flow, temperature measurement data, element content of each battery slice, and a wettability model to be trained; the element content of the battery slice is obtained by scanning each slice area of the target battery;
[0021] Substituting the battery heat flow and the temperature measurement data as calculation parameters into the heat conduction equation of the target battery to obtain the thermal conductivity distribution of the target battery;
[0022] Using the ultrasonic transmission map, the thermal conductivity distribution, and the element content of each battery slice, the wettability model to be trained is trained to obtain a target wettability model;
[0023] The ultrasonic transmission map and thermal conductivity distribution of the battery to be measured are input into the target wettability model to obtain the electrolyte wettability information of the battery to be measured.
[0024] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:
[0025] Obtaining an ultrasonic transmission map of a target battery, battery heat flow, temperature measurement data, element content of each battery slice, and a wettability model to be trained; the element content of the battery slice is obtained by scanning each slice area of the target battery;
[0026] Substituting the battery heat flow and the temperature measurement data as calculation parameters into the heat conduction equation of the target battery to obtain the thermal conductivity distribution of the target battery;
[0027] Using the ultrasonic transmission map, the thermal conductivity distribution, and the element content of each battery slice, the wettability model to be trained is trained to obtain a target wettability model;
[0028] The ultrasonic transmission map and thermal conductivity distribution of the battery to be measured are input into the target wettability model to obtain the electrolyte wettability information of the battery to be measured.
[0029] The above-mentioned battery electrolyte wettability measurement method, device, computer equipment, storage medium and computer program product combining ultrasonic scanning with thermal conductivity measurement obtains the ultrasonic transmission map, battery heat flow, temperature measurement data, element content of each battery slice and the wettability model to be trained of the target battery; the element content of the battery slice is obtained by scanning each slice area of the target battery; the battery heat flow and temperature measurement data are used as calculation parameters and substituted into the heat conduction equation of the target battery to obtain the thermal conductivity distribution of the target battery; the ultrasonic transmission map, thermal conductivity distribution and element content of each battery slice are used to train the wettability model to obtain the target wettability model; the ultrasonic transmission map and thermal conductivity distribution of the battery to be measured are input into the target wettability model to obtain the electrolyte wettability information of the battery to be measured.
[0030] By integrating multiple sources of information, including the target battery's ultrasonic transmission spectrum, battery heat flow and temperature measurement data, and the elemental content of each battery slice, combined with the thermal conductivity distribution obtained from the heat conduction equation, the multidimensional factors affecting electrolyte wettability are comprehensively captured, and the wettability model is accurately trained. Ultimately, the target wettability model can be used to efficiently and accurately assess the electrolyte wettability of the battery under test. This method not only accurately detects electrolyte wettability in lithium batteries without damage, but also accurately estimates the electrolyte content of the battery in situ. This method will facilitate future modeling of electrode aging mechanisms, thereby improving the efficiency of battery quality monitoring and safety evaluation, and providing strong support for battery production and maintenance processes. BRIEF DESCRIPTION OF THE DRAWINGS
[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0032] Figure 1 A diagram illustrating an application environment of a battery electrolyte wettability measurement method combining ultrasonic scanning with thermal conductivity measurement in one embodiment;
[0033] Figure 2 Schematic diagram of a flow chart of a method for measuring battery electrolyte wettability by combining ultrasonic scanning with thermal conductivity measurement in one embodiment;
[0034] Figure 3 1 is a flow chart of a method for obtaining a target infiltration model in one embodiment;
[0035] Figure 4 Schematic diagram of a flow chart of a method for determining electrolyte content in one embodiment;
[0036] Figure 5 Schematic diagram of a process for obtaining a target infiltration model in another embodiment;
[0037] Figure 6 Schematic diagram of a process for obtaining thermal conductivity distribution in one embodiment;
[0038] Figure 7 A schematic flow chart of a method for obtaining thermal conductivity distribution in another embodiment;
[0039] Figure 8 Schematic diagram of a target infiltration model training process in one embodiment;
[0040] Figure 9 is a schematic diagram of a target infiltration model application process in one embodiment;
[0041] Figure 10 A structural block diagram of a battery electrolyte wettability measurement device combining ultrasonic scanning with thermal conductivity measurement in one embodiment;
[0042] Figure 11 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION
[0043] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.
[0044] The embodiment of the present application provides a battery electrolyte wettability measurement method combining ultrasonic scanning with thermal conductivity measurement, which can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 obtains the ultrasonic transmission map, battery heat flow, temperature measurement data, element content of each battery slice and the wettability model to be trained of the target battery from the terminal 102; the element content of the battery slice is obtained by scanning each slice area of the target battery; the battery heat flow and temperature measurement data are used as calculation parameters and substituted into the heat conduction equation of the target battery to obtain the thermal conductivity distribution of the target battery; the ultrasonic transmission map, thermal conductivity distribution and element content of each battery slice are used to train the wettability model to obtain the target wettability model; the ultrasonic transmission map and thermal conductivity distribution of the battery to be measured are input into the target wettability model to obtain the electrolyte wettability information of the battery to be measured. Terminal 102 may include, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers.
[0045] In an exemplary embodiment, Figure 2 As shown, a battery electrolyte wettability measurement method combining ultrasonic scanning and thermal conductivity measurement is provided. Figure 1 The server in FIG. 1 is taken as an example to illustrate the method, including the following steps 202 to 210. Among them:
[0046] Step 202: Obtain the ultrasonic transmission map of the target battery, the battery heat flow, the temperature measurement data, the element content of each battery slice, and the wettability model to be trained.
[0047] Among them, the ultrasonic transmission map can be an image generated by using the reflection and transmission information when ultrasonic waves penetrate an object, which can show the density and uniformity differences of the internal structure of the material.
[0048] The battery heat flow may be the steady amount of heat added to the target battery.
[0049] The temperature measurement data may be data of the temperature field on the battery surface captured by a thermal imager.
[0050] The element content of a battery slice may be the specific content distribution of a specified chemical element in each slice after the battery is divided into several slices.
[0051] The wettability model to be trained may be a neural network model that has not been trained and is used to learn and predict the wettability of the electrolyte in the battery.
[0052] Specifically, multi-dimensional measurement data of the target battery is collected, including ultrasonic transmission maps, battery heat flow, temperature measurement data and element content of each slice.
[0053] During the ultrasound transmission map process, the ultrasound probe position distribution settings are set. A total of 4 ultrasound probes are used. Probes 1 and 2 are relatively distributed on both sides of the surface to be transmitted in order to obtain the projection signal; probes 3 and 4 are located on both sides of the surface to be transmitted, but are not opposite to any probe. The purpose is to obtain the reflected signals from the front and back as the ultrasound transmission map.
[0054] For battery heat flow, keep the ambient temperature constant to avoid factors such as air convection that affect heat conduction. Control the heater attached to one side of the battery (usually the back) to transfer heat to the target battery surface, and record the power it provides (heat flux density (battery heat flow) q = P / A, where P is power and A is the area of the heater).
[0055] For temperature measurement data, after starting the heat flow, use a thermal imager or thermocouple to measure the temperature on the large surface of the battery, and record the temperature changes at different locations over time until a steady state is reached.
[0056] For the element content of each battery slice, first cut and sample different sites of the battery electrode, perform EDS scanning on the sampled electrode slice, and use the content of the target element in the EDS analysis result of the electrode slice as the element content of the battery slice, such as the phosphorus content.
[0057] For the wettability model to be trained, a deep learning convolutional neural network based on the pytorch framework is used through the physical characteristics of the target battery.
[0058] Step 204 : Substitute the battery heat flow and temperature measurement data as calculation parameters into the heat conduction equation of the target battery to obtain the thermal conductivity distribution of the target battery.
[0059] Among them, the heat conduction equation can be a mathematical formula that describes the transfer of heat in a material. The diffusion of heat flow in the material is usually determined based on conditions such as the thermal conductivity, temperature, and heat source of the material.
[0060] Among them, the thermal conductivity distribution can be the spatial variation of the thermal conductivity of different areas of the battery, reflecting the thermal conduction characteristics of the material at different positions.
[0061] Specifically, the temperature distribution T(x, y) at different positions on the battery surface is obtained by a thermal imager, where x and y are the coordinates of the battery surface.
[0062] According to Fourier's law:
[0063]
[0064] in:
[0065] q is the known heat flux density (battery heat flow) (a constant heat flow provided by experiments).
[0066] k is the local thermal conductivity, which is the quantity that needs to be calculated.
[0067] is the temperature gradient (derived from the experimentally obtained temperature distribution).
[0068] Under steady-state conditions, the heat conduction equation can be written as:
[0069]
[0070] Assuming that the thermal conductivity k(x,y) at each point is a local constant, we can calculate the thermal conductivity at different locations through the relationship between temperature gradient and heat flux density:
[0071]
[0072] By counting the thermal conductivity coefficients at different positions, the thermal conductivity distribution of the target battery is determined.
[0073] Step 206 , using the ultrasonic transmission map, the thermal conductivity distribution, and the element content of each battery slice, the wettability model to be trained is trained to obtain a target wettability model.
[0074] Among them, the target wettability model can be a wettability prediction model based on the training, which can use data such as the battery's ultrasonic spectrum and thermal conductivity distribution to efficiently and accurately infer the electrolyte's wettability.
[0075] Specifically, feature extraction is performed on the ultrasonic transmission map and thermal conductivity distribution obtained above to obtain the ultrasonic transmission map features and thermal conductivity features of the target battery; then, based on the indirect measurement method of the electrolyte distribution, the electrolyte content is calculated from the element content of each battery slice, and the electrolyte content of the target battery is calculated in combination with the concentration of the electrolyte solution, and a distribution label corresponding to the electrolyte content is established, and the ultrasonic transmission map features and thermal conductivity features of the target battery are input into the wettability model to be trained, and the parameters of the wettability model to be trained are iteratively updated using the gradient descent optimizer provided by the framework of the wettability model to be trained. If the performance of the wettability model to be trained fails to meet the preset requirements, the ultrasonic transmission map, battery heat flow, temperature measurement data, and element content of each battery slice of another target battery are obtained, and the model is continued to be trained after processing the data of the target battery until the performance of the wettability model to be trained meets the preset requirements, that is, the model converges, and the wettability model to be trained is used as the target wettability model. Figure 8 As shown, Figure 8 This paper describes the training steps for a neural network model for battery electrolyte distribution estimation. First, the battery's input feature data is collected using various methods: an ultrasonic scanner is used to obtain an ultrasonic transmission spectrum of the battery, which reflects the battery's internal structure and material properties; thermal conductivity measurement methods are used to obtain the thermal conductivity distribution at different locations in the battery, characterizing the thermal conduction characteristics. Simultaneously, the battery is disassembled and the lithium hexafluorophosphate (LiPF6) mass content in specific areas is measured using techniques such as EDS analysis. The resulting electrolyte distribution serves as the model's output label. These input features and label data are then fed into a convolutional neural network for training. Through continuous parameter adjustment and optimization, the neural network gradually learns the mapping relationship between the ultrasonic spectrum and the thermal conductivity distribution to the electrolyte content, until the model converges and ultimately forms a reliable target wettability model.
[0076] Step 208 : Input the ultrasonic transmission map and thermal conductivity distribution of the battery to be measured into the target wettability model to obtain electrolyte wettability information of the battery to be measured.
[0077] The electrolyte wettability information may be the distribution and degree of electrolyte in the battery, which is used to judge the health status and performance stability of the battery.
[0078] Specifically, during the actual testing process, the ultrasonic transmission spectrum and thermal conductivity distribution of the battery to be tested are input into the trained target wettability model. The model uses feedforward calculation to quickly output electrolyte wettability information based on the input features, revealing the electrolyte infiltration distribution and degree of infiltration in the battery. This output not only provides quantitative indicators for health monitoring of battery performance status, but also reflects abnormal phenomena inside the battery in real time. Figure 9 As shown, Figure 9This paper describes the estimation process of a trained neural network in practical applications. For a battery with unknown electrolyte distribution, its ultrasonic transmission spectrum and thermal conductivity distribution are used as input features and fed into a trained target wettability model. Based on the mapping relationships learned during training, the network outputs an estimated distribution of the electrolyte within the battery. Calculation of the target wettability model enables non-destructive and rapid detection of electrolyte wettability within the battery, thereby assessing battery performance and assisting in safety analysis and quality control.
[0079] In the above-mentioned battery electrolyte wettability measurement method combining ultrasonic scanning with thermal conductivity measurement, the ultrasonic transmission map, battery heat flow, temperature measurement data, element content of each battery slice and the wettability model to be trained of the target battery are obtained; the element content of the battery slice is obtained by scanning each slice area of the target battery; the battery heat flow and temperature measurement data are used as calculation parameters and substituted into the heat conduction equation of the target battery to obtain the thermal conductivity distribution of the target battery; the wettability model to be trained is trained using the ultrasonic transmission map, thermal conductivity distribution and element content of each battery slice to obtain the target wettability model; the ultrasonic transmission map and thermal conductivity distribution of the battery to be measured are input into the target wettability model to obtain the electrolyte wettability information of the battery to be measured.
[0080] By integrating multiple sources of information, including the target battery's ultrasonic transmission spectrum, battery heat flow and temperature measurement data, and the elemental content of each battery slice, combined with the thermal conductivity distribution obtained from the heat conduction equation, the multidimensional factors affecting electrolyte wettability are comprehensively captured, and the wettability model is accurately trained. Ultimately, the target wettability model can be used to efficiently and accurately assess the electrolyte wettability of the battery under test. This method not only accurately detects electrolyte wettability in lithium batteries without damage, but also accurately estimates the electrolyte content of the battery in situ. This method will facilitate future modeling of electrode aging mechanisms, thereby improving the efficiency of battery quality monitoring and safety evaluation, and providing strong support for battery production and maintenance processes.
[0081] In an exemplary embodiment, Figure 3 As shown, the wettability model to be trained is trained using the ultrasonic transmission spectrum, the thermal conductivity distribution, and the element content of each battery slice to obtain the target wettability model, including steps 302 to 304.
[0082] Step 302 : Determine the electrolyte content corresponding to each slice area according to the corresponding relationship between the element content of each battery slice and each slice area.
[0083] The electrolyte content can be the specific distribution and concentration level of the electrolyte in various regions inside the battery, and is usually measured as the amount of electrolyte per unit volume or mass.
[0084] Specifically, different sites of the battery electrode are first cut to obtain each slice area of the target battery. The content and distribution of key elements in each slice are scanned using EDS and scanning electron microscopy (SEM). The mass ratio of the key elements in the electrolyte of the slice area of the target battery is calculated, combined with the role of each element in the electrolyte and its proportion, the electrolyte content in each slice area is further calculated.
[0085] Step 304 : Using the distribution of each electrolyte content as an output label, the ultrasonic transmission map and the thermal conductivity distribution are used to train the wettability model to obtain a target wettability model.
[0086] Specifically, the calculated electrolyte content distribution of each slice area is integrated into a complete electrolyte wettability map to show the electrolyte distribution at different positions inside the battery in detail. The distribution map is set as the output label of the model training to provide a clear and physically meaningful target output for the wettability model. During the model training process, the collected ultrasonic transmission map and the calculated thermal conductivity distribution are input into the wettability model to be trained. The ultrasonic map provides information about the internal structure of the battery, while the thermal conductivity distribution reflects the heat transfer characteristics of the material. The two respectively reveal the key characteristics of the electrolyte distribution and wettability in the battery. With the electrolyte content distribution as the training label, the model is gradually optimized through supervised learning, identifying the complex relationship between the ultrasonic and thermal conductivity characteristics and the electrolyte infiltration pattern, and finally training a target wettability model with generalization ability, which can accurately predict the electrolyte distribution of other batteries and realize the effective quantification of electrolyte wettability.
[0087] In this embodiment, by accurately calculating the element content of each battery slice area and determining its electrolyte content, using the electrolyte distribution as the output label, and combining the ultrasonic transmission map and thermal conductivity distribution to train the wettability model, a high-precision target wettability model can be obtained. This model can deeply understand the physical and chemical properties inside the battery, achieve accurate prediction of the electrolyte wettability, and provide a reliable basis for subsequent battery performance analysis and safety monitoring. This solution significantly improves the accuracy of electrolyte distribution detection, effectively supports non-destructive testing of the internal structure of the battery, and makes battery quality control and safety assessment more forward-looking and scientific.
[0088] In an exemplary embodiment, Figure 4 As shown, according to the corresponding relationship between the element content of each battery slice and each slice area, the electrolyte content corresponding to each slice area is determined, including steps 402 to 406.
[0089] Step 402: Obtain a preset concentration of the electrolyte.
[0090] Step 404 : determining the electrolyte content of each slice region according to the mass ratio of the battery slice element content to the electrolyte in each slice region.
[0091] The preset concentration of the electrolyte may be a standard concentration value of the electrolyte in the electrolyte solution, which is usually obtained by battery design or experimental measurement to ensure the effectiveness and stability of the electrochemical reaction in the battery.
[0092] The electrolyte content may be the actual content of the electrolyte in the slice area.
[0093] Specifically, the proportion of key elements in the electrolyte in each slice area is analyzed in detail, and their actual measured values are converted into corresponding electrolyte contents using the known proportions of these elements in the electrolyte. For example, if the mass ratio of an element in the electrolyte is known, the ratio of its content to other elements can reflect the electrolyte content. This method of inferring the electrolyte by element content enables an accurate estimation of the electrolyte content in each slice area, providing key data support for the calculation of electrolyte content.
[0094] Step 406 : Obtain the electrolyte content corresponding to each slice region according to the electrolyte content of each slice region and the preset electrolyte concentration.
[0095] Specifically, the electrolyte content of each slice area is combined with the preset electrolyte concentration to infer the actual electrolyte content. Specifically, according to the electrolyte content of each slice area, the preset concentration is converted into the corresponding electrolyte volume or mass distribution, so as to accurately obtain the electrolyte wettability of each area. This step systematically converts the microscopic components into a macroscopic expression of the electrolyte content by comparing the electrolyte concentration with the preset value, providing a reliable and quantitative reference for the electrolyte distribution inside the battery, so that the subsequent wettability model training can be optimized based on the accurate electrolyte wettability map.
[0096] In this embodiment, by obtaining the preset concentration of the electrolyte and combining it with the electrolyte mass ratio of the battery element content in each slice area, the electrolyte content of each slice area is accurately calculated, and then the electrolyte content is determined based on the electrolyte content and the preset concentration, thereby achieving an accurate characterization of the electrolyte distribution inside the battery. This method can non-destructively and accurately reveal the infiltration of the electrolyte inside the battery, ensuring the quality consistency and safety of the battery during production and use. Through the refined analysis of the electrolyte distribution, a scientific basis can be provided for battery design optimization, performance improvement and service life extension, making battery manufacturing and monitoring more scientific and efficient.
[0097] In an exemplary embodiment, Figure 5As shown, the distribution of each electrolyte content is used as the output label, and the ultrasonic transmission spectrum and thermal conductivity coefficient distribution are used to train the wettability model to obtain the target wettability model, including steps 502 to 506.
[0098] Step 502 : Input the ultrasound transmission map and the thermal conductivity distribution into the wettability model to be trained to obtain the predicted electrolyte content of each slice region.
[0099] The predicted electrolyte content may be estimated data obtained by estimating the quantity and distribution of electrolyte in each slice area of the battery based on input data (such as ultrasonic transmission maps and thermal conductivity distribution) through a model.
[0100] Specifically, the experimentally acquired ultrasound transmission maps and thermal conductivity distributions are fed as input features into the wettability model to be trained. The ultrasound transmission maps provide physical characteristics of the battery's internal microstructure, revealing material density and distribution, while the thermal conductivity distribution reflects the efficiency of heat conduction in different regions of the battery, characterizing the thermodynamic properties of different materials. Based on these input features and combined with its initial parameters, the model calculates and outputs a preliminary prediction of the electrolyte content in each slice region.
[0101] Step 504 : adjusting the model parameters of the wettability model to be trained according to the difference between the predicted electrolyte content and the electrolyte content of each slice region to obtain an adjusted wettability model.
[0102] The adjusted wettability model may be a model obtained by optimizing the internal parameters of the model to reduce the error according to the difference between the electrolyte content predicted by the model and the actual measured value during the training process.
[0103] Specifically, after obtaining preliminary predictions, the model's predicted electrolyte content is compared with the actual measured electrolyte content on a slice-by-slice basis to calculate the difference between the two. Based on these differences, optimization algorithms such as gradient descent are applied to gradually adjust the model parameters to reduce prediction errors, allowing the model to optimize its feature recognition capabilities, making the predicted output more accurately match the actual electrolyte distribution, and enhancing the model's generalization ability, enabling it to more effectively capture the complex relationship between ultrasonic and thermal conductivity characteristics and electrolyte wetting patterns, thereby obtaining an adjusted wettability model.
[0104] In step 506, the adjusted wettability model is used as the wettability model to be trained, and the ultrasonic transmission map, battery heat flow, temperature measurement data, and element content of each battery slice of another battery are obtained. Then, the step of substituting the battery heat flow and temperature measurement data as calculation parameters into the heat conduction equation of the target battery to obtain the thermal conductivity coefficient distribution of the target battery is returned.
[0105] Specifically, the adjusted wettability model with parameter adjustment is used as the new wettability model to be trained, and the ultrasonic transmission map, battery heat flow, temperature measurement data, and element content of each battery slice of another battery are obtained at the terminal and used as a round of data input, prediction, and error correction. In each iteration, the model reinterprets the input features using the updated parameters, thereby further optimizing the prediction accuracy in the new training data cycle. As the number of training times increases, the model gradually learns and grasps the deep connection between the input features and the electrolyte distribution, so that its prediction performance continues to improve and gradually tends to the ideal accuracy.
[0106] Step 508 : When the difference between the predicted electrolyte content and the electrolyte content of each slice region is less than the set value of the electrolyte prediction difference, a target wettability model is obtained.
[0107] Specifically, when the error between the model's predicted electrolyte content and the electrolyte content in each slice area is less than the preset allowable difference value, the training process ends and the model reaches the predetermined accuracy standard. At this point, the model, which has undergone multiple iterations of optimization, is the final target wettability model, capable of effectively predicting battery electrolyte distribution under new data conditions, providing a highly accurate prediction tool for internal electrolyte wettability analysis of batteries, thereby supporting efficient battery performance and safety assessment.
[0108] In this embodiment, by inputting the ultrasonic transmission map and thermal conductivity distribution into the wettability model to be trained, the predicted electrolyte content of the slice area is obtained, and the model parameters are gradually adjusted to reduce the prediction error, the accuracy and generalization ability of the model can be effectively improved. Through multiple cycles of training, the performance of the model on different battery samples is gradually optimized, and finally a high-precision target wettability model with an error lower than the set value is obtained. This method can non-destructively and accurately predict the distribution state of the electrolyte inside the battery, making battery quality detection more refined and standardized, providing important guarantees for battery safety and performance optimization, and significantly improving detection efficiency and reliability.
[0109] In an exemplary embodiment, Figure 6 As shown, the battery heat flow and temperature measurement data are used as calculation parameters and substituted into the heat conduction equation of the target battery to obtain the thermal conductivity distribution of the target battery, including steps 602 to 604.
[0110] Step 602: Calculate the measured temperature gradient of the target battery perpendicular to the large surface according to the temperature measurement data.
[0111] The measured temperature gradient can be the rate at which the temperature changes with spatial position inside or on the surface of an object, usually expressed as the temperature difference per unit distance.
[0112] Specifically, these measurement data are differentially calculated along the spatial direction to obtain the temperature gradient of the battery in the large surface direction, that is, the temperature change rate per unit distance. The uniformity of the sensor layout and the complexity of the battery structure must be considered in this gradient calculation process to ensure that the temperature gradient can accurately characterize the heat flow transfer in each layer of the battery. The final temperature gradient data will serve as an important input for the subsequent calculation of the thermal conductivity coefficient, providing a reliable basis for further heat conduction analysis. Among them, the large surface direction of the battery refers to the parallel plane direction with the largest area in the battery structure, usually the front and back of the battery, which is usually perpendicular to the thickness direction of the battery. The heat conduction and heat dissipation characteristics in the large surface direction often have an important influence on the overall thermal management and performance stability of the battery. Therefore, focusing on the temperature changes and heat flow transfer in this direction in thermal analysis can help to more accurately understand the heat dissipation behavior of the battery under different operating conditions.
[0113] Step 604 : Calculate the ratio of the battery heat flow to the measured temperature gradient to obtain a thermal conductivity distribution.
[0114] Specifically, based on the known battery heat flow data, a ratio operation is performed on it and the temperature gradient calculated in the previous step to determine the thermal conductivity of each region, and the thermal conductivity distribution is obtained by statistically analyzing the thermal conductivity of each region. Among them, the thermal conductivity calculation needs to be performed region by region to ensure the matching of heat flow and temperature gradient, so that each ratio can reflect the actual thermal conductivity of the corresponding location. The thermal conductivity distribution map generated by this ratio operation can reveal the response characteristics of different regions of the battery in heat flow transfer, thereby providing in-depth information about the battery's heat dissipation performance and internal structural characteristics.
[0115] In this embodiment, by accurately calculating the temperature gradient in the large surface direction of the battery and using its ratio to the battery heat flow to determine the thermal conductivity distribution, the thermal conductivity characteristics of each area of the battery can be fully revealed. This method provides a detailed thermal conductivity distribution map, which accurately characterizes the heat dissipation capabilities of different materials and structures within the battery. This process not only helps optimize the battery's thermal management design and reduce the risk of thermal runaway, but also improves the battery's performance under efficient and stable operating conditions, thereby effectively extending the battery life and ensuring its safety.
[0116] In an exemplary embodiment, Figure 7 As shown, the ratio of the battery heat flow to the measured temperature gradient is calculated to obtain the thermal conductivity distribution, including steps 702 to 706.
[0117] Step 702 : Segment the large surface of the target battery according to the measured temperature gradient to obtain segmented regions of the large surface of each battery.
[0118] The battery large surface segmentation area can be to divide the surface of the battery large surface into several small areas, and the temperature change rate in each area is relatively uniform, so as to more accurately analyze the heat conduction characteristics of the part.
[0119] Specifically, based on the distribution of the measured temperature gradient across the large surface of the battery, the large surface is finely segmented to form multiple small areas with relatively uniform temperature change rates. The temperature gradient change rate within each segmented area is less than a preset threshold to ensure that the temperature changes in each area are relatively smooth. This segmentation strategy can create multiple sub-areas with similar thermal characteristics on large surfaces with complex temperature changes, avoiding errors caused by excessive temperature gradients. This provides ideal boundary conditions for the calculation of the thermal conductivity coefficient of each area, ensuring the accuracy of heat flow analysis and the consistency of regional thermal conductivity behavior.
[0120] Step 704 , calculate the battery heat flow and the temperature change rate corresponding to each battery large surface segmented area to obtain the thermal conductivity of each battery.
[0121] The temperature change rate may be the rate at which the temperature of an object in a certain direction changes with position, and is usually expressed as a temperature difference per unit distance.
[0122] Battery thermal conductivity is a parameter that describes the thermal conductivity efficiency of a battery material or structural region, reflecting the ability of heat to spread within that region. A higher thermal conductivity indicates that the material in that region is more likely to transfer heat.
[0123] Specifically, in the divided battery large surface segmentation area, the thermal conductivity of each battery large surface segmentation area is calculated in combination with the measured battery heat flow data. Specifically, the heat flux density in each battery large surface segmentation area is combined with the corresponding temperature change rate, and the battery thermal conductivity of each battery large surface segmentation area is obtained by the ratio of the two. This step reflects the heat conduction efficiency of each sub-area and reveals the thermal response characteristics of the material at that location. The region-by-region calculation method ensures the spatial accuracy of the thermal conductivity coefficient, so that the thermal conductivity performance of each segmentation area can be presented in detail, forming a more accurate picture of heat flow transfer.
[0124] Step 706 : Determine the thermal conductivity distribution according to the thermal conductivity of each battery.
[0125] Specifically, the thermal conductivity coefficients of all sub-areas of the battery's large surface are integrated into a comprehensive thermal conductivity distribution map. This distribution map shows the differences in thermal conductivity performance across the battery's large surface. By summarizing the thermal conductivity coefficients of each area, an overall thermal conductivity distribution is obtained, which can detail the impact of different materials or structures on heat transfer across the battery's large surface. This distribution not only provides a reference for optimizing heat dissipation under various operating conditions, but also provides key data for battery thermal management design, thermal runaway prevention, and battery performance improvement.
[0126] In this embodiment, the battery's large surface is segmented into temperature gradients, ensuring that the temperature change rate of each segmented area is within a controllable range. The ratio of heat flux to temperature change rate in each area is then calculated to determine the thermal conductivity distribution. This method accurately displays the thermal conductivity of different battery regions. This method not only provides a detailed thermal conductivity distribution map, helping to identify uneven heat flow, but also reveals potential hotspots or heat dissipation bottlenecks, providing scientific guidance for battery heat dissipation optimization and thermal management, effectively improving battery safety and long-term stability.
[0127] It should be understood that, although the steps in the flowcharts of the above embodiments are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the flowcharts of the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of steps or stages in other steps.
[0128] Based on the same inventive concept, the embodiments of the present application also provide a battery electrolyte wettability measurement device that combines ultrasonic scanning and thermal conductivity measurement for implementing the above-mentioned battery electrolyte wettability measurement method that combines ultrasonic scanning and thermal conductivity measurement. The implementation solution provided by the device is similar to the implementation solution described in the above-mentioned method. Therefore, the specific limitations of one or more embodiments of the battery electrolyte wettability measurement device that combines ultrasonic scanning and thermal conductivity measurement provided below can be found in the above-mentioned limitations of the battery electrolyte wettability measurement method that combines ultrasonic scanning and thermal conductivity measurement, and will not be repeated here.
[0129] In an exemplary embodiment, Figure 10 As shown, a battery electrolyte wettability measurement device combining ultrasonic scanning and thermal conductivity measurement is provided, comprising: a battery data acquisition module 1002, a coefficient distribution acquisition module 1004, a wettability model training module 1006, and a wettability calculation module 1008, wherein:
[0130] Battery data acquisition module 1002 is used to obtain the ultrasonic transmission map of the target battery, battery heat flow, temperature measurement data, element content of each battery slice, and the wettability model to be trained; the element content of the battery slice is obtained by scanning each slice area of the target battery;
[0131] The coefficient distribution obtaining module 1004 is used to substitute the battery heat flow and temperature measurement data as calculation parameters into the heat conduction equation of the target battery to obtain the thermal conductivity distribution of the target battery;
[0132] The wettability model training module 1006 is used to train the wettability model to be trained using the ultrasonic transmission map, the thermal conductivity distribution, and the element content of each battery slice to obtain a target wettability model;
[0133] The wettability calculation module 1008 is used to input the ultrasonic transmission map and thermal conductivity distribution of the battery to be measured into the target wettability model to obtain the electrolyte wettability information of the battery to be measured.
[0134] In one embodiment, the wettability model training module 1006 is also used to determine the electrolyte content corresponding to each slice area based on the correspondence between the element content of each battery slice and each slice area; using each electrolyte content as an output label, the ultrasonic transmission map and the thermal conductivity coefficient distribution are used to train the wettability model to obtain a target wettability model.
[0135] In one embodiment, the wettability model training module 1006 is also used to obtain a preset concentration of the electrolyte; determine the electrolyte content of each slice area based on the mass ratio of the battery slice element content to the electrolyte in each slice area; and obtain the electrolyte content corresponding to each slice area based on the electrolyte content of each slice area and the preset concentration of the electrolyte.
[0136] In one embodiment, the wettability model training module 1006 is also used to input the ultrasonic transmission map and the thermal conductivity distribution into the wettability model to be trained to obtain the predicted electrolyte content of each slice area; adjust the model parameters of the wettability model to be trained according to the difference between the predicted electrolyte content and the electrolyte content of each slice area to obtain an adjusted wettability model; use the adjusted wettability model as the wettability model to be trained, obtain the ultrasonic transmission map, battery heat flow, temperature measurement data and element content of each battery slice of another battery, and return to execute the step of substituting the battery heat flow and temperature measurement data as calculation parameters into the heat conduction equation of the target battery to obtain the thermal conductivity distribution of the target battery; when the difference between the predicted electrolyte content and the electrolyte content of each slice area is less than the electrolyte prediction difference set value, the target wettability model is obtained.
[0137] In one embodiment, the coefficient distribution obtaining module 1004 is further used to calculate the measured temperature gradient of the target battery perpendicular to the large surface direction based on the temperature measurement data; calculate the ratio of the battery heat flow to the measured temperature gradient to obtain the thermal conductivity distribution.
[0138] In one embodiment, the coefficient distribution obtaining module 1004 is also used to segment the large surface of the target battery according to the measured temperature gradient to obtain each battery large surface segmented area; each battery large surface segmented area has a temperature change rate less than a preset temperature change rate; calculate the battery heat flow and the temperature change rate corresponding to each battery large surface segmented area to obtain the thermal conductivity of each battery; and determine the thermal conductivity distribution according to the thermal conductivity of each battery.
[0139] Each module in the aforementioned battery electrolyte wettability measurement device combining ultrasonic scanning with thermal conductivity measurement can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a computer device memory in software form, so that the processor can call and execute the corresponding operations of each module.
[0140] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Figure 11 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. 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 computer program in the non-volatile storage medium. The database of the computer device is used to store server data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a battery electrolyte infiltration measurement method combining ultrasonic scanning with thermal conductivity measurement is implemented.
[0141] Those skilled in the art will understand that Figure 11 The structure shown in the figure is only a block diagram of a part of the 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 component arrangement.
[0142] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.
[0143] In one embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the steps in the above-mentioned method embodiments when executed by a processor.
[0144] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps of each of the above-described method embodiments.
[0145] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.
[0146] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.
[0147] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0148] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.
Claims
1. A method for measuring the wettability of battery electrolyte by combining ultrasonic scanning with thermal conductivity measurement, characterized in that: The method comprises: Obtaining the ultrasonic transmission spectrum, battery heat flow, temperature measurement data, element content of each battery slice, and wettability model to be trained of the target battery; the element content of the battery slice is obtained by scanning each slice area of the target battery; Substituting the battery heat flow and the temperature measurement data as calculation parameters into the heat conduction equation of the target battery to obtain the thermal conductivity distribution of the target battery; Using the ultrasonic transmission map, the thermal conductivity distribution and the element content of each of the battery slices, the wettability model to be trained is trained to obtain a target wettability model; The ultrasonic transmission spectrum and thermal conductivity distribution of the battery to be measured are input into the target wettability model to obtain the electrolyte wettability information of the battery to be measured.
2. The method according to claim 1, characterized in that The method of training the wettability model to be trained by using the ultrasonic transmission map, the thermal conductivity distribution and the element content of each battery slice to obtain a target wettability model includes: Determining the electrolyte content corresponding to each of the slice areas according to the corresponding relationship between the element content of each of the battery slices and each of the slice areas; The distribution of each electrolyte content is used as an output label, and the ultrasonic transmission map and the thermal conductivity distribution are used to train the wettability model to be trained to obtain a target wettability model.
3. The method according to claim 2, characterized in that The determining the electrolyte content corresponding to each of the slice areas according to the correspondence between the element content of each of the battery slices and each of the slice areas comprises: Obtaining the preset concentration of electrolyte; Determining the electrolyte content of each of the slice regions according to the mass ratio of the battery slice element content to the electrolyte in each of the slice regions; The electrolyte content corresponding to each of the slicing regions is obtained according to the electrolyte content of each of the slicing regions and the preset concentration of the electrolyte.
4. The method according to claim 2, characterized in that: The method uses the distribution of each electrolyte content as an output label, uses the ultrasonic transmission spectrum and the thermal conductivity distribution to train the wettability model to be trained, and obtains a target wettability model, including: Inputting the ultrasonic transmission map and the thermal conductivity distribution into the wettability model to be trained to obtain the predicted electrolyte content of each slice area; According to the difference between the predicted electrolyte content and the electrolyte content of each slice region, the model parameters of the infiltration model to be trained are adjusted to obtain an adjusted infiltration model; Using the adjusted wettability model as the wettability model to be trained, obtaining an ultrasonic transmission map, battery heat flow, temperature measurement data, and element content of each battery slice of another battery, and returning to execute the step of using the battery heat flow and the temperature measurement data as calculation parameters, substituting them into the heat conduction equation of the target battery, and obtaining the thermal conductivity distribution of the target battery; In the case where the difference between the predicted electrolyte content and the electrolyte content of each of the slice regions is less than the set value of the electrolyte prediction difference, the target wettability model is obtained.
5. The method according to any one of claims 1 to 4, characterized in that: Substituting the battery heat flow and the temperature measurement data as calculation parameters into the heat conduction equation of the target battery to obtain the thermal conductivity distribution of the target battery includes: Calculating the measured temperature gradient of the target battery perpendicular to the large surface according to the temperature measurement data; The ratio of the battery heat flow to the measured temperature gradient is calculated to obtain the thermal conductivity distribution.
6. The method according to claim 5, characterized in that The calculating the ratio of the battery heat flow to the measured temperature gradient to obtain the thermal conductivity distribution includes: According to the measured temperature gradient, the large surface of the target battery is segmented to obtain each battery large surface segmented area; each of the battery large surface segmented areas has a temperature change rate less than a preset rate; Calculating the battery heat flow and the temperature change rate corresponding to each of the battery large surface divided areas to obtain the thermal conductivity of each battery; The thermal conductivity distribution is determined according to the thermal conductivity of each battery.
7. A battery electrolyte wettability measurement device combining ultrasonic scanning with thermal conductivity measurement, characterized in that: The device comprises: A battery data acquisition module, used to acquire the ultrasonic transmission map of the target battery, the battery heat flow, the temperature measurement data, the element content of each battery slice, and the wettability model to be trained; the element content of the battery slice is obtained by scanning each slice area of the target battery; A coefficient distribution obtaining module, used to substitute the battery heat flow and the temperature measurement data as calculation parameters into the heat conduction equation of the target battery to obtain the thermal conductivity distribution of the target battery; A wettability model training module, used to train the wettability model to be trained using the ultrasonic transmission map, the thermal conductivity distribution and the element content of each battery slice to obtain a target wettability model; The wettability calculation module is used to input the ultrasonic transmission spectrum and thermal conductivity distribution of the battery to be measured into the target wettability model to obtain the electrolyte wettability information of the battery to be measured.
8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.
10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.