A Multi-Parameter Collaborative Optimization Method for Predicting Solid-State Battery Lifetime

Abstract

This invention relates to a multi-parameter collaborative optimization method for predicting the lifespan of solid-state batteries. It comprehensively considers the main parameters affecting solid-state battery lifespan, including ionic conductivity, electrode materials, operating temperature, and charge / discharge rate. The key to solid-state battery lifespan assessment lies in capacity decay and internal resistance growth. By establishing a multi-parameter collaborative optimization mathematical model, the accuracy and reliability of solid-state battery lifespan prediction are improved. Optimization algorithms are used to optimize the model parameters, quickly finding the optimal parameter combination and improving prediction efficiency. The prediction method of this invention has strong generalization ability and can be applied to different types and specifications of solid-state batteries, providing strong support for the design, manufacturing, and application of solid-state batteries.

Description

Technical Field

[0001] This invention belongs to the field of solid-state battery technology, and particularly relates to a multi-parameter collaborative optimization method for predicting the lifetime of solid-state batteries. Background Technology

[0002] Solid-state batteries, as a novel battery technology, possess advantages such as high energy density, good safety, and fast charging speed, and are considered the future direction of battery technology development. However, the lifespan of solid-state batteries is affected by various factors, such as the performance of the solid electrolyte, the performance of electrode materials, the battery's operating temperature, and the charge / discharge rate, making accurate prediction of solid-state battery lifespan difficult. Currently, existing battery lifespan prediction methods mainly include experimental methods, physical model-based methods, and data-driven methods. While experimental methods can directly obtain battery lifespan data, they are time-consuming and labor-intensive, and it is difficult to consider all influencing factors; physical model-based methods require a deep understanding of the battery's internal mechanisms, and the determination of model parameters is relatively complex; data-driven methods require a large amount of experimental data to train the model, and the model's generalization ability is limited. Therefore, a solid-state battery lifespan prediction method that can comprehensively consider the influence of multiple factors is needed to improve the accuracy and reliability of predictions. Summary of the Invention

[0003] The purpose of this invention is to provide a multi-parameter collaborative optimization method for predicting the lifespan of solid-state batteries. By establishing a mathematical model, multiple parameters affecting the lifespan of solid-state batteries are collaboratively optimized, thereby improving the accuracy of solid-state battery lifespan prediction.

[0004] Based on current research, the main factors (parameters) affecting the lifespan of solid-state batteries include ionic conductivity, electrode materials, operating temperature, and charge / discharge rate. The key to assessing the lifespan of solid-state batteries lies in their capacity decay and internal resistance increase. Generally speaking, when the capacity of a solid-state battery decays to a certain extent, it is considered that the battery is unusable; or when the internal resistance increases to a certain extent, it can also be considered a condition for the solid-state battery to be unusable.

[0005] To achieve the above objectives, the technical solution of this invention is: a multi-parameter collaborative optimization method for predicting the lifetime of solid-state batteries, characterized by comprising the following steps:

[0006] Step 1: Co-optimize the parameters affecting solid-state battery lifespan and construct a lifespan prediction model. These parameters include ionic conductivity, electrode material, operating temperature, and charge / discharge rate. The key to solid-state battery lifespan assessment lies in capacity decay and internal resistance growth. The capacity decay model is C(n) = C0(1-αn). β ), where n represents the number of charge-discharge cycles, C0 represents the initial capacity of the battery, and the relationship between ionic conductivity σ and capacity decay coefficient α is assumed to be: a1 and m1 are obtained by fitting experimental data; for the capacity decay model C(n)=C0(1-αn) β Let β in ) be related to ionic conductivity as follows: Where b1 and k1 are constants; let the relationship between the specific capacity Q of the electrode material and α be: Here, a2 and m2 are obtained by fitting experimental data; the lower the specific capacity of the electrode material, the faster the capacity decay; the relationship between β and Q is... b2 and k2 are corresponding constants; consider the form of the Arrhenius equation. Where a3 is the pre-factor, E a1 It is the activation energy related to capacity decay, R is the gas constant, and T is the operating temperature; for β, let... b3 is a constant, E a2 It is the activation energy related to β; Where a4 and m4 are constants, the higher the charge / discharge rate, the greater the capacity decay coefficient; b4 and k4 are constants.

[0007] Taking all the above factors into account, the expression for the capacity attenuation coefficient α is:

[0008]

[0009] Taking all the above factors into consideration,

[0010] The expression for the capacity decay coefficient α is:

[0011]

[0012] The expression for β is:

[0013]

[0014] Internal resistance growth model R(n)=R0(1+γn) δ ), where n represents the number of charge-discharge cycles, R0 represents the initial internal resistance, and the relationship between the internal resistance growth coefficient γ and the ionic conductivity σ is assumed to be: c1 and n1 are constants; the lower the ionic conductivity, the faster the internal resistance increases. For the internal resistance growth model, R(n) = R0(1 + γn). δ In ) δ, assuming d1 and p1 are constants; c2 and n2 are constants. The lower the specific capacity of the electrode material, the greater the internal resistance growth coefficient. d2 and p2 are constants; Where c3 is the pre-factor, Ea3 It is the activation energy related to the increase of internal resistance; d3 is a constant, E a4 It is the activation energy related to δ; c4 and n4 are constants. The higher the charge / discharge rate, the faster the internal resistance increases. d4 and p4 are constants;

[0015] In summary,

[0016] The expression for the internal resistance growth coefficient γ is:

[0017]

[0018] The expression for δ is:

[0019]

[0020] The lifetime prediction model assumes that the end-of-life condition of a solid-state battery is either capacity decay to x% of the initial capacity or internal resistance increase to y% of the initial internal resistance. Therefore, the lifetime N of the solid-state battery can be expressed as:

[0021]

[0022] Substituting the above expressions for α, β, γ, and δ into the lifetime prediction model, we obtain a complete mathematical model for predicting the lifetime of solid-state batteries based on multiple parameters.

[0023] Step 2: Collect parameter information and input the collected parameter information into the life prediction model to complete the life prediction of solid-state batteries.

[0024] Preferably, in the lifetime prediction model of step one, the lifetime N of the solid-state battery is affected by parameters, where the capacity decay part involves parameters α, β, n, and x%, and the internal resistance growth part involves parameters γ, δ, n, and y%. α, β, γ, and δ are related to the ionic conductivity σ, the specific capacity Q of the electrode material, the operating temperature T, and the charge / discharge rate C. The model is optimized with the goal of minimizing the lifetime prediction error, and an optimization objective function is established. Where C pred,i and R pred,i The capacitance and internal resistance are predicted based on the current parameter combination, C actual,i and R actual,i These are the actual measured capacitance and internal resistance.

[0025] Preferably, the optimization steps for the lifetime prediction model are as follows: 1) data acquisition, 2) data preprocessing, and 3) model parameter estimation.

[0026] Preferably, data acquisition includes: measuring the ionic conductivity of the solid-state battery at different temperatures and charge / discharge states using an electrochemical impedance spectroscopy (EIS) instrument, and obtaining a set of ionic conductivity data σ1, σ2, ..., σ n By conducting charge-discharge experiments, the specific capacity of the electrode materials was measured, and the specific capacity data Q1, Q2, ..., Q were obtained. n During battery charging and discharging, a temperature sensor is used to record the operating temperature, obtaining temperature data T1, T2, ..., T. n Set different charge / discharge rates, use a charge / discharge device to record the battery's charge / discharge data at different rates, and calculate the charge / discharge rate data C1, C2, ..., C n .

[0027] Preferably, data preprocessing specifically involves checking for outliers in the data, such as data points that significantly deviate from the normal range. If outliers are found, median filtering is used to remove them. Min-max normalization is then used to normalize data such as ionic conductivity, specific capacity, temperature, and charge / discharge rate, mapping each data point to the [0,1] interval. Assuming the original data is X = {x1, x2, ..., x...} n The normalized data is

[0028]

[0029] Preferably, the model parameter estimation is performed by identifying multiple parameters of the solid-state battery lifetime assessment model using an improved adaptive genetic algorithm. The main improvements include adaptive crossover probability (p... c ) and mutation probability (p m Adjustments to ) and the introduction of disaster recovery operations and return mechanisms.

[0030] Preferably, an improved adaptive genetic algorithm has the following steps:

[0031] ① Encoding: The parameters that need to be optimized are encoded in binary. Based on the empirical values ​​of the parameters, the range of parameter values ​​is set, and each range is represented by a binary string of a certain length.

[0032] ② Initialize the population: Based on the binary encoding method, a certain number of individuals are randomly generated to form the initial population, with each individual representing a possible combination of parameters;

[0033] ③ Calculate fitness: For each individual, decode its corresponding parameters and substitute them into the mathematical model for lifespan prediction.

[0034] In the middle, calculate its fitness value;

[0035] Among them, C0, α, β, γ, and δ are obtained by encoding and decoding the individual;

[0036] ④ Selection Operator: Based on the fitness value of an individual, a roulette wheel selection operator is used to select individuals for the next generation of the population. Roulette wheel selection: The proportion of an individual on the roulette wheel is calculated based on its fitness value, and then random selection is performed. Individuals with higher fitness values ​​have a greater probability of being selected. ⑤ Crossover Operation: An adaptive crossover probability adjustment is designed. The crossover probability is dynamically adjusted according to an adaptive formula. When the fitness value of an individual is large and there are many individuals in the population with fitness values ​​greater than the average fitness value, the crossover probability will be reduced accordingly to protect superior gene combinations. The adaptive crossover probability and mutation probability formulas are designed as follows:

[0037]

[0038] Among them, the values ​​"0.8" and "0.1" are empirical values, f max f represents the maximum fitness value in the population. avg f represents the average fitness value of the population, f' represents the larger of the two fitness values ​​of the crossover individuals, and f' represents the fitness value of the individual to be mutated. min M1 represents the minimum fitness value in the population; M2 represents the number of individuals in each generation with a fitness value greater than the average fitness value; λ is an infinitesimal positive number, mainly to prevent the denominator from equaling 0; other parameters can be defined based on empirical values, such as p. c1 =0.9,p c2 =0.6,p m1 =0.1,p m2 =0.01; Perform a crossover operation on the selected individuals to generate new individuals;

[0039] ⑥ Mutation operation: An adaptive mutation probability adjustment is designed, with the mutation probability adjusted according to an adaptive formula. When an individual's fitness value is low or the population shows a premature convergence trend, the mutation probability will increase to increase population diversity and avoid getting trapped in local optima. The mutation probability formula is:

[0040]

[0041] Where f' represents the fitness value of the individual to be mutated, and the value "0.1" is an empirical value. Other parameters have the same meaning as in the crossover probability formula; p m1 =0.1,p m2 =0.01, perform mutation operation on the newly generated individual to introduce new genes;

[0042] ⑦ Catastrophe operation judgment. Assume the number of iterations is M. After the m-th (m < M) generation, most individuals have a high similarity with the optimal individual (only the optimal and the worst individuals have low similarity). At this time, perform the following operations to generate a new population: Save the current population state, the optimal individual, and its fitness value; Retain a small number of individuals with poor fitness values; Delete most of the individuals with excellent fitness values in the population; Randomly generate new individuals for supplementation.

[0043] a. Judgment condition:

[0044] After an appropriate number of iterations, judge whether the conditions for the catastrophe operation are met; It can be judged according to factors such as the diversity index of the population and the distribution of fitness values. For example, calculate the similarity between individuals in the population. If most individuals have a high similarity with the optimal individual (only the optimal and the worst individuals have low similarity), it is considered that the conditions for the catastrophe operation are met.

[0045] b. Mathematical description:

[0046] Assume that the catastrophe operation judgment is carried out when the number of iterations reaches T. Calculate the similarity between individuals in the population using the Euclidean distance method. If the Euclidean distance between most individuals and the optimal individual is less than a certain threshold, it is considered that the conditions for the catastrophe operation are met.

[0047] ⑧ Return mechanism: To improve the global search performance of the algorithm and enhance the traversability of the search, a return operation is added; When the crossover and mutation operations fail, that is, when the average fitness level of the newly generated population is lower than the average fitness level of the original population, this operation is negated and restored to the previous population state.

[0048] ⑨ Iteration: Repeat steps ③ to ⑧ until the stop condition is met, such as reaching the maximum number of iterations or the fitness value reaching a certain accuracy requirement.

[0049] The technical effects of the present invention are as follows:

[0050] 1. The present invention comprehensively considers the influence of various factors on the life of solid-state batteries. By establishing a mathematical model for multi-parameter collaborative optimization, it improves the accuracy and reliability of solid-state battery life prediction. At the same time, using an optimization algorithm to optimize the model parameters can quickly find the optimal parameter combination and improve the prediction efficiency; The prediction method of the present invention has strong generalization ability and can be applied to different types and specifications of solid-state batteries, providing strong support for the design, manufacture, and application of solid-state batteries.

[0051] 2. By continuously adjusting the crossover and mutation probabilities, as well as executing catastrophe operations and return mechanisms, the algorithm can better balance global and local searches, avoid premature convergence, and thus more effectively find the optimal combination of multiple parameters, enabling the lifetime prediction model to more accurately predict the lifetime of solid-state batteries. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of an improved adaptive genetic algorithm. Detailed Implementation

[0053] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments.

[0054] Unless otherwise specified, the terms "first," "second," etc., used in this invention are used to distinguish different objects and are not used to indicate size or sequence, nor should they be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0055] The term "and / or" in this invention is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, B existing alone, and A and B existing simultaneously. Clearly, the described embodiments are only a portion of the embodiments of this invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0056] Current research indicates that the main factors (parameters) affecting the lifespan of solid-state batteries include ionic conductivity, electrode materials, operating temperature, and charge / discharge rate. The key to assessing the lifespan of solid-state batteries lies in their capacity decay and internal resistance increase. Generally, when the capacity of a solid-state battery decays to a certain level, it is considered unusable; similarly, an increase in internal resistance to a certain level can also be considered a condition for battery obsolescence. The mathematical modeling for solid-state battery lifespan assessment is described in detail below:

[0057] 1) Considering capacity decay

[0058] Capacity decay model C(n)=C0(1-αn) β ), where n represents the number of charge-discharge cycles, C0 represents the initial capacity of the battery, and other parameters are detailed below.

[0059] 1. The effect of ionic conductivity on capacity decay

[0060] Let the relationship between ionic conductivity σ and capacity decay coefficient α be: a1 and m1 need to be obtained by fitting experimental data. For the capacity decay model C(n)=C0(1-αn) β Let β in ) be related to ionic conductivity as follows: Where b1 and k1 are constants.

[0061] 2. The effect of electrode material specific capacity on capacity decay

[0062] Let the specific capacity Q of the electrode material be related to α as follows: Here, a2 and m2 need to be obtained by fitting experimental data. The lower the specific capacity of the electrode material, the faster the capacity decays. Meanwhile, the relationship between β and Q is... b2 and k2 are the corresponding constants.

[0063] 3. The effect of operating temperature on capacity decay

[0064] Consider the form of the Arrhenius equation, Where a3 is the pre-factor, E a1 It is the activation energy related to capacity decay, R is the gas constant, and T is the operating temperature.

[0065] For β, let b3 is a constant, E a2 It is the activation energy related to β.

[0066] 4. The effect of charge / discharge rate on capacity decay

[0067] Where a4 and m4 are constants, the higher the charge / discharge rate, the greater the capacity decay coefficient. b4 and k4 are constants.

[0068] Taking all the above factors into account, the expression for the capacity attenuation coefficient α is:

[0069]

[0070] Taking all the above factors into consideration,

[0071] The expression for the capacity decay coefficient α is:

[0072]

[0073] The expression for β is:

[0074]

[0075] 2) Considering the increase in internal resistance

[0076] Internal resistance growth model R(n)=R0(1+γn)δ ), where n represents the number of charge-discharge cycles, R0 represents the initial internal resistance value, and other parameters are detailed below.

[0077] 1. The effect of ionic conductivity on the increase of internal resistance

[0078] Let the relationship between the internal resistance growth coefficient γ and the ionic conductivity σ be: c1 and n1 are constants. The lower the ionic conductivity, the faster the internal resistance increases.

[0079] For the internal resistance growth model R(n) = R0(1 + γn) δ In ) δ, assuming d1 and p1 are constants.

[0080] 2. The effect of electrode material specific capacitance on the increase of internal resistance

[0081] c2 and n2 are constants. The lower the specific capacity of the electrode material, the greater the internal resistance growth coefficient. d2 and p2 are constants.

[0082] 3. The effect of operating temperature on the increase of internal resistance

[0083] Where c3 is the pre-factor, E a3 It is the activation energy related to the increase of internal resistance. d3 is a constant, E a4 It is the activation energy related to δ.

[0084] 4. The effect of charge / discharge rate on the increase of internal resistance

[0085] c4 and n4 are constants. The higher the charge / discharge rate, the faster the internal resistance increases.

[0086] d4 and p4 are constants.

[0087] In summary,

[0088] The expression for the internal resistance growth coefficient γ is:

[0089]

[0090] The expression for δ is:

[0091]

[0092] Lifetime prediction model

[0093] Assuming the end-of-life condition for a solid-state battery is either capacity decay to x% of its initial capacity or internal resistance increase to y% of its initial internal resistance, then the lifespan N of the solid-state battery can be expressed as:

[0094]

[0095] Substituting the expressions for α, β, γ, and δ into the lifespan prediction model yields a complete mathematical model for predicting the lifespan of solid-state batteries based on multiple parameters. This model comprehensively considers the synergistic effects of multiple parameters, such as ionic conductivity, electrode material specific capacity, operating temperature, and charge / discharge rate, on the lifespan of solid-state batteries. The capacity decay model and internal resistance growth model in the model respectively introduce reasonable physicochemical mechanisms related to each parameter. For example, the capacity decay model considers the effect of temperature on the reaction rate using the Arrhenius equation, and the internal resistance growth model similarly considers the mechanisms of action of various factors, making the model not only a result of data fitting but also possessing clear physical meaning and theoretical support, which helps to deeply understand the intrinsic causes of battery lifespan degradation. The model provides explicit analytical expressions for the capacity decay coefficients α and β, and the internal resistance growth coefficients γ and δ. These analytical expressions describe in detail the quantitative relationships between each parameter and capacity decay and internal resistance growth. Through these analytical expressions, the degree of influence of different parameter changes on battery lifespan can be analyzed more intuitively, and specific theoretical guidance can be provided for the optimization of battery design and usage conditions.

[0096] Parameter Cooperative Algorithm Design

[0097] In the above lifetime prediction model, the lifetime N of a solid-state battery is affected by multiple parameters. The capacity decay component involves parameters α, β, n, and x%, while the internal resistance growth component involves parameters γ, δ, n, and y%. Furthermore, α and β are related to parameters such as ionic conductivity σ, electrode material specific capacity Q, operating temperature T, and charge / discharge rate C, with specific relationships as described above (e.g., ...).

[0098] Similarly, γδ is also related to these parameters.

[0099] To minimize lifetime prediction error, an objective function is established. Where C pred,i and R pred,i The capacitance and internal resistance are predicted based on the current parameter combination, C actual,i and R actual,i These are the actual measured capacitance and internal resistance.

[0100] 1. Data Collection

[0101] The ionic conductivity of this solid-state battery at different temperatures and charge / discharge states was measured using electrochemical impedance spectroscopy, yielding a set of ionic conductivity data σ1, σ2, ..., σ n ;

[0102] The specific capacity of the electrode material was measured through charge-discharge experiments, and the specific capacity data Q1, Q2, ..., Q were obtained. n ;

[0103] During battery charging and discharging, a temperature sensor is used to record the operating temperature, obtaining temperature data T1, T2, ..., T. n ;

[0104] Different charge / discharge rates are set, and the battery charge / discharge data at different rates is recorded using a charge / discharge device. The charge / discharge rate data C1, C2, ..., C are then calculated. n .

[0105] 2. Data Preprocessing

[0106] Check the data for outliers, such as data points that significantly deviate from the normal range. If outliers are found, remove them using median filtering.

[0107] The min-max normalization method was used to normalize the data such as ionic conductivity, specific capacity, temperature and charge / discharge rate, mapping each data point to the interval [0,1].

[0108] Assume the original data is X = {x1, x2, ..., x...} n The normalized data is

[0109]

[0110] 3. Model parameter estimation

[0111] To achieve multi-parameter identification in the aforementioned solid-state battery lifetime assessment model, an improved adaptive genetic algorithm (IAGA) was designed. Its main improvements include adaptive crossover probability (p... c ) and mutation probability (p m Adjustments were made to the search algorithm, and a catastrophe operation and return mechanism were introduced to avoid premature convergence and improve the traversability of the search.

[0112] like Figure 1 As shown, the specific algorithm design steps are as follows:

[0113] ① Encoding: The parameters that need to be optimized are encoded in binary. Based on the empirical values ​​of the parameters, the range of parameter values ​​is set, and each range is represented by a binary string of a certain length.

[0114] ② Population Initialization: Based on the binary encoding method, a certain number of individuals are randomly generated to form the initial population. Each individual represents a set of possible parameter combinations; for example, for the objective function, the preprocessed data is substituted into the expression for the capacity decay coefficient.

[0115] and

[0116] An initial set of parameters will be randomly generated:

[0117] (a1,a2,a3,a4,m1,m2,E a1 ,m4,b1,b2,b3,b4,k1,k2,E a2 ,k4)

[0118] ③ Calculate fitness: For each individual, decode its corresponding parameters and substitute them into the mathematical model for lifespan prediction.

[0119] In the middle, calculate its fitness value.

[0120] Among them, parameters such as C0, α, β, γ, and δ are obtained by encoding and decoding the individual.

[0121] ④ Selection Operator: Based on the fitness value of an individual, a roulette wheel selection operator is used to select individuals for the next generation of the population. Roulette wheel selection: The proportion of an individual on the roulette wheel is calculated based on its fitness value, and then random selection is performed. Individuals with higher fitness values ​​have a greater probability of being selected. ⑤ Crossover Operation: An adaptive crossover probability adjustment is designed, and the crossover probability is dynamically adjusted according to an adaptive formula. For example, when an individual's fitness value is large and there are many individuals in the population with fitness values ​​greater than the average fitness value, the crossover probability will be reduced accordingly to protect superior gene combinations.

[0122] The adaptive crossover probability and mutation probability formulas are designed as follows:

[0123]

[0124] The value "0.8" is an empirical value, f max f represents the maximum fitness value in the population. avg f represents the average fitness value of the population, f' represents the larger of the two fitness values ​​of the crossover individuals, and f' represents the fitness value of the individual to be mutated. min M1 represents the minimum fitness value in the population; M2 represents the number of individuals in each generation with a fitness value greater than the average fitness value; λ is an infinitesimal positive number, mainly to prevent the denominator from equaling 0; other parameters can be defined based on empirical values, such as p. c1 =0.9,p c2 =0.6,p m1 =0.1,p m2 =0.01. Perform a crossover operation on the selected individuals to generate new individuals.

[0125] ⑥ Mutation operation: Design an adaptive mutation probability adjustment. The mutation probability is also adjusted according to an adaptive formula. When the fitness value of an individual is small or the population shows a premature trend, the mutation probability will increase to increase the diversity of the population and avoid falling into local optimality. The mutation probability formula is as follows:

[0126]

[0127] where f' represents the fitness value of the individual to be mutated, the value "0.1" is an empirical value, and the other parameters have the same meaning as in the crossover probability formula; p m1 = 0.1, p m2 = 0.01. Perform mutation operations on the newly generated individuals to introduce new genes.

[0128] ⑦ Catastrophe operation judgment: Assume the number of iterations is M. After the m-th (m < M) generation, if most individuals have a high similarity to the optimal individual (only the optimal individual and the worst individual have low similarity), then perform the following operations to generate a new population: Save the current population state, the optimal individual, and its fitness value; Retain a small number of individuals with poor fitness values; Delete most of the individuals with excellent fitness values in the population; Randomly generate new individuals for supplementation.

[0129] a. Judgment conditions:

[0130] After an appropriate number of iterations, judge whether the conditions for the catastrophe operation are met. Usually, it can be judged according to factors such as the diversity index of the population and the distribution of fitness values. For example, calculate the similarity between individuals in the population. If most individuals have a high similarity to the optimal individual (only the optimal individual and the worst individual have low similarity), it is considered that the conditions for the catastrophe operation are met.

[0131] b. Mathematical description:

[0132] Assume that the catastrophe operation judgment is performed when the number of iterations reaches T. Calculate the similarity between individuals in the population using the Euclidean distance method. If the Euclidean distance between most individuals and the optimal individual is less than a certain threshold, it is considered that the conditions for the catastrophe operation are met. For example, there is a population with a size of N = 50. When the number of iterations reaches T = 100, the catastrophe operation judgment is performed. Calculate the Euclidean distance between each individual and the optimal individual and find that 40 individuals have a Euclidean distance less than 0.5 from the optimal individual, then it is considered that the conditions for the catastrophe operation are met. Perform the catastrophe operation, retain the 10 individuals with the worst fitness values, delete the remaining 40 individuals, and then randomly generate 40 new individuals to form a new population.

[0133] ⑧ Return Mechanism: To improve the algorithm's global search performance and search traversal, a return operation is added. When a crossover or mutation operation fails, i.e., the average (optimal) fitness level of the newly generated population is lower than the average (optimal) fitness level of the original population, the operation is rejected, and the population returns to its previous state. For example, after a crossover or mutation operation, the newly generated population has N = 50 individuals, and its average fitness value is calculated to be f'. avg The original population's average fitness value was f. avg If f' avg <f avg If the previous state is not reached, a return operation will be performed, restoring the population to its previous state.

[0134] 9. Iteration: Repeat steps ③ to ⑧ until a stopping condition is met, such as reaching the maximum number of iterations or achieving a certain level of fitness accuracy. Throughout the process, by continuously adjusting the crossover and mutation probabilities, and executing catastrophe operations and return mechanisms, the algorithm can better balance global and local searches, avoid premature convergence, and thus more effectively find the optimal combination of multiple parameters, enabling the lifetime prediction model to more accurately predict the lifetime of solid-state batteries.

[0135] This invention comprehensively considers the impact of multiple factors on solid-state battery lifespan. By establishing a multi-parameter collaborative optimization mathematical model, it improves the accuracy and reliability of solid-state battery lifespan prediction. Simultaneously, by employing optimization algorithms to optimize the model parameters, the optimal parameter combination can be quickly found, improving prediction efficiency. The prediction method of this invention has strong generalization ability and can be applied to different types and specifications of solid-state batteries, providing strong support for the design, manufacturing, and application of solid-state batteries.

[0136] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0137] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0138] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-parameter collaborative optimization method for predicting the lifetime of solid-state batteries, characterized in that, The steps include: Step 1: Collaboratively optimize the parameters affecting the lifespan of solid-state batteries and construct a lifespan prediction model. The parameters include ionic conductivity, electrode materials, operating temperature, and charge / discharge rate. The key to assessing the lifespan of solid-state batteries lies in their capacity decay and internal resistance growth. Capacity decay model ,in, Indicates the number of charge-discharge cycles. This indicates the initial capacity of the battery and the capacity decay coefficient. The expression is: ， The expression is: ， Internal resistance growth model ,in Indicates the number of charge-discharge cycles. This represents the initial internal resistance value. Internal resistance growth coefficient The expression is: ， The way to express it is: ， Among them, and The data was obtained by fitting experimental data, where and It is a constant. and Obtained by fitting experimental data. and For the corresponding constant, It is a pre-factor. It is the activation energy related to capacity decay. It is the gas constant. It is the operating temperature. It is a constant. Is with The relevant activation energy, and It is a constant. and It is a constant. and It is a constant. and It is a constant. and It is a constant. It is a pre-factor. It is the activation energy related to the increase of internal resistance. It is a constant. Is with , and It is a constant. and It is a constant; and It is the ionic conductivity. Where C is the specific capacity of the electrode material, and C is the charge / discharge rate. Lifetime prediction model The end-of-life condition for solid-state batteries is capacity decay back to their initial capacity. Or the internal resistance increases to the initial internal resistance. Then the lifespan of solid-state batteries Represented as: ， The above , , , Substituting the expression into the lifetime prediction model, we obtain a complete mathematical model for predicting the lifetime of solid-state batteries based on multiple parameters. Step 2: Collect parameter information and input the collected parameter information into the life prediction model to complete the life prediction of solid-state batteries.

2. The method for predicting the lifetime of a solid-state battery through multi-parameter collaborative optimization according to claim 1, characterized in that, In the lifetime prediction model in step one, the lifetime of the solid-state battery... The capacity decay component is affected by parameters, among which... ,β, and The internal resistance growth section involves parameters. , , and ,and , and And also related to ionic conductivity Electrode material specific capacity Operating temperature Charge / discharge rate Relatedly, the model is optimized with the goal of minimizing lifetime prediction error, and an optimization objective function is established. ,in and The capacity and internal resistance are predicted based on the current parameter combination. and These are the actual measured capacitance and internal resistance.

3. The method for predicting the lifetime of a solid-state battery through multi-parameter collaborative optimization according to claim 2, characterized in that, The optimization steps for the lifetime prediction model are as follows: 1) Data acquisition, 2) Data preprocessing, and 3) Model parameter estimation.

4. The method for predicting the lifetime of a solid-state battery through multi-parameter collaborative optimization according to claim 3, characterized in that, Data acquisition included: measuring the ionic conductivity of the solid-state battery at different temperatures and charge / discharge states using an electrochemical impedance spectroscopy (EIS) instrument, obtaining a set of ionic conductivity data. The specific capacity of the electrode material was measured through charge-discharge experiments to obtain specific capacity data. During battery charging and discharging, a temperature sensor is used to record the operating temperature and obtain temperature data. Set different charge / discharge rates, use a charge / discharge device to record the battery's charge / discharge data at different rates, and calculate the charge / discharge rate data. .

5. The method for predicting the lifetime of a solid-state battery through multi-parameter collaborative optimization according to claim 4, characterized in that, Data preprocessing specifically involves checking for outliers in the data. If outliers are found, median filtering is used to remove them. Min-max normalization is then used to normalize data such as ionic conductivity, specific capacity, temperature, and charge / discharge rate, mapping each data point to the [0,1] interval. The original data is... The normalized data is 6. The method for predicting the lifetime of a solid-state battery through multi-parameter collaborative optimization according to claim 5, characterized in that, Model parameter estimation involves multi-parameter identification of a solid-state battery lifetime assessment model using an improved adaptive genetic algorithm, with improvements including adaptive crossover probabilities. and mutation probability Adjustments were made, and disaster recovery operations and return mechanisms were introduced.

7. The method for predicting the lifetime of a solid-state battery through multi-parameter collaborative optimization according to claim 6, characterized in that, An improved adaptive genetic algorithm has the following steps: ① Encoding: The parameters that need to be optimized are encoded in binary. Based on the empirical values ​​of the parameters, the range of parameter values ​​is set, and each range is represented by a binary string of a certain length. ② Initialize the population: Based on the binary encoding method, a certain number of individuals are randomly generated to form the initial population, with each individual representing a possible combination of parameters; ③ Calculate fitness: For each individual, decode its corresponding parameters and substitute them into the mathematical model for lifespan prediction. In the middle, calculate its fitness value; in, Obtained by encoding and decoding of an individual; ④ Selection operator: Based on the fitness value of an individual, the roulette wheel selection operator is used to select individuals for the next generation of the population; Roulette wheel selection: The proportion of an individual on the roulette wheel is calculated based on its fitness value, and then random selection is performed. Individuals with higher fitness values ​​have a greater probability of being selected. ⑤ For the crossover operation, an adaptive crossover probability adjustment is designed, with the crossover probability dynamically adjusted according to an adaptive formula; the adaptive crossover probability and mutation probability formulas are designed as follows: ， Among them, the values ​​"0.8" and "0.1" are empirical values. Represents the maximum fitness value in the population. The average fitness value of the population. This represents the larger of the fitness values ​​of the two individuals at the crossover. The fitness value represents the individual to be mutated. This represents the minimum fitness value in the population. This represents the number of individuals in each generation whose fitness value is greater than the average fitness value. This represents the number of individuals in each generation whose fitness value is less than the average fitness value. The value is an infinitesimal positive number to prevent the denominator from being equal to 0; other parameters can be defined based on empirical values; crossover operations are performed on the selected individuals to generate new individuals; , ; ⑥ Mutation operation: Design an adaptive mutation probability adjustment, where the mutation probability is adjusted according to an adaptive formula; the mutation probability formula is: ， in, This represents the fitness value of the individual to be mutated. The value "0.1" is an empirical value. Other parameters have the same meaning as in the crossover probability formula. , The newly generated individuals are subjected to mutation operations to introduce new genes; ⑦ Disaster response judgment, iteration count is , in the , After a generation, most individuals have a high similarity to the best individual. At this point, the following operations are performed to generate a new population: save the current population state, the best individual and its fitness value; retain a small number of individuals with poor fitness values; delete most of the individuals with excellent fitness in the population; and randomly generate new individuals to replenish the population. a. Judgment conditions: After an appropriate number of iterations, determine whether the conditions for a catastrophic operation are met. The determination is based on the population diversity index and the distribution of fitness values. If most individuals have a high similarity to the best individual, then the conditions for a catastrophic operation are considered to be met. b. Mathematical description: When the number of iterations reaches In order to determine the catastrophe operation, the similarity between individuals in the population is calculated and the Euclidean distance method is used. If the Euclidean distance between the majority of individuals and the best individual is less than a certain threshold, the conditions for catastrophe operation are considered to be met. ⑧ Return mechanism: In order to improve the global search performance and search traversal, a return operation is added; when the crossover or mutation operation fails, that is, when the average fitness level of the newly generated population is lower than the average fitness level of the original population, the operation is rejected and the population is restored to the previous state.

9. Iteration: Repeat steps 3 to 8 until the stopping condition is met. The stopping condition is reaching the maximum number of iterations or the fitness value reaches the accuracy requirement.

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