Fault location method and system for sodium ion battery energy storage based on photovoltaic power source

Abstract

The invention discloses a fault location method and system for sodium ion battery energy storage based on photovoltaic power supply, and relates to the technical field of fault location of sodium ion battery energy storage, comprising the following steps: obtaining impedance measurement accuracy data and impedance measurement stability data of EIS fault location analysis under prepared samples with different electrode surface roughnesses; constructing a sample preparation evaluation model based on a convolutional neural network; constructing an electrode surface roughness-EIS fault location performance display model; according to the experimental preparation restrictions and preset demand restrictions of EIS fault location, constraining the electrode surface roughness-EIS fault location performance display model; analyzing to obtain the balanced roughness of the electrode surface; the application is used to solve the problem of unbalanced electrode surface roughness, which will lead to an increase in contact resistance, thereby affecting the accuracy of electrochemical impedance measurement, and failing to truly reflect the interface state and performance of the battery.

Description

Technical Field

[0001] The present invention relates to the technical field of fault location of sodium ion battery energy storage, and more specifically, to a fault location method and system for sodium ion battery energy storage based on a photovoltaic power source. Background Art

[0002] Photovoltaic power is a clean, renewable energy technology that converts solar energy into electrical energy. With the rapid development of renewable energy, photovoltaic power generation has become an important part of the global power supply. Combining photovoltaic power with energy storage systems can balance the time difference between power generation and power consumption and improve energy efficiency. Especially when solar power generation fluctuates greatly, the role of energy storage systems is particularly important.

[0003] Sodium-ion batteries are an emerging battery technology that works similarly to lithium-ion batteries, but uses sodium as the main charge carrier. Sodium is abundant and low-cost, which helps reduce the overall cost of large-scale energy storage systems. The energy density of sodium-ion batteries is gradually increasing, and the cycle life is relatively long, which is suitable for power load balancing and the integration of renewable energy. As the technology matures, sodium-ion batteries have great potential for application in the field of energy storage.

[0004] In energy storage systems, faults may lead to power loss, equipment damage, and even safety hazards. Developing effective fault location methods can help quickly identify and solve problems and ensure the stable operation of the system. Fault location methods may involve technologies such as data acquisition, signal processing, and fault diagnosis algorithms. Sensors are used to monitor the battery status, combined with data analysis technologies such as machine learning or deep learning to monitor the health status of the battery in real time and detect abnormal conditions in a timely manner. By implementing this fault location method, the reliability of sodium-ion battery energy storage systems can be significantly improved, downtime can be reduced, and operation and maintenance costs can be reduced, thereby improving the overall economy and efficiency of photovoltaic power systems.

[0005] As an emerging energy storage technology, sodium-ion batteries have attracted widespread attention due to their abundant sodium resources and low cost. Solid electrolytes (SEs) play a key role in sodium-ion batteries because of their higher safety and better cycle performance. However, the interfacial stability and ionic conductivity of solid electrolytes may be affected under different working conditions, resulting in battery performance degradation or failure. Therefore, it is particularly important to develop effective solid electrolyte fault detection methods.

[0006] For example, the invention patent announcement with announcement number: CN118589458A discloses a hybrid energy storage system and its working method applied to an independent photovoltaic microgrid. For the field of energy storage technology, it can give full play to the advantages of high-power charging and discharging of supercapacitors, fast and high-rate charging and discharging of sodium-ion batteries, and long cycle performance, high safety, and greater discharge depth of iron-chromium flow batteries. The three complement each other's advantages, improve the safety and charging and discharging performance of the hybrid energy storage system, and construct a hybrid energy storage system. The hybrid energy storage system and its working method applied to an independent photovoltaic microgrid include a photovoltaic power generation module, a sodium-ion battery module, a supercapacitor module, an iron-chromium flow battery module, a DC-DC module, a DC-AC module, a power load module, and a hybrid battery management system; the sodium-ion battery, the supercapacitor, and the iron-chromium flow battery are respectively connected in parallel to the hybrid battery management system through the DC-DC module; the photovoltaic power generation module is connected to the power load module through the DC-DC module, the DC bus, the DC-AC module, and the AC bus.

[0007] For example, the invention patent with announcement number: CN113848508B discloses a method for locating a single-point grounding fault on the AC side of a battery energy storage system through leakage current, including: measuring the leakage current of a three-phase energy storage unit of a cascaded H-bridge battery energy storage system; and using a curve fitting method to detect and locate a grounding fault according to the leakage current size and distribution law of the three-phase energy storage unit. The present invention can locate a single-point grounding fault on the AC side of a cascaded H-bridge battery energy storage system in a power grid with an ungrounded center point. The present invention can measure the leakage current using a leakage current sensor at the outlet of the CHB-BESS energy storage unit on the AC side. It is not necessary to measure the voltage between the center point of the CHB-BESS and the ground. The measurement signal is small, and the ferromagnetic resonance that may be caused by the voltage transformer is avoided. It is safe and convenient, and is conducive to the fault inspection and maintenance of the CHB-BESS.

[0008] In the above disclosed technical solution, there are at least the following technical problems: the existing electrochemical impedance spectroscopy analysis is widely used in sodium ion battery fault location, mainly by measuring the impedance characteristics of the battery to identify the failure of the electrolyte and electrode interface. EIS can provide detailed information about ionic conductivity, interface reaction kinetics and internal impedance of the battery, helping researchers to analyze the working status and fault characteristics of the battery at different frequencies. The advantage is that it can perform real-time monitoring non-destructively, detect potential faults in time, and avoid serious damage caused by performance degradation. At the same time, it provides a scientific basis for optimizing battery design and material selection, thereby improving the safety and reliability of the battery.

[0009] In the electrochemical impedance spectroscopy analysis of sodium-ion batteries, the surface treatment of the sample is crucial to the accuracy of the results. First, if the electrode surface is too rough, it may lead to an increase in contact resistance, which will not only affect the accuracy of the impedance measurement, but also cause the measurement results to fail to truly reflect the interface state of the battery, thereby misleading fault diagnosis. On the other hand, if the surface is too smooth, it may lead to instability of the interface reaction, making the experimental results unrepresentative under actual operating conditions. Such extreme cases of surface characteristics may lead to misjudgment of battery performance, thereby affecting the effectiveness of overall fault location.

[0010] The problem of unbalanced electrode surface roughness in existing technologies will lead to increased contact resistance, thereby affecting the accuracy of electrochemical impedance measurement and failing to truly reflect the interface state and performance of the battery.

[0011] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0012] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a fault location method and system for sodium-ion battery energy storage based on photovoltaic power source, which solves the problem of unbalanced electrode surface roughness, which will lead to increased contact resistance, thereby affecting the accuracy of electrochemical impedance measurement and failing to truly reflect the interface state and performance problems of the battery through analysis of different sample surfaces.

[0013] To achieve the above object, the present invention provides the following technical solutions:

[0014] The fault location method of sodium ion battery energy storage based on photovoltaic power source includes the following steps: obtaining impedance measurement accuracy data and impedance measurement stability data of EIS fault location analysis under prepared samples with different electrode surface roughness; constructing a sample preparation evaluation model based on a convolutional neural network according to the impedance measurement accuracy data and the impedance measurement stability data; constructing an electrode surface roughness-EIS fault location performance display model according to the results of the sample preparation evaluation model of the EIS fault location analysis under prepared samples with different electrode surface roughness and the corresponding electrode surface roughness; constraining the electrode surface roughness-EIS fault location performance display model according to the experimental preparation restrictions and preset demand restrictions of the EIS fault location; and performing data analysis on the electrode surface roughness-EIS fault location performance display model after the constraint processing to obtain the electrode surface equilibrium roughness.

[0015] In a preferred embodiment, the impedance measurement accuracy data includes a fitting accuracy anomaly coefficient and an ion conductivity influence coefficient; the method for obtaining the fitting accuracy anomaly coefficient is as follows: a basic fitting value is obtained based on the electrode fitting tightness data of the EIS measurement data obtained by the sensor; based on the electrochemical simulation software, the ion migration inside the electrode is simulated to obtain the ion migration rate and the preset maximum migration speed, and the ratio is used as the ion migration influence factor; based on the direction of the reaction and the electrode surface state angle, the surface state influence factor is calculated by the cosine function; based on the material properties of the battery and the reaction rate formula, the reaction rate of the battery is calculated, and the migration speed of the reaction at different nodes is calculated in combination with the electrode geometry and surface treatment conditions; the basic fitting value is corrected in combination with the migration speed, surface state influence factor and ion migration influence factor, and the fitting accuracy anomaly coefficient is calculated.

[0016] In a preferred embodiment, the specific method for obtaining the ion conductivity influence coefficient is as follows: collect historical data of similar projects in the past, the historical data includes various types of conductivity problems, classify the collected conductivity problem records by type, assign problem weights to different types of problems to reflect the degree of impact of the problem on battery performance, and calculate the conductivity project evaluation coefficient according to the type and weight of the problem record; obtain the temperature, humidity and material properties affected by the battery conductivity problem, and calculate the affected environmental coefficient; obtain the type of project-related materials, the material type includes conductive materials, insulating materials, and composite materials, and set material scores for the material type according to the conductivity; calculate the ion conductivity influence coefficient in combination with the affected environmental coefficient, the conductive project evaluation coefficient and the material score.

[0017] In a preferred embodiment, the impedance measurement stability data includes an interface reaction kinetic uniformity coefficient; the specific method for obtaining the interface reaction kinetic uniformity coefficient is as follows: obtain several reaction rates during EIS measurement; the reaction standard deviation and reaction average of the reaction rates at the same time interval during EIS measurement; calculate the reaction rate variation coefficient based on the reaction standard deviation and the reaction average; calculate the interface reaction kinetic uniformity coefficient based on the reaction rate variation coefficient based on a preset interface reaction kinetic uniformity coefficient calculation formula.

[0018] In a preferred embodiment, the electrode surface roughness-EIS fault location performance display model is constructed according to the results of the sample preparation evaluation model of the EIS fault location analysis under the prepared samples with different electrode surface roughnesses and the corresponding electrode surface roughnesses, specifically: the sample preparation evaluation coefficients of the outputs of the prepared samples with electrode surface roughnesses under different settings are obtained from the sample preparation evaluation model under the prepared samples with different electrode surface roughnesses; the horizontal axis is represented as the prepared samples with electrode surface roughness, and the vertical axis is represented as the sample preparation evaluation coefficients, to construct the electrode surface roughness-EIS fault location performance display model; the sample preparation evaluation coefficients corresponding to the prepared samples with each electrode surface roughness are marked on the constructed electrode surface roughness-EIS fault location performance display model.

[0019] In a preferred embodiment, the electrode surface roughness-EIS fault location performance display model is constrained according to the experimental preparation limitations of the EIS fault location and the preset demand limitations, specifically: collecting and analyzing the surface roughness range existing in the experiment to determine the acceptable roughness value range; setting EIS performance indicators according to preset requirements, and the EIS performance indicators including the accuracy and stability requirements of impedance measurement; incorporating the roughness value and EIS performance indicator into the constraint conditions; deleting the data that does not meet the constraint conditions in the electrode surface roughness-EIS fault location performance display model according to the constraint conditions.

[0020] In a preferred embodiment, the electrode surface roughness after constraint processing-EIS fault location performance display model is subjected to data analysis to obtain the electrode surface equilibrium roughness, specifically: the electrode surface roughness corresponding to the sample preparation evaluation coefficient with the slowest change rate of the electrode surface roughness after constraint processing-EIS fault location performance display model is taken as the electrode surface equilibrium roughness.

[0021] The technical effects and advantages of the fault location method and system of the sodium ion battery energy storage based on photovoltaic power supply of the present invention are as follows:

[0022] 1. The present invention quantifies the accuracy and stability of impedance measurement by acquiring and analyzing EIS measurement data under different electrode surface roughness, and forms key indicators such as fitting accuracy anomaly coefficient, ion conductivity influence coefficient and interface reaction dynamic uniformity coefficient. These indicators can not only accurately identify preparation defects, but also provide an important basis for optimizing electrode design. Furthermore, the sample preparation evaluation model constructed based on the convolutional neural network realizes a comprehensive evaluation of the preparation process, thereby improving the stability and safety of battery performance and promoting the efficiency of fault location. Overall, the method of the present invention has the advantages of quantitative evaluation, accurate identification, feedback mechanism, etc., which lays a solid foundation for technological progress in the battery field.

[0023] 2. The present invention significantly improves the controllability and measurement accuracy of the electrode preparation process by constructing a display model of electrode surface roughness and EIS fault location performance. Specifically, by analyzing the sample preparation evaluation model under different electrode surface roughness, the sample preparation evaluation coefficient corresponding to each roughness can be clearly displayed, so as to intuitively identify the optimal electrode surface treatment conditions. Subsequently, based on the experimental preparation restrictions and preset demand restrictions, the model was constrained to ensure that only valid data that meets the performance indicators is retained, further improving the accuracy and stability of EIS measurements. Finally, by analyzing the model after constraint processing, the electrode surface equilibrium roughness was obtained. This indicator provides a scientific basis for electrode optimization design and promotes the accuracy and reliability of battery system fault location. Overall, this method has the advantages of accurate data analysis and clear performance optimization, and contributes new ideas to the technological progress of the battery industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a structural schematic diagram of the fault location method of sodium ion battery energy storage based on photovoltaic power supply of the present invention. DETAILED DESCRIPTION

[0025] 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.

[0026] Embodiment 1, Figure 1 The present invention provides a fault location method for sodium ion battery energy storage based on photovoltaic power source, comprising the following steps:

[0027] S1, obtain impedance measurement accuracy data and impedance measurement stability data for EIS fault location analysis under prepared samples with different electrode surface roughness.

[0028] The impedance measurement accuracy data includes a fitting accuracy anomaly coefficient and an ion conductivity influence coefficient, and the impedance measurement stability data includes an interface reaction dynamics uniformity coefficient.

[0029] The fitting accuracy anomaly coefficient is an important indicator used to evaluate the fitting results of electrochemical impedance spectroscopy (EIS) data. It reflects the reliability and accuracy of the data by quantifying the degree of deviation of the model parameters during the fitting process. A lower fitting accuracy anomaly coefficient means that the model has a higher degree of fit with the actual data, indicating that the sample preparation quality is good and can truly reflect the interface state and performance of the battery; while a higher anomaly coefficient may indicate problems in sample preparation, such as unbalanced electrode surface roughness, which leads to increased contact resistance, thus affecting the accuracy of EIS measurement and the effectiveness of fault location.

[0030] Analyzing the fitting accuracy anomaly coefficient has the following advantages in solving the problem of uneven electrode surface roughness, which will lead to increased contact resistance, thus affecting the accuracy of electrochemical impedance measurement and failing to truly reflect the interface state and performance of the battery:

[0031] Quantitative evaluation: By providing a specific numerical indicator, the fitting accuracy anomaly coefficient can effectively quantify the impact of electrode surface roughness on contact resistance, helping researchers to clarify the severity of the problem.

[0032] Identify preparation defects: A high anomaly coefficient can quickly indicate defects in sample preparation, such as uneven surface treatment or improper material selection, prompting timely adjustments to the preparation process.

[0033] Optimizing battery performance: By monitoring and optimizing the fitting accuracy anomaly coefficient, researchers are able to improve the battery’s contact resistance, thereby increasing the accuracy of electrochemical impedance measurements and providing a more realistic reflection of the battery’s interface state and performance.

[0034] Support model improvement: This coefficient can serve as a feedback mechanism to guide the improvement of battery design and material selection, so that the electrode surface treatment is more ideal and the overall battery performance is optimized.

[0035] Enhance the reliability of fault location: Reducing the fitting accuracy anomaly coefficient can improve the accuracy of EIS fault location analysis and help identify potential faults more effectively, thereby improving the safety and reliability of the battery system.

[0036] The method for obtaining the fitting accuracy anomaly coefficient is as follows:

[0037] Obtaining a basic fitting value based on the electrode fitting tightness data of the EIS measurement data obtained by the sensor;

[0038] The ion migration inside the electrode is simulated based on the electrochemical simulation software to obtain the ion migration rate and the preset maximum migration speed, and the ratio is used as the influencing factor of ion migration;

[0039] The surface state influence factor is calculated by the cosine function based on the reaction direction and the electrode surface state angle;

[0040] The reaction rate of the battery is calculated based on the material properties of the battery and the reaction rate formula, and the migration speed of the reaction at different nodes is calculated based on the electrode geometry and surface treatment conditions;

[0041] The basic fitting value was corrected by combining the migration velocity, surface state influencing factor and ion migration influencing factor, and the fitting accuracy anomaly coefficient was calculated.

[0042] The specific calculation formula of the ion migration influencing factor is as follows:

[0043]

[0044] The specific calculation formula of the surface state influencing factor is as follows:

[0045]

[0046] The migration speed calculation formula is specifically:

[0047]

[0048] The specific calculation formula of the fitting accuracy anomaly coefficient is as follows:

[0049]

[0050] In the formula, is the fitting accuracy anomaly coefficient, is the migration speed of node i, is the basic fitting value, is the current ion migration rate, is the maximum migration speed allowed, is the reaction angle, is the electrode surface state angle, and is the reaction characteristics and quantum migration adjustment coefficient, is the reaction rate of the battery, is the migration speed.

[0051] The ionic conductivity influence coefficient is an important indicator used to measure the effect of the electrode surface state on the ability of ions to migrate at the interface. This coefficient reflects how ions are restricted by the surface structure during battery operation by quantifying the effect of the roughness of the electrode surface on ionic conductivity. A higher ionic conductivity influence coefficient means that the roughness of the electrode surface significantly hinders the migration of ions, which may lead to an increase in contact resistance, thereby affecting the accuracy of electrochemical impedance measurement and failing to truly reflect the interface state and overall performance of the battery. This indicator plays an important role in EIS fault location analysis and helps to identify and solve performance problems caused by unbalanced electrode surface roughness.

[0052] Analyzing the ionic conductivity influence coefficient has the following advantages in solving the problem of uneven electrode surface roughness, which will lead to increased contact resistance, thus affecting the accuracy of electrochemical impedance measurement and failing to truly reflect the interface state and performance of the battery:

[0053] Accurately identify problems: By quantifying the effect of electrode surface roughness on ion migration, the ionic conductivity influence coefficient can accurately identify the problem of increased contact resistance caused by surface imbalance, facilitating timely adjustment of the preparation process.

[0054] Guiding optimized design: The analysis results of this coefficient can provide guidance for electrode design and help optimize the surface treatment process, thereby improving ionic conductivity, reducing contact resistance, and improving overall battery performance.

[0055] Enhanced measurement accuracy: Improving ionic conductivity can reduce contact resistance and improve the accuracy of electrochemical impedance measurements, making the evaluation of battery interface state and performance more reliable.

[0056] Feedback mechanism: The ionic conductivity influence coefficient serves as a feedback mechanism, which can help researchers monitor the state changes of the electrode surface in real time and adjust the experimental conditions and sample preparation methods in time to maintain optimal performance.

[0057] Facilitate fault location: By monitoring and optimizing this factor, the sensitivity of EIS fault location analysis can be improved, making the identification of potential faults faster and more accurate, thereby improving the safety and reliability of the battery.

[0058] The specific method for obtaining the ionic conductivity influence coefficient is as follows:

[0059] Collect historical data of similar projects in the past, including various types of conductivity problems. Classify the collected conductivity problem records by type, assign problem weights to different types of problems to reflect the degree of impact of the problems on battery performance, and calculate the conductivity project evaluation coefficient based on the type and weight of the problem records;

[0060] Obtain the temperature, humidity and material properties affected by battery conductivity issues, and calculate the affected environmental coefficients;

[0061] Obtain the types of project-related materials, including conductive materials, insulating materials, and composite materials, and set material scores for the material types based on their conductivity;

[0062] The ionic conductivity impact coefficient is calculated by combining the affected environment coefficient, the conductive project assessment coefficient and the material score.

[0063] The specific calculation formula of the conductive project evaluation coefficient is as follows:

[0064]

[0065] The specific calculation formula of the affected environment coefficient is as follows:

[0066]

[0067] The specific calculation formula of the ionic conductivity influence coefficient is as follows:

[0068]

[0069] In the formula, is the evaluation coefficient of the conductive project, is the weight of the ith conductivity problem, is the number of times the i-th conductivity problem occurs, is the number of times the conductivity problem type occurs, V is the total number of conductivity problems, is the affected environment factor, is the number of conductive items affected, is the temperature, is humidity, P is material property, is the ionic conductivity influence coefficient.

[0070] The interface reaction kinetic uniformity coefficient is an important indicator for measuring the reaction rate and mechanism consistency of the electrode surface, and can reflect the uniformity of the interface reaction. A rough electrode surface may lead to uneven reactions, which in turn affects the reaction kinetics, making the impedance characteristics in the EIS analysis unstable. A higher interface reaction kinetic uniformity coefficient indicates that the interface reaction process is more consistent, which helps to accurately evaluate the interface state and overall performance of the battery. This coefficient plays an important role in EIS fault location analysis, and can help identify the problem of increased contact resistance caused by unbalanced electrode surface roughness, thereby ensuring the accuracy and reliability of electrochemical impedance measurements.

[0071] Analyzing the uniformity coefficient of the interface reaction kinetics has the following advantages in solving the problem of uneven electrode surface roughness, which will lead to increased contact resistance, thus affecting the accuracy of electrochemical impedance measurement and failing to truly reflect the interface state and performance of the battery:

[0072] Reflects reaction consistency: This coefficient can effectively quantify the uniformity of the interface reaction and help identify inconsistent reaction rates caused by surface roughness, thereby clarifying the root cause of the problem.

[0073] Optimizing electrode design: By monitoring and optimizing the interface reaction kinetic uniformity coefficient, researchers can specifically adjust the electrode surface treatment process to improve the consistency and stability of the reaction and reduce contact resistance.

[0074] Improve measurement reliability: Good interfacial reaction dynamics uniformity can improve the accuracy of electrochemical impedance measurement and ensure that the test results can truly reflect the interface state and performance of the battery.

[0075] Promote performance improvement: By enhancing the consistency of interfacial reactions, the ionic conductivity and overall battery performance can be improved, making the battery more stable in practical applications.

[0076] Effective feedback mechanism: As a feedback mechanism, the interfacial reaction kinetic uniformity coefficient can guide improvements in subsequent experiments and preparation processes, thereby continuously optimizing battery performance and fault location.

[0077] The specific method for obtaining the interface reaction dynamic uniformity coefficient is as follows:

[0078] Get several reaction rates during EIS measurement;

[0079] The standard deviation and average value of the reaction rate at the same time interval during EIS measurement;

[0080] The coefficient of variation of the reaction rate was calculated based on the reaction standard deviation and the reaction mean;

[0081] The reaction rate variation coefficient is used to calculate the interface reaction kinetic uniformity coefficient based on a preset interface reaction kinetic uniformity coefficient calculation formula.

[0082] The specific calculation formula of the reaction rate variation coefficient is as follows:

[0083]

[0084] The specific calculation formula of the interface reaction dynamic uniformity coefficient is as follows:

[0085]

[0086] In the formula, is the coefficient of variation of the reaction rate, is the reaction rate at the jth moment, is the total number of moments collected during EIS measurement, and j is the moment number; is the interface reaction kinetic uniformity coefficient.

[0087] S2, construct a sample preparation evaluation model based on a convolutional neural network according to the impedance measurement accuracy data and the impedance measurement stability data.

[0088] The sample preparation evaluation model is constructed based on the convolutional neural network according to the impedance measurement accuracy data and the impedance measurement stability data, specifically:

[0089] The obtained fitting accuracy anomaly coefficient, ion conductivity influence coefficient and interface reaction kinetic uniformity coefficient are used to establish a sample preparation evaluation model to generate a sample preparation evaluation coefficient;

[0090] The calculation formula of the sample preparation evaluation coefficient is as follows:

[0091]

[0092] Where TS is the sample preparation evaluation coefficient, is the preset proportional coefficient of the fitting accuracy anomaly coefficient, is the preset proportional coefficient of the ionic conductivity influence coefficient, is the preset proportional coefficient of the interface reaction dynamic uniformity coefficient, is the fitting accuracy anomaly coefficient, is the ionic conductivity influence coefficient, is the interface reaction kinetic uniformity coefficient.

[0093] This embodiment quantifies the accuracy and stability of impedance measurement by acquiring and analyzing EIS measurement data under different electrode surface roughness, and forms key indicators such as fitting accuracy anomaly coefficient, ion conductivity influence coefficient and interface reaction dynamic uniformity coefficient. These indicators can not only accurately identify preparation defects, but also provide an important basis for optimizing electrode design. Furthermore, the sample preparation evaluation model constructed based on the convolutional neural network realizes a comprehensive evaluation of the preparation process, thereby improving the stability and safety of battery performance and promoting the efficiency of fault location. Overall, the method of the present invention has the advantages of quantitative evaluation, accurate identification, feedback mechanism, etc., which lays a solid foundation for technological progress in the field of batteries.

[0094] Example 2, S3, constructs an electrode surface roughness-EIS fault location performance display model based on the results of the sample preparation evaluation model of the EIS fault location analysis under the prepared samples with different electrode surface roughness and the corresponding electrode surface roughness.

[0095] According to the results of the sample preparation evaluation model of the EIS fault location analysis under the prepared samples with different electrode surface roughness and the corresponding electrode surface roughness, an electrode surface roughness-EIS fault location performance display model is constructed, specifically:

[0096] Obtaining sample preparation evaluation coefficients of outputs of prepared samples of electrode surface roughness under different settings from a sample preparation evaluation model under prepared samples of electrode surface roughness;

[0097] The horizontal axis represents the prepared sample of the electrode surface roughness, and the vertical axis represents the sample preparation evaluation coefficient, and the electrode surface roughness-EIS fault location performance display model is constructed;

[0098] The sample preparation evaluation coefficient corresponding to the prepared sample of each electrode surface roughness is marked on the constructed electrode surface roughness-EIS fault location performance display model.

[0099] S4, based on the experimental preparation limitations of EIS fault location and the preset demand limitations, the electrode surface roughness-EIS fault location performance display model is constrained.

[0100] According to the experimental preparation restrictions and preset demand restrictions of EIS fault location, the electrode surface roughness-EIS fault location performance display model is constrained, specifically:

[0101] Collect and analyze the surface roughness range in the experiment to determine the acceptable roughness range;

[0102] According to preset requirements, EIS performance indicators are set, wherein the EIS performance indicators include accuracy and stability requirements of impedance measurement;

[0103] Incorporate roughness values ​​and EIS performance indicators into constraints;

[0104] According to the constraints, the data that does not meet the constraints in the electrode surface roughness-EIS fault location performance display model are deleted.

[0105] S5, performing data analysis on the electrode surface roughness-EIS fault location performance display model after constraint processing to obtain the electrode surface equilibrium roughness.

[0106] The electrode surface roughness after constraint processing-EIS fault location performance display model is subjected to data analysis to obtain the electrode surface equilibrium roughness, specifically:

[0107] The electrode surface roughness corresponding to the sample preparation evaluation coefficient with the slowest change rate of the electrode surface roughness-EIS fault location performance display model after constraint processing is taken as the electrode surface equilibrium roughness.

[0108] This embodiment significantly improves the controllability and measurement accuracy of the electrode preparation process by constructing a display model of electrode surface roughness and EIS fault location performance. Specifically, by analyzing the sample preparation evaluation model under different electrode surface roughness, the sample preparation evaluation coefficient corresponding to each roughness can be clearly displayed, so as to intuitively identify the optimal electrode surface treatment conditions. Subsequently, based on the experimental preparation restrictions and preset demand restrictions, the model was constrained to ensure that only valid data that meets the performance indicators is retained, further improving the accuracy and stability of the EIS measurement. Finally, by analyzing the model after constraint processing, the electrode surface equilibrium roughness was obtained. This indicator provides a scientific basis for electrode optimization design and promotes the accuracy and reliability of battery system fault location. Overall, this method has the advantages of accurate data analysis and clear performance optimization, and contributes new ideas to the technological progress of the battery industry.

[0109] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0110] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0111] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0112] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0113] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0114] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A fault location method for sodium ion battery energy storage based on photovoltaic power source, characterized in that: The steps include: Obtain impedance measurement accuracy data and impedance measurement stability data for EIS fault location analysis under prepared samples with different electrode surface roughness; A sample preparation evaluation model is constructed based on a convolutional neural network according to the impedance measurement accuracy data and the impedance measurement stability data; According to the results of the sample preparation evaluation model of the EIS fault location analysis under the prepared samples with different electrode surface roughness and the corresponding electrode surface roughness, an electrode surface roughness-EIS fault location performance display model is constructed; According to the experimental preparation limitations of EIS fault location and the preset demand limitations, the electrode surface roughness-EIS fault location performance display model is constrained. The data of the electrode surface roughness-EIS fault location performance display model after constraint processing was analyzed to obtain the equilibrium roughness of the electrode surface.

2. The fault location method for sodium ion battery energy storage based on photovoltaic power source according to claim 1 is characterized in that: The impedance measurement accuracy data includes a fitting accuracy anomaly coefficient and an ion conductivity influence coefficient; the method for obtaining the fitting accuracy anomaly coefficient is as follows: Obtaining a basic fitting value based on the electrode fitting tightness data of the EIS measurement data obtained by the sensor; The ion migration inside the electrode is simulated based on the electrochemical simulation software to obtain the ion migration rate and the preset maximum migration speed, and the ratio is used as the influencing factor of ion migration; The surface state influence factor is calculated by the cosine function based on the reaction direction and the electrode surface state angle; The reaction rate of the battery is calculated based on the material properties of the battery and the reaction rate formula, and the migration speed of the reaction at different nodes is calculated based on the electrode geometry and surface treatment conditions; The basic fitting value was corrected by combining the migration velocity, surface state influencing factor and ion migration influencing factor, and the fitting accuracy anomaly coefficient was calculated.

3. The fault location method for sodium ion battery energy storage based on photovoltaic power source according to claim 2 is characterized in that: The specific method for obtaining the ionic conductivity influence coefficient is as follows: Collect historical data of similar projects in the past, including various types of conductivity problems. Classify the collected conductivity problem records by type, assign problem weights to different types of problems to reflect the degree of impact of the problems on battery performance, and calculate the conductivity project evaluation coefficient based on the type and weight of the problem records; Obtain the temperature, humidity and material properties affected by battery conductivity issues, and calculate the affected environmental coefficients; Obtain the types of project-related materials, including conductive materials, insulating materials, and composite materials, and set material scores for the material types based on their conductivity; The ionic conductivity impact coefficient is calculated by combining the affected environment coefficient, the conductive project assessment coefficient and the material score.

4. The fault location method for sodium ion battery energy storage based on photovoltaic power source according to claim 3 is characterized in that: The impedance measurement stability data includes the interface reaction dynamic uniformity coefficient; the specific method for obtaining the interface reaction dynamic uniformity coefficient is as follows: Get several reaction rates during EIS measurement; The standard deviation and average value of the reaction rate at the same time interval during EIS measurement; The coefficient of variation of the reaction rate was calculated based on the reaction standard deviation and the reaction mean; The reaction rate variation coefficient is used to calculate the interface reaction kinetic uniformity coefficient based on a preset interface reaction kinetic uniformity coefficient calculation formula.

5. The fault location method for sodium ion battery energy storage based on photovoltaic power source according to claim 4 is characterized in that: According to the results of the sample preparation evaluation model of the EIS fault location analysis under the prepared samples with different electrode surface roughness and the corresponding electrode surface roughness, an electrode surface roughness-EIS fault location performance display model is constructed, specifically: Obtaining sample preparation evaluation coefficients of outputs of prepared samples of electrode surface roughness under different settings from a sample preparation evaluation model under prepared samples of electrode surface roughness; The horizontal axis represents the prepared sample of the electrode surface roughness, and the vertical axis represents the sample preparation evaluation coefficient, and the electrode surface roughness-EIS fault location performance display model is constructed; The sample preparation evaluation coefficient corresponding to the prepared sample of each electrode surface roughness is marked on the constructed electrode surface roughness-EIS fault location performance display model.

6. The fault location method for sodium ion battery energy storage based on photovoltaic power source according to claim 5, characterized in that: According to the experimental preparation restrictions and preset demand restrictions of EIS fault location, the electrode surface roughness-EIS fault location performance display model is constrained, specifically: Collect and analyze the surface roughness range in the experiment to determine the acceptable roughness range; According to preset requirements, EIS performance indicators are set, wherein the EIS performance indicators include accuracy and stability requirements of impedance measurement; Incorporate roughness values ​​and EIS performance indicators into constraints; According to the constraints, the data that does not meet the constraints in the electrode surface roughness-EIS fault location performance display model are deleted.

7. The fault location method for sodium ion battery energy storage based on photovoltaic power source according to claim 6 is characterized in that: The electrode surface roughness after constraint processing-EIS fault location performance display model is subjected to data analysis to obtain the electrode surface equilibrium roughness, specifically: The electrode surface roughness corresponding to the sample preparation evaluation coefficient with the slowest change rate of the electrode surface roughness-EIS fault location performance display model after constraint processing is taken as the electrode surface equilibrium roughness.

8. The fault location method for sodium ion battery energy storage based on photovoltaic power source according to claim 7, characterized in that: The specific calculation formula of the ion migration influencing factor is as follows: The specific calculation formula of the surface state influencing factor is as follows: The migration speed calculation formula is specifically: The specific calculation formula of the fitting accuracy anomaly coefficient is as follows: In the formula, is the fitting accuracy anomaly coefficient, is the migration speed of node i, is the basic fitting value, is the current ion migration rate, is the maximum migration speed allowed, is the reaction angle, is the electrode surface state angle, and is the reaction characteristics and quantum migration adjustment coefficient, is the reaction rate of the battery, is the migration speed.

9. The fault location method for sodium ion battery energy storage based on photovoltaic power source according to claim 8, characterized in that: The specific calculation formula of the conductive project evaluation coefficient is as follows: The specific calculation formula of the affected environment coefficient is as follows: The specific calculation formula of the ionic conductivity influence coefficient is as follows: In the formula, is the evaluation coefficient of the conductive project, is the weight of the ith conductivity problem, is the number of times the i-th conductivity problem occurs, is the number of times the conductivity problem type occurs, V is the total number of conductivity problems, is the affected environment factor, is the number of conductive items affected, is the temperature, is humidity, P is material property, is the ionic conductivity influence coefficient.

10. A system using the fault location method of sodium ion battery energy storage based on photovoltaic power source according to any one of claims 1 to 9, characterized in that: It includes data acquisition module, preparation and evaluation model module, display model module, constraint module and roughness balance module; A data acquisition module, used to obtain impedance measurement accuracy data and impedance measurement stability data for EIS fault location analysis under prepared samples with different electrode surface roughness; A preparation evaluation model module, used for building a sample preparation evaluation model based on a convolutional neural network according to the impedance measurement accuracy data and the impedance measurement stability data; A display model module is used to construct an electrode surface roughness-EIS fault location performance display model based on the results of the sample preparation evaluation model of the EIS fault location analysis under the prepared samples with different electrode surface roughness and the corresponding electrode surface roughness; The constraint module is used to constrain the electrode surface roughness-EIS fault location performance display model according to the experimental preparation constraints and pre-examination requirement constraints of EIS fault location; The roughness balance module is used to perform data analysis on the electrode surface roughness-EIS fault location performance display model after constraint processing to obtain the electrode surface balance roughness.

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