Semi-solid-state battery electrolyte performance optimization method and semi-solid-state battery

Abstract

The invention relates to a semi-solid-state battery electrolyte performance optimization method and a semi-solid-state battery, and relates to the technical field of battery performance optimizing.The method comprises the steps that battery operation environment temperature characteristics are collected, and high-temperature and low-temperature time coefficients, a high-temperature coefficient and a low-temperature coefficient are obtained through calculation; predicting high-temperature operation stability and low-temperature operation conductivity of the electrolyte according to the filler parameters to obtain a stability parameter and a conductivity parameter, and calculating to obtain a time performance value by combining a high-temperature time coefficient and a low-temperature time coefficient; according to the stability, the conductivity parameters, the high-temperature coefficient and the low-temperature coefficient, a two-dimensional performance value is obtained through calculation, a comprehensive performance value is calculated, and filler parameters are optimized to obtain optimal filler parameters. The technical problem that the comprehensive performance of the battery is insufficient due to the fact that an inorganic filler scheme matched with the working environment of the battery cannot be accurately set in a traditional method can be solved; the scientificity and accuracy of inorganic filler scheme setting can be improved, and the comprehensive performance of the battery in a practical application environment is remarkably enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of battery performance optimization, and in particular to a method for optimizing the performance of a semi-solid-state battery electrolyte and a semi-solid-state battery. Background Art

[0002] One of the core components of semi-solid-state batteries is the electrolyte, which is usually composed of materials such as polymers, organic liquid electrolytes and lithium salts. It has structural stability, good conductivity and low leakage risk.

[0003] However, traditional semi-solid-state battery electrolyte optimization methods mainly rely on static material performance optimization and fail to fully consider the impact of the actual battery operating environment on electrolyte performance. For example, when the battery operates in a high-temperature environment, the stability of the electrolyte may be affected by the temperature increase, resulting in structural changes or performance degradation; while in a low-temperature environment, the ionic conductivity of the electrolyte usually decreases significantly, affecting the discharge performance of the battery.

[0004] Therefore, traditional battery electrolyte optimization methods often cannot take into account the performance requirements of the battery under different temperature conditions, especially the structural stability at high temperature and the conductivity at low temperature, resulting in insufficient overall performance of the battery. Summary of the Invention

[0005] The present invention aims to solve the technical problem that traditional semi-solid-state battery electrolyte performance optimization methods cannot accurately set the inorganic filler solution that is compatible with the battery working environment, thereby leading to insufficient overall battery performance. A semi-solid-state battery electrolyte performance optimization method and a semi-solid-state battery are provided to solve the problem.

[0006] The technical solution of the present invention to solve the above technical problems is as follows:

[0007] In a first aspect, the present invention provides a method for optimizing the performance of a semi-solid-state battery electrolyte, comprising: constructing a filler parameter space for adding inorganic fillers to a semi-solid-state battery electrolyte, and randomly selecting filler parameters; collecting the ambient temperature characteristics of the semi-solid-state battery operation, and calculating the high-temperature time coefficient, low-temperature time coefficient, high-temperature coefficient, and low-temperature coefficient; predicting the high-temperature operation stability and low-temperature operation conductivity of the semi-solid-state battery electrolyte based on the filler parameters, obtaining stability parameters and conductivity parameters, and calculating the time performance value in combination with the high-temperature time coefficient and low-temperature time coefficient; calculating the two-dimensional performance value based on the stability parameter, conductivity parameter, high-temperature coefficient, and low-temperature coefficient, and calculating the comprehensive performance value in combination with the time performance value, evaluating and optimizing the filler parameters, and obtaining the optimal filler parameters as the performance optimization result.

[0008] Optionally, the method for optimizing the performance of a semi-solid-state battery electrolyte further includes: obtaining the type and amount of inorganic fillers added to the semi-solid-state battery electrolyte; traversing and combining the types and amounts of inorganic fillers to obtain multiple sample filler parameters and constructing a filler parameter space; and randomly selecting filler parameters within the filler parameter space.

[0009] Optionally, the method for optimizing the performance of a semi-solid-state battery electrolyte also includes: collecting a temperature sequence within the operating environment of the semi-solid-state battery as an ambient temperature characteristic; extracting the highest temperature and the lowest temperature within the ambient temperature characteristic, and respectively calculating the deviation ratios of the highest temperature and the lowest temperature from the preset temperature to obtain a highest deviation ratio and a lowest deviation ratio; allocating and calculating a high temperature coefficient and a low temperature coefficient based on the highest deviation ratio and the lowest deviation ratio; and calculating a high temperature time coefficient and a low temperature time coefficient based on the ambient temperature characteristic.

[0010] Optionally, the method for optimizing the performance of a semi-solid-state battery electrolyte also includes: extracting temperatures greater than the preset temperature within the ambient temperature characteristics, and calculating the proportion as a high-temperature time coefficient; extracting temperatures less than or equal to the preset temperature within the ambient temperature characteristics, and calculating the proportion as a low-temperature time coefficient.

[0011] Optionally, the method for optimizing the performance of a semi-solid-state battery electrolyte also includes: obtaining design test data of the semi-solid-state battery electrolyte and collecting a sample filler parameter set; collecting the operating stability parameters of the semi-solid-state battery electrolyte at a preset high temperature and the conductivity parameters at a preset low temperature under different sample filler parameters, and marking them as a sample stability parameter set and a sample conductivity parameter set; dividing the sample filler parameter set, the sample stability parameter set and the sample conductivity parameter set to obtain a training set, a validation set and a test set; using machine learning to construct an electrolyte performance predictor, and using the training set, validation set and test set to perform supervised training, validation and testing on the electrolyte performance predictor until the requirements are met; inputting the filler parameters into the electrolyte performance predictor, and outputting the stability parameters and conductivity parameters.

[0012] Optionally, the method for optimizing the performance of a semi-solid-state battery electrolyte also includes: allocating a first high-temperature weight and a first low-temperature weight based on the high-temperature time coefficient and the low-temperature time coefficient; using the first high-temperature weight and the first low-temperature weight to perform a weighted calculation on the ratio of the stability parameter and the conductivity parameter to the preset stability parameter and the preset conductivity parameter to obtain a time performance value.

[0013] Optionally, the semi-solid-state battery electrolyte performance optimization method also includes: allocating a second high-temperature weight and a second low-temperature weight based on the high-temperature coefficient and the low-temperature coefficient; using the second high-temperature weight and the second low-temperature weight, performing a weighted calculation on the ratio of the stability parameter and the conductivity parameter to the preset stability parameter and the preset conductivity parameter to obtain a two-dimensional performance value; calculating a comprehensive performance value based on the time performance value and the two-dimensional performance value; continuing the random selection of filler parameters and the evaluation calculation of the comprehensive performance value to complete the optimization of the filler parameters, obtain the filler parameters with the largest comprehensive performance value, obtain the optimal filler parameters and prepare the electrolyte as the performance optimization result.

[0014] In a second aspect, the present invention provides a semi-solid-state battery, wherein the electrolyte of the semi-solid-state battery is prepared using any one of the semi-solid-state battery electrolyte performance optimization methods described in the first aspect.

[0015] The beneficial effects of the present invention are as follows: by constructing a filler parameter space for adding inorganic fillers to a semi-solid battery electrolyte and randomly selecting any filler parameter; then collecting the ambient temperature characteristics of the semi-solid battery operation and calculating the high temperature time coefficient, low temperature time coefficient, high temperature coefficient and low temperature coefficient; then, based on the filler parameters, predicting the high temperature operation stability and low temperature operation conductivity of the semi-solid battery electrolyte to obtain stability parameters and conductivity parameters; further calculating a time performance value based on the stability parameter, conductivity parameter, high temperature time coefficient and low temperature time coefficient; on the other hand, calculating a two-dimensional performance value based on the stability parameter, conductivity parameter, high temperature coefficient and low temperature coefficient, and calculating a comprehensive performance value in combination with the time performance value; then continuing to randomly select filler parameters and evaluate and calculate the comprehensive performance value until the optimization converges, selecting the filler parameter with the largest comprehensive performance value as the optimal filler parameter and preparing the electrolyte as the performance optimization result; that is, by optimizing the inorganic filler parameters in combination with the battery operation environment, the scientificity and accuracy of the inorganic filler scheme setting in the electrolyte can be improved, thereby effectively improving the high temperature stability and low temperature conductivity of the battery, and significantly enhancing the comprehensive performance of the semi-solid battery in actual application environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic flow chart of a method for optimizing the performance of a semi-solid-state battery electrolyte provided by the present invention;

[0017] Figure 2 A schematic diagram of a process for obtaining stability parameters and conductivity parameters in a semi-solid battery electrolyte performance optimization method provided by the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the specified features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0020] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or illustration". Any embodiment of the present invention described as "for example" is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed herein.

[0021] Example 1, as Figure 1 As shown, an embodiment of the present invention provides a method for optimizing the performance of a semi-solid-state battery electrolyte, comprising:

[0022] S100: Construct a filler parameter space for adding inorganic fillers to semi-solid battery electrolytes and randomly select filler parameters.

[0023] Furthermore, step S100 of the present invention further includes:

[0024] S110: Obtain the type and amount of inorganic fillers added to the semi-solid battery electrolyte; S120: Traverse and combine the types and amounts of inorganic fillers to obtain multiple sample filler parameters and construct a filler parameter space; S130: Randomly select filler parameters in the filler parameter space.

[0025] Specifically, first, the type and amount of inorganic fillers added to the semi-solid battery electrolyte are obtained. The type and amount of inorganic fillers will affect the structure and conductivity of the electrolyte, and can be set according to the battery type and actual scenario. The types of inorganic fillers include but are not limited to oxides, fluorides, silicates, carbonates, etc., such as aluminum oxide, silicon oxide, calcium silicate, magnesium silicate, calcium carbonate, etc.; the amount of inorganic filler added refers to the control range of the filler type, such as the control range of aluminum oxide is 5% to 20%, and the control range of silicon oxide is 3% to 12%.

[0026] Next, the types and addition amounts of inorganic fillers are traversed and combined. By traversing different types of fillers and their different addition amounts, a variety of sample filler parameters are obtained, where each sample filler parameter represents a possible electrolyte formula, and a filler parameter space is constructed based on the multiple sample filler parameters. As shown in Table 1, the column of inorganic filler type lists different types of inorganic fillers that can be used in the electrolyte (such as aluminum oxide, silicon oxide, calcium silicate, etc.); the columns of filler 1, filler 2, filler 3, and filler 4 addition amount show the possible different addition amounts of each filler in the electrolyte (such as 5%, 10%, 15%, 20%, etc.). The combination of each filler type and addition amount constitutes a filler parameter.

[0027]

[0028] Table 1

[0029] Finally, any set of filler parameters is randomly selected in the filler parameter space as the filler parameters to be analyzed (such as 10% alumina, 6% silicon oxide, 2% calcium carbonate, etc.) for subsequent filler parameter optimization analysis.

[0030] S200: collecting the ambient temperature characteristics of the semi-solid-state battery during operation, and calculating and obtaining the high temperature time coefficient, the low temperature time coefficient, the high temperature coefficient, and the low temperature coefficient.

[0031] Furthermore, step S200 of the present invention further includes:

[0032] S210: Collect the temperature sequence in the operating environment of the semi-solid-state battery as the environmental temperature feature; S220: Extract the highest temperature and the lowest temperature in the environmental temperature feature, calculate the deviation ratio of the highest temperature and the lowest temperature to the preset temperature respectively, and obtain the highest deviation ratio and the lowest deviation ratio; S230: According to the highest deviation ratio and the lowest deviation ratio, allocate and calculate to obtain the high temperature coefficient and the low temperature coefficient.

[0033] Specifically, real-time temperature data of the battery operating environment is collected through temperature sensors or environmental monitoring equipment. Temperature sensors can be installed in different locations in the battery environment to reflect the temperature impact of the battery during actual operation. Then, the temperature sequence of the semi-solid-state battery operating environment within a preset historical time zone (such as the past year) is collected, where the temperature refers to the daily average operating environment temperature within the preset historical time zone. By recording the average ambient temperature of each day, the temperature change trend within a year is formed, and the temperature sequence is obtained. The temperature sequence is used as the ambient temperature feature.

[0034] Next, the maximum and minimum temperatures within the ambient temperature feature are extracted, e.g., if the maximum temperature is 55 degrees Celsius and the minimum is -15 degrees Celsius. A preset temperature is then obtained, which is the ideal operating temperature for a semi-solid-state battery, such as a rated operating temperature of 25 degrees Celsius (this can be set based on the battery's operating environment in actual applications). Furthermore, the deviation ratio between the maximum temperature and the preset temperature is calculated. This is the ratio of the absolute value of the temperature difference between the maximum temperature and the preset temperature to the preset temperature, which is set as the maximum deviation ratio. For example, if the maximum temperature is 55 degrees Celsius and the preset temperature is 25 degrees Celsius, the maximum deviation ratio is (55-25) / 25, which equals 1. 2; on the other hand, calculate the deviation ratio between the minimum temperature and the preset temperature, that is, the ratio of the absolute value of the temperature difference between the minimum temperature and the preset temperature to the preset temperature, and set it as the minimum deviation ratio. For example, assuming that the minimum temperature is minus 15 degrees Celsius and the preset temperature is 25 degrees Celsius, the minimum deviation ratio is (25+15) / 25, which is equal to 1.6; obtain the maximum deviation ratio and the minimum deviation ratio, wherein the deviation ratio can reflect the adaptability of the ambient temperature change to the battery design, that is, if the maximum deviation ratio is larger, it means that the performance of the battery may be greatly affected in a high temperature environment; if the minimum deviation ratio is larger, it means that the performance of the battery may be greatly affected in a low temperature environment.

[0035] Then, the high temperature coefficient and the low temperature coefficient are calculated according to the maximum deviation ratio and the minimum deviation ratio. First, the maximum deviation ratio and the minimum deviation ratio are summed to obtain the total deviation ratio (such as 1.2 plus 1.6 equals 2.8); then the ratio of the maximum deviation ratio to the total deviation ratio (such as 1.2 / 2.8 is approximately equal to 0.43) is set as the high temperature coefficient, and the ratio of the minimum deviation ratio to the total deviation ratio (such as 1.6 / 2.8 is approximately equal to 0.57) is set as the low temperature coefficient, where the high temperature coefficient reflects the proportion of the high temperature part in the temperature fluctuation. The larger the value, the greater the impact of high temperature on battery performance, which indicates that the battery has a higher stability requirement in a high temperature environment; the low temperature coefficient reflects the proportion of the low temperature part in the temperature fluctuation. The larger the value, the greater the impact of low temperature on battery performance, which indicates that the conductivity optimization of the battery in a low temperature environment is more important.

[0036] By calculating the high temperature coefficient and low temperature coefficient, the influence of ambient temperature fluctuations can be quantitatively described, thereby guiding the optimization of semi-solid battery electrolytes and ensuring the optimal performance of the battery under different temperature conditions, thereby improving the scientificity, rationality and accuracy of filler parameter optimization.

[0037] S240: Calculate and obtain a high temperature time coefficient and a low temperature time coefficient according to the ambient temperature characteristics.

[0038] Furthermore, step S240 of the present invention further includes:

[0039] S241: extracting temperatures greater than the preset temperature in the ambient temperature feature, and calculating the proportion as the high temperature time coefficient; S242: extracting temperatures less than or equal to the preset temperature in the ambient temperature feature, and calculating the proportion as the low temperature time coefficient.

[0040] Specifically, the amount of high-temperature data greater than the preset temperature in the ambient temperature feature is extracted, and the ratio of the high-temperature data to the total temperature data is set as the high-temperature time coefficient, that is, the number of days in a year with a temperature higher than the preset temperature is counted and divided by the total number of days (usually 365 days) to obtain the high-temperature time coefficient. The high-temperature time coefficient represents the proportion of time in the environment that is higher than the preset temperature. The higher the coefficient, the longer the environment is in a high-temperature state, and the higher the requirement for the high-temperature stability of the battery. On the other hand, the amount of low-temperature data less than or equal to the preset temperature in the ambient temperature feature is extracted, and the ratio of the low-temperature data to the total temperature data is set as the low-temperature time coefficient, that is, the number of days in a year with a temperature lower than or equal to the preset temperature is counted and divided by the total number of days to obtain the low-temperature time coefficient. The low-temperature time coefficient represents the proportion of time in the environment that is lower than or equal to the preset temperature. The higher the coefficient, the longer the environment is in a low-temperature state, and the higher the requirement for optimizing the low-temperature conductivity of the battery.

[0041] By calculating the high-temperature time coefficient and the low-temperature time coefficient, the long-term impact of ambient temperature can be quantitatively analyzed, and the optimization of semi-solid-state battery electrolytes can be guided to ensure the best performance of the battery under different temperature conditions.

[0042] S300: Predicting the high-temperature operation stability and low-temperature operation conductivity of the semi-solid battery electrolyte according to the filler parameters, obtaining stability parameters and conductivity parameters, and calculating and obtaining a time performance value by combining the high-temperature time coefficient and the low-temperature time coefficient.

[0043] Further, if Figure 2 As shown, step S300 of the present invention further includes:

[0044] S310: Obtain design test data of semi-solid battery electrolytes and collect sample filler parameter sets; S320: Collect operating stability parameters of semi-solid battery electrolytes at preset high temperature and conductivity parameters at preset low temperature under different sample filler parameters, and mark them as sample stability parameter set and sample conductivity parameter set; S330: Divide the sample filler parameter set, sample stability parameter set and sample conductivity parameter set to obtain training set, validation set and test set; S340: Use machine learning to construct an electrolyte performance predictor, and use the training set, validation set and test set to supervise training, validation and testing of the electrolyte performance predictor until the requirements are met; S350: Input the filler parameters into the electrolyte performance predictor, and output the stability parameters and conductivity parameters.

[0045] Specifically, first, obtain the design test data of the semi-solid battery electrolyte (including parameters such as the performance of the semi-solid battery electrolyte under different filler parameters), and collect different sample filler parameters in the design test data to construct a sample filler parameter set. Next, collect the operating stability parameters of the semi-solid battery electrolyte under a preset high temperature (which can be set according to the actual test scenario, such as 50 degrees Celsius) under different sample filler parameters. The operating stability parameters can be obtained by quantitatively evaluating the fracture strength of the electrolyte during the test (measuring the mechanical strength of the material at high temperature, the breaking point of the test sample under external force), elongation at break (assessing the ductility of the material, especially how much elongation the material can withstand under high temperature conditions without breaking). By quantifying the fracture strength and elongation at break, the high-temperature stability of the electrolyte under different filler parameters is evaluated. The larger the operating stability parameter, the better the high-temperature stability, which is marked as the sample stability parameter. On the other hand, the conductivity parameters of the semi-solid battery electrolyte at a preset low temperature (such as minus 10 degrees Celsius) under different sample filling parameters can be obtained after evaluation through parameters such as ionic conductivity (an indicator to measure the ionic conductivity of the electrolyte at low temperature. The higher the ionic conductivity, the better the conductivity performance of the electrolyte at low temperature). They are marked as sample conductivity parameters, and a set of sample stability parameters and a set of sample conductivity parameters are obtained. Among them, the sample filling parameters have a corresponding relationship with the sample stability parameters and the sample conductivity parameters.

[0046] The sample filling parameter set, sample stability parameter set and sample conductivity parameter set are divided according to a predetermined ratio, usually 70% for the training set (used to train the model to help the model learn the relationship between input and output), 15% for the validation set (used to adjust hyperparameters and verify the performance of the model on different data), and 15% for the test set (used to test the generalization ability of the final model to ensure that the model can perform well on unseen data), to obtain the training set, validation set and test set.

[0047] Then, machine learning is used to construct an electrolyte performance predictor, for example, an electrolyte performance predictor is constructed based on a BP neural network, wherein the electrolyte performance predictor is used to predict the high-temperature stability and low-temperature conductivity of the electrolyte under different filler parameters, thereby optimizing the filler formula and improving battery performance; the electrolyte performance predictor includes an input layer, multiple hidden layers and an output layer, wherein the input data of the input layer is the filler parameter, and the output data of the output layer is the stability parameter and the conductivity parameter; the training set, validation set and test set are further used to supervise the training, validation and testing of the electrolyte performance predictor, first, the filler parameters and ambient temperature characteristics of the training set are input into the BP neural network, and the predicted output of the neural network is calculated, including the high-temperature stability parameter and the low-temperature conductivity parameter; then, the mean square error (MSE) is used to measure the gap between the predicted value and the true value, and the neural network weight is adjusted by gradient descent (SGD / Adam) to reduce the error and improve the model fitting ability; then the model is continuously optimized until the training error converges or the set accuracy requirement is met. Next, in the validation phase, the validation set data is fed into the trained model to predict stability and conductivity parameters. The model's prediction accuracy on unseen data is then evaluated to prevent overfitting. If the validation error is large, the learning rate, number of neurons, and regularization strategy are optimized to improve the model's generalization ability. In the testing phase, the test set data is fed into the trained model, and the test error is calculated to ensure the model maintains high prediction accuracy on unseen data, validating its generalization ability and stability. If the test results meet the requirements, the model can be used to predict electrolyte performance under new filler parameters, providing support for optimization analysis, and a trained electrolyte performance predictor is obtained.

[0048] Finally, the filler parameters are input into the trained electrolyte performance predictor, which outputs stability and conductivity parameters. By using machine learning to construct an electrolyte performance predictor for performance prediction, intelligent and automated predictions can be made, allowing for rapid and accurate predictions of high-temperature stability and low-temperature conductivity for different filler parameters. This significantly improves prediction efficiency and accuracy, providing a scientific basis for optimizing filler parameters for semi-solid-state batteries.

[0049] Furthermore, step S300 of the present invention further includes:

[0050] S360: Allocate a first high temperature weight and a first low temperature weight according to the high temperature time coefficient and the low temperature time coefficient; S370: Use the first high temperature weight and the first low temperature weight to perform a weighted calculation on the ratio of the stability parameter and the conductivity parameter to the preset stability parameter and the preset conductivity parameter to obtain a time performance value.

[0051] Specifically, a first high-temperature weight and a first low-temperature weight are assigned based on the high-temperature time coefficient and the low-temperature time coefficient. Specifically, the high-temperature time coefficient (the percentage of time the battery operates in a high-temperature environment) is set as the first high-temperature weight, and the low-temperature time coefficient (the percentage of time the battery operates in a low-temperature environment) is set as the first low-temperature weight. Next, a preset stability parameter and a preset conductivity parameter are obtained. The preset parameters can be set based on the average performance data of other semi-solid-state batteries and used as a benchmark for comparison.

[0052] Then, the product of the first high temperature weight multiplied by the ratio of the stability parameter to the preset stability parameter is added to the product of the first low temperature weight multiplied by the ratio of the conductivity parameter to the preset conductivity parameter, and the sum of the two is used as the time performance value. For example, assuming that the first high temperature weight is 0.4, the first low temperature weight is 0.6, the stability parameter is 0.5, the preset stability parameter is 0.4, the conductivity parameter is 0.25, and the preset conductivity parameter is 0.5, then the time performance value is 0.4*(0.5 / 0.4)+0.6*(0.25 / 0.5) equals 0.8, wherein the higher the time performance value, the better the comprehensive performance of the electrolyte characterizing the filler parameters in the long-term operating environment. Through the calculation and optimization analysis of the time performance value, the electrolyte filler parameters that are most suitable for a specific operating environment can be intelligently screened to improve the long-term stability and reliability of the semi-solid state battery.

[0053] S400: Calculate and obtain a two-dimensional performance value based on the stability parameter, conductivity parameter, high temperature coefficient, and low temperature coefficient. Combined with the time performance value, calculate and obtain a comprehensive performance value. Evaluate and optimize the filler parameters to obtain the optimal filler parameters as the performance optimization result.

[0054] Furthermore, step S400 of the present invention further includes:

[0055] S410: Allocate a second high temperature weight and a second low temperature weight according to the high temperature coefficient and the low temperature coefficient; S420: Use the second high temperature weight and the second low temperature weight to perform weighted calculation on the ratio of the stability parameter and the conductivity parameter to the preset stability parameter and the preset conductivity parameter to obtain a two-dimensional performance value; S430: Calculate and obtain a comprehensive performance value according to the time performance value and the two-dimensional performance value; S440: Continue to perform random selection of filler parameters and evaluation calculation of comprehensive performance values, complete the optimization of filler parameters, obtain filler parameters with the largest comprehensive performance value, obtain optimal filler parameters and prepare electrolyte as a performance optimization result.

[0056] Specifically, the high-temperature coefficient is set as the second high-temperature weight, and the low-temperature coefficient is set as the second low-temperature weight. The high-temperature coefficient and the low-temperature coefficient reflect the performance requirements of the battery under different temperature environments. A larger coefficient means that the environment has a greater impact on the battery, and the corresponding weight is larger. Next, the product of the second high-temperature weight and the ratio of the stability parameter to the preset stability parameter is added to the product of the second low-temperature weight and the ratio of the conductivity parameter to the preset conductivity parameter. The sum is used to obtain a two-dimensional performance value. The two-dimensional performance value is used to quantify the comprehensive performance of the electrolyte filler parameters under different temperature conditions. The higher the two-dimensional performance value, the better the comprehensive performance of the electrolyte characterized by the filler parameters in high and low temperature environments.

[0057] Then, the time performance value and the two-dimensional performance value are averaged to obtain a comprehensive performance value. The higher the comprehensive performance value, the electrolyte characterizing the filler parameters has better comprehensive performance under different environmental conditions such as long time, high temperature, and low temperature. Selecting this filler scheme can improve the overall operating stability and high and low temperature adaptability of the battery.

[0058] Using the same method, the comprehensive performance values ​​of other filler parameters within the filler parameter space are evaluated and calculated in sequence until all filler parameter analyses are completed, obtaining multiple comprehensive performance values ​​for multiple filler parameters. The filler parameter with the largest comprehensive performance value is selected as the optimal filler parameter, and an electrolyte is prepared based on the optimal filler parameter as the performance optimization result. By screening out the filler parameter with the largest comprehensive performance value, it is possible to ensure that the electrolyte maintains good high-temperature stability and low-temperature conductivity under various temperature environments, allowing the prepared electrolyte to exhibit a longer service life and stronger environmental adaptability in practical applications, thereby improving the overall performance of the semi-solid-state battery.

[0059] The embodiment of the present invention provides a method for optimizing the performance of a semi-solid-state battery electrolyte, which has at least the following technical effects:

[0060] A filler parameter space for adding inorganic fillers to a semi-solid battery electrolyte is constructed, and any filler parameter is randomly selected; the ambient temperature characteristics of the semi-solid battery operation are then collected, and the high-temperature time coefficient, low-temperature time coefficient, high-temperature coefficient, and low-temperature coefficient are calculated; then, based on the filler parameters, the high-temperature operation stability and low-temperature operation conductivity of the semi-solid battery electrolyte are predicted to obtain stability parameters and conductivity parameters; further, a time performance value is calculated based on the stability parameter, conductivity parameter, high-temperature time coefficient, and low-temperature time coefficient; on the other hand, a two-dimensional performance value is calculated based on the stability parameter, conductivity parameter, high-temperature coefficient, and low-temperature coefficient, and a comprehensive performance value is calculated in combination with the time performance value; then, random selection of filler parameters and evaluation and calculation of comprehensive performance values ​​are continued until the optimization converges, and the filler parameter with the largest comprehensive performance value is selected as the optimal filler parameter and the electrolyte is prepared as the performance optimization result; that is, by optimizing the inorganic filler parameters in combination with the battery operating environment, the scientificity and accuracy of the inorganic filler scheme setting in the electrolyte can be improved, thereby effectively improving the high-temperature stability and low-temperature conductivity of the battery, and significantly enhancing the comprehensive performance of the semi-solid battery in actual application environments.

[0061] Example 2: Based on the same inventive concept as the method for optimizing the performance of a semi-solid-state battery electrolyte provided in Example 1, an embodiment of the present invention further provides a semi-solid-state battery, wherein the electrolyte of the semi-solid-state battery is prepared using any one of the methods for optimizing the performance of a semi-solid-state battery electrolyte in the above-mentioned Example 1.

[0062] Although preferred embodiments of the present invention have been described, additional changes and modifications to these embodiments may occur to those skilled in the art once the basic inventive concepts become known.

[0063] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing the performance of a semi-solid battery electrolyte, characterized in that: Methods include: Construct a filler parameter space for adding inorganic fillers to semi-solid battery electrolytes and randomly select filler parameters; Collect the ambient temperature characteristics of the semi-solid-state battery operation and calculate the high temperature time coefficient, low temperature time coefficient, high temperature coefficient and low temperature coefficient; According to the filler parameters, the high-temperature operation stability and low-temperature operation conductivity of the semi-solid battery electrolyte are predicted to obtain stability parameters and conductivity parameters, and the time performance value is calculated by combining the high-temperature time coefficient and the low-temperature time coefficient; According to the stability parameter, conductivity parameter, high temperature coefficient and low temperature coefficient, the two-dimensional performance value is calculated and obtained. Combined with the time performance value, the comprehensive performance value is calculated and optimized for the filler parameters to obtain the optimal filler parameters as the performance optimization result.

2. The method for optimizing the performance of a semi-solid-state battery electrolyte according to claim 1, wherein: Construct a filler parameter space for adding inorganic fillers to semi-solid battery electrolytes and randomly select filler parameters, including: Obtain the type and amount of inorganic fillers added to semi-solid battery electrolytes; The types and addition amounts of inorganic fillers are traversed and combined to obtain various sample filler parameters and construct filler parameter space; The packing parameters are randomly selected within the packing parameter space.

3. The method for optimizing the performance of a semi-solid-state battery electrolyte according to claim 1, wherein: Collect the ambient temperature characteristics of the semi-solid-state battery operation and calculate the high temperature time coefficient, low temperature time coefficient, high temperature coefficient and low temperature coefficient, including: Collect the temperature sequence in the semi-solid-state battery operating environment as the ambient temperature feature; Extracting the maximum temperature and the minimum temperature within the ambient temperature feature, and calculating the deviation ratios of the maximum temperature and the minimum temperature from the preset temperature, respectively, to obtain a maximum deviation ratio and a minimum deviation ratio; According to the highest deviation ratio and the lowest deviation ratio, a high temperature coefficient and a low temperature coefficient are obtained by allocation calculation; According to the ambient temperature characteristics, a high temperature time coefficient and a low temperature time coefficient are calculated.

4. The method for optimizing the performance of a semi-solid-state battery electrolyte according to claim 3, wherein: According to the ambient temperature characteristics, the high temperature time coefficient and the low temperature time coefficient are calculated, including: Extracting temperatures greater than the preset temperature in the ambient temperature feature and calculating the proportion as a high temperature time coefficient; The temperatures less than or equal to the preset temperature in the ambient temperature feature are extracted, and the proportion is calculated as the low temperature time coefficient.

5. The method for optimizing the performance of a semi-solid-state battery electrolyte according to claim 1, wherein: According to the filler parameters, the high-temperature operation stability and low-temperature operation conductivity of the semi-solid battery electrolyte are predicted to obtain stability parameters and conductivity parameters, including: Obtain design test data for semi-solid battery electrolytes and collect sample filler parameter sets; Collect the operational stability parameters of the semi-solid battery electrolyte at a preset high temperature and the conductivity parameters at a preset low temperature under different sample filling parameters, and mark them as a sample stability parameter set and a sample conductivity parameter set; Dividing the sample filler parameter set, the sample stability parameter set, and the sample conductivity parameter set to obtain a training set, a validation set, and a test set; Using machine learning to construct an electrolyte performance predictor, and using the training set, validation set, and test set to perform supervised training, validation, and testing on the electrolyte performance predictor until requirements are met; The filler parameters are input into the electrolyte performance predictor, and the stability parameters and the conductivity parameters are obtained as output.

6. The method for optimizing the performance of a semi-solid-state battery electrolyte according to claim 1, wherein: The time performance value is calculated by combining the high temperature time coefficient and the low temperature time coefficient, including: Allocating a first high temperature weight and a first low temperature weight according to the high temperature time coefficient and the low temperature time coefficient; The first high-temperature weight and the first low-temperature weight are used to perform weighted calculation on the ratios of the stability parameter and the conductivity parameter to the preset stability parameter and the preset conductivity parameter to obtain a time performance value.

7. The method for optimizing the performance of a semi-solid-state battery electrolyte according to claim 1, wherein: Based on the stability parameter, conductivity parameter, high temperature coefficient and low temperature coefficient, a two-dimensional performance value is calculated and obtained. Combined with the time performance value, a comprehensive performance value is calculated and optimized to evaluate the filler parameters and obtain the optimal filler parameters as the performance optimization result, including: Allocating a second high temperature weight and a second low temperature weight according to the high temperature coefficient and the low temperature coefficient; Using the second high-temperature weight and the second low-temperature weight, a weighted calculation is performed on the ratio of the stability parameter and the conductivity parameter to the preset stability parameter and the preset conductivity parameter to obtain a two-dimensional performance value; Calculating a comprehensive performance value based on the time performance value and the two-dimensional performance value; Continue to randomly select filler parameters and evaluate and calculate the comprehensive performance value, complete the optimization of filler parameters, obtain the filler parameters with the largest comprehensive performance value, obtain the optimal filler parameters and prepare the electrolyte as the performance optimization result.

8. A semi-solid-state battery, characterized in that: The electrolyte of the semi-solid-state battery is prepared by the semi-solid-state battery electrolyte performance optimization method according to any one of claims 1 to 7.

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