An energy storage system and a method for analyzing abnormal operation of an energy storage battery

Abstract

The present application discloses an energy storage system and a method for analyzing the abnormal operation of an energy storage battery. After determining that the voltage deviation of the energy storage battery is greater than a preset voltage threshold and the dispersion degree of the internal resistance parameter deviation of the energy storage battery is greater than a preset dispersion threshold, it is determined that the energy storage battery is operating abnormally. That is, the present application comprehensively evaluates whether the energy storage battery is in an abnormal state through the voltage difference and the internal resistance parameter difference, improves the fault tolerance rate of diagnosis, reduces the influence of other interference factors on the diagnosis result, can effectively prevent internal short circuit in the energy storage battery, and further can avoid the problem that the energy storage battery causes battery thermal runaway due to internal short circuit failure, and finally leads to battery combustion, fire or even explosion accidents.

Description

Technical Field

[0001] This application relates to the technical field of energy storage analysis, and particularly relates to an energy storage system and a method for analyzing abnormal operation of energy storage batteries. Background Art

[0002] With the gradual increase in the proportion of new energy power generation, the accommodation of new energy power generation has become a key issue. Energy storage has been widely promoted due to its inherent advantages.

[0003] As the core unit of the energy storage system, the safe operation of the energy storage battery is related to the stability of the entire energy storage system. The most significant problem affecting the safety of the energy storage battery is the internal short - circuit fault. Most of the internal mechanism defect problems of the energy storage battery will gradually evolve into internal short - circuit faults, and later trigger battery thermal runaway, ultimately leading to battery combustion, fire, or even explosion accidents. Summary of the Invention

[0004] Based on the above - mentioned deficiencies of the prior art, this application provides a method for analyzing abnormal operation of an energy storage system and an energy storage battery to solve the problem that the existing energy storage battery causes battery thermal runaway due to internal short - circuit faults, ultimately leading to battery combustion, fire, or even explosion accidents.

[0005] To achieve the above object, this application provides the following technical solutions:

[0006] In the first aspect of this application, a method for analyzing abnormal operation of an energy storage battery is provided, including:

[0007] Respectively determine whether the voltage deviation of the energy storage battery is greater than a preset voltage threshold and whether the discrete degree of the internal resistance parameter deviation of the energy storage battery is greater than a preset discrete threshold;

[0008] If it is determined that the voltage deviation of the energy storage battery is greater than the preset voltage threshold and the discrete degree of the internal resistance parameter deviation of the energy storage battery is greater than the preset discrete threshold, it is determined that the energy storage battery is operating abnormally.

[0009] Optionally, in the above - mentioned method for analyzing abnormal operation of an energy storage battery, if it is determined that the voltage deviation of the energy storage battery is not greater than the preset voltage threshold, and / or, the discrete degree of the internal resistance parameter deviation of the energy storage battery is not greater than the preset discrete threshold, it is determined that the energy storage battery is operating normally.

[0010] Optionally, in the above - mentioned method for analyzing abnormal operation of an energy storage battery, after determining that the energy storage battery is operating abnormally, it further includes:

[0011] Generating an abnormal alarm signal, where the abnormal alarm signal is used to indicate that the energy storage battery is operating abnormally.

[0012] Optionally, in the above method for analyzing the abnormal operation of the energy storage battery, after generating the abnormal alarm signal, it further includes:

[0013] Reporting the abnormal alarm signal to the battery management system.

[0014] Optionally, in the above method for analyzing the abnormal operation of the energy storage battery, determining whether the voltage deviation of the energy storage battery is greater than a preset voltage threshold includes:

[0015] Respectively determining each measured voltage and each theoretical voltage of the energy storage battery within a preset time period;

[0016] Calculating the cumulative deviation value between each measured voltage and each theoretical voltage within the preset time period;

[0017] Determining whether the cumulative deviation value is greater than the preset voltage threshold;

[0018] If it is determined that the cumulative deviation value is greater than the preset voltage threshold, it is determined that the voltage deviation of the energy storage battery is greater than the preset voltage threshold;

[0019] If it is determined that the cumulative deviation value is not greater than the preset voltage threshold, it is determined that the voltage deviation of the energy storage battery is not greater than the preset voltage threshold.

[0020] Optionally, in the above method for analyzing the abnormal operation of the energy storage battery, determining each measured voltage of the energy storage battery within a preset time period includes:

[0021] Collecting the operating voltages of the energy storage battery within a preset time period respectively to obtain each measured voltage of the energy storage battery within the preset time period.

[0022] Optionally, in the above method for analyzing the abnormal operation of the energy storage battery, determining each theoretical voltage of the energy storage battery within a preset time period includes:

[0023] According to the equivalent circuit model of the energy storage battery, establishing an input-output relationship expression of the energy storage battery;

[0024] Processing the input-output relationship expression of the energy storage battery to obtain the input-output difference equation of the energy storage battery;

[0025] Calculating based on the least squares iterative formula and the input-output difference equation to obtain each theoretical voltage of the energy storage battery within the preset time period.

[0026] Optionally, in the above method for analyzing the abnormal operation of the energy storage battery, before determining whether the discrete degree of the internal resistance parameter deviation of the energy storage battery is greater than a preset discrete threshold, it further includes:

[0027] Determine the offline identification internal resistance parameters and online identification internal resistance parameters of the energy storage battery within a preset time period respectively;

[0028] Use the entropy weight method to perform deviation analysis on the offline identification internal resistance parameters and online identification internal resistance parameters within the preset time period, and obtain the deviation dispersion degree of the internal resistance parameters of the energy storage battery.

[0029] Optionally, in the above operation anomaly analysis method of the energy storage battery, determining the offline identification internal resistance parameters of the energy storage battery within a preset time period includes:

[0030] Determine the pulse charge and discharge data in the pulse charge and discharge section of the equivalent circuit model of the energy storage battery;

[0031] Fit the pulse charge and discharge data to obtain the offline identification internal resistance parameters of the energy storage battery within a preset time period.

[0032] Optionally, in the above operation anomaly analysis method of the energy storage battery, determining the online identification internal resistance parameters of the energy storage battery within a preset time period includes: [[ID=URL]]

[0033] Establish an input-output relationship expression of the energy storage battery according to the equivalent circuit model of the energy storage battery;

[0034] Process the input-output relationship expression of the energy storage battery to obtain the input-output difference equation of the energy storage battery;

[0035] Based on the difference equation coefficients in the input-output difference equation of the energy storage battery and the internal resistance parameter conversion relationship expression, calculate the online identification internal resistance parameters of the energy storage battery within a preset time period.

[0036] Optionally, in the above operation anomaly analysis method of the energy storage battery, using the entropy weight method to perform deviation analysis on the offline identification internal resistance parameters and online identification internal resistance parameters within the preset time period, and obtaining the deviation dispersion degree of the internal resistance parameters of the energy storage battery includes:

[0037] Calculate the relative error between the offline identification internal resistance parameter and the online identification internal resistance parameter corresponding to each moment within the preset time period;

[0038] Perform standardization processing on the relative error between the offline identification internal resistance parameter and the online identification internal resistance parameter corresponding to each moment respectively, and obtain the standardized relative error corresponding to each moment;

[0039] Calculate the information entropy of each standardized relative error corresponding to each moment respectively;

[0040] Based on the information entropy of each of the standardized relative errors corresponding to each moment, calculate the weight of each standardized relative error;

[0041] Perform weighted calculations on the weights of each of the standardized relative errors respectively to obtain the degree of dispersion of the internal resistance parameter deviation of the energy storage battery.

[0042] A second aspect of the present application provides an energy storage system, including: a controller and at least one energy storage battery; the controller is configured to perform the method for analyzing the abnormal operation of the energy storage battery according to any one of the aspects disclosed in the first aspect on the energy storage battery to achieve the analysis of the abnormal operation of the energy storage battery.

[0043] Optionally, in the above energy storage system, the controller is a cloud server, or a station-side server, or an edge-layer application device.

[0044] The present application provides a method for analyzing the abnormal operation of an energy storage battery. After determining that the voltage deviation of the energy storage battery is greater than a preset voltage threshold and the degree of dispersion of the internal resistance parameter deviation of the energy storage battery is greater than a preset dispersion threshold, it is determined that the energy storage battery is operating abnormally. That is, the present application comprehensively evaluates whether the energy storage battery is in an abnormal state through the voltage difference and the internal resistance parameter difference, improves the fault tolerance rate of the diagnosis, reduces the influence of other interference factors on the diagnosis result, can effectively prevent internal short circuits in the energy storage battery, and further can avoid problems such as battery thermal runaway caused by internal short circuit faults in the energy storage battery, and ultimately lead to battery combustion, fire, or even explosion accidents. Description of the Drawings

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0046] Figure 1 It is a flowchart of a method for analyzing the abnormal operation of an energy storage battery provided by an embodiment of the present application;

[0047] Figure 2 It is a flowchart for judging the magnitude relationship between the voltage deviation and the preset voltage threshold provided by an embodiment of the present application;

[0048] Figure 3 It is a flowchart for determining the theoretical voltage of an energy storage battery provided by an embodiment of the present application;

[0049] Figure 4It is a circuit topology diagram of an equivalent circuit model of an energy storage battery provided by an embodiment of the present application;

[0050] Figure 5 It is a flowchart of another method for analyzing the abnormal operation of an energy storage battery provided by an embodiment of the present application;

[0051] Figures 6 to 9 It is a flowchart of another four methods for analyzing the abnormal operation of an energy storage battery provided by an embodiment of the present application;

[0052] Figure 10 It is a flowchart for determining the off-line identification internal resistance parameter of an energy storage battery provided by an embodiment of the present application;

[0053] Figure 11 It is a curve graph of the charge and discharge pulse voltage and time relationship of an energy storage battery provided by an embodiment of the present application;

[0054] Figure 12 It is a flowchart for determining the on-line identification internal resistance parameter of an energy storage battery provided by an embodiment of the present application;

[0055] Figure 13 It is a flowchart for determining the deviation dispersion degree of the internal resistance parameter of an energy storage battery provided by an embodiment of the present application. Detailed implementation manners

[0056] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0057] First of all, it should be noted that through the research of the inventor, it is found that the main electrical external characteristics causing the internal short circuit of the energy storage battery are that the voltage signal drops suddenly and then rises or continues to be lower than the theoretical parameters, the AC or DC internal impedance increases, and the charge and discharge duration and capacity change. And the detection and evaluation of the voltage and internal resistance of the energy storage battery are effective ways to prevent and judge the internal short circuit of the energy storage battery.

[0058] In this regard, the present application provides a method for analyzing the abnormal operation of an energy storage battery to solve the problem that the existing energy storage battery causes battery thermal runaway due to internal short circuit faults, and finally leads to battery combustion, fire or even explosion accidents.

[0059] Please refer to Figure 1 , the method for analyzing the abnormal operation of the energy storage battery may include the following steps:

[0060] S101. Determine whether the voltage deviation of the energy storage battery is greater than the preset voltage threshold and whether the discrete degree of the internal resistance parameter deviation of the energy storage battery is greater than the preset discrete threshold respectively.

[0061] Among them, the specific values of the preset voltage threshold and the preset discrete threshold can be determined according to the specific application environment and user requirements. As long as it is ensured that when the voltage deviation of the energy storage battery is greater than the preset voltage threshold and the discrete degree of the internal resistance parameter deviation of the energy storage battery is greater than the preset discrete threshold, the energy storage battery is in an abnormal state.

[0062] In some embodiments, for the specific process of determining whether the voltage deviation of the energy storage battery is greater than the preset voltage threshold in step S101, reference can be made to Figure 2 , which mainly includes the following steps:

[0063] S401. Determine each measured voltage and each theoretical voltage of the energy storage battery within the preset time period respectively.

[0064] Among them, the specific value of the preset time period can be determined according to the application environment and user requirements. For example, half an hour, or dozens of minutes, or a few minutes... are all within the protection scope of this application.

[0065] In practical applications, the operating voltages of the energy storage battery within the preset time period can be collected respectively to obtain each measured voltage of the energy storage battery within the preset time period.

[0066] As Figure 3 shown, the specific process of determining each theoretical voltage of the energy storage battery within the preset time period can include:

[0067] S501. According to the equivalent circuit model of the energy storage battery, establish an input-output relationship expression of the energy storage battery.

[0068] In practical applications, the equivalent circuit model of the energy storage battery can be obtained by identifying the off-line calibration test data of the energy storage battery. Among them, the equivalent circuit model includes the functional curve relationship between each equivalent parameter in the energy storage battery and the SOC (state of charge of the battery).

[0069] Among them, the off-line calibration test data of the energy storage battery can be obtained by performing a calibration test on the energy storage battery. Specifically, the calibration test is a common and necessary work for the energy storage battery. The test method can be that the energy storage battery performs a complete full charge and full discharge cycle each time, and during the charge and discharge process, short-term pulse charge and discharge and long-term static are performed at intervals of a certain SOC (such as 5%), and the voltage and current data during the charge and discharge process of the energy storage battery are obtained.

[0070] In practical applications, the equivalent circuit model of the energy storage battery can be a second-order RC model, which equivalently describes the changes in the electrical characteristics of the energy storage battery during the charge and discharge process. As Figure 4 shown, by adding two first-order RC links, the equivalent circuit model is used to simultaneously characterize the electrochemical polarization effect and the concentration difference polarization effect of the energy storage battery, thereby reflecting the dynamic characteristics of the energy storage battery.

[0071] It should be noted that the input-output relationship expression of the energy storage battery can be a transfer function. The input of the transfer function is the voltage and current of the energy storage battery at the previous few moments, and the output is the predicted voltage at the next moment. Among them, the transfer function expression can be:

[0072] E is the open-circuit voltage, U is the voltage of the energy storage battery, I is the current of the energy storage battery, R0 is the ohmic resistance, R s is the electrochemical polarization resistance, C s is the electrochemical polarization capacitance, R p is the concentration difference polarization capacitance, Cp is the concentration difference polarization capacitance, and s represents the conversion value of the input signal in the S (Laplace transform) domain. Specifically, the open-circuit voltage E can be derived through the SOC fitting relationship, and the derivation method is a well-known technology.

[0073] S502. Process the input-output relationship expression of the energy storage battery to obtain the input-output difference equation of the energy storage battery.

[0074] In practical applications, the input-output relationship expression of the energy storage battery can be subjected to bipolar transformation and discretization processing, so as to convert the input-output relationship expression of the energy storage battery into the input-output difference equation of the energy storage battery.

[0075] Among them, the expression of the input-output difference equation of the energy storage battery can be: y(k) = E(k) - U(k) = a1y(k - 1) + a2y(k - 2) + a3I(k) + a4I(k - 1) + a5I(k - 2), where y represents the system output value, that is, the difference between the open-circuit voltage (OCV) of the energy storage battery and the terminal voltage of the energy storage battery at each moment; k represents the label of each sampling moment of the discrete signal (k represents the current moment, k - 1 represents the previous sampling moment, and k - 2 represents the previous previous sampling moment); a1, a2, a3, a4, and a5 represent each input term in the discretized equation, that is, a1 is the constant coefficient of y(k - 1), a2 is the constant coefficient of y(k - 2), a3 is the constant coefficient of I(k), a4 is the constant coefficient of I(k - 1), and a5 is the constant coefficient of I(k - 2).

[0076] S503. Calculate based on the least squares iterative formula and the input-output difference equation to obtain each theoretical voltage of the energy storage battery within the preset time period.

[0077] In practical applications, the output value of the input-output difference equation at each moment can be calculated through the iterative formula of the forgetting factor recursive least squares method. Among them, the output value includes the theoretical voltage of the energy storage battery and the difference equation coefficients.

[0078] Among them, the iterative formula of the forgetting factor recursive least squares method can be:

[0079] θ is the coefficient matrix, and the specific interpretations of a1, a2, a3, a4, and a5 are the same as those in the above formula, and can be collectively referred to as the difference equation coefficient values. Φ is the input-output parameter matrix, λ is the forgetting factor, P is the covariance matrix, K is the gain term, and I is the identity matrix. Specifically, the input-output parameter matrix Φ is the sampled known parameter, and the forgetting factor λ is a fixed value.

[0080] It should be noted that the above parameters are all intermediate variables.

[0081] S402. Calculate the cumulative deviation value between each measured voltage and each theoretical voltage within a preset time period.

[0082] In practical applications, the formula ε u = ABS(U 理论 - U 实测 ) / U 理论 can be used to calculate the deviation value between each measured voltage and the corresponding theoretical voltage within a preset time period, and then the deviation values between each measured voltage and the corresponding theoretical voltage within the preset time period are superimposed to obtain the cumulative deviation value between each measured voltage and each theoretical voltage within the preset time period. Among them, ε u represents the deviation value, U 理论 represents the theoretical voltage of the energy storage battery, and U 实测 represents the measured voltage of the energy storage battery.

[0083] In addition, it is also possible to first calculate the deviation value between each measured voltage and the corresponding theoretical voltage within a preset time period through the formula ε u = ABS(U 理论 - U 实测 ) / U 理论 , and then take the overall average of the deviations between each measured voltage and the corresponding theoretical voltage within the preset time period, and use this average value as the cumulative deviation value between each measured voltage and each theoretical voltage within the preset time period.

[0084] It can be understood that the cumulative deviation value between each measured voltage and each theoretical voltage within a preset time period can be obtained through the formula ε = AVG[ABS(U 理论 - U 实测 ) / U 理论 .

[0085] S403. Determine whether the cumulative deviation value is greater than a preset voltage threshold.

[0086] If it is determined that the cumulative deviation value is greater than the preset voltage threshold, step S404 can be executed; if it is determined that the cumulative deviation value is not greater than the preset voltage threshold, step S405 can be executed.

[0087] S404. Determine that the voltage deviation of the energy storage battery is greater than the preset voltage threshold.

[0088] In practical applications, if it is determined that the cumulative deviation value is greater than the preset voltage threshold, it can be determined that the voltage deviation of the energy storage battery is greater than the preset voltage threshold.

[0089] S405. Determine that the voltage deviation of the energy storage battery is not greater than the preset voltage threshold.

[0090] In practical applications, if it is determined that the cumulative deviation value is not greater than the preset voltage threshold, it can be determined that the voltage deviation of the energy storage battery is not greater than the preset voltage threshold.

[0091] It should be noted that determining that the voltage deviation of the energy storage battery is not greater than the preset voltage threshold can indicate that the operation of the energy storage battery meets the expectations and there is no abnormal risk for the time being. Determining that the voltage deviation of the energy storage battery is greater than the preset voltage threshold can indicate that the operation data of the energy storage battery is abnormal and abnormal characteristics may appear.

[0092] In practical applications, in the specific process of executing step S101, which is to respectively determine whether the voltage deviation of the energy storage battery is greater than the preset voltage threshold and whether the discrete degree of the internal resistance parameter deviation of the energy storage battery is greater than the preset discrete threshold, it is possible to first determine whether the voltage deviation of the energy storage battery is greater than the preset voltage threshold, and then determine whether the discrete degree of the internal resistance parameter deviation of the energy storage battery is greater than the preset discrete threshold, that is Figure 5 as shown; conversely, it is also possible to first determine whether the discrete degree of the internal resistance parameter deviation of the energy storage battery is greater than the preset discrete threshold, and then determine whether the voltage deviation of the energy storage battery is greater than the preset voltage threshold, which is not shown; of course, it is also possible to execute simultaneously, that is Figure 1 as shown, and it can be determined according to the specific application environment and user requirements, and all are within the protection scope of this application.

[0093] Preferably, it is possible to first determine whether the voltage deviation of the energy storage battery is greater than the preset voltage threshold, and after determining that the voltage deviation of the energy storage battery is greater than the preset voltage threshold, then determine whether the discrete degree of the internal resistance parameter deviation of the energy storage battery is greater than the preset discrete threshold, and comprehensively evaluate the abnormal characteristics of the energy storage battery through the progressive judgment logic of voltage analysis and internal resistance analysis.

[0094] It should be noted that after it is determined that the voltage deviation of the energy storage battery is greater than the preset voltage threshold, it indicates that the operation data of the energy storage battery is abnormal and there may be abnormal characteristics. To eliminate the interference introduced by data iterative calculation, it is possible to further verify the abnormal state of the energy storage battery by determining whether the discrete degree of the internal resistance parameter deviation of the energy storage battery is greater than the preset discrete threshold.

[0095] If it is determined that the voltage deviation of the energy storage battery is greater than the preset voltage threshold and the discrete degree of the internal resistance parameter deviation of the energy storage battery is greater than the preset discrete threshold, then step S102 is executed.

[0096] S102. Determine that the energy storage battery is operating abnormally.

[0097] In practical applications, if it is determined that the voltage deviation of the energy storage battery is greater than the preset voltage threshold, and the discrete degree of the internal resistance parameter deviation of the energy storage battery is greater than the preset discrete threshold, it can be explained that at this time the voltage of the energy storage battery decreases, the internal resistance is abnormal, the operation state of the energy storage battery is abnormal, and it has the characteristics of internal short circuit evolution. Therefore, it can be determined that the energy storage battery is operating abnormally.

[0098] Based on the above principle, in the method for analyzing the abnormal operation of the energy storage battery provided in this embodiment, after it is determined that the voltage deviation of the energy storage battery is greater than the preset voltage threshold and the discrete degree of the internal resistance parameter deviation of the energy storage battery is greater than the preset discrete threshold, it is determined that the energy storage battery is operating abnormally. That is, in this application, the voltage difference and the internal resistance parameter difference are used to comprehensively evaluate whether the energy storage battery is in an abnormal state, improving the fault tolerance rate of the diagnosis and reducing the influence of other interference factors on the diagnosis result. It can effectively prevent the internal short circuit of the energy storage battery, and further avoid the problem of battery thermal runaway caused by the internal short circuit fault of the energy storage battery, and finally lead to battery combustion, fire or even explosion accidents.

[0099] It is worth noting that there is also an existing analysis method, but it mainly focuses on collecting and analyzing the voltage deviation between series strings, lacking necessary means to evaluate the difference between the measured voltage and the theoretical voltage, and it is impossible to accurately evaluate whether the energy storage battery really has the risk of internal short circuit only through voltage detection; while this application can collect the operation data of each single energy storage battery, analyze each single energy storage battery respectively through the voltage deviation corresponding to each single energy storage battery, and can comprehensively evaluate the state of the energy storage battery by combining the voltage difference and the internal resistance parameter difference, improving the accuracy of the evaluation.

[0100] In addition, since the parameters required for the voltage difference and the internal resistance parameter difference can be obtained through the existing system's inherent data sampling equipment, there is no need to additionally attach hardware detection costs.

[0101] Optionally, in another embodiment provided by the present application, after performing the steps of respectively determining whether the voltage deviation of the energy storage battery is greater than a preset voltage threshold and whether the discrete degree of the internal resistance parameter deviation of the energy storage battery is greater than a preset discrete threshold, if it is determined that the voltage deviation of the energy storage battery is not greater than the preset voltage threshold, and / or, the discrete degree of the internal resistance parameter deviation of the energy storage battery is not greater than the preset discrete threshold, then as Figure 6 shown, the method for analyzing the abnormal operation of the energy storage battery may further include step S1001 of determining that the energy storage battery operates normally.

[0102] In practical applications, if it is determined that the voltage deviation of the energy storage battery is not greater than the preset voltage threshold, and / or, the discrete degree of the internal resistance parameter deviation of the energy storage battery is not greater than the preset discrete threshold, it can be explained that the possibility of the abnormal state of the energy storage battery is small at this time, and it does not have the characteristics of internal short-circuit evolution, so it can be determined that the energy storage battery operates normally.

[0103] Optionally, in another embodiment provided by the present application, after performing step S102 of determining that the energy storage battery operates abnormally, please refer to Figure 7 and the method for analyzing the abnormal operation of the energy storage battery may further include step S201 of generating an abnormal alarm signal.

[0104] Wherein, the abnormal alarm signal is used to prompt that the energy storage battery operates abnormally.

[0105] Specifically, the manner of generating the abnormal alarm signal can be determined according to the specific application environment and user requirements. For example, emitting a beeping sound, and / or, flashing an indicator light. No matter which method is adopted, it is within the protection scope of the present application.

[0106] In practical applications, an abnormal alarm signal can be generated after determining that the energy storage battery operates abnormally to prompt the operation and maintenance personnel to conduct a fault investigation, and further avoid the problem of battery thermal runaway caused by the internal short-circuit fault of the energy storage battery, and finally lead to battery combustion, fire or even explosion accidents.

[0107] Optionally, in another embodiment provided by the present application, after performing step S201 of generating an abnormal alarm signal, please refer to Figure 8 and the method for analyzing the abnormal operation of the energy storage battery may further include step S301 of reporting the abnormal alarm signal to the battery management system.

[0108] In practical applications, after generating the abnormal alarm signal, the abnormal alarm signal can be reported to the battery management system through a communication link to prompt the operation and maintenance personnel to conduct a fault investigation in a timely manner through the battery management system.

[0109] Optionally, in another embodiment provided by the present application, before performing the step of determining whether the discrete degree of the internal resistance parameter deviation of the energy storage battery is greater than the preset discrete threshold in step S101, please refer toFigure 9 , the method for analyzing the abnormal operation of the energy storage battery may further include:

[0110] S601. Determine the respective off-line identified internal resistance parameters and on-line identified internal resistance parameters of the energy storage battery within a preset time period.

[0111] Among them, the off-line identified internal resistance parameters and on-line identified internal resistance parameters may include the following five items: ohmic resistance, electrochemical polarization resistance, electrochemical polarization capacitance, concentration difference polarization capacitance, and concentration difference polarization capacitance.

[0112] In practical applications, the specific process of determining the respective off-line identified internal resistance parameters of the energy storage battery within a preset time period may be as Figure 10 shown, mainly including the following steps:

[0113] S701. Determine the pulse charge-discharge data in the pulse charge-discharge section of the equivalent circuit model of the energy storage battery.

[0114] Among them, the pulse charge-discharge data in the pulse charge-discharge section can be obtained by analyzing the SOC function curve relationship of the equivalent circuit model of the energy storage battery obtained through calibration testing. The SOC function relationship curve represents the relationship between the charge-discharge voltage U and time T for each SOC interval.

[0115] Specifically, the relationship between the charge-discharge voltage and time of the energy storage battery can be as Figure 11 shown, which can be divided into four stages. Among them, the first stage is the AB section, indicating that the energy storage battery changes from no-load static state to loaded discharge, and the charge-discharge voltage shows a stepwise decrease. According to the second-order equivalent circuit of the energy storage battery, the voltage across the capacitor cannot change suddenly, and the sudden decrease in the charge-discharge voltage is caused by the voltage drop across the ohmic resistance R0. The ohmic resistance R0 can be calculated through the relationship between the sudden voltage drop and current in the AB section or CD section. Taking the CD section as an example, the calculation formula is: U D represents the charge-discharge voltage at point D, and U C represents the charge-discharge voltage at point C.

[0116] The second stage is the BC section, indicating that as the discharge time increases, the charge-discharge voltage slowly decreases in an exponential change trend until the last moment of charge-discharge, and the charge-discharge voltage reaches the minimum value. This is exactly affected by the time constants of the two RC links. The time constants of the two RC links can be calculated through the charge-discharge end fitting equation, specifically: And the values of the two polarization resistances can be deduced through the exponential fitting of the static section, and then the capacitance values of the polarization capacitors can be calculated inversely, specifically:

[0117]

[0118] Based on the above, it can be seen that the five RC parameters of the charging and discharging stages of the energy storage battery can be calculated through the first stage AB and the second stage BC, that is, each parameter in the off-line identification of the internal resistance parameter.

[0119] It should be noted that in the third stage CD and the fourth stage DE, the energy storage battery changes from loaded discharge to no-load static state. The charging and discharging voltage responses also experience a process of jump-up and exponential slow increase, and the principle is the same as that of the first and second stages.

[0120] S702. Fit the pulse charge and discharge data to obtain each off-line identified internal resistance parameter of the energy storage battery within a preset time period.

[0121] In practical applications, after determining the pulse charge and discharge data in the pulse charge and discharge section of the equivalent circuit model of the energy storage battery, taking the example of performing pulse charge and discharge once every 5% SOC conventionally, the corresponding RC parameters can be calculated for each 5% SOC through the pulse fitting method, and then the fitting is extended through the spline interpolation method to obtain the RC parameters of the energy storage battery in the charging and discharging stages and the theoretical reference curve of SOC. Among them, the RC parameters of the energy storage battery in the charging and discharging stages are each off-line identified internal resistance parameter of the energy storage battery within a preset time period.

[0122] In practical applications, the specific process of determining each on-line identified internal resistance parameter of the energy storage battery within a preset time period can be as Figure 12 shown, mainly including the following steps:

[0123] S801. According to the equivalent circuit model of the energy storage battery, establish an input-output relationship expression of the energy storage battery.

[0124] It should be noted that for the relevant description of step S801, refer to step S501, which will not be elaborated here.

[0125] S802. Process the input-output relationship expression of the energy storage battery to obtain the input-output difference equation of the energy storage battery.

[0126] It should be noted that for the relevant description of step S802, refer to step S502, which will not be elaborated here.

[0127] S803. Based on the difference equation coefficients in the input-output difference equation of the energy storage battery and the conversion relationship expression of the internal resistance parameters, calculate each on-line identified internal resistance parameter of the energy storage battery within a preset time period.

[0128] In practical applications, according to the conversion relationship expression between the difference equation coefficients in the input-output difference equation and the internal resistance parameters, the difference equation coefficients can be calculated first and then each on-line identified internal resistance parameter of the energy storage battery within a preset time period can be deduced. The specific formula is:

[0129] Let \(T\) be the sampling time, and the others are all \(RC\) parameters, that is, the internal resistance parameters are identified online.

[0130] Based on the above, it can be understood that through the online parameter representation, the theoretical voltage at the next moment and the online identified internal resistance parameters can be calculated from the measured voltage at the previous moment.

[0131] S602. Use the entropy weight method to perform deviation analysis on each offline identified internal resistance parameter and each online identified internal resistance parameter within a preset time period to obtain the deviation dispersion degree of the internal resistance parameters of the energy storage battery.

[0132] In practical applications, when performing step S602 and using the entropy weight method to perform deviation analysis on each offline identified internal resistance parameter and each online identified internal resistance parameter within a preset time period to obtain the deviation dispersion degree of the internal resistance parameters of the energy storage battery, the specific process can be as Figure 13 shown, mainly including the following steps:

[0133] S901. Calculate the relative error between the offline identified internal resistance parameter and the online identified internal resistance parameter corresponding to each moment within the preset time period.

[0134] Also assume that the energy storage battery performs pulse charge and discharge and sampling every 5% SOC as an example. The preset time period is a certain 5% SOC charge and discharge time period. Then, through the calculation formula the relative error \(X_i\) between the offline identified internal resistance parameter and the online identified internal resistance parameter corresponding to each moment within this 5% SOC charge and discharge time period can be calculated. Here, \(X\) i represents the relative error, and \(i = x1, x2, x3, x4, x5\).

[0135] S902. Standardize the relative error between the offline identified internal resistance parameter and the online identified internal resistance parameter corresponding to each moment respectively to obtain the standardized relative error corresponding to each moment.

[0136] In practical applications, the maximum - minimum standardization can be used to process the relative error between the offline identified internal resistance parameter and the online identified internal resistance parameter corresponding to each moment. The processing formula can be: where \(y_{ij}\) is the standardized relative error, \(i\) represents different internal resistance parameters, and \(j\) represents different sampling moments.

[0137] S903. Calculate the information entropy of each standardized relative error corresponding to each moment respectively.

[0138] In practical applications, the information entropy \(E_j\) of each standardized relative error corresponding to each moment can be calculated through the formula In the formula, \(E\) is the information entropy, \(i\) represents different internal resistance parameters, and \(j\) represents different sampling moments.

[0139] S904. Calculate the weight of each normalized relative error based on the information entropy of the normalized relative errors corresponding to each moment.

[0140] In practical applications, the weight of each normalized relative error can be calculated through the formula where \(W\) represents the weight of the normalized relative error, and \(i\) represents different internal resistance parameters.

[0141] S905. Perform weighted calculations on the weights of each normalized relative error respectively to obtain the discrete degree of the internal resistance parameter deviation of the energy storage battery.

[0142] In practical applications, the discrete degree of the internal resistance parameter deviation of the energy storage battery can be calculated through the formula where \(W_{i}\) represents the weight of the normalized relative error corresponding to different internal resistance parameters at each moment, and \(x_{i}\) ij represents the value of different internal resistance parameters at each moment. ij It should be noted that the above is only a specific example of using the entropy weight method to analyze the deviation of each offline identified internal resistance parameter and each online identified internal resistance parameter within a preset period, and obtain the discrete degree of the internal resistance parameter deviation of the energy storage battery. However, in practical applications, the deviation of each offline identified internal resistance parameter and each online identified internal resistance parameter within a preset period can also be analyzed through other existing methods to obtain the discrete degree of the internal resistance parameter deviation of the energy storage battery. No matter which method is adopted, it is within the protection scope of this application.

[0143] In this embodiment, it is proposed to score the discrete degree of the internal resistance parameter deviation of the energy storage battery by using the entropy weight method, and evaluate the abnormal characteristics of the energy storage battery based on this. In addition, compared with the existing method of offline detecting the AC impedance of the energy storage battery through hardware means such as EIS test equipment, this application can also detect the dynamic change and abnormality of the internal resistance of the energy storage battery online through practical and effective means, which can further improve the accuracy of the abnormal operation analysis of the energy storage battery.

[0144] [[ID= twenty-five ]]

[0145] ​Based on the method for analyzing the abnormal operation of the energy storage battery provided in the above embodiments, it can be understood that the present application first establishes an equivalent circuit model of the energy storage battery, and uses the pulse fitting method for off-line parameter identification by means of the pre-set off-line calibration test data of the energy storage battery to obtain the internal resistance parameters of the theoretical equivalent model of the energy storage battery operating normally, which are used as the basis for diagnosis reference; then, the voltage and current data of the energy storage battery during the operation process are collected in real time, and the forgetting factor recursive least squares method is used for on-line parameter identification to estimate the theoretical voltage of the energy storage battery operation and calculate the real-time internal resistance parameters; furthermore, the deviation between the measured voltage and the theoretical voltage is calculated, and the entropy weight method is used to calculate the dispersion degree of the internal resistance parameters of the equivalent circuit model, and whether the energy storage battery is in an abnormal state is comprehensively evaluated through the difference in voltage and the difference in internal resistance parameters, that is, the equivalent circuit model and the parameter identification theory are applied to the fault identification of the energy storage battery, and through the combination of off-line identification and on-line identification, all the static characteristics and dynamic characteristics of the energy storage battery are analyzed and compared and evaluated; moreover, it covers the off-line degree analysis of the two important electrical characteristics of the voltage and internal resistance parameters of the energy storage battery related to battery faults, and proposes to use the entropy weight method to comprehensively evaluate the length of the internal resistance deviation, and through the progressive judgment logic of voltage analysis and internal resistance analysis, comprehensively evaluate the abnormal characteristics of the energy storage battery, improve the diagnostic fault tolerance rate, and reduce the influence of other interference factors.

[0146] Optionally, another embodiment of the present application further provides an energy storage system, which may include: a controller and at least one energy storage battery; the controller is used to execute the method for analyzing the abnormal operation of the energy storage battery as described in any of the above embodiments on the energy storage battery to realize the analysis of the abnormal operation of the energy storage battery.

[0147] In practical applications, the controller may be a cloud server, or a station-side server, and may also be an edge-layer application device; it can be determined according to the specific application environment and user requirements, and all are within the protection scope of the present application.

[0148] It should be noted that for the relevant descriptions of the method for analyzing the abnormal operation of the energy storage battery, reference can be made to the corresponding above embodiments, and details are not described here again. For the relevant descriptions of the energy storage system, reference can be made to the prior art, and details are not described here again.

[0149] In this embodiment, since the parameter calculations for voltage and internal resistance in the method for analyzing the abnormal operation of the energy storage battery both rely on the existing system's inherent data sampling equipment, there is no additional hardware detection cost, and the computing power requirement is low, it can be flexibly embedded in a cloud server, a station-side server, or an edge-layer application device, so as to realize the operation analysis of each single energy storage battery in the energy storage system.

[0150] Those skilled in the art may further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0151] In this application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0152] The above description of the disclosed embodiments enables those skilled in the art to implement or use this application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application will not be limited to the embodiments shown herein, but rather is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for analyzing abnormal operation of an energy storage battery, characterized in that, Including: Respectively determining whether the voltage deviation of the energy storage battery is greater than a preset voltage threshold and whether the discrete degree of the internal resistance parameter deviation of the energy storage battery is greater than a preset discrete threshold, where the voltage deviation of the energy storage battery is the cumulative deviation between the measured voltage and the theoretical voltage within a preset period, and using the entropy weight method to perform deviation analysis on each offline identified internal resistance parameter and each online identified internal resistance parameter within the preset period to obtain the discrete degree of the internal resistance parameter deviation of the energy storage battery; If it is determined that the voltage deviation of the energy storage battery is greater than the preset voltage threshold and the discrete degree of the internal resistance parameter deviation of the energy storage battery is greater than the preset discrete threshold, it is determined that the energy storage battery is operating abnormally.

2. The method for analyzing abnormal operation of an energy storage battery according to claim 1, wherein If it is determined that the voltage deviation of the energy storage battery is not greater than the preset voltage threshold, and / or the discrete degree of the internal resistance parameter deviation of the energy storage battery is not greater than the preset discrete threshold, it is determined that the energy storage battery is operating normally.

3. The method for analyzing abnormal operation of the energy storage battery according to claim 1, wherein After determining that the energy storage battery is operating abnormally, it further includes: Generating an abnormal alarm signal, which is used to prompt that the energy storage battery is operating abnormally.

4. The method for analyzing the abnormal operation of the energy storage battery according to claim 3, wherein, After generating the abnormal alarm signal, it further includes: Reporting the abnormal alarm signal to the battery management system.

5. The method for analyzing abnormal operation of the energy storage battery according to claim 1, wherein Determining whether the voltage deviation of the energy storage battery is greater than the preset voltage threshold includes: Respectively determining each measured voltage and each theoretical voltage of the energy storage battery within a preset period; Calculating the cumulative deviation value between each measured voltage and each theoretical voltage within the preset period; Judging whether the cumulative deviation value is greater than the preset voltage threshold; If it is determined that the cumulative deviation value is greater than the preset voltage threshold, it is determined that the voltage deviation of the energy storage battery is greater than the preset voltage threshold; If it is determined that the cumulative deviation value is not greater than the preset voltage threshold, it is determined that the voltage deviation of the energy storage battery is not greater than the preset voltage threshold.

6. The method for analyzing the abnormal operation of the energy storage battery according to claim 5, characterized in that, Determining each measured voltage of the energy storage battery within a preset period includes: Respectively collecting the operating voltages of the energy storage battery within a preset period to obtain each measured voltage of the energy storage battery within the preset period.

7. The method for analyzing the abnormal operation of the energy storage battery according to claim 5, characterized in that, Determining each theoretical voltage of the energy storage battery within a preset period includes: According to the equivalent circuit model of the energy storage battery, establishing an input-output relationship expression of the energy storage battery; Processing the input-output relationship expression of the energy storage battery to obtain the input-output difference equation of the energy storage battery; Calculating based on the least squares iterative formula and the input-output difference equation to obtain each theoretical voltage of the energy storage battery within the preset period.

8. The method for analyzing the abnormal operation of the energy storage battery according to claim 5, characterized in that Before judging whether the discrete degree of the internal resistance parameter deviation of the energy storage battery is greater than the preset discrete threshold, it further includes: Respectively determining each offline identified internal resistance parameter and each online identified internal resistance parameter of the energy storage battery within a preset period; Using the entropy weight method to perform deviation analysis on each offline identified internal resistance parameter and each online identified internal resistance parameter within the preset period to obtain the discrete degree of the internal resistance parameter deviation of the energy storage battery.

9. The method for analyzing abnormal operation of an energy storage battery according to claim 8, characterized in that, Determining each offline identified internal resistance parameter of the energy storage battery within a preset period includes: Determine the pulse charge and discharge data in the pulse charge and discharge section of the equivalent circuit model of the energy storage battery; Fit the pulse charge and discharge data to obtain each of the off-line identified internal resistance parameters of the energy storage battery within a preset time period.

10. The method for analyzing the abnormal operation of the energy storage battery according to claim 8, wherein, Determine each of the on-line identified internal resistance parameters of the energy storage battery within a preset time period, including: Based on the equivalent circuit model of the energy storage battery, establish an input-output relationship expression of the energy storage battery; Process the input-output relationship expression of the energy storage battery to obtain the input-output difference equation of the energy storage battery; Based on the difference equation coefficients and the internal resistance parameter conversion relationship expression in the input-output difference equation of the energy storage battery, calculate each of the on-line identified internal resistance parameters of the energy storage battery within a preset time period.

11. The method for analyzing abnormal operation of an energy storage battery according to claim 8, wherein Use the entropy weight method to perform deviation analysis on each of the off-line identified internal resistance parameters and each of the on-line identified internal resistance parameters within the preset time period to obtain the deviation dispersion degree of the internal resistance parameters of the energy storage battery, including: Calculate the relative error between the off-line identified internal resistance parameter and the on-line identified internal resistance parameter corresponding to each moment within the preset time period; Perform standardization processing on the relative error between the off-line identified internal resistance parameter and the on-line identified internal resistance parameter corresponding to each moment respectively to obtain the standardized relative error corresponding to each moment; Calculate the information entropy of each of the standardized relative errors corresponding to each moment respectively; Based on the information entropy of each of the standardized relative errors corresponding to each moment, calculate the weight of each of the standardized relative errors; Perform weighted calculation on the weights of each of the standardized relative errors respectively to obtain the deviation dispersion degree of the internal resistance parameters of the energy storage battery.

12. An energy storage system, characterized in that, Including: A controller and at least one energy storage battery; The controller is used to execute the method for analyzing the abnormal operation of the energy storage battery according to any one of claims 1-11 on the energy storage battery to realize the analysis of the abnormal operation of the energy storage battery.

13. The energy storage system according to claim 12, characterized in that, The controller is a cloud server, or a station-side server, or an edge-layer application device.

Images