Performance prediction method of thermal battery considering actual load equivalence

Abstract

The application provides a storage thermal battery performance prediction method considering actual load equivalence, which comprises: establishing a single thermal battery degradation model; establishing a degradation model of each component of the single thermal battery to the single thermal battery through performance decoupling of the single thermal battery; considering the equivalent results of the parallel-connection and series-connection structure of the single thermal battery group and the actual load inside and outside the shell, establishing a unit thermal battery performance model, and calculating the unit thermal battery capacity prediction result. Based on the component coupling relationship, the actual load equivalence inside and outside the shell, and the consideration of degradation consistency and dispersion, the performance prediction model is established from bottom to top for the storage performance degradation stage of the thermal battery, which can adapt to thermal batteries with different components and packaging structures, so as to ensure that the prediction result is accurate and can be popularized and used among different battery models, and has obvious practicability.

Description

Technical Field

[0001] The present invention belongs to the technical field of thermal batteries, and in particular relates to a method for predicting the performance of a thermal storage battery taking actual load equivalence into consideration. Background Art

[0002] A thermal battery is a primary battery that uses a room-temperature solid inorganic molten salt as an electrolyte. During operation, a device activates a heat source, causing the electrolyte to melt and connect the positive and negative electrodes, generating an electrochemical reaction to output electrical energy. Because they are non-conductive when stored at room temperature, thermal batteries have the characteristics of high reliability, long storage life, and strong environmental adaptability, and are widely used in weapons and equipment. In recent years, as weapons and equipment have increased their requirements for the storage life of power sources, predicting the performance of thermal batteries after long-term storage has become a problem that needs to be solved. Therefore, it is particularly important to evaluate the performance degradation of thermal batteries during long-term storage during the design stage and formulate corresponding material design and battery packaging structure design strategies, as well as to deeply understand their degradation laws.

[0003] Thermal batteries are primarily subjected to constant environmental stresses and temperature fluctuations during storage. Common degradation modeling approaches focus solely on the overall storage state of the battery. Due to the isolation of the battery housing, these models typically do not consider environmental stress variables, only time variables. Consequently, the degradation models developed are time-series models of degradation under specific storage conditions. These models present several challenges. First, thermal battery samples are expensive, making it difficult to obtain a highly accurate and cost-effective degradation time-series model. Second, time-series models lack environmental stress variables, making it difficult to evaluate thermal battery performance under various storage conditions. Third, the modeling process ignores component degradation and the actual loads that cause degradation. Consequently, the results provide only performance evaluation capabilities and lack guidance for improving thermal battery design. Therefore, to address these storage modeling challenges, it is imperative to develop performance prediction methods for thermal batteries that consider actual load equivalence, ensuring accuracy, generalizability, and mechanistic guidance. Summary of the Invention

[0004] In response to the above-mentioned deficiencies in the prior art, the present invention proposes a method for predicting the performance of thermal storage batteries that takes into account actual load equivalence. The method includes establishing a degradation model for a single thermal battery; establishing a degradation model for each component of the single thermal battery to the single thermal battery by decoupling the performance of the single thermal battery; establishing a unit thermal battery performance model by taking into account the equivalent results of the series-parallel structure of the single thermal battery group and the actual load inside and outside the shell, and calculating the predicted results of the unit thermal battery capacity. Based on the considerations of the component coupling relationship, the equivalence of the actual load inside and outside the shell, and the consistency and dispersion of degradation, the present invention establishes a performance prediction model from the bottom up for the complete thermal battery storage performance degradation stage. It can adapt to thermal batteries with different compositions and packaging structures, thereby ensuring the accuracy of the prediction results and the ability to promote and use them among different battery models, and has good practicality.

[0005] The present invention provides a method for predicting the performance of a thermal storage battery taking into account actual load equivalence, which comprises the following steps:

[0006] S1. Establish a single thermal battery degradation model: Based on the performance degradation caused by material corrosion during the storage stage, the degradation process of the single thermal battery is modeled:

[0007]

[0008] Where α0 and β represent the initial first acceleration factor parameter and the second acceleration factor parameter respectively; X i and α i They represent the i-th environmental stress and its corresponding first acceleration factor parameter, i is a positive integer from 1 to number, number represents the number of stress types considered in the modeling; t represents the degradation time; Y represents the performance prediction value of the single thermal battery; exp represents the exponential function;

[0009] S2. By decoupling the performance of single thermal batteries, a degradation model of each component of a single thermal battery to a single thermal battery is established: considering the structure of the single thermal battery and the participation of each component in the discharge process of the single thermal battery, the capacity short board formula and the reaction competition formula are introduced to establish the final capacity model of the single thermal battery:

[0010]

[0011] in, represents the estimated value of the single degradation amount; A represents the determination coefficient of the short plate component; Respectively represent the degradation prediction values ​​of the positive electrode sheet, separator sheet and negative electrode sheet; r1, r2, r3 represent the first competitive reaction parameter, the second competitive reaction parameter and the third competitive reaction parameter, respectively, reflecting the relationship between the competitive reactions of different materials; max represents the maximum value;

[0012] S3. Considering the equivalent results of the series-parallel structure of the single thermal battery group and the actual load inside and outside the shell, a unit thermal battery performance model is established;

[0013] S31. Based on the shell leakage and gas content, an equivalent model of the gas inside and outside the shell is established;

[0014] S32, using Δn as a rate indicator and the change of each gas component as a result, the gas component change rate is calculated, and an equivalent kinetic model of the gas inside and outside the shell is established based on the relative humidity change and oxygen concentration change of the equivalent model of the gas inside and outside the shell;

[0015] S33, predicting actual storage single thermal battery performance;

[0016] S34. Considering the dispersion of single thermal batteries, a unit thermal battery performance coupling model is established:

[0017] S35. Calculate the electrical performance of the unit thermal battery based on the structure of the single thermal battery pack:

[0018] S351. The circuit model of the circuit structure couples the performance of each single thermal battery into the performance of the unit-level single thermal battery. The battery pack is connected in series first and then in parallel. The circuit unit thermal battery is connected in series and parallel with the internal resistance. The performance of each single thermal battery is substituted into the performance of the single thermal battery to predict the performance R of the unit thermal battery. 单元 for:

[0019] R 单元 =(R1+R2+…+R num1 ) / / (R (num1)+1 +R (num1)+2 +...+R num2 ) (9)

[0020] Among them, R s Indicates the performance of the s-th single thermal battery, s ranges from 1 to num2; num1 and num2 represent the first constant integer and the second constant integer respectively; num1 and num2 represent the first constant integer and the second constant integer respectively;

[0021] S352. Based on the discharge characteristics of the single thermal battery, determine the capacity performance of the series-connected module unit thermal battery as follows:

[0022] Q pack =max (i) (Q cell ) (10)

[0023] Among them, Q pack Indicates the capacity degradation of the series structure; Q cell Indicates the degradation of the monomer capacity in the series structure;

[0024] S353. Considering the internal resistance shunting during the calculation of the parallel capacitance of the two series modules, the final unit thermal battery performance is determined as:

[0025]

[0026] Among them, Q pack_all Z represents the predicted capacity degradation of the unit thermal battery; cell Indicates the degradation of a single cell; the left formula overall represents the capacity prediction process considering current distribution, and the right formula indicates that the capacity of the parallel stack is determined by the capacity degradation of the module with the smallest capacity; min indicates the minimum value;

[0027] So far, Q is calculated pack_all Unit thermal battery capacity prediction results.

[0028] Furthermore, the step S33 specifically includes the following steps:

[0029] S331. Arrange and accumulate the increments of the relative humidity differential dRH and the oxygen differential dO2 at a fixed interval dt on the time axis to obtain the temporal changes of the relative humidity RH and the oxygen concentration O2 RH(t) and O2(t);

[0030] S332, substitute RH(t) and O2(t) inside the shell, taking the temperature inside and outside the shell equal, and predict the performance of the actual storage single thermal battery;

[0031] The step S34 specifically includes the following steps:

[0032] S341, taking the power capacity as the performance attribute, the unit thermal battery performance modeling is performed considering the performance dispersion and series-parallel structure;

[0033] S342, modifying the single thermal battery degradation model according to the equivalent results of the internal and external environments of the shell;

[0034] S343. Consider the case where there is only one single thermal battery in the shell. There is performance dispersion among the single thermal batteries. The dispersion results of the single thermal batteries are statistically analyzed:

[0035]

[0036] Wherein, Q represents the predicted capacity value of a single thermal battery; Q0 ​​represents the initial value of the capacity of a single thermal battery; F represents the distribution of the capacity value, and η and γ represent the first distribution parameter and the second distribution parameter, respectively.

[0037] Preferably, the equivalent model of the gas inside and outside the shell in step S31 is:

[0038]

[0039] Where n represents the amount of the leaked component; R represents the gas molar constant; T represents the temperature; q represents the shell leakage rate; P represents the local pressure; P0 represents the pressure inside the shell; the amount of the leaked component n is the cumulative result Δn of the interval climate state, that is:

[0040]

[0041] Where Δt represents the degradation time interval.

[0042] Preferably, the equivalent dynamic model of the gas inside and outside the shell in step S31 is:

[0043]

[0044] in, and Respectively represent the rate of change of relative humidity and oxygen with time; c(H2O) and c(O2) represent the concentration of water molecules and oxygen outside the shell, respectively; and They represent the rate constants of water molecules and oxygen consumption in the shell respectively; and Respectively represent the influence coefficients of the relative humidity and oxygen concentration inside the shell on the reaction rate; ac1 and ac2 represent the first acceleration parameter and the second acceleration parameter, respectively; f represents the function of the rate of change of the environment inside the shell based on the amount of substance; According to the meaning of each part of the kinetic equation, the equivalent kinetic model of the gas inside and outside the shell is simplified to:

[0045]

[0046] Among them, RH inner and RH real Respectively represent the relative humidity values ​​inside and outside the shell; and Respectively represent the influence coefficients of relative humidity and oxygen concentration in the shell on the reaction rate; parm leak Indicates pending parameters; parm reaction represents the internal reaction rate parameter.

[0047] Preferably, the unit thermal battery performance coupling model in step S34 is a p-value test result, and the p-value is specifically calculated as follows:

[0048]

[0049] Where D represents the absolute difference between the cumulative distribution of the calculated sample and the fitted distribution; N represents the number of samples; j represents the series term; and ∞ represents infinity.

[0050] Preferably, the performance of the single thermal battery is coupled in step S2, specifically the capacity performance of the single thermal battery is determined by a capacity short board model, and the correction of the degradation curve between components is affected by a reaction competition model.

[0051] Preferably, the series-parallel structure in step S341 considers the shunt effect of the resistance model, and the equivalent result of the internal and external environments of the shell in step S342 considers the isolation and leakage effects of the shell packaging on the environmental load.

[0052] Preferably, the equivalent model of the gas inside and outside the shell in step S31 is affected by the storage environment outside the shell, the gas leakage rate of the shell, the assembly environment and the reaction rate inside the shell.

[0053] Preferably, the single thermal battery in step S1 includes a positive electrode sheet, a separator and a negative electrode sheet.

[0054] Compared with the prior art, the technical effects of the present invention are:

[0055] 1. The present invention designs a method for predicting the performance of thermal storage batteries that takes actual load equivalence into consideration. It establishes a thermal battery degradation model from the bottom up at the material level, which facilitates clarifying the impact of each electrode on the overall degradation of the thermal battery. The experimental data is derived from material-level samples, which greatly saves experimental costs. At the same time, the analysis of the component coupling process can adjust design indicators in the design stage and estimate the performance of the design optimization, which has obvious advantages over direct modeling of unit cells.

[0056] 2. The present invention designs a method for predicting the performance of thermal batteries that takes actual load equivalence into account. It considers the environmental loads borne by thermal batteries in various storage stages as model inputs. Compared with traditional time-series degradation models, it is more in line with actual storage conditions and can predict thermal battery performance in changing storage environments, making the performance prediction model more adaptable.

[0057] 3. The present invention designs a method for predicting the performance of thermal storage batteries that takes actual load equivalence into consideration. It combines a variety of environmental load equivalence methods, including the gas exchange kinetics inside and outside the shell and reaction competition. It truly overcomes the difficult problem of no direct contact between the test load and sensitive components in battery storage performance research, making the model prediction results more accurate and more capable of explaining the mechanism, and can guide the processes of thermal battery material selection, packaging structure design, battery pack structure design, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Other features, objects and advantages of the present application will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings.

[0059] Figure 1 This is a flow chart of the method for predicting the performance of a thermal storage battery taking into account actual load equivalence of the present invention;

[0060] Figure 2a is a schematic diagram of a side cross-section of a single thermal battery in one embodiment of the present invention;

[0061] Figure 2b Schematic top view of various components of a single thermal battery in one embodiment of the present invention;

[0062] Figure 3 A schematic diagram of a thermal battery according to one embodiment of the present invention;

[0063] Figure 4 This is a schematic diagram of the degradation curve of component materials and single thermal battery capacity in a preferred embodiment of the present invention;

[0064] Figure 5a Schematic diagram of the actual load evolution curve of relative humidity in a preferred embodiment of the present invention;

[0065] Figure 5b Schematic diagram of the actual oxygen load evolution curve in a preferred embodiment of the present invention;

[0066] Figure 6 Schematic diagram of the capacity degradation curve and confidence interval analysis results of a single thermal battery in a preferred embodiment of the present invention;

[0067] Figure 7 Schematic diagram of the analysis results of the capacity degradation amount and confidence interval of a single thermal battery in a preferred embodiment of the present invention;

[0068] Figure 8 Schematic diagram of the dispersion and distribution fitting results of the degradation amount in a unit thermal battery in a preferred embodiment of the present invention;

[0069] Figure 9 This is a comparison diagram of the unit thermal battery degradation amount and its confidence interval and the single thermal battery prediction in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0070] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the relevant invention are shown in the accompanying drawings.

[0071] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0072] Figure 1The present invention shows a method for predicting the performance of a thermal storage battery considering actual load equivalence, which includes the following steps:

[0073] S1. Establishing a single thermal battery degradation model: As shown in Figure 2, a single thermal battery consists of three main components: the negative electrode 1, the separator 2, and the positive electrode 3. Based on the performance degradation caused by material corrosion during the storage stage, the degradation process of the single thermal battery is modeled using the Arrhenius equation:

[0074]

[0075] Where α0 and β represent the initial first acceleration factor parameter and the second acceleration factor parameter respectively; X i and α i They represent the i-th environmental stress and its corresponding first acceleration factor parameter, respectively. i is a positive integer from 1 to number. Number represents the number of stress types considered in the modeling. In a specific embodiment, it is 3, which are temperature, humidity, and oxygen respectively. t represents the degradation time. Y represents the performance prediction value of the single thermal battery. exp represents an exponential function.

[0076] S2. By decoupling the performance of single thermal batteries, a degradation model of each component of a single thermal battery to a single thermal battery is established: considering the structure of the single thermal battery and the participation of each component in the discharge process of the single thermal battery, the capacity short board formula and the reaction competition formula are introduced to establish the final capacity model of the single thermal battery:

[0077]

[0078] Where Z^ represents the estimated value of the single degradation amount; A represents the determination coefficient of the short-board component; They represent the predicted degradation values ​​of the positive electrode, separator, and negative electrode respectively; r1, r2, and r3 represent the first, second, and third competitive reaction parameters, respectively, reflecting the relationship between the competitive reactions of different materials; max represents the maximum value.

[0079] The performance of single thermal batteries is coupled, specifically the capacity performance of single thermal batteries is determined by the capacity short board model, and the correction of degradation curves between components is affected by the reaction competition model.

[0080] S3. Considering the equivalent results of the series-parallel structure of the single thermal battery group and the actual loads inside and outside the shell, a unit thermal battery performance model is established: the single thermal battery does not directly contact the external environmental load of the shell. At the same time, the final unit thermal battery performance is obtained by forming a battery group by connecting dozens of single thermal batteries in series and parallel.

[0081] S31. Based on the shell leakage and gas content, establish an equivalent model of the gas inside and outside the shell:

[0082]

[0083] Where n represents the amount of the leaked component; R represents the gas molar constant; T represents the temperature; q represents the shell leakage rate; P represents the local pressure; and P0 represents the pressure inside the shell. The formula is adjusted based on the gas data and the test difficulty. The amount of the leaked component, n, is the cumulative result Δn of the interval climate state, that is:

[0084]

[0085] Where Δt represents the degradation time interval.

[0086] The equivalent model of the gas inside and outside the shell is affected by factors including but not limited to the storage environment outside the shell, the shell leakage rate, the assembly environment and the reaction rate inside the shell.

[0087] S32. Using Δn as the rate indicator and the change of each gas component as the result, the gas component change rate is calculated. Based on the change of relative humidity and oxygen concentration of the equivalent model of the gas inside and outside the shell, an equivalent kinetic model of the gas inside and outside the shell is established:

[0088]

[0089] in, and Respectively represent the rate of change of relative humidity and oxygen with time; c(H2O) and c(O2) represent the concentration of water molecules and oxygen outside the shell, respectively; and They represent the rate constants of water molecules and oxygen consumption in the shell respectively; and Respectively represent the coefficients of influence of relative humidity and oxygen concentration inside the shell on the reaction rate; ac1 and ac2 represent the first acceleration parameter and the second acceleration parameter, respectively; f represents the function of the rate of change of the environment inside the shell based on the amount of substance; According to the meaning of each part of the kinetic equation, the equivalent kinetic model of the gas inside and outside the shell is simplified to:

[0090]

[0091] Among them, RH inner and RH real Respectively represent the relative humidity values ​​inside and outside the shell; and Respectively represent the influence coefficients of relative humidity and oxygen concentration in the shell on the reaction rate; parm leak Indicates pending parameters; parm reaction represents the internal reaction rate parameter.

[0092] The values ​​that do not change much, such as external pressure, ideal gas constant, temperature, etc., are condensed into the undetermined parameters parmleak , extract the environmental variables that mainly affect the leakage, such as leakage rate, external gas concentration, internal gas concentration, and define the internal reaction rate parameter parm reaction .

[0093] S33. Predict actual storage single-cell thermal battery performance.

[0094] S331. Arrange and accumulate the increments of the relative humidity differential dRH and the oxygen differential dO2 at a fixed interval dt on the time axis to obtain the changes of the relative humidity RH and the oxygen concentration O2 over time RH(t) and O2(t).

[0095] S332. Substitute the RH(t) and O2(t) inside the shell, take the temperature inside and outside the shell as equal, and predict the performance of the actual storage single thermal battery.

[0096] S34. Considering the dispersion of single thermal batteries, a unit thermal battery performance coupling model is established.

[0097] S341. Taking the power capacity as the performance attribute, the unit thermal battery performance modeling is carried out considering the performance dispersion and series-parallel structure; the series-parallel structure considers the shunt effect of the resistance model.

[0098] S342. Modify the degradation model of the single thermal battery according to the equivalent results of the internal and external environments of the shell; the equivalent results of the internal and external environments of the shell take into account the isolation and leakage effects of the shell packaging on the environmental load.

[0099] S343. Consider the case where there is only one single thermal battery in the shell. There is performance dispersion among the single thermal batteries. The dispersion results of the single thermal batteries are statistically analyzed:

[0100]

[0101] Wherein, Q represents the predicted capacity value of a single thermal battery; Q0 ​​represents the initial value of the capacity of a single thermal battery; F represents the distribution of the capacity value, and η and γ represent the first distribution parameter and the second distribution parameter, respectively.

[0102] The optimization method of the unit thermal battery performance coupling model is the p-value test result. The specific calculation method of the p-value is:

[0103]

[0104] Where D represents the absolute difference between the cumulative distribution of the calculated sample and the fitted distribution; N represents the number of samples; j represents the series term; and ∞ represents infinity.

[0105] S35. Calculate the electrical performance of the unit thermal battery based on the structure of the single thermal battery pack.

[0106] S351. The circuit model of the circuit structure couples the performance of each single thermal battery into the performance of the unit-level single thermal battery. The battery pack is connected in series first and then in parallel. The circuit unit thermal battery is connected in series and parallel with the internal resistance. The performance of each single thermal battery is substituted into the performance of the single thermal battery to predict the performance R of the unit thermal battery. 单元 for:

[0107] R 单元 =(R1+R2+...+R num1 ) / / (R (num1)+1 +R (num1)+2 +...+R num2 ) (9)

[0108] Among them, R s Indicates the performance of the s-th single thermal battery, s ranges from 1 to num2; num1 and num2 represent the first constant integer and the second constant integer respectively; num1 and num2 represent the first constant integer and the second constant integer respectively.

[0109] S352. Based on the discharge characteristics of the single thermal battery, determine the capacity performance of the series-connected module unit thermal battery as follows:

[0110] Q pack =max (i) (Q cell ) (10)

[0111] Among them, Q pack Indicates the capacity degradation of the series structure; Q cell Indicates the capacity degradation of a single cell in a series structure.

[0112] S353. Considering the internal resistance shunting during the calculation of the parallel capacitance of the two series modules, the final unit thermal battery performance is determined as:

[0113]

[0114] Among them, Q pack_all Z represents the predicted capacity degradation of the unit thermal battery; cell The left equation represents the capacity prediction process taking current distribution into account. The right equation indicates that the capacity of the parallel stack is determined by the capacity degradation of the module with the smallest capacity. Min represents the minimum value. In one specific embodiment, num1 is 28 and num2 is 56.

[0115] So far, Q is calculated pack_all Unit thermal battery capacity prediction results.

[0116] In a specific embodiment, using a certain type of thermal battery as an example, a thermal battery capacity prediction model is established from the bottom up by decomposing the thermal battery's functions at the material level, the monomer level, and the unit level. This model can adapt to the performance prediction requirements of thermal battery products with different compositions and packaging designs, and can evaluate the thermal battery storage performance in advance during the design phase. This method does not require testing the entire battery after the thermal battery design parameters are finalized. Instead, it achieves capacity prediction of the thermal battery system by acquiring performance data at the material level and decoupling the functions. Compared with traditional overall thermal battery degradation experiments and data regression methods, the modeling process of the present invention can obtain an explanation of the degradation mechanism of all thermal battery components to the thermal battery performance. The results provide support for thermal battery design. At the same time, the present invention introduces the difference between actual load and environmental load, and through the establishment of a gas equivalent kinetic equation, the two can be converted into each other, solving the problems of unclear loads, high dependence of modeling on time variables, and low scenario applicability in traditional methods.

[0117] In order to demonstrate the applicability of the present invention, it is applied to a thermal battery system example, where the objects and the single thermal batteries modeled by the method are connected in series and packaged to form a Figure 3 The thermal battery 4 shown in the figure has a terminal 5 provided on its end surface. The method for predicting the performance of a thermal storage battery of the present invention specifically comprises the following steps:

[0118] S1. Use the Arrhenius formula to establish a single thermal battery degradation model.

[0119] According to the degradation process model of each component of the single thermal battery obtained in S1, the capacity degradation trajectory of the negative electrode, positive electrode and separator is as follows: Figure 4 As shown by the first, second, and fourth curves from top to bottom, the degradation trajectory obtained here reflects the independent degradation effects of the three components, each of which independently consumes the environmental stress during the degradation process. Further consideration is needed to comprehensively consider the degradation effects of the three thermal battery cells and the environmental stress results of the three components competing for the same effect.

[0120] S2. By decoupling the performance of the single thermal battery, a degradation model of each component of the single thermal battery to the single thermal battery is established.

[0121] According to the single thermal battery coupling model, the fusion Figure 4 The first, second, and fourth curves from top to bottom are the capacity degradation curves of single thermal batteries. Since the three components compete to react to environmental stress during single-component degradation, the actual degradation point of the component will be slower than the degradation process of a single component. That is, the horizontal axis time corresponding to the circle in the figure is less than the 200h dotted line of the schematic prediction data, and the single-component degradation process will be slower than the degradation curve of the fastest-degrading component. The degradation trajectory of the single component is shown in the figure. Figure 4 As shown in the third independent curve from top to bottom, the circles in the figure represent the measured monomer performance, which are distributed on both sides.

[0122] S3. Considering the equivalent results of the series-parallel structure of the single thermal battery group and the actual loads inside and outside the shell, a unit thermal battery performance model is established.

[0123] By combining the shell leakage dynamics equation with the shell reaction rate equation, taking relative humidity and oxygen as an example, the thermal battery environmental load evolution model of the embodiment is solved as follows: Figure 5a 、 Figure 5b As shown, due to the high relative humidity of the actual storage environment, generally between 50% and 70%, the actual load of relative humidity presents a linear upward trend, while the oxygen concentration increases with the leakage, and the storage degradation and leakage present a stable trend.

[0124] Figure 6 The figure shows the capacity degradation curve of a single thermal battery after switching to actual load. To facilitate the subsequent unit-level performance coupling, the capacity degradation results are converted to the degradation amount. Figure 7 Shown is the degradation curve of a single thermal battery.

[0125] Figure 8 The figure shows the results of the dispersion distribution model of the degradation amount of a single thermal battery. When establishing the unit battery model, this distribution model is used for sampling evaluation.

[0126] The final unit thermal battery degradation curve is as follows: Figure 9 As shown, compared with the degradation amount of single thermal batteries, the unit thermal batteries have a greater degradation amount in the same period. Compared with the actual thermal batteries stored for 10 years, the capacity value degradation result is 3-6%, which is consistent with the prediction range, and the average absolute error with the predicted value is within 3%, which proves that the storage thermal battery performance prediction method considering the actual load equivalence established by the present invention has excellent effect.

[0127] The present invention proposes a method for predicting the performance of a storage thermal battery that takes actual load equivalence into consideration. A thermal battery degradation model is established from the bottom up at the material level, which facilitates clarifying the influence of each electrode on the overall degradation of the thermal battery. The experimental data is derived from material-level samples, which greatly saves experimental costs. At the same time, the analysis of the component coupling process can adjust the design indicators in the design stage and estimate the performance of the design optimization, which has obvious advantages over modeling directly for unit cells. The environmental loads borne by the thermal battery in various storage stages are considered as model inputs, which is more in line with the actual storage conditions than the traditional time-series degradation model. The thermal battery performance can be predicted in a changing storage environment, making the performance prediction model more adaptable. A variety of environmental load equivalence methods, including the gas exchange kinetics inside and outside the shell and reaction competition, are combined to truly overcome the difficult problem of no direct contact between the test load and the sensitive components in the battery storage performance research, making the model prediction results more accurate and more capable of explaining the mechanism, and can guide the processes of thermal battery material selection, packaging structure design, battery pack structure design, etc.

[0128] Finally, it should be noted that the above embodiments are only intended to illustrate rather than limit the technical solutions of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the present invention can still be modified or replaced by equivalents. Any modification or partial replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for predicting the performance of a thermal storage battery considering actual load equivalence, characterized in that: It includes the following steps: S1. Establish a single thermal battery degradation model: Based on the performance degradation caused by material corrosion during the storage stage, the degradation process of the single thermal battery is modeled: （1）； in, and represent the initial first acceleration factor parameter and the second acceleration factor parameter respectively; and They represent the i-th environmental stress and its corresponding first acceleration factor parameter, i is a positive integer from 1 to number, and number represents the number of stress types considered in the modeling; represents the degradation time; represents the performance prediction value of a single thermal battery; exp represents an exponential function; S2. By decoupling the performance of single thermal batteries, a degradation model of each component of a single thermal battery to a single thermal battery is established: considering the structure of the single thermal battery and the participation of each component in the discharge process of the single thermal battery, the capacity short board formula and the reaction competition formula are introduced to establish the final capacity model of the single thermal battery: （2）； in, represents the estimated value of monomer degradation; represents the coefficient of determination of the short plate component; Represent the predicted degradation values ​​of the positive electrode sheet, separator sheet and negative electrode sheet respectively; They represent the first competitive reaction parameter, the second competitive reaction parameter, and the third competitive reaction parameter, respectively, reflecting the relationship between the competitive reactions of different materials; max represents the maximum value; S3. Considering the equivalent results of the series-parallel structure of the single thermal battery group and the actual load inside and outside the shell, a unit thermal battery performance model is established; S31. Based on the shell leakage and gas content, an equivalent model of the gas inside and outside the shell is established; S32, As a rate indicator, the change of each gas component is used as a result to calculate the gas component change rate, and according to the change of relative humidity and oxygen concentration of the equivalent model of the gas inside and outside the shell, an equivalent kinetic model of the gas inside and outside the shell is established; S33, predicting actual storage single thermal battery performance; S34. Considering the dispersion of single thermal batteries, a unit thermal battery performance coupling model is established: S35. Calculate the electrical performance of the unit thermal battery based on the structure of the single thermal battery pack: S351. The circuit model of the circuit structure couples the performance of each single thermal battery into the performance of the unit-level single thermal battery. The battery pack is connected in series first and then in parallel. The circuit unit thermal battery represented by the internal resistance is connected in series and parallel. The performance of each single thermal battery is substituted into the performance of the single thermal battery to predict the performance of the unit thermal battery. for: （9）； in, Indicates the performance of the s-th thermal battery, where s ranges from 1 to num2; and denote the first constant integer and the second constant integer respectively; S352. Based on the discharge characteristics of the single thermal battery, determine the capacity performance of the series-connected module unit thermal battery as follows: （10）； in, Indicates the capacity degradation of the series structure; Indicates the degradation of the monomer capacity in the series structure; S353. Considering the internal resistance shunting during the calculation of the parallel capacitance of the two series modules, the final unit thermal battery performance is determined as: （11）； in, represents the predicted capacity degradation of the unit thermal battery; Indicates the degradation of a single cell; the left formula overall represents the capacity prediction process considering current distribution, and the right formula indicates that the capacity of the parallel stack is determined by the capacity degradation of the module with the smallest capacity; min indicates the minimum value; Obtained by calculation Unit thermal battery capacity prediction results.

2. The method for predicting the performance of a heat storage battery considering actual load equivalence according to claim 1, characterized in that: The step S33 specifically includes the following steps: S331, the fixed interval Lower relative humidity differential and oxygen differential The increments are arranged and accumulated on the time axis to obtain the relative humidity and oxygen concentration Changes over time and ; S332, put the shell and Substituting the values ​​of the temperature inside and outside the shell into equal values, the performance of the actual storage single thermal battery is predicted; The step S34 specifically includes the following steps: S341, taking the power capacity as the performance attribute, the unit thermal battery performance modeling is performed considering the performance dispersion and series-parallel structure; S342, modifying the single thermal battery degradation model according to the equivalent results of the internal and external environments of the shell; S343. Consider the case where there is only one single thermal battery in the shell. There is performance dispersion among the single thermal batteries. The dispersion results of the single thermal batteries are statistically analyzed: （7）； in, Indicates the predicted capacity value of a single thermal battery; Indicates the initial value of the single thermal battery capacity; Represents the distribution of capacitance values; and represent the first distribution parameter and the second distribution parameter respectively.

3. The method for predicting the performance of a thermal storage battery considering actual load equivalence according to claim 1, characterized in that: The equivalent model of the gas inside and outside the shell in step S31 is: （3）； in, The amount of substance representing the leaked component; represents the gas molar constant; Indicates temperature; Indicates the shell leakage rate; Indicates the local pressure; Indicates the pressure inside the shell; the amount of the leaked component n is the cumulative result of the interval climate state ,Right now: （4）； in, Represents the degradation time interval.

4. The method for predicting the performance of a heat storage battery considering actual load equivalence according to claim 3, characterized in that: The equivalent dynamic model of the gas inside and outside the shell in step S31 is: （5）； in, and represent the rates of change of relative humidity and oxygen with time, respectively; and represent the concentrations of water molecules and oxygen outside the shell respectively; and They represent the rate constants of water molecules and oxygen consumption in the shell respectively; and represent the influence coefficients of relative humidity and oxygen concentration inside the shell on the reaction rate; and represent the first acceleration parameter and the second acceleration parameter respectively; f represents the function of the rate of change of the environment inside the shell based on the amount of substance; according to the meaning of each part of the kinetic equation, the equivalent kinetic model of the gas inside and outside the shell is simplified to: （6）； in, and Respectively represent the relative humidity values ​​inside and outside the shell; Indicates pending parameters; represents the internal reaction rate parameter.

5. The method for predicting the performance of a thermal storage battery considering actual load equivalence according to claim 1, characterized in that: The unit thermal battery performance coupling model in step S34 is: The value test results, The specific calculation method of the value is: （8）； in, Indicates the absolute difference between the cumulative distribution of the sample and the fitted distribution; Indicates the number of samples; represents a series term; Indicates infinity.

6. The method for predicting the performance of a thermal storage battery considering actual load equivalence according to claim 1, characterized in that: In step S2, the performance of the single thermal battery is coupled, specifically, the capacity performance of the single thermal battery is determined by the capacity short board model, and the correction of the degradation curve between components is affected by the reaction competition model.

7. The method for predicting the performance of a heat storage battery considering actual load equivalence according to claim 2, characterized in that: In step S341, the series-parallel structure considers the shunt effect of the resistance model, and in step S342, the equivalent result of the internal and external environments of the shell considers the isolation and leakage effects of the shell packaging on the environmental load.

8. The method for predicting the performance of a heat storage battery considering actual load equivalence according to claim 1, characterized in that: In step S31, the equivalent model of the gas inside and outside the shell is affected by the storage environment outside the shell, the gas leakage rate of the shell, the assembly environment and the reaction rate inside the shell.

9. The method for predicting the performance of a heat storage battery considering actual load equivalence according to claim 1, characterized in that: In step S1 , the single thermal battery includes a positive electrode sheet, a separator, and a negative electrode sheet.

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