Thermal storage battery performance prediction method considering actual load equivalence

Abstract

The invention provides a thermal storage battery performance prediction method considering actual load equivalence. The method comprises the following steps: establishing a single thermal battery degradation model; through performance decoupling of the single thermal battery, establishing a degradation model from each part of the single thermal battery to the single thermal battery; and establishing a unit thermal battery performance model by considering an equivalent result of a series-parallel structure of the single thermal battery pack and actual loads inside and outside the shell, and calculating to obtain a unit thermal battery capacity prediction result. According to the invention, on the basis of consideration of a part coupling relationship, equivalent actual loads inside and outside a shell, degradation consistency and dispersibility, a performance prediction model is established from bottom to top in a thermal battery storage performance degradation stage, and thermal batteries with different components and packaging structures can be adapted; therefore, it is ensured that the prediction result is accurate, and the method can be popularized and used among different battery models, and has obvious practicability.

Description

Technical Field

[0001] The 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 solid inorganic molten salt at room temperature as an electrolyte. When working, the device is activated to trigger a heat source, causing the electrolyte to melt and connect the positive and negative electrodes to produce an electrochemical reaction to output electrical energy. Because it is 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 higher requirements for the storage life of power sources, the performance prediction 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 in the design stage and formulate corresponding material design and battery packaging structure design strategies, and to have a deep understanding of their degradation laws.

[0003] Thermal batteries are mainly subjected to constant environmental stress and temperature alternating stress during the storage stage. Common degradation modeling is directly carried out for the overall storage state of the battery. Due to the isolation of the shell, the model usually does not involve environmental stress variables, but only time variables. The degradation model actually established is a degradation time series model under a specific storage environment. There are several problems with such a model. First, the thermal battery sample consumption is large, and it is difficult to obtain a degradation time series model with high accuracy and low experimental cost; second, the time series model lacks environmental stress variables and cannot evaluate the performance of thermal batteries in various storage environment changes; third, the model establishment process ignores the actual load that causes component degradation and the degradation. The result only has a performance evaluation function but does not have a guiding significance for the improvement of thermal battery design. Therefore, in order to solve the modeling problem of thermal batteries in the storage stage, it is urgent and necessary to seek a storage thermal battery performance prediction method that considers the equivalent of actual loads to achieve the accuracy, generalization ability and mechanism guidance of the performance prediction model. Summary of the invention

[0004] In view of the defects in the above-mentioned prior art, the present invention proposes a method for predicting the performance of thermal storage batteries that takes into account the equivalence of actual loads. The method includes establishing a degradation model for a single thermal battery; establishing a degradation model for each component of a single thermal battery to a single thermal battery by decoupling the performance of the single thermal battery; considering the equivalent results of the series-parallel structure of the single thermal battery group and the actual loads inside and outside the shell, establishing a unit thermal battery performance model, and calculating the prediction results of the unit thermal battery capacity. Based on the considerations of the coupling relationship of components, the equivalence of actual loads 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, which can adapt to thermal batteries with different components and packaging structures, thereby ensuring that the prediction results are accurate and can be promoted and used 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] Among them, α 0 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 the single thermal battery, a degradation model of each component of the single thermal battery to the single thermal battery is established: considering the structure of the single thermal battery and the participation of each component of the single thermal battery in the discharge process, 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 monomer degradation amount; A represents the determination coefficient of the short-board component; Respectively represent the predicted values ​​of degradation of the positive electrode sheet, separator sheet and negative electrode sheet; r 1 ,r 2 ,r 3 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 means taking the maximum value;

[0012] 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;

[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 index 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 according to 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 monomer 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 circuit unit thermal battery is connected in series and parallel by using the battery pack represented by 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 =(R 1 +R 2 +…+R num1 ) / / (R (num1)+1 +R (num1)+2 +...+R num2 ) (9)

[0020] Among them, R s represents the performance of the s-th single thermal battery, s is 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, the capacity performance of the series-connected module unit thermal battery is determined as follows:

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

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

[0024] S353, considering the internal resistance shunt in the process of calculating the parallel capacity of two groups of series modules, the final unit thermal battery performance is determined as:

[0025]

[0026] Among them, Q pack_all Represents the predicted capacity degradation of the unit thermal battery; Z cell Indicates the degradation of a single cell; the left formula as a whole indicates 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 taking the minimum value;

[0027] So far, we can calculate Q pack_all Unit thermal battery capacity prediction results.

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

[0029] S331, relative humidity differential dRH and oxygen differential dO at fixed interval dt 2 The increments are arranged and accumulated on the time axis to obtain the relative humidity RH and oxygen concentration O 2 Changes of RH(t) and O over time 2 (t);

[0030] S332, RH(t) in the shell and O 2 (t) is substituted, and the temperature inside and outside the shell is taken as equal, and the performance of the actual storage monomer thermal battery is predicted;

[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 the 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. Considering 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] Where Q represents the predicted capacity value of the single thermal battery; Q 0 represents the initial value of the capacity of the single thermal battery; F represents the distribution of the capacity value, 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 leaking component; R represents the gas molar constant; T represents the temperature; q represents the shell leakage rate; P represents the local pressure; P 0 represents the pressure inside the shell; the amount of substance n of the leaked component 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(H 2 O) and c(O 2 ) 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 due to corrosion aging, respectively; and Respectively represent the influence coefficients of relative humidity and oxygen concentration in the shell on the reaction rate; ac 1 and ac 2 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 specific calculation method of the p-value is:

[0048]

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

[0050] Preferably, in step S2, the performance of the single thermal battery is coupled, 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 environment inside and outside 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 a thermal storage 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 direct modeling of unit cells.

[0056] 2. The present invention designs a storage thermal battery performance prediction method that considers actual load equivalence, which takes into account the environmental loads borne by thermal batteries in various storage stages as model input. Compared with traditional time-series degradation models, it is more in line with actual storage conditions and can predict thermal battery performance in a changing storage environment, 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, and truly overcomes the difficult problem of no direct contact between the test load and sensitive components in the study of battery storage performance. This makes the model prediction results more accurate and more capable of explaining the mechanism, and can guide the selection of thermal battery materials, packaging structure design, battery pack structure design and other processes. 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 a method for predicting the performance of a thermal storage battery taking actual load equivalence into consideration according to 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 It is a 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 in one embodiment of the present invention;

[0063] Figure 4 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 It is a schematic diagram of the actual load evolution curve of relative humidity in a preferred embodiment of the present invention;

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

[0066] Figure 6 It is a schematic diagram of the capacity degradation curve of a single thermal battery and its confidence interval analysis result in a preferred embodiment of the present invention;

[0067] Figure 7 It is a 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 It is a 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] Fig. 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 in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It should also be noted that, for ease of description, only the parts related 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 in the embodiments of the present 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 1 The present invention shows a method for predicting the performance of a thermal storage battery considering actual load equivalence, the method comprising the following steps:

[0073] S1. Establish a single thermal battery degradation model: As shown in Figure 2, a single thermal battery includes three main components: a negative electrode sheet 1, a separator sheet 2, and a positive electrode sheet 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 formula:

[0074]

[0075] Among them, α 0 and β represent the initial first acceleration factor parameter and the second acceleration factor parameter respectively; X i and α i They respectively represent the i-th environmental stress and its corresponding first acceleration factor parameter, i takes a positive integer from 1 to number, number represents the number of stress types considered in the modeling, and in a specific embodiment takes 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 the single thermal battery, a degradation model of each component of the single thermal battery to the single thermal battery is established: considering the structure of the single thermal battery and the participation of each component of the single thermal battery in the discharge process, 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 monomer degradation; A represents the determination coefficient of the short-board component; Respectively represent the predicted values ​​of degradation of the positive electrode sheet, separator sheet and negative electrode sheet; r 1 ,r 2 ,r 3 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 means taking 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, and 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, an equivalent model of the gas inside and outside the shell is established:

[0082]

[0083] Where n represents the amount of the leaking component; R represents the gas molar constant; T represents the temperature; q represents the shell leakage rate; P represents the local pressure; P 0 Indicates the pressure inside the shell. Combined with the gas data and the test difficulty, the formula expression is adjusted. The amount of substance n of the leaked component 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 gases inside and outside the shell is affected by factors including but not limited to the storage environment outside the shell, the gas leakage rate of the shell, the assembly environment and the reaction rate inside the shell.

[0087] S32. Taking Δn as the rate index and the change of each gas component as the result, the gas component change rate is calculated, and the equivalent kinetic model of the gas inside and outside the shell is established according to the change of relative humidity and oxygen concentration of the equivalent model of the gas inside and outside the shell:

[0088]

[0089] in, and Respectively represent the rate of change of relative humidity and oxygen with time; c(H 2 O) and c(O 2 ) 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 due to corrosion aging, respectively; and Respectively represent the influence coefficients of relative humidity and oxygen concentration in the shell on the reaction rate; ac 1 and ac 2 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 ​​of the external pressure, ideal gas constant, temperature, etc. that do not change much are condensed into the undetermined parameters parm leak , 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, relative humidity differential dRH and oxygen differential dO at fixed interval dt 2 The increments are arranged and accumulated on the time axis to obtain the relative humidity RH and oxygen concentration O 2 Changes of RH(t) and O over time 2 (t).

[0095] S332, RH(t) in the shell and O 2 (t) is substituted, and the temperature inside and outside the shell is taken as equal, and the performance of the actual storage single thermal battery is predicted.

[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 the series-parallel structure; the series-parallel structure considers the shunt effect of the resistance model.

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

[0099] S343. Considering 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] Where Q represents the predicted capacity value of the single thermal battery; Q 0represents the initial value of the capacity of the single thermal battery; F represents the distribution of the capacity value, 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 calculated sample cumulative distribution 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 circuit unit thermal battery is connected in series and parallel by using the battery pack represented by 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 =(R 1 +R 2 +...+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 is 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, the capacity performance of the series-connected module unit thermal battery is determined 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 shunt in the process of calculating the parallel capacity of two groups of series modules, the final unit thermal battery performance is determined as:

[0113]

[0114] Among them, Q pack_all Represents the predicted capacity degradation of the unit thermal battery; Z cell represents the degradation of a single cell; the left formula as a whole represents the capacity prediction process considering current distribution; the right formula represents 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 a specific embodiment, num1 is 28 and num2 is 56.

[0115] So far, we can calculate Q pack_all Unit thermal battery capacity prediction results.

[0116] In a specific embodiment, a certain type of thermal battery is used as an example. By decomposing the thermal battery material level, monomer level and unit level functions, a thermal battery capacity prediction model is established from the bottom up. It can adapt to the performance prediction requirements of thermal battery products with different components and packaging designs, and can evaluate the thermal battery storage performance in advance during the design stage. This method does not require the overall battery to be tested after the thermal battery design parameters are finalized, but achieves capacity prediction of the thermal battery system by acquiring performance data at the material level and decoupling functions. Compared with the traditional overall thermal battery degradation experiment and data regression method, the modeling process of the present invention can obtain the mechanism explanation of the degradation contribution of all thermal battery components to the thermal battery performance, and the results have a supporting role in the design of thermal batteries. At the same time, the present invention introduces the difference between actual load and environmental load, and enables the two to be converted into each other by establishing the form of gas equivalent kinetic equation, which solves the problems of unclear load, 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, in which the single thermal batteries modeled by the object and method are connected in series and packaged to form a Figure 3 The thermal battery 4 shown in the figure has a terminal 5 on the end surface of the thermal battery 4. The thermal storage battery performance prediction method 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 sheet, positive electrode sheet and separator sheet is as follows: Figure 4 As shown in the first, second and fourth curves from top to bottom, the degradation trajectory obtained at this time is affected by the independent degradation effects of the three components, and the environmental stress of the degradation process is consumed independently. It is further necessary to comprehensively consider the degradation effect of the thermal battery monomer composed of the three components, as well as the environmental stress results of the competitive reaction of the three components.

[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 the single thermal battery. Since the three components compete to react to environmental stress when the single component degrades, 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 degradation process will be slower than the degradation curve of the fastest degraded component. The degradation trajectory of the single component is as follows: Figure 4 As shown in the third independent curve from top to bottom, the circles in the figure represent the actual 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 excessive relative humidity of the actual storage environment, generally 50%-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 the single thermal battery after the actual load is used. To facilitate the subsequent unit-level performance coupling, the capacity degradation result is converted into the expression of 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. This distribution model is used for sampling evaluation when establishing the unit battery model.

[0126] The final unit thermal battery degradation curve is as follows: Fig. 9 As shown, compared with the degradation amount of single thermal batteries, the unit thermal batteries have more degradation in the same period. Compared with the actual thermal batteries stored for 10 years, the capacity value degradation result is 3-6%, which is equivalent to the prediction interval, 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 storage thermal battery performance prediction method that considers actual load equivalence. A thermal battery degradation model is established from the material level to the bottom up, which is convenient for 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 the experimental cost. 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 direct modeling of 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 problem that the test load is not in direct contact with 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: 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; 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: considering the structure of the single thermal battery and the participation of each component of the single thermal battery in the discharge process, the capacity short board formula and the reaction competition formula are introduced to establish the final capacity model of the single thermal battery: in, represents the estimated value of the monomer degradation amount; A represents the determination coefficient of the short-board component; Respectively represent the predicted values ​​of degradation of the positive electrode sheet, the separator sheet and the 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; 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; S31. Based on the shell leakage and gas content, an equivalent model of the gas inside and outside the shell is established; S32, using Δn as a rate index 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 according to the relative humidity change and oxygen concentration change of the equivalent model of the gas inside and outside the shell; S33, predicting actual storage monomer 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 circuit unit thermal battery is connected in series and parallel by using the battery pack represented by 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: R 单元 =(R1+R2+...+R num1 ) / / (R (num1)+1 +R (num1)+2 +...+R num2 ) (9) Among them, R s represents 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; S352. Based on the discharge characteristics of the single thermal battery, the capacity performance of the series-connected module unit thermal battery is determined as follows: Q pack =max (i) (Q cell ) (10) 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; S353, considering the internal resistance shunt in the process of calculating the parallel capacity of two groups of series modules, the final unit thermal battery performance is determined as: Among them, Q pack_all Represents the predicted capacity degradation of the unit thermal battery; Z cell Indicates the degradation of a single cell; the left formula as a whole indicates 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 taking the minimum value; Q is obtained by calculation pack_all Unit thermal battery capacity prediction results.

2. The method for predicting the performance of a thermal storage battery considering actual load equivalence according to claim 1, characterized in that: The step S33 specifically includes the following steps: S331, arranging and accumulating 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); S332, substitute RH(t) and O2(t) in the shell, take the temperature inside and outside the shell as equal, and predict the performance of the actual storage single thermal battery; 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 the 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. Considering 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: Wherein, Q represents the predicted capacity value of the single thermal battery; Q0 ​​represents the initial value of the capacity of the single thermal battery; F represents the distribution of the capacity value; η and γ 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: Where n represents the amount of the substance of the leaking 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; where the amount of the substance of the leaking component n is the cumulative result of the interval climate state Δn, that is: Where Δt represents the degradation time interval.

4. The method for predicting the performance of a thermal storage battery considering actual load equivalence according to claim 1, characterized in that: The equivalent dynamic model of the gas inside and outside the shell in step S31 is: in, and They represent the rate of change of relative humidity and oxygen with time respectively; 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 due to corrosion aging, respectively; and Respectively represent the influence coefficients of the relative humidity and oxygen concentration in 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 in 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: 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.

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 a p-value test result, and the specific calculation method of the p-value is: Where D represents the absolute difference between the calculated sample cumulative distribution and the fitted distribution; N represents the number of samples; j represents the series term; and ∞ represents 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 thermal 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 environment inside and outside 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 thermal 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 thermal 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 sheet and a negative electrode sheet.

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