SOC pile service life prediction method, system, device and medium

Abstract

The invention discloses an SOC pile service life prediction method, system and device and a medium, and relates to the technical field of pile service life prediction. The invention provides an SOC electric pile service life prediction method comprising the following steps: obtaining cobalt loading capacity and oxygen vacancy attenuation characteristics of an electric pile cathode in a preset time period, the preset time period being a time period when an electric pile is charged and discharged; determining a first predicted life according to the cobalt load attenuation characteristic of the stack cathode; determining a second predicted life according to the attenuation characteristic of the oxygen vacancy; determining a third predicted life according to the working current and the theoretical current of at least one time node in the preset time period; and obtaining the predicted service life of the galvanic pile according to the first predicted life, the second predicted life and the third predicted life.

Description

Technical Field

[0001] The present application relates to the technical field of battery stack service life prediction, and in particular to a method, system, device and medium for predicting the service life of a SOC battery stack. Background Art

[0002] High-temperature solid oxide cell (SOC) is a general term for high-temperature solid oxide fuel cells (SOFC) and high-temperature solid oxide electrolyzers (SOEC). SOC stacks are battery packs composed of multiple identical single cell units stacked together.

[0003] During the use of SOC stacks, their performance will gradually decline with the increase of charge and discharge cycles, which is mainly attributed to the aging of materials and structural changes inside the stack. In order to accurately evaluate the service life of SOC stacks and improve the reliability and economy of the system, an effective service life prediction method is urgently needed. Summary of the invention

[0004] The main purpose of this application is to provide a SOC stack service life prediction method, system, device and medium, aiming to accurately evaluate the service life of the SOC stack and provide strong support for the maintenance and management of the SOC stack.

[0005] To achieve the above objectives, in a first aspect, the present application provides a method for predicting the service life of a SOC stack, comprising:

[0006] Obtaining attenuation characteristics of the cobalt loading and oxygen vacancies of the cathode of the battery stack within a preset time period, wherein the preset time period is a time period during which the battery stack has undergone charge and discharge;

[0007] Determine a first predicted life according to the cobalt loading attenuation characteristics of the cathode of the battery stack;

[0008] Determining a second predicted lifetime according to the decay characteristics of oxygen vacancies;

[0009] Determine a third predicted life according to the operating current and the theoretical current at at least one time node within a preset time period;

[0010] The predicted service life of the fuel cell stack is obtained based on the first predicted life, the second predicted life and the third predicted life.

[0011] Optionally, the step of determining the first predicted life according to the cobalt loading attenuation characteristics of the cathode of the battery stack includes:

[0012] Obtain the actual number of full charge and discharge times of the battery stack within a preset time period;

[0013] Obtaining a cobalt loading attenuation curve of the cathode of the battery stack according to the cobalt loading data at several time points within a preset time period;

[0014] According to the cobalt loading attenuation curve and the actual full charging and discharging times, a predicted full charging and discharging times when the cobalt loading of the cathode of the battery stack decays to a preset threshold is obtained;

[0015] A first predicted lifespan is obtained according to the predicted number of full charging and discharging times and the actual number of full charging and discharging times.

[0016] Optionally, the step of obtaining the actual number of full charge and discharge times of the battery stack within a preset time period includes:

[0017] Obtain the number of times the battery stack is charged and discharged within a preset time period, as well as the data of the charge and discharge amount each time;

[0018] Based on the charging and discharging data of each time, the actual number of full charging and discharging times within the preset time period is obtained.

[0019] Optionally, the calculation formula for obtaining the actual number of full charging and discharging times within a preset time period is:

[0020]

[0021] In the formula, Q charge,i is the charge capacity of the i-th charge, n is the total number of charging times, Q discharge,j is the discharge amount of the jth discharge, m is the total number of discharge times, the min function represents the smaller value of the total charge amount and the total discharge amount, Q rated Indicates the rated capacity of the battery stack.

[0022] Optionally, the calculation formula for the first predicted lifespan is:

[0023]

[0024] Where P threshold represents the preset threshold, P0 represents the cobalt loading at the initial time of the preset time period, μ represents the attenuation coefficient of the cobalt loading, N 实际 Indicates the actual number of full charge and discharge times within the preset time period.

[0025] Optionally, the calculation formula for determining the second predicted lifetime according to the decay characteristics of oxygen vacancies is:

[0026]

[0027] In the formula, O V0 represents the initial number of oxygen vacancies in a preset time period, O VC represents the critical number of oxygen vacancies, O Vs represents the number of oxygen vacancies at the sth time node within the preset time period, Δt s Indicates the time interval from the initial time of the preset time period to the sth time node.

[0028] Optionally, the calculation formula for determining the third predicted life according to the working current and the theoretical current at at least one time node within the preset time period is:

[0029]

[0030] In the formula, K represents a constant, y represents the number of time nodes selected within the preset time period, and I work,x Represents the working current at the xth time node, I theory,x represents the theoretical current at the xth time node, Indicates the absolute value of the relative deviation between the working current and the theoretical current at the xth time node.

[0031] In a second aspect, the present application provides a SOC stack service life prediction system, comprising:

[0032] Attenuation characteristic acquisition module: configured to acquire the attenuation characteristics of the cobalt loading and oxygen vacancies of the cathode of the stack within a preset time period, wherein the preset time period is the time period during which the stack has undergone charge and discharge;

[0033] A first prediction module: configured to determine a first predicted life according to a cobalt loading attenuation characteristic of a cathode of a stack;

[0034] A second prediction module: configured to determine a second predicted lifetime according to the decay characteristics of oxygen vacancies;

[0035] A third prediction module: configured to determine a third predicted life according to the working current and the theoretical current at at least one time node within a preset time period;

[0036] Service life prediction module: configured to obtain the predicted service life of the fuel cell stack based on the first predicted service life, the second predicted service life and the third predicted service life.

[0037] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above method.

[0038] In a fourth aspect, the present application provides a computer storage medium, wherein a computer program is stored on the computer-readable storage medium, and a processor executes the computer program to implement the above method.

[0039] Beneficial effects that this application can achieve:

[0040] The present application proposes a method, system, device and medium for predicting the service life of a SOC battery stack, including: obtaining the attenuation characteristics of the cobalt load and oxygen vacancies of the battery stack cathode within a preset time period, wherein the preset time period is the time period during which the battery stack has undergone charge and discharge; determining a first predicted life according to the attenuation characteristics of the cobalt load of the battery stack cathode; determining a second predicted life according to the attenuation characteristics of the oxygen vacancies; determining a third predicted life according to the working current and theoretical current of at least one time node within the preset time period; obtaining the predicted service life of the battery stack according to the first predicted life, the second predicted life and the third predicted life. The service life of the battery stack is predicted by combining the attenuation characteristics of the cobalt load of the battery stack cathode, the attenuation characteristics of the oxygen vacancies, and the relationship between the working current and the theoretical current, which can more comprehensively consider the factors affecting the life of the battery stack, thereby improving the accuracy of the prediction and providing strong support for the use, maintenance and optimization of the battery stack. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Schematic diagram of the process flow of the method for predicting the service life of a battery stack according to an embodiment of the present application.

[0042] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0045] In the present invention, unless otherwise clearly specified and limited, the terms "connection", "fixation", etc. should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0046] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the meaning of "and / or" appearing in the full text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0047] Example 1

[0048] Reference Figure 1 The first embodiment of the present application provides a method for predicting the service life of a SOC stack, comprising the following steps:

[0049] S10. Obtaining attenuation characteristics of the cobalt loading and oxygen vacancies of the cathode of the fuel cell stack within a preset time period, wherein the preset time period is a time period during which the fuel cell stack has undergone charge and discharge.

[0050] Optionally, a preset time period is determined, which is the time period of charge and discharge cycles that the stack has experienced. This time period should be long enough to reflect the attenuation trend of the stack performance. Within the preset time period, the cobalt loading data and oxygen vacancy data of the stack cathode are regularly collected. The cobalt loading can be measured by measuring the cobalt content in the catalyst by X-ray fluorescence spectroscopy, electrochemical impedance spectroscopy (EIS) testing, etc. X-ray fluorescence spectroscopy uses X-rays to excite the cobalt element in the sample, causing it to emit characteristic fluorescent X-rays. By detecting the intensity of the fluorescent X-rays, the cobalt content in the sample can be determined. Electrochemical impedance spectroscopy is an effective electrochemical analysis technique that can measure the change of impedance of solid oxide fuel cells with frequency. By analyzing the impedance spectrum, the conductivity and interface reaction characteristics of the cathode material can be understood, thereby indirectly reflecting the change in cobalt loading. Oxygen vacancies can be detected by X-ray diffraction (XRD), electron paramagnetic resonance (EPR), transmission electron microscopy (TEM) and other technologies. X-ray diffraction (XRD) can infer the existence and quantity of oxygen vacancies by analyzing the X-ray diffraction pattern of the material. Electron paramagnetic resonance (EPR) uses the resonance phenomenon of unpaired electrons in a magnetic field to detect the presence of oxygen vacancies. The collected cobalt loading and oxygen vacancy data are analyzed to obtain their decay characteristics within a preset time period, such as decay rate, decay mode, etc.

[0051] Among them, cobalt loading refers to the cobalt content in the cathode of the stack, usually expressed as mass percentage (wt%). Elements such as cobalt (Co), iron (Fe) and lanthanum (La) are usually used as cathode catalyst materials or structural materials in the stack, and these elements may be reduced due to high temperature and electrochemical reactions. In particular, cobalt (Co) may dissolve or migrate during long-term charging and discharging, resulting in a decrease in its content. The size of the cobalt loading directly affects the performance and life of the fuel cell. A higher cobalt loading generally provides a better catalytic effect. The size of the cobalt loading directly affects the catalytic activity and stability of the cathode of the fuel cell. A lower cobalt loading may lead to insufficient catalytic activity, affecting the performance and life of the stack. Oxygen vacancies refer to the missing position of oxygen atoms in metal oxides. In solid physics and materials science, oxygen vacancies are a common point defect that affects the physical and chemical properties of materials. In fuel cells, oxygen vacancies can affect the activity, stability and conductivity of electrocatalysts. Oxygen vacancies can affect the active sites and electronic structure of electrocatalysts, thereby affecting the performance and life of the stack.

[0052] S20. Determine a first predicted life according to the cobalt loading attenuation characteristics of the cathode of the fuel cell stack.

[0053] Optionally, a prediction model is established based on the attenuation characteristics of the cobalt loading. This model can take into account the initial value of the cobalt loading, the attenuation rate, and possible accelerated attenuation factors. Using the established model, the life of the stack cathode when the cobalt loading decays to a preset threshold is predicted, i.e., the first predicted life. This threshold can be determined based on the performance requirements of the stack and the actual application scenario.

[0054] S30. Determine a second predicted lifetime according to the decay characteristics of oxygen vacancies.

[0055] Optionally, analyze the effect of oxygen vacancies on the performance of the stack, including its effect on conductivity, catalytic activity, etc. Establish a prediction model based on the decay characteristics of oxygen vacancies. This model can take into account the initial number of oxygen vacancies, decay rate, and possible influencing factors. Use the oxygen vacancy decay model to predict the life of the stack when the number of oxygen vacancies reaches a certain critical value, that is, the second predicted life. This critical value can be determined based on the performance stability and safety requirements of the stack.

[0056] S40. Determine a third predicted lifespan according to the operating current and the theoretical current at at least one time node within a preset time period.

[0057] Optionally, within a preset time period, select at least one time node and obtain the working current and theoretical current data of these time nodes. The working current can be obtained through actual measurement, while the theoretical current can be calculated through the design parameters and performance model of the battery stack. Calculate the deviation between the working current and the theoretical current, and analyze the changing trend of the deviation. This deviation can reflect the performance fluctuation and attenuation of the battery stack in actual work. Based on the deviation analysis results, establish a prediction model to predict the life of the battery stack when the deviation between the working current and the theoretical current reaches a certain preset range, that is, the third predicted life. This preset range can be determined according to the performance stability and reliability requirements of the battery stack.

[0058] S50. Obtain a predicted service life of the fuel cell stack according to the first predicted service life, the second predicted service life, and the third predicted service life.

[0059] Optionally, the obtained first predicted life, second predicted life and third predicted life data are processed and standardized, and the final service life prediction can be obtained through the three predicted lifespans by weighted averaging, taking the minimum value or taking the maximum value.

[0060] Example 2

[0061] Based on Example 1, this embodiment provides a method for predicting the service life of an SOC stack, including the following steps:

[0062] S10. Obtaining attenuation characteristics of the cobalt loading and oxygen vacancies of the cathode of the fuel cell stack within a preset time period, wherein the preset time period is a time period during which the fuel cell stack has undergone charge and discharge.

[0063] S20. Determine a first predicted life according to the cobalt loading attenuation characteristics of the cathode of the fuel cell stack.

[0064] Optionally, the step of determining the first predicted life according to the cobalt loading attenuation characteristics of the cathode of the battery stack includes:

[0065] S201, obtaining the actual number of full charge and discharge times of the battery stack within a preset time period;

[0066] Optionally, the step of obtaining the actual number of full charge and discharge times of the battery stack within a preset time period includes:

[0067] S2011. Obtain the number of times the battery stack is charged and discharged within a preset time period, as well as the charging and discharging data for each time.

[0068] Specifically, determine a preset time period, such as one month, two months, half a year, one year, two years or longer. Collect all charge and discharge records of the battery stack within the preset time period, including the timestamp, charge amount and discharge amount of each charge and discharge. Ensure the accuracy and completeness of the data for subsequent calculations.

[0069] S2012. Obtain the actual number of full charge and discharge times within a preset time period based on the charge and discharge data for each time.

[0070] Optionally, the calculation formula for obtaining the actual number of full charge and discharge times within a preset time period is:

[0071]

[0072] In the formula, Q charge,i is the charge capacity of the i-th charge, n is the total number of charging times, Q discharge,j is the discharge amount of the jth discharge, m is the total number of discharge times, the min function represents the smaller value of the total charge amount and the total discharge amount, Q rated It indicates the rated capacity of the fuel cell stack, which is the nominal capacity when the fuel cell stack is designed. Indicates the total charge capacity within the preset time period. Indicates the total discharge capacity within a preset time. The min function is used to take the smaller value of the total charge capacity and the total discharge capacity to ensure the accuracy of the calculation.

[0073] S202. Obtain a cobalt loading attenuation curve of the cathode of the battery stack according to the cobalt loading data at several time points within a preset time period.

[0074] Specifically, several time points are selected within a preset time period, and these time points can be evenly distributed or selected according to a specific number of charge and discharge cycles or time intervals. At each time point, the cobalt loading of the cathode of the battery stack is measured and the measured value is recorded. Using a chart or data fitting tool, the measured cobalt loading data is associated with the corresponding time point to draw a curve of the cobalt loading over time, that is, the cobalt loading attenuation curve.

[0075] S203, according to the cobalt loading attenuation curve and the actual full charging and discharging times, obtaining the predicted full charging and discharging times when the cobalt loading of the cathode of the battery stack decays to a preset threshold.

[0076] Specifically, a preset threshold of the cobalt loading is determined, which may be the cobalt loading value when the stack performance begins to decline significantly. The cobalt loading attenuation curve is analyzed to determine the changing trend of the cobalt loading over time and the attenuation rate μ. An appropriate mathematical model or statistical method is used to predict the number of full charge and discharge cycles required for the cobalt loading to decay to the preset threshold based on the attenuation trend and rate of the cobalt loading.

[0077] S204: Obtain a first predicted lifespan according to the predicted number of full charge, discharge and full discharge times and the actual number of full charge, discharge and full discharge times.

[0078] Optionally, the calculation formula for the first predicted life span is:

[0079]

[0080] Where P threshold represents the preset threshold, P0 represents the cobalt loading at the initial time of the preset time period, and μ represents the attenuation coefficient of the cobalt loading, which reflects the rate at which the cobalt loading decreases with the increase in the number of full-charge and discharge cycles. The larger the attenuation coefficient, the faster the cobalt loading decays. 实际 Indicates the actual number of full charge and discharge cycles within the preset time period. L1 indicates the remaining number of full charge and discharge cycles required for the cobalt loading of the cathode of the battery stack to decay to the preset threshold value under the current attenuation coefficient (or converted to time, if the time required for each cycle is known).

[0081] This is a natural logarithm term, where P threshold is the preset cobalt loading threshold, indicating that when the cobalt loading decays to this value, the performance of the battery stack will drop significantly; P0 is the cobalt loading at the beginning of the preset time period. This logarithmic term represents the logarithmic value of the "relative attenuation" required for the cobalt loading to decay from the initial value to the threshold. The above formula calculates the number of full charge and discharge cycles that the battery stack can perform before reaching the preset threshold by combining the attenuation characteristics of the cobalt loading (represented by the attenuation coefficient μ and the logarithmic value of the relative attenuation) and the number of cycles that have occurred. The attenuation coefficient μ and the logarithmic value of the relative attenuation jointly determine the total number of cycles required for the cobalt loading to decay to the threshold (if the number of cycles that have occurred is not taken into account). Then, subtract the number of cycles that have occurred from this total number of cycles to obtain the remaining predicted life.

[0082] For ease of understanding, an example is given below:

[0083] Assume that there is an existing battery stack with a rated capacity of 100Ah. It is necessary to predict the service life of this battery stack in the future based on the decay characteristics of its cathode cobalt loading.

[0084] S2011: Get charge and discharge times and data:

[0085] Over the past year (preset time period), we have recorded every charge and discharge of the battery stack.

[0086] There are a total of n = 50 charges, each with a different charge capacity, but the total is 4800Ah;

[0087] There are a total of m = 50 discharges, the discharge capacity of each time is also different, but the total is 4700Ah.

[0088] S2012: Calculate the actual number of full, full and discharge times:

[0089] Substitute the data into the above formula,

[0090] S202: Obtaining the cobalt loading attenuation curve:

[0091] Over the past year, we have measured the cobalt loading of the cathode of the battery stack regularly (e.g., every month). We obtained the cobalt loading data at several time points and plotted a decay curve of the cobalt loading over time.

[0092] S203: Obtaining the predicted full and full discharge times:

[0093] The cobalt loading attenuation curve was analyzed to determine the attenuation trend and rate of the cobalt loading. Assume that the preset threshold is 80% of the initial cobalt loading and the attenuation coefficient μ is 0.05% for each full charge and discharge cycle.

[0094] S204: Calculate the first predicted lifespan:

[0095] Assuming that the initial cobalt loading P0 is 1 (expressed as a relative value, i.e., the initial cobalt loading is set to 100%), then

[0096] Bring in data:

[0097] It should be noted that the data in the above examples are only for illustrating the application of basic steps and formulas.

[0098] S30. Determine a second predicted lifetime according to the decay characteristics of oxygen vacancies.

[0099] Optionally, the calculation formula for determining the second predicted lifetime according to the decay characteristics of oxygen vacancies is:

[0100]

[0101] In the formula, O V0 represents the initial number of oxygen vacancies in a preset time period, which is the initial number of oxygen vacancies in the material before observation or experiment; VC represents the critical oxygen vacancy number, which is the number of oxygen vacancies when the material properties begin to change significantly or reach a critical point; O Vs represents the number of oxygen vacancies at the sth time node within the preset time period, Δt s Indicates the time interval from the initial time of the preset time period to the sth time node.

[0102] Specifically, It represents the logarithmic ratio between the initial number of oxygen vacancies and the critical number of oxygen vacancies, reflecting the degree of change of oxygen vacancies in the material from the initial state to the critical state. It represents the ratio of the logarithmic ratio of the change in the number of oxygen vacancies in a specific time interval to the time interval, that is, the rate at which oxygen vacancies decay. The second predicted lifetime can be understood as the "equivalent time" required for the material to reach the critical state from the initial state, based on the current decay rate.

[0103] To make it easier to understand, let's take a specific example:

[0104] Assuming that the initial number of oxygen vacancies O V0 =1000, critical oxygen vacancy number O VC = 500. During the experiment, we recorded the s = 5th time node (Δt s =100 hours) of oxygen vacancies O Vs =800.

[0105] Substitute the above data into the formula for calculation:

[0106] (1)

[0107] (2)

[0108] (3)

[0109] Therefore, based on the current oxygen vacancy decay rate, it is predicted that the equivalent time required for the material to reach the critical state from the initial state is about 31.07 hours. Then, based on the relationship between time and the number of full charge and discharge times, the time can be converted into the number of full charge and discharge times. It should be noted that the data in the above example is only to illustrate the basic steps and how to apply the formula.

[0110] S40. Determine a third predicted lifespan according to the operating current and the theoretical current at at least one time node within a preset time period.

[0111] Optionally, the calculation formula for determining the third predicted life according to the working current and the theoretical current at at least one time node within the preset time period is:

[0112]

[0113] Where K is a constant used to adjust the scale or unit of the predicted lifespan. This constant may depend on the specific equipment, system or application scenario. y is the number of time nodes selected within the preset time period. These time nodes can be equally spaced or selected according to specific conditions. I work,x Indicates the working current at the xth time node, that is, the current value of the device or system during actual operation; I theory,x It represents the theoretical current at the xth time node, that is, the current value of the device or system under ideal or designed conditions; The absolute value of the relative deviation between the working current and the theoretical current at the xth time node reflects the degree of difference between the actual current and the theoretical current. The smaller the sum of the relative deviations, the closer the current performance of the equipment or system is to the theoretical value, and therefore the predicted life may be longer.

[0114] For ease of understanding, the calculation of the third predicted life is given as an example as follows:

[0115] Assume that there is a battery stack that selects 4 time nodes within a preset time period to monitor the working current and theoretical current.

[0116] Time node 1:

[0117] Working current I work,1 =10A,

[0118] Theoretical current I theory,1 =12A,

[0119] Relative deviation |(10-12) / 12|=0.1667.

[0120] Time Node 2:

[0121] Working current I work,2 =11A,

[0122] Theoretical current I theory,2 =12A,

[0123] Relative deviation |(11-12) / 12|=0.0833.

[0124] Time Node 3:

[0125] Working current I work,3 =12A,

[0126] Theoretical current I theory,3 =12A,

[0127] Relative deviation |(12-12) / 12|=0.

[0128] Time Node 4:

[0129] Working current I work,4 =13A,

[0130] Theoretical current I theory,4 =12A,

[0131] Relative deviation |(13-12) / 12|=0.0833.

[0132] Add the absolute values ​​of the relative deviations of all time nodes: 0.1667+0.0833+0+0.0833=0.3333

[0133] Assuming that the constant k is 100, the third predicted life span is: L3 = 100 / 0.3333≈300 (the unit may be hours, days or other units, depending on the definition of k). It should be noted that the data in the above example is only for illustrating the basic steps and application of the formula.

[0134] S50. Obtain a predicted service life of the fuel cell stack according to the first predicted service life, the second predicted service life, and the third predicted service life.

[0135] Optionally, the predicted service life of the battery stack is calculated as follows: L 预测 =L1×ω1+L2×ω2+L3×ω3. In the formula, L1 is the first predicted life, L2 is the second predicted life, L3 is the third predicted life, ω1, ω2 and ω3 are the weights of the first predicted life, the second predicted life and the third predicted life respectively, and ω1+ω2+ω3=1. Before predicting the predicted service life of the battery stack, it is necessary to preprocess the data of the first predicted life, the second predicted life and the third predicted life respectively, unify or standardize the units, and the number of full charge and discharge or time can be selected as the same unit.

[0136] If a prediction method has performed more accurately in the past for the life prediction of similar fuel cells, it should be assigned a higher weight. If a prediction method is based on a failure mode that has a greater impact on the overall performance of the fuel cell, it should also be assigned a higher weight. If the data required by a prediction method is easier to obtain and more reliable, then consider increasing its weight.

[0137] For example, assuming that we believe that the capacity attenuation data on which the first predicted life (L1) is based is very reliable and has the greatest impact on the performance of the battery stack, we assign it a weight of 0.5; the oxygen vacancy attenuation characteristic data on which the second predicted life (L2) is based is relatively reliable, but has a slightly smaller impact, so we assign it a weight of 0.3; the third predicted life (L3) is based on the difference data between the working current and the theoretical current, which has a certain degree of uncertainty, so we assign it a weight of 0.2.

[0138] Example 3

[0139] Based on Example 1, this embodiment provides a SOC stack service life prediction system, including:

[0140] Attenuation characteristic acquisition module: It is configured to obtain the attenuation characteristics of the cobalt loading and oxygen vacancies of the cathode of the fuel cell stack within a preset time period, wherein the preset time period is the time period during which the fuel cell stack has undergone charge and discharge.

[0141] The first prediction module is configured to determine a first predicted life according to the cobalt loading attenuation characteristics of the cathode of the fuel cell stack.

[0142] The second prediction module is configured to determine a second predicted lifetime according to the decay characteristics of oxygen vacancies.

[0143] The third prediction module is configured to determine a third predicted life according to the working current and the theoretical current at at least one time node within a preset time period.

[0144] Service life prediction module: configured to obtain the predicted service life of the fuel cell stack based on the first predicted service life, the second predicted service life and the third predicted service life.

[0145] Optionally, the above functional modules are further summarized as follows:

[0146] Attenuation characteristics acquisition module:

[0147] Used to obtain the attenuation characteristics of the cobalt loading and oxygen vacancies in the cathode of the battery stack within a preset time period.

[0148] Input: Relevant data of the preset time period (the charging and discharging time period that the battery stack has experienced), including the number of charge and discharge cycles, and the conditions of each cycle (such as temperature, pressure, current, etc.).

[0149] Output: data on the change of cobalt loading in the cathode of the fuel cell stack over time, and data on the change of the number or concentration of oxygen vacancies over time.

[0150] How it is implemented: Through laboratory testing, online monitoring, or extraction from historical data through data analysis algorithms.

[0151] First prediction module:

[0152] Function: Determine the first predicted life based on the cobalt loading attenuation characteristics of the stack cathode.

[0153] Input: Cobalt loading attenuation data output by the attenuation characteristics acquisition module.

[0154] Output: First predicted life (i.e., the stack service life predicted based on the cobalt loading attenuation).

[0155] Implementation method: Use mathematical models (such as linear regression, exponential decay model, etc.) to fit the cobalt loading decay trend and extrapolate it to the preset life endpoint.

[0156] Second prediction module:

[0157] Function: Determine the second predicted lifetime based on the decay characteristics of oxygen vacancies.

[0158] Input: Oxygen vacancy decay data output by the decay characteristics acquisition module.

[0159] Output: Second predicted life (i.e., stack service life predicted based on oxygen vacancy decay).

[0160] Implementation method: The mathematical model is also used to fit the oxygen vacancy decay trend, and its impact on the performance of the fuel cell is considered and extrapolated to the end of life.

[0161] The third prediction module:

[0162] Function: Determine a third predicted life according to the working current and theoretical current at at least one time node within a preset time period.

[0163] Input: operating current data within a preset time period, theoretical current data (which may be based on the design specifications of the battery stack or historical data).

[0164] Output: The third predicted life (i.e., the service life of the battery stack predicted based on the difference between the operating current and the theoretical current).

[0165] Implementation method: Calculate the relative deviation between the working current and the theoretical current, analyze its changing trend over time, and predict the life of the battery stack based on this.

[0166] Service life prediction module:

[0167] Function: Get the predicted service life of the fuel cell stack based on the first predicted life, the second predicted life and the third predicted life.

[0168] Input: predicted lifespan output by the first, second, and third prediction modules.

[0169] Output: Comprehensive predicted life (i.e., estimated stack service life after considering all prediction methods).

[0170] Implementation method: The weighted average method or the minimum method can be used to combine the accuracy and importance of each prediction method to obtain the final predicted lifespan.

[0171] Example 4

[0172] Based on Example 1, this embodiment provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the SOC stack service life prediction method as described above.

[0173] Optionally, the computer device may include: a processor, such as a central processing unit (CPU), a communication bus, a user interface, a network interface, and a memory. Among them, the communication bus is used to realize the connection and communication between these components. The user interface may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface may also include a standard wired interface and a wireless interface. The network interface may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory may optionally be a storage device independent of the aforementioned processor, and the memory may be a high-speed random access memory (Random Access Memory, RAM) memory, or it may be a stable non-volatile memory (Non-Volatile Memory, NVM), such as at least one disk storage; the processor may be a general-purpose processor, including a central processing unit, a network processor, etc., and may also be a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0174] The memory as a storage medium may include an operating system, a network communication module, a user interface module, and an application program for implementing the cleaning method of a photovoltaic power station.

[0175] The network interface is mainly used for data communication with the network server; the user interface is mainly used for data interaction with the user; the processor and memory in the present application can be set in an electronic device, and the electronic device calls the method for implementing the SOC battery stack service life prediction method stored in the memory through the processor.

[0176] Example 5

[0177] Based on Example 1, a computer program is stored on the computer-readable storage medium, and the processor executes the computer program to implement the SOC stack service life prediction method as described above.

[0178] In some embodiments, the computer readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or various devices including one or any combination of the above memories. The computer may be various computing devices including intelligent terminals and servers.

[0179] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for predicting the service life of a SOC stack, characterized in that: include: Obtaining attenuation characteristics of the cobalt loading and oxygen vacancies of the cathode of the battery stack within a preset time period, wherein the preset time period is a time period during which the battery stack has undergone charge and discharge; Determine a first predicted life according to the cobalt loading attenuation characteristics of the cathode of the battery stack; Determining a second predicted lifetime according to the decay characteristics of oxygen vacancies; Determine a third predicted life according to the operating current and the theoretical current at at least one time node within a preset time period; The predicted service life of the fuel cell stack is obtained based on the first predicted life, the second predicted life and the third predicted life.

2. The SOC stack service life prediction method according to claim 1, characterized in that: The step of determining the first predicted life according to the cobalt loading attenuation characteristics of the cathode of the battery stack includes: Obtain the actual number of full charge and discharge times of the battery stack within a preset time period; Obtaining a cobalt loading attenuation curve of the cathode of the battery stack according to the cobalt loading data at several time points within a preset time period; According to the cobalt loading attenuation curve and the actual full charging and discharging times, a predicted full charging and discharging times when the cobalt loading of the cathode of the battery stack decays to a preset threshold is obtained; A first predicted lifespan is obtained according to the predicted number of full charging and discharging times and the actual number of full charging and discharging times.

3. The SOC stack service life prediction method according to claim 2, characterized in that: The step of obtaining the actual number of full charge and discharge times of the battery stack within a preset time period includes: Obtain the number of times the battery stack is charged and discharged within a preset time period, as well as the data of the charge and discharge amount each time; Based on the charging and discharging data of each time, the actual number of full charging and discharging times within the preset time period is obtained.

4. The SOC stack service life prediction method according to claim 2, characterized in that: The calculation formula for obtaining the actual number of full charge and discharge times within the preset time period is: In the formula, Q charge,i is the charge capacity of the i-th charge, n is the total number of charges, Q discharge,j is the discharge amount of the jth discharge, m is the total number of discharges, the min function represents the smaller value of the total charge amount and the total discharge amount, Q rated Indicates the rated capacity of the battery stack.

5. The SOC stack service life prediction method according to claim 4, characterized in that: The calculation formula for the first predicted life span is: Where P threshold represents the preset threshold, P0 represents the cobalt loading at the initial time of the preset time period, μ represents the attenuation coefficient of the cobalt loading, N 实际 Indicates the actual number of full charge and discharge times within the preset time period.

6. The SOC stack service life prediction method according to claim 1, characterized in that: The calculation formula for determining the second predicted lifetime according to the decay characteristics of oxygen vacancies is: In the formula, O V0 represents the initial number of oxygen vacancies in a preset time period, O VC represents the critical number of oxygen vacancies, O Vs represents the number of oxygen vacancies at the sth time node within the preset time period, Δt s Indicates the time interval from the initial time of the preset time period to the sth time node.

7. The SOC stack service life prediction method according to claim 1, characterized in that: The calculation formula for determining the third predicted life according to the working current and the theoretical current at at least one time node within the preset time period is: In the formula, K represents a constant, y represents the number of time nodes selected within the preset time period, and I work,x Represents the working current at the xth time node, I theory,x represents the theoretical current at the xth time node, Indicates the absolute value of the relative deviation between the working current and the theoretical current at the xth time node.

8. A SOC stack service life prediction system, characterized in that: include: Attenuation characteristic acquisition module: configured to acquire the attenuation characteristics of the cobalt loading and oxygen vacancies of the cathode of the stack within a preset time period, wherein the preset time period is the time period during which the stack has undergone charge and discharge; A first prediction module: configured to determine a first predicted life according to a cobalt loading attenuation characteristic of a cathode of a stack; A second prediction module: configured to determine a second predicted lifetime according to the decay characteristics of oxygen vacancies; A third prediction module: configured to determine a third predicted life according to the working current and the theoretical current at at least one time node within a preset time period; Service life prediction module: configured to obtain the predicted service life of the fuel cell stack based on the first predicted service life, the second predicted service life and the third predicted service life.

9. A computer device, characterized in that: The computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the processor executes the computer program to implement the method according to any one of claims 1 to 7.

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