Energy storage battery abnormality identification method, system, device and energy storage station

Abstract

The application discloses a kind of energy storage battery abnormality identification method, system, device and energy storage station for energy storage station, which comprises obtaining the charge-discharge state data of energy storage battery;Based on the charge-discharge state data, the internal resistance evaluation method is used to evaluate the charge internal resistance Rc and discharge internal resistance Rd of energy storage battery;Based on the charge internal resistance Rc and discharge internal resistance Rd, the charge internal resistance score Sc, discharge internal resistance score Sd and battery internal resistance score St of energy storage battery are calculated;Based on the charge internal resistance score Sc, discharge internal resistance score Sd and battery internal resistance score St, the energy storage battery that appears abnormality is identified.The application can quickly, accurately and timely identify the abnormality of energy storage battery, to ensure the safety of energy storage battery in use.

Description

Technical Field

[0001] This invention relates to the field of energy storage battery status identification technology, and more specifically, to an energy storage battery anomaly identification method, system, device, and energy storage station. Background Technology

[0002] The energy storage battery mentioned in this invention refers to a battery that stores electrical energy from the power grid or renewable energy grid and outputs the stored electrical energy to electrical equipment at appropriate times. Types include rechargeable batteries and dedicated energy storage battery packs. In recent years, my country's new energy industry has continued to develop, and the energy storage market has experienced explosive growth, with electrochemical energy storage showing the most rapid development.

[0003] However, electrochemical energy storage is still in its development stage, and the safety of energy storage batteries is of paramount importance in this process. Due to the inconsistencies in the usage time, manufacturing processes, and battery materials and structures of various energy storage batteries (consistency issues), even identical energy storage batteries can have differences in charge and discharge performance, thus posing certain safety hazards during use.

[0004] In particular, for the application of cascaded batteries, retired power batteries also have more requirements in terms of consistency, safety, and reverse source tracking.

[0005] Existing energy storage battery evaluation methods mainly include ampere-hour integration, model-based evaluation methods, and machine learning. These methods are primarily designed for ordinary batteries and require sufficiently long testing and data analysis times. They are complex, involve large amounts of data computation, and cannot quickly, accurately, or timely identify anomalies in energy storage batteries during use, which can easily lead to safety hazards.

[0006] In view of this, to address the above problems, it is necessary to design an anomaly identification method, system, device, and energy storage station applicable to energy storage batteries, which can quickly and accurately identify abnormal energy storage batteries and ensure their safety during use. Summary of the Invention

[0007] The purpose of this invention is to provide a method, system, device, and energy storage station for identifying abnormalities in energy storage batteries, so as to solve the problem that existing technologies cannot quickly, accurately, and timely identify abnormalities in energy storage batteries, thereby ensuring the safety of energy storage batteries during use.

[0008] To achieve the above objectives, the present invention provides a method for identifying abnormalities in energy storage batteries for energy storage stations, comprising the following steps:

[0009] Acquire charge and discharge state data of energy storage batteries;

[0010] Based on charge and discharge state data, the charging internal resistance Rc and discharging internal resistance Rd of the energy storage battery are evaluated using an internal resistance evaluation method.

[0011] Based on the charging internal resistance Rc and the discharging internal resistance Rd, calculate the charging internal resistance score Sc, the discharging internal resistance score Sd, and the battery internal resistance score St of the energy storage battery.

[0012] Based on the charging internal resistance score Sc, the discharging internal resistance score Sd, and the battery internal resistance score St, abnormal energy storage batteries are identified.

[0013] By adopting the technical solution disclosed in this invention, abnormal energy storage batteries can be detected quickly, timely, and accurately, enabling safety monitoring and risk alarms in the energy storage safety management process and timely detection of safety issues.

[0014] In one embodiment of the above-mentioned method for identifying abnormal energy storage batteries for energy storage stations, the energy storage battery includes at least one secondary power battery and / or a dedicated energy storage battery pack; or, a retired power battery pack from a vehicle or a retired power battery pack mounted on a bracket.

[0015] In one embodiment of the above-mentioned method for identifying abnormal energy storage batteries in energy storage stations, the internal resistance evaluation method is the dynamic periodic internal resistance weighted average method.

[0016] In one embodiment of the above-described method for identifying abnormal energy storage batteries in energy storage stations, the dynamic periodic internal resistance weighted average method further includes the following steps:

[0017] Obtain the charging state data of the energy storage battery for the most recent T cycles, which contains m charging processes, and perform validity filtering, where T and m are integers greater than 1;

[0018] Obtain the discharge state data of the energy storage battery for the most recent T cycles, which contains n discharge processes, and perform validity filtering, where T and n are integers greater than 1;

[0019] For each charging process m, obtain the charging internal resistance sequence Rc1,...,Rcm. Take the weighted average of the charging internal resistance sequence, with the formula Rc=sum(i / m*Rci) / sum(i / m),1≤i≤m, to obtain the charging internal resistance Rc, where sum is the summation formula and i is an integer.

[0020] For each discharge process n, obtain the discharge internal resistance sequence Rd1,...,Rdn. Take the weighted average of the discharge internal resistance sequence, with the formula Rd=sum(j / n*Rdj) / sum(j / n),1≤j≤n, to obtain the discharge internal resistance Rd, where sum is the summation formula and j is an integer.

[0021] In one embodiment of the above-mentioned method for identifying abnormal energy storage batteries in energy storage stations, the method for obtaining the charging and discharging internal resistance sequence is the full-circuit ohm constant method.

[0022] In one embodiment of the above-described method for identifying abnormal energy storage batteries in energy storage stations, the full-circuit ohm coefficient method further includes:

[0023] Obtain the highest single-cell voltages U1 and U2, and the total currents I1 and I2 at an effective observation point at an adjacent time step during charging process m or discharging process n. Then, obtain the charging or discharging internal resistance sequence using the formula R_point = (U1 - U2) / (I1 - I2) or R_point = (U1 - U2) / ((I1 + I2) / 2), where R_point is the resistance at an effective observation point at an adjacent time step; or...

[0024] Obtain the highest single-cell voltages U1 and U2, and the total currents I1 and I2 at k adjacent effective observation points during charging process m or discharging process n. Use the formula R_point=(U1-U2) / (I1-I2) or R_point=(U1-U2) / ((I1+I2) / 2) to obtain the charging or discharging internal resistance at the k adjacent effective observation points. Then, use the formula R_pross=avg(R_point1,...,R_pointk) to obtain the charging or discharging internal resistance sequence, where k is an integer, R_point is the resistance at an adjacent effective observation point, R_pross is the average resistance value of the k effective observation points, avg is the formula for calculating the average value, and R_pointk is the resistance at the kth adjacent effective observation point.

[0025] In one embodiment of the above-mentioned method for identifying abnormalities in energy storage batteries for energy storage stations, the method for calculating the charging internal resistance score Sc, discharging internal resistance score Sd, and battery internal resistance score St of the energy storage battery is a benchmark internal resistance calculation method.

[0026] In one embodiment of the above-mentioned method for identifying abnormal energy storage batteries in energy storage stations, the reference internal resistance calculation method further includes:

[0027] The charging internal resistance score Sc is calculated based on the charging internal resistance Rc, and the formula is Sc=1-Rc / Rcbase, where Rcbase is a preset parameter;

[0028] The discharge internal resistance score Sd is calculated based on the discharge internal resistance Rd, and the formula is Sd=1-Rd / Rdbase, where Rdbase is a preset parameter;

[0029] The minimum value between the charging internal resistance Rc and the discharging internal resistance Rd is obtained as the battery internal resistance score St, and the formula is St=min(Sc,Sd), where min is the formula for finding the minimum value.

[0030] In one embodiment of the above-mentioned method for identifying abnormal energy storage batteries in energy storage stations, the method for identifying abnormal energy storage batteries is as follows: if the charging internal resistance score Sc is less than a preset parameter threshold alpha_c, or the discharging internal resistance score Sd is less than a preset parameter threshold alpha_d, or the battery internal resistance score St is less than a preset parameter threshold alpha_t, then the energy storage battery is determined to be abnormal.

[0031] To better achieve the purpose of the invention, the present invention also provides an energy storage battery anomaly identification system for energy storage stations, comprising:

[0032] The data acquisition module is used to acquire the charge and discharge status data of the energy storage battery;

[0033] The internal resistance evaluation module is used to evaluate the charging internal resistance Rc and discharging internal resistance Rd of the energy storage battery based on the charge and discharge state data and using an internal resistance evaluation method.

[0034] The internal resistance rating module is used to calculate the charging internal resistance rating Sc, discharging internal resistance rating Sd, and battery internal resistance rating St of the energy storage battery based on the charging internal resistance Rc and the discharging internal resistance Rd.

[0035] The status recognition module is used to identify abnormal energy storage batteries based on the charging internal resistance score Sc, the discharging internal resistance score Sd, and the battery internal resistance score St.

[0036] In one embodiment of the above-mentioned abnormal identification system for energy storage batteries used in energy storage stations, the energy storage battery includes at least one secondary power battery and / or a dedicated energy storage battery pack; or, a retired power battery pack from a vehicle or a retired power battery pack mounted on a bracket.

[0037] In one embodiment of the above-mentioned energy storage battery anomaly identification system for energy storage stations, the internal resistance evaluation method is the dynamic periodic internal resistance weighted average method.

[0038] In one embodiment of the above-mentioned energy storage battery anomaly identification system for energy storage stations, the dynamic periodic internal resistance weighted average method further includes the following steps:

[0039] Obtain the charging state data of the energy storage battery for the most recent T cycles, which contains m charging processes, and perform validity filtering, where T and m are integers greater than 1;

[0040] Obtain the discharge state data of the energy storage battery for the most recent T cycles, which contains n discharge processes, and perform validity filtering, where T and n are integers greater than 1;

[0041] For each charging process m, obtain the charging internal resistance sequence Rc1,...,Rcm. Take the weighted average of the charging internal resistance sequence, with the formula Rc=sum(i / m*Rci) / sum(i / m),1≤i≤m, to obtain the charging internal resistance Rc, where sum is the summation formula and i is an integer.

[0042] For each discharge process n, obtain the discharge internal resistance sequence Rd1,...,Rdn. Take the weighted average of the discharge internal resistance sequence, with the formula Rd=sum(j / n*Rdj) / sum(j / n),1≤j≤n, to obtain the discharge internal resistance Rd, where sum is the summation formula and j is an integer.

[0043] In one embodiment of the above-mentioned energy storage battery anomaly identification system for energy storage stations, the method for obtaining the charging and discharging internal resistance sequence is the full-circuit ohm coefficient method.

[0044] In one embodiment of the above-mentioned energy storage battery anomaly identification system for energy storage stations, the full-circuit ohm coefficient method further includes:

[0045] Obtain the highest single-cell voltages U1 and U2, and the total currents I1 and I2, at an effective observation point at an adjacent time step during charging process m or discharging process n. Then, use the formula R_point = (U1 - U2) / (I1 - I2) or R_point = (U1 - U2) / ((I1 + I2) / 2) to obtain the charging or discharging internal resistance sequence, where R_point is the resistance at an effective observation point at an adjacent time step; or...

[0046] Obtain the highest single-cell voltages U1 and U2, and the total currents I1 and I2 at k adjacent effective observation points during charging process m or discharging process n. Use the formula R_point=(U1-U2) / (I1-I2) or R_point=(U1-U2) / ((I1+I2) / 2) to obtain the charging or discharging internal resistance at the k adjacent effective observation points. Then, use the formula R_pross=avg(R_point1,...,R_pointk) to obtain the charging or discharging internal resistance sequence, where k is an integer, R_point is the resistance at an adjacent effective observation point, R_pross is the average resistance value of the k effective observation points, avg is the formula for calculating the average value, and R_pointk is the resistance at the kth adjacent effective observation point.

[0047] In one embodiment of the above-mentioned energy storage battery anomaly identification system for energy storage stations, the method for calculating the charging internal resistance score Sc, discharging internal resistance score Sd, and battery internal resistance score St of the energy storage battery is the benchmark internal resistance calculation method.

[0048] In one embodiment of the above-mentioned energy storage battery anomaly identification system for energy storage stations, the reference internal resistance calculation method further includes:

[0049] The charging internal resistance score Sc is calculated based on the charging internal resistance Rc, and the formula is Sc=1-Rc / Rcbase, where Rcbase is a preset parameter;

[0050] The discharge internal resistance score Sd is calculated based on the discharge internal resistance Rd, and the formula is Sd=1-Rd / Rdbase, where Rdbase is a preset parameter;

[0051] The minimum value between the charging internal resistance Rc and the discharging internal resistance Rd is obtained as the battery internal resistance score St, and the formula is St=min(Sc,Sd), where min is the formula for finding the minimum value.

[0052] In one embodiment of the above-mentioned abnormal energy storage battery identification system for energy storage stations, the method for identifying abnormal energy storage batteries is as follows: if the charging internal resistance score Sc is less than a preset parameter threshold alpha_c, or the discharging internal resistance score Sd is less than a preset parameter threshold alpha_d, or the battery internal resistance score St is less than a preset parameter threshold alpha_t, then the energy storage battery is determined to be abnormal.

[0053] In one embodiment of the above-mentioned energy storage battery anomaly identification system for energy storage stations, it further includes an energy storage battery database, a data interaction module, a local module and / or a data processing module;

[0054] The local module is used to collect the charging internal resistance Rc, discharging internal resistance Rd, charging internal resistance score Sc, discharging internal resistance score Sd, and battery internal resistance score St of the energy storage battery in the energy storage station, and upload them to the energy storage battery database through the data interaction module in a periodic or real-time transmission manner.

[0055] The energy storage battery database is used to store the charging internal resistance Rc, discharging internal resistance Rd, charging internal resistance score Sc, discharging internal resistance score Sd, and battery internal resistance score St of energy storage batteries in multiple energy storage stations.

[0056] The data processing module is used to analyze the charging internal resistance Rc, discharging internal resistance Rd, charging internal resistance score Sc, discharging internal resistance score Sd, and battery internal resistance score St in the energy storage battery database to determine preset parameters Rcbase, Rdbase, or preset parameter thresholds alpha_c, alpha_d, and alpha_t.

[0057] To better achieve the purpose of the invention, the present invention also provides a computer-readable storage medium having a computer program stored thereon, the computer program being configured to execute the method described above when run.

[0058] To better achieve the purpose of the invention, the present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the method described above.

[0059] To better achieve the purpose of the invention, the present invention also provides an energy storage station, which is equipped with or connected to a network with the energy storage battery anomaly identification system as described above.

[0060] In addition, this invention combines charging and discharging states to comprehensively detect energy storage batteries, further improving the accuracy of energy storage battery anomaly identification.

[0061] Of course, the energy storage battery anomaly identification system, medium, electronic device, and energy storage station provided by this invention correspond to the above-described method and have the same beneficial technical effects.

[0062] To provide a better understanding of the above and other aspects of the present invention, specific embodiments are described below in conjunction with the accompanying drawings, but these are not intended to limit the scope of protection of the present invention. Attached Figure Description

[0063] Figure 1 This is a flowchart of the steps of an energy storage battery anomaly identification method according to an embodiment of the present invention.

[0064] Figure 2 This is a flowchart illustrating the steps of the dynamic periodic internal resistance weighted average method according to an embodiment of the present invention.

[0065] Figure 3 This is a flowchart illustrating the steps of the full-circuit ohm constant method according to an embodiment of the present invention.

[0066] Figure 4 This is a flowchart illustrating the steps of a reference internal resistance calculation method according to an embodiment of the present invention.

[0067] Figure 5 This is a structural block diagram of an energy storage battery anomaly identification system according to an embodiment of the present invention.

[0068] Figure 6 This is a structural block diagram of an energy storage battery anomaly identification system according to another embodiment of the present invention.

[0069] Figure 7 This is a schematic diagram of an energy storage station according to an embodiment of the present invention.

[0070] Figure 8 A schematic diagram of an energy storage station according to another embodiment of the present invention.

[0071] In the attached figures, the following labels are used:

[0072] Steps of the S1~S4-Energy Storage Battery Anomaly Identification Method

[0073] Steps of the S21~S24-Dynamic Periodic Internal Resistance Weighted Average Method

[0074] S25~S253, S25~S254' - Steps of the Ohm's Law Method for the Entire Circuit

[0075] Steps for calculating reference internal resistance (S31~S33)

[0076] Rc - Charging internal resistance

[0077] Rd - Discharge internal resistance

[0078] R_point - The resistance of a valid observation point at a neighboring time.

[0079] R_pross - the average resistance value of k valid observation points

[0080] R_pointk - Resistance at the effective observation point at the kth adjacent time step

[0081] Sc-charging internal resistance rating

[0082] Sd-discharge internal resistance score

[0083] St-Battery Internal Resistance Rating

[0084] Rcbase, Rdbase - Preset Parameters

[0085] 1, 1', 2 - Energy Storage Battery Anomaly Identification System

[0086] 11, 21 - Data Acquisition Module

[0087] 12, 22 - Internal Resistance Assessment Module

[0088] 13, 23 - Internal Resistance Scoring Module

[0089] 14, 24 - Status Recognition Module

[0090] 15, 25 - Data Interaction Module

[0091] 16, 26 - Local Modules

[0092] 17, 27 - Data Processing Module

[0093] 3, 3'-Energy Storage Station

[0094] 4, 4', 4" - Energy Storage Battery

[0095] D-Energy Storage Battery Database

[0096] sum - to calculate the summation

[0097] avg - calculate the average value

[0098] min - find the minimum value

[0099] U1, U2 - Highest unit voltage

[0100] I1, I2 - Total current

[0101] alpha_c, alpha_d, alpha_t - Preset parameter thresholds

[0102] T, m, n, i, j, k - integers Detailed Implementation

[0103] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments to further understand the purpose, solution and beneficial technical effects of the present invention, but this is not intended to limit the scope of protection of the appended claims.

[0104] The core of this invention is to provide a method, system, device, and energy storage station for identifying abnormalities in energy storage batteries. Based on internal resistance, the method identifies abnormalities in the energy storage battery, balancing accuracy and computational complexity. It can quickly, accurately, and promptly reflect the safety status of the energy storage battery, improving its safety during use and facilitating its application.

[0105] Please see Figure 1 , Figure 1 This is a flowchart illustrating the steps of an energy storage battery anomaly identification method according to an embodiment of the present invention. Specifically, it includes the following steps:

[0106] S1: Obtain the charge / discharge status data of the energy storage battery.

[0107] Specifically, charge / discharge status data of the energy storage battery can be obtained through a battery data acquisition system or a battery management system, and the energy storage battery preferably includes at least one secondary-use power battery and / or a dedicated energy storage battery pack; or a retired power battery pack from a vehicle or a retired power battery pack mounted on a bracket. Secondary-use power batteries are those that have experienced degradation after a period of use (e.g., as power batteries in electric vehicles), and after degradation, are insufficient to meet the current application requirements of the equipment (e.g., electric vehicles), but the battery still has a certain remaining capacity to meet the needs of electrical equipment (e.g., charging electric vehicles waiting for charging equipment).

[0108] For dedicated energy storage battery packs, these are new batteries specifically designed for energy storage applications from the outset. Furthermore, energy storage batteries include lithium-ion batteries, sodium-sulfur batteries, flow batteries, lead-acid batteries, lead-carbon batteries, nickel-metal hydride batteries, etc., and this invention is not limited to these. Therefore, the technical solution of this invention has a wide range of applications and is not limited to the type of energy storage battery.

[0109] Of course, in order to make it easier to achieve consistency, safety and traceability in the application of secondary power batteries, retired power batteries of the same electric vehicle model or different models (including retired power batteries in whole packs fixed on brackets) can be selected. This can make full use of the charging and discharging battery performance data of specific electric vehicle models, or even specific electric vehicle power batteries, collected and accumulated by the charging network on the big data platform during the long-term charging and discharging process of electric vehicles.

[0110] S2: Based on charge / discharge state data, use the internal resistance evaluation method to evaluate the charging internal resistance Rc and discharging internal resistance Rd of the energy storage battery.

[0111] Please refer to the following: Figure 2 Preferably, the internal resistance assessment method is a dynamic periodic internal resistance weighted average method, which further includes the following steps:

[0112] S21: Obtain the charging state data of the energy storage battery for the most recent T cycles, containing m charging processes, and perform validity filtering, where T and m are integers greater than 1. Validity filtering includes voltage filtering, current filtering, and / or power filtering, but the present invention is not limited thereto. In one specific embodiment, obtain the charging state data of the energy storage battery for the most recent 7 days, which includes 10 charging processes, and perform validity filtering on data with voltage greater than 5.0 volts or less than 3.5 volts, current greater than 300 amps or less than 5 amps, and power greater than 500 watts or less than 5 watts;

[0113] S22: Obtain the discharge state data of the energy storage battery for the most recent T cycles, containing n discharge processes, and perform validity filtering, where T and n are integers greater than 1. Validity filtering includes voltage filtering, current filtering, and / or power filtering, but this invention is not limited thereto. In one specific embodiment, obtain the discharge state data of the energy storage battery for the most recent 7 days, which includes 11 discharge processes, and perform validity filtering on data with voltage greater than 5.0 volts or less than 3.5 volts, current greater than 300 amps or less than 5 amps, and power greater than 500 watts or less than 5 watts.

[0114] S23: Based on the results of S21, for each charging process m, obtain the charging internal resistance sequence Rc1,...,Rcm, and take the weighted average of the charging internal resistance sequence. The formula is Rc=sum(i / m*Rci) / sum(i / m),1≤i≤m(1) to obtain the charging internal resistance Rc, where sum is the summation formula and i is an integer. In a specific embodiment, the charging status data of the energy storage battery for the most recent 7 days are obtained, which includes 10 charging processes and the charging internal resistance sequence {0.26556,0.28814,0.3315,0.41375,0.2615,0.297,0.29589,0.31118,0.2673,0.27738}. The charging internal resistance sequence is taken as a weighted average by formula (1) to obtain the charging internal resistance Rc of 0.31 ohms.

[0115] S24: Based on the results of S22, for each discharge process n, obtain the discharge internal resistance sequence Rd1,...,Rdn, and take the weighted average of the discharge internal resistance sequence. The formula is Rd=sum(j / n*Rdj) / sum(j / n),1≤j≤n(2), to obtain the discharge internal resistance Rd, where sum is the summation formula and j is an integer. In a specific embodiment, the discharge state data of the energy storage battery for the most recent 7 days are obtained, which includes 11 discharge processes and the discharge internal resistance sequence {0.42869,0.47831,0.44712,0.48262,0.5215,0.46312,0.53381,0.47175,0.51906,0.43656,0.51569}. The discharge internal resistance sequence is taken as a weighted average by formula (2) to obtain the discharge internal resistance Rd as 0.47 ohms.

[0116] The dynamic periodic internal resistance weighted average method used to evaluate internal resistance in this embodiment of the invention is based on multiple charge and discharge processes over multiple charge and discharge cycles. While taking into account both the charging and discharging processes, it also has a sufficient amount of data samples to ensure the accuracy of the calculation results.

[0117] Furthermore, by filtering the collected data for validity, the accuracy of internal resistance assessment was further improved.

[0118] Furthermore, this invention performs a weighted average of the charging internal resistance sequence and the discharging internal resistance sequence, calculating each data point according to reasonable weights, to further comprehensively evaluate the charging internal resistance and the discharging internal resistance.

[0119] The dynamic periodic internal resistance weighted average method has the characteristics of being fast, timely, and highly accurate, and can be applied to various energy storage batteries mentioned above, solving the consistency problem between different energy storage batteries.

[0120] Please see Figure 3 , Figure 3The flowchart shows steps S25-S253 and S25-S254' of the full-circuit ohmmeter calibration method. Preferably, the full-circuit ohmmeter calibration method for obtaining the charging and discharging internal resistance sequence further includes:

[0121] Obtain the highest single-cell voltages U1 and U2, and the total currents I1 and I2 at an effective observation point at an adjacent time step during charging process m or discharging process n. Then, obtain the charging or discharging internal resistance sequence using the formulas R_point=(U1-U2) / (I1-I2)(3) or R_point=(U1-U2) / ((I1+I2) / 2)(4), where R_point is the resistance at an effective observation point at an adjacent time step; or...

[0122] Obtain the highest single-cell voltages U1 and U2, and the total currents I1 and I2 at the effective observation points at k adjacent time points during the charging process m or the discharging process n. Use the formula R_point=(U1-U2) / (I1-I2)(3) or R_point=(U1-U2) / ((I1+I2) / 2)(4) to obtain the charging or discharging internal resistance at the effective observation points at the k adjacent time points. Then, use the formula R_pross=avg(R_point1,...,R_pointk)(5) to obtain the charging or discharging internal resistance sequence, where k is an integer, R_point is the resistance at an effective observation point at an adjacent time point, R_pross is the average resistance value of the k effective observation points, avg is the formula for calculating the average value, and R_pointk is the resistance at the effective observation point at the kth adjacent time point.

[0123] In one specific embodiment, the complete charging process of the energy storage battery is detected. After acquiring the highest single-cell voltages U1 and U2, and the total currents I1 and I2 of one charging process, the charging internal resistance of nine effective observation points can be calculated using the above formula. The charging internal resistance is {0.313, 0.276, 0.239, 0.222, 0.220, 0.238, 0.256, 0.128, 0.498}. Then, the charging internal resistance corresponding to this charging process is obtained as 0.26556 ohms using formula (5). The above steps are repeated for the remaining charging and discharging processes to obtain the charging internal resistance sequence and discharging internal resistance sequence in steps S23 and S24.

[0124] As shown in the formulas and data above, calculating the charging and discharging internal resistance only requires obtaining the highest single-cell voltage and the total package current data. Therefore, even with a large amount of data, the calculation can still be performed quickly and timely to obtain the charging and discharging internal resistance data.

[0125] Furthermore, the charging internal resistance and discharging internal resistance are obtained based on data from each complete charging and discharging process. The data sample size is sufficient, which makes the calculated charging internal resistance and discharging internal resistance corresponding to each charging and discharging process highly accurate, thereby ensuring the accuracy of the charging and discharging internal resistance after weighted average calculation.

[0126] S3: Based on the charging internal resistance Rc and the discharging internal resistance Rd, calculate the charging internal resistance score Sc, the discharging internal resistance score Sd, and the battery internal resistance score St of the energy storage battery.

[0127] Please see Figure 4 , Figure 4 The flowchart shows steps S31 to S33 of the benchmark internal resistance calculation method. Preferably, the benchmark internal resistance calculation method for calculating the charging internal resistance score Sc, discharging internal resistance score Sd, and battery internal resistance score St of the energy storage battery further includes the following steps:

[0128] S31: Calculate the charging internal resistance score Sc based on the charging internal resistance Rc. The formula is Sc=1-Rc / Rcbase, where Rcbase is a preset parameter.

[0129] S32: The discharge internal resistance score Sd is calculated based on the discharge internal resistance Rd. The formula is Sd=1-Rd / Rdbase, where Rdbase is a preset parameter.

[0130] S33: The minimum value between the charging internal resistance Rc and the discharging internal resistance Rd is the battery internal resistance score St, and the formula is St=min(Sc,Sd), where min is the formula for finding the minimum value.

[0131] In one specific embodiment, the charging internal resistance Rc of the energy storage battery is 0.31 ohms, the discharging internal resistance Rd is 0.47 ohms, and the preset parameters Rcbase and Rdbase are 1.0 ohms. The calculated charging internal resistance score Sc is 0.69, the discharging internal resistance score Sd is 0.53, and the battery internal resistance score St is 0.53. The preset parameters Rcbase and Rdbase are obtained based on historical data analysis and can be flexibly set and adjusted as needed. In other embodiments, the charging internal resistance score Sc, the discharging internal resistance score Sd, and the battery internal resistance score St obtained according to the above method are shown in the table below:

[0132] 1 0.3884 0.5758 0.9445 0.9177 0.9177 2 0.4144 0.5914 0.9408 0.9155 0.9155 3 0.4175 0.6329 0.9404 0.9096 0.9096 4 0.9339 0.5316 0.943 0.9241 0.9241 5 1.1136 2.0948 0.8409 0.7007 0.7007

[0133] The reference internal resistance calculation method in this embodiment of the invention is based on the charging internal resistance and discharging internal resistance obtained above. It does not require additional data and can save the calculation process while ensuring the accuracy of the calculation results. Thus, the charging internal resistance score Sc, the discharging internal resistance score Sd, and the battery internal resistance score St can be obtained quickly.

[0134] In addition, the minimum value between the charging internal resistance score Sc and the discharging internal resistance score Sd is obtained as the battery internal resistance score St. This process does not require tedious calculations and takes accuracy into account, further optimizing the speed of energy storage battery anomaly identification.

[0135] In addition, for dedicated energy storage battery packs, all internal resistance data during use can be obtained, and preset parameters Rcbase and Rdbase can be determined by combining the state analysis during use.

[0136] For secondary-use power batteries (including retired power battery packs from complete vehicles or retired power battery packs mounted on racks), their traceability data is also analyzed. Specifically, when determining the preset parameters Rcbase and Rdbase, not only is the internal resistance data of the secondary-use power batteries analyzed during their use in energy storage stations, but also the raw data collected and accumulated by the charging network on the big data platform during their long-term charge-discharge process before retirement is analyzed. This allows for a more scientific and accurate assessment of whether the energy storage batteries are experiencing abnormalities. Because the preset parameters Rcbase and Rdbase are based on extensive data analysis and can be adjusted according to actual conditions, the accuracy of the data is fully guaranteed, thereby ensuring the safety of the energy storage batteries used in energy storage stations.

[0137] S4: Identify abnormal energy storage batteries based on charging internal resistance score Sc, discharging internal resistance score Sd, and battery internal resistance score St.

[0138] Specifically, if the charging internal resistance score Sc is less than a preset parameter threshold alpha_c, the discharging internal resistance score Sd is less than a preset parameter threshold alpha_d, or the battery internal resistance score St is less than a preset parameter threshold alpha_t, then the energy storage battery is determined to be abnormal. In one specific embodiment, the charging internal resistance score Sc is 0.6949, the discharging internal resistance score Sd is 0.5255, and the battery internal resistance score St is 0.5255; the preset parameter thresholds alpha_c, alpha_d, and alpha_t are 0.3, respectively; therefore, the energy storage battery can be determined to be normal. The preset parameter thresholds alpha_c, alpha_d, and alpha_t are obtained based on historical data analysis and can also be flexibly set and adjusted as needed.

[0139] The anomaly identification method in this embodiment of the invention is based on three variables: charging internal resistance score Sc, discharging internal resistance score Sd, and battery internal resistance score St. By comparing these scores with preset parameter thresholds alpha_c, alpha_d, and alpha_t, the safety of the energy storage battery during charging and discharging is comprehensively evaluated, resulting in a more comprehensive and accurate safety assessment. Furthermore, the preset parameter thresholds alpha_c, alpha_d, and alpha_t are set in the same way as the preset parameters Rcbase and Rdbase, and have the same beneficial technical effects, which will not be elaborated further here.

[0140] The present invention provides a method for identifying anomalies in energy storage batteries for energy storage stations. This method compares the internal resistance of the energy storage battery under charging and discharging states based on charging and discharging data, and then compares this internal resistance with preset parameters for the battery's internal resistance during charging and discharging to obtain an internal resistance score. Based on this, the present invention's method for identifying anomalies in energy storage batteries is implemented by analyzing the internal resistance of the battery under charging and discharging states. In contrast, existing technologies for identifying anomalies in energy storage batteries employ integral methods, model-based evaluation methods, machine learning, etc., which are complex and involve large amounts of data computation. Therefore, the present invention can quickly, timely, and accurately identify abnormal energy storage batteries, enabling safety monitoring and risk alarms in the energy storage safety management process, timely detection of safety issues, reduction of potential safety hazards in practical applications, and ensuring the safety of energy storage batteries in energy storage stations during use.

[0141] Please see Figure 5 , Figure 5 A structural block diagram of an energy storage battery anomaly identification system 1 according to an embodiment of the present invention is shown, comprising:

[0142] The data acquisition module 11 is used to acquire the charge and discharge state data of the energy storage battery. Preferably, the energy storage battery includes at least one secondary-use power battery and / or a dedicated energy storage battery pack; or a retired power battery pack from a vehicle or a retired power battery pack mounted on a bracket. Secondary-use power batteries are those that have experienced degradation after a period of use (e.g., as power batteries in electric vehicles), and after degradation, are insufficient to meet the application requirements of current equipment (e.g., electric vehicles), but the battery still has a certain remaining capacity to meet the needs of electrical equipment (e.g., charging electric vehicles waiting for charging equipment).

[0143] For dedicated energy storage battery packs, these are new batteries specifically designed for energy storage applications from the outset. Furthermore, energy storage batteries include lithium-ion batteries, sodium-sulfur batteries, flow batteries, lead-acid batteries, lead-carbon batteries, nickel-metal hydride batteries, etc., and this invention is not limited to these. Therefore, the technical solution of this invention has a wide range of applications and is not limited to the type of energy storage battery.

[0144] Of course, in order to make it easier to achieve consistency, safety and traceability in the application of secondary power batteries, retired power batteries of the same electric vehicle model or different models (including retired power batteries in whole packs fixed on brackets) can be selected. This can make full use of the charging and discharging battery performance data of specific electric vehicle models, or even specific electric vehicle power batteries, collected and accumulated by the charging network on the big data platform during the long-term charging and discharging process of electric vehicles.

[0145] The internal resistance evaluation module 12 is used to evaluate the charging internal resistance Rc and discharging internal resistance Rd of the energy storage battery based on the charge / discharge state data using an internal resistance evaluation method. Preferably, the internal resistance evaluation method used in the internal resistance evaluation module 12 is the dynamic periodic internal resistance weighted average method, the calculation method and specific implementation of which are as described in step S2 above, and will not be repeated here.

[0146] The internal resistance scoring module 13 is used to calculate the charging internal resistance score Sc, discharging internal resistance score Sd, and battery internal resistance score St of the energy storage battery based on the charging internal resistance Rc and the discharging internal resistance Rd. Preferably, the method for calculating the internal resistance score in the internal resistance scoring module 13 is the benchmark internal resistance calculation method, the calculation method and specific implementation of which are as described in step S3 above, and will not be repeated here.

[0147] The status identification module 14 is used to identify energy storage batteries that have malfunctioned based on the charging internal resistance score Sc, the discharging internal resistance score Sd, and the battery internal resistance score St. Preferably, the identification method and specific implementation of the status identification module 14 are as described in step S4 above, and will not be repeated here.

[0148] Furthermore, please refer to Figure 6 , Figure 6 This is a structural block diagram of an energy storage battery anomaly identification system 1' according to another embodiment of the present invention, which further includes an energy storage battery database D, a data interaction module 15, a local module 16, and / or a data processing module 17, wherein:

[0149] Local module 16 is used to collect the charging internal resistance Rc, discharging internal resistance Rd, charging internal resistance score Sc, discharging internal resistance score Sd, and battery internal resistance score St of the energy storage battery in the energy storage station, and upload them to the energy storage battery database D through data interaction module 15 in a periodic or real-time transmission manner.

[0150] The energy storage battery database D is used to store the charging internal resistance Rc, discharging internal resistance Rd, charging internal resistance score Sc, discharging internal resistance score Sd, and battery internal resistance score St of the energy storage batteries of multiple energy storage stations.

[0151] Data processing module 17 is used to analyze the charging internal resistance Rc, discharging internal resistance Rd, charging internal resistance score Sc, discharging internal resistance score Sd, and battery internal resistance score St in the energy storage battery database D, in order to determine preset parameters Rcbase, Rdbase or preset parameter thresholds alpha_c, alpha_d, alpha_t.

[0152] In one specific embodiment, multiple energy storage stations located in different regions are equipped with energy storage battery anomaly identification systems 1' and 2. Local modules 16 and 26 in each of these systems collect the charging internal resistance Rc, discharging internal resistance Rd, charging internal resistance score Sc, discharging internal resistance score Sd, and battery internal resistance score St of the energy storage batteries at their respective local energy storage stations. These data are then uploaded to the energy storage battery database D via data interaction modules 15 and 25 in a periodic or real-time transmission manner. Therefore, the energy storage battery database D stores the internal resistance data of all energy storage batteries from multiple energy storage stations with different specifications, types, and locations. Data processing modules 17 and 27 can retrieve all internal resistance data from the local energy storage stations and the energy storage battery database D through data interaction modules 15 and 25 and perform big data analysis to determine a preset parameter and / or preset parameter threshold for judging whether an energy storage battery is abnormal, thereby making the anomaly detection more accurate.

[0153] The energy storage battery anomaly identification system provided in this embodiment of the invention forms a database about energy storage batteries. It can not only analyze the data of all energy storage batteries in a local energy storage station, but also perform comprehensive analysis of the data of energy storage batteries in multiple energy storage stations. The data in the database is complete and comprehensive, which makes the accuracy of the preset parameter thresholds high.

[0154] In addition, the energy storage battery database also includes traceability data of secondary power batteries, which further optimizes the accuracy of preset parameter thresholds and makes the judgment of anomalies of energy storage batteries more accurate.

[0155] It should be noted that the system proposed above can also be implemented in other ways. For example, the system embodiments described above are merely illustrative, and the division of modules described above is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed.

[0156] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, the computer program being configured to execute the above-described energy storage battery anomaly identification method when running, which will not be described in detail here.

[0157] An embodiment of the present invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program to execute the above-described energy storage battery anomaly identification method, which will not be described in detail here.

[0158] An embodiment of the present invention also provides an energy storage station, which is equipped with or connected via a network to the energy storage battery anomaly identification system as described above, for identifying abnormal energy storage batteries in the energy storage station to ensure the safety of the energy storage station.

[0159] Please see Figure 7 This is a schematic diagram of an energy storage station 3 according to an embodiment of the present invention. The energy storage station 3 is equipped with energy storage batteries 4, 4', and 4''. In this embodiment, the energy storage station 3 is equipped with a complete energy storage battery anomaly identification system 1, which can identify anomalies in the energy storage batteries in the energy storage station 3. It can also interact with the energy storage battery database D in the cloud through a data interaction module, and process the internal resistance data in the energy storage battery database D locally or in the cloud to determine preset parameters and preset parameter thresholds.

[0160] The energy storage station provided in this embodiment of the invention can analyze the data of the energy storage battery by setting up a complete energy storage battery anomaly identification system on its own end, either locally or in the cloud. Even when the network is unstable, it can analyze all the energy storage batteries in the local energy storage station, which improves the stability of the system and ensures the accuracy of anomaly identification.

[0161] Please refer to the following: Figure 8 This is a schematic diagram of an energy storage station 3' according to another embodiment of the present invention. The energy storage station 3' is equipped with energy storage batteries 4, 4', and 4''. In this embodiment, the energy storage station 3' does not have an energy storage battery anomaly identification system 1 installed at its local end. The complete energy storage battery anomaly identification system 1 is entirely located in the cloud. The energy storage station 3' only performs the functions of online connection or host computer. The energy storage station 3' controls the energy storage battery anomaly identification system 1 located in the cloud to identify anomalies in the energy storage batteries in the energy storage station 3', and can process the internal resistance data in the energy storage battery database D to determine preset parameters and preset parameter thresholds.

[0162] The energy storage station provided in this embodiment of the invention sets the energy storage battery anomaly identification system in the cloud, so it can be used without local setup, which improves the convenience and economy of anomaly identification in the application process.

[0163] Furthermore, the operation and maintenance of the cloud system are not limited by time or location; only the cloud server needs maintenance, enabling timely response and feedback of issues, thus improving operational and maintenance efficiency. The cloud system also possesses a certain degree of functional scalability, supporting secondary development of an anomaly identification system for energy storage batteries based on internal resistance. For example, other influencing factors can be incorporated into the anomaly identification method, facilitating further optimization of the system.

[0164] In summary, the energy storage battery anomaly identification method provided by this invention can quickly, accurately, and timely identify abnormal energy storage batteries in energy storage stations, which can improve the safety of energy storage batteries during use. Furthermore, the identification method can simultaneously balance accuracy and computational complexity, making it easy to apply.

[0165] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of the claims of this application.

[0166] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device, or fabricating them separately as individual integrated circuit modules, or fabricating multiple modules or steps as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0167] Of course, the present invention may have other various embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and modifications according to the present invention, but these corresponding changes and modifications should all fall within the protection scope of the appended claims.

Claims

1. A method for identifying abnormalities in energy storage batteries for energy storage stations, characterized in that, Includes the following steps: Acquire charge and discharge state data of energy storage batteries; Based on the charge and discharge state data, the charging internal resistance Rc and discharging internal resistance Rd of the energy storage battery are evaluated using an internal resistance evaluation method. Based on the charging internal resistance Rc and the discharging internal resistance Rd, calculate the charging internal resistance score Sc, the discharging internal resistance score Sd, and the battery internal resistance score St of the energy storage battery. Based on the charging internal resistance score Sc, the discharging internal resistance score Sd, and the battery internal resistance score St, abnormal energy storage batteries are identified; wherein, The method for calculating the charging internal resistance score Sc, discharging internal resistance score Sd, and battery internal resistance score St of the energy storage battery is a benchmark internal resistance calculation method, which further includes: The charging internal resistance score Sc is calculated based on the charging internal resistance Rc, and the formula is Sc=1-Rc / Rcbase, where Rcbase is a preset parameter; The discharge internal resistance score Sd is calculated based on the discharge internal resistance Rd, and the formula is Sd=1-Rd / Rdbase, where Rdbase is a preset parameter; The minimum value between the charging internal resistance Rc and the discharging internal resistance Rd is obtained as the battery internal resistance score St, and the formula is St=min(Sc,Sd), where min is the formula for finding the minimum value.

2. The method for identifying abnormal energy storage batteries in an energy storage station according to claim 1, characterized in that, The energy storage battery includes at least one secondary power battery and / or a dedicated energy storage battery pack; or, a retired power battery pack from a vehicle or a retired power battery pack mounted on a bracket.

3. The method for identifying abnormal energy storage batteries in an energy storage station according to claim 1 or 2, characterized in that, The internal resistance assessment method is the dynamic periodic internal resistance weighted average method.

4. The method for identifying abnormal energy storage batteries in an energy storage station according to claim 3, characterized in that, The dynamic periodic internal resistance weighted average method further includes the following steps: Obtain the charging state data of the energy storage battery for the most recent T cycles, which contains m charging processes, and perform validity filtering, where T and m are integers greater than 1; Obtain the discharge state data of the energy storage battery for the most recent T cycles, which contains n discharge processes, and perform validity filtering, where T and n are integers greater than 1; For each charging process m, obtain the charging internal resistance sequence Rc1,...,Rcm, and take the weighted average of the charging internal resistance sequence with the formula Rc=sum(i / m*Rci) / sum(i / m), 1≤i≤m, to obtain the charging internal resistance Rc, where sum is the summation formula and i is an integer; For each discharge process n, obtain the discharge internal resistance sequence Rd1,...,Rdn, and take the weighted average of the discharge internal resistance sequence using the formula Rd=sum(j / n*Rdj) / sum(j / n), 1≤j≤n, to obtain the discharge internal resistance Rd, where sum is the summation formula and j is an integer.

5. The method for identifying abnormal energy storage batteries in an energy storage station according to claim 4, characterized in that, The method for obtaining the charging and discharging internal resistance sequence is the full-circuit ohm constant method.

6. The method for identifying abnormal energy storage batteries in an energy storage station according to claim 5, characterized in that, The ohmic constant method for the entire circuit further includes: Obtain the highest single-cell voltages U1 and U2, and the total currents I1 and I2 at an effective observation point at an adjacent moment in the charging process m or discharging process n. Then, obtain the charging or discharging internal resistance sequence using the formula R_point = (U1-U2) / (I1-I2) or R_point = (U1-U2) / ((I1+I2) / 2), where R_point is the resistance at an effective observation point at an adjacent moment; or... Obtain the highest single-cell voltages U1 and U2, and the total currents I1 and I2 at k adjacent effective observation points during the charging process m or the discharging process n. Calculate the charging or discharging internal resistance at the k adjacent effective observation points using the formula R_point = (U1-U2) / (I1-I2) or R_point = (U1-U2) / ((I1+I2) / 2). Then, obtain the charging or discharging internal resistance sequence using the formula R_pross = avg(R_point1,...,R_pointk), where k is an integer, R_point is the resistance at an adjacent effective observation point, R_pross is the average resistance value of the k effective observation points, avg is the formula for calculating the average value, and R_pointk is the resistance at the kth adjacent effective observation point.

7. The method for identifying abnormal energy storage batteries in an energy storage station according to claim 1 or 2, characterized in that, The method for identifying abnormal energy storage batteries is as follows: if the charging internal resistance score Sc is less than a preset parameter threshold alpha_c, or the discharging internal resistance score Sd is less than a preset parameter threshold alpha_d, or the battery internal resistance score St is less than a preset parameter threshold alpha_t, then the energy storage battery is determined to be abnormal.

8. An abnormal identification system for energy storage batteries in an energy storage station, characterized in that, include: The data acquisition module is used to acquire the charge and discharge status data of the energy storage battery; An internal resistance evaluation module is used to evaluate the charging internal resistance Rc and discharging internal resistance Rd of the energy storage battery based on the charge and discharge state data and using an internal resistance evaluation method. The internal resistance scoring module is used to calculate the charging internal resistance score Sc, the discharging internal resistance score Sd, and the battery internal resistance score St of the energy storage battery based on the charging internal resistance Rc and the discharging internal resistance Rd. The status identification module is used to identify abnormal energy storage batteries based on the charging internal resistance score Sc, the discharging internal resistance score Sd, and the battery internal resistance score St; wherein, The method for calculating the charging internal resistance score Sc, discharging internal resistance score Sd, and battery internal resistance score St of the energy storage battery is a benchmark internal resistance calculation method, which further includes: The charging internal resistance score Sc is calculated based on the charging internal resistance Rc, and the formula is Sc=1-Rc / Rcbase, where Rcbase is a preset parameter; The discharge internal resistance score Sd is calculated based on the discharge internal resistance Rd, and the formula is Sd=1-Rd / Rdbase, where Rdbase is a preset parameter; The minimum value between the charging internal resistance Rc and the discharging internal resistance Rd is obtained as the battery internal resistance score St, and the formula is St=min(Sc,Sd), where min is the formula for finding the minimum value.

9. The energy storage battery anomaly identification system for an energy storage station according to claim 8, characterized in that, The energy storage battery includes at least one secondary power battery and / or a dedicated energy storage battery pack; or, a retired power battery pack from a vehicle or a retired power battery pack mounted on a bracket.

10. The energy storage battery anomaly identification system for an energy storage station according to claim 9, characterized in that, The internal resistance assessment method is the dynamic periodic internal resistance weighted average method.

11. The energy storage battery anomaly identification system for an energy storage station according to claim 10, characterized in that, The dynamic periodic internal resistance weighted average method further includes the following steps: Obtain the charging state data of the energy storage battery for the most recent T cycles, which contains m charging processes, and perform validity filtering, where T and m are integers greater than 1; Obtain the discharge state data of the energy storage battery for the most recent T cycles, which contains n discharge processes, and perform validity filtering, where T and n are integers greater than 1; For each charging process m, obtain the charging internal resistance sequence Rc1,...,Rcm, and take the weighted average of the charging internal resistance sequence with the formula Rc=sum(i / m*Rci) / sum(i / m), 1≤i≤m, to obtain the charging internal resistance Rc, where sum is the summation formula and i is an integer; For each discharge process n, obtain the discharge internal resistance sequence Rd1,...,Rdn, and take the weighted average of the discharge internal resistance sequence using the formula Rd=sum(j / n*Rdj) / sum(j / n), 1≤j≤n, to obtain the discharge internal resistance Rd, where sum is the summation formula and j is an integer.

12. The energy storage battery anomaly identification system for an energy storage station according to claim 11, characterized in that, The method for obtaining the charging and discharging internal resistance sequence is the full-circuit ohm constant method.

13. The energy storage battery anomaly identification system for an energy storage station according to claim 12, characterized in that, The ohmic constant method for the entire circuit further includes: Obtain the highest single-cell voltages U1 and U2, and the total currents I1 and I2 at an effective observation point at an adjacent moment in the charging process m or discharging process n. Then, obtain the charging or discharging internal resistance sequence using the formula R_point = (U1-U2) / (I1-I2) or R_point = (U1-U2) / ((I1+I2) / 2), where R_point is the resistance at an effective observation point at an adjacent moment; or... Obtain the highest single-cell voltages U1 and U2, and the total currents I1 and I2 at k adjacent effective observation points during the charging process m or the discharging process n. Calculate the charging or discharging internal resistance at the k adjacent effective observation points using the formula R_point = (U1-U2) / (I1-I2) or R_point = (U1-U2) / ((I1+I2) / 2). Then, obtain the charging or discharging internal resistance sequence using the formula R_pross = avg(R_point1,...,R_pointk), where k is an integer, R_point is the resistance at an adjacent effective observation point, R_pross is the average resistance value of the k effective observation points, avg is the formula for calculating the average value, and R_pointk is the resistance at the kth adjacent effective observation point.

14. The energy storage battery anomaly identification system for an energy storage station according to claim 9, characterized in that, The method for identifying abnormal energy storage batteries is as follows: if the charging internal resistance score Sc is less than a preset parameter threshold alpha_c, or the discharging internal resistance score Sd is less than a preset parameter threshold alpha_d, or the battery internal resistance score St is less than a preset parameter threshold alpha_t, then the energy storage battery is determined to be abnormal.

15. The energy storage battery anomaly identification system for an energy storage station according to claim 8 or 14, characterized in that, It also includes an energy storage battery database, a data interaction module, a local module, and / or a data processing module; The local module is used to collect the charging internal resistance Rc, discharging internal resistance Rd, charging internal resistance score Sc, discharging internal resistance score Sd, and battery internal resistance score St of the energy storage battery in the energy storage station, and upload them to the energy storage battery database through the data interaction module in a periodic or real-time transmission manner. The energy storage battery database is used to store the charging internal resistance Rc, discharging internal resistance Rd, charging internal resistance score Sc, discharging internal resistance score Sd, and battery internal resistance score St of the energy storage batteries of multiple energy storage stations. The data processing module is used to analyze the charging internal resistance Rc, discharging internal resistance Rd, charging internal resistance score Sc, discharging internal resistance score Sd, and battery internal resistance score St in the energy storage battery database to determine the preset parameters Rcbase, Rdbase, or the preset parameter thresholds alpha_c, alpha_d, and alpha_t.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is configured to execute the method of any one of claims 1-7 when it is run.

17. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method of any one of claims 1-7.

18. An energy storage station, which is equipped with or connected via a network to an energy storage battery anomaly identification system as described in any one of claims 8-15.

Images