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 and nominal capacity of energy storage battery;Based on charge-discharge data, using capacity evaluation method, the charge capacity and discharge capacity of energy storage battery are evaluated;Based on charge capacity, discharge capacity and nominal capacity, the charge efficiency, discharge efficiency and conversion efficiency of energy storage battery are calculated;Based on charge efficiency, discharge efficiency and conversion efficiency, capacity score is carried out to energy storage battery;Based on capacity score, the energy storage battery that appears abnormality is identified.The application can quickly, accurately and timely identify the abnormality of energy storage battery, 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] Obtain the charge / discharge status data and nominal capacity Cs of the energy storage battery;

[0010] Based on state-of-charge data, a capacity evaluation method is used to evaluate the charging capacity Cc and discharging capacity Cd of the energy storage battery.

[0011] Calculate the charging efficiency, discharging efficiency, and conversion efficiency of the energy storage battery based on the charging capacity Cc, discharging capacity Cd, and nominal capacity Cs.

[0012] Energy storage batteries are rated for capacity based on charging efficiency, discharging efficiency, and conversion efficiency.

[0013] Energy storage batteries exhibiting abnormalities are identified based on capacity scoring.

[0014] By adopting the technical solution disclosed in this invention, abnormal energy storage batteries can be identified quickly, timely, and accurately, enabling safety monitoring and risk alarms in the energy storage safety management process and timely detection of safety issues.

[0015] 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.

[0016] In one embodiment of the above-mentioned method for identifying abnormal energy storage batteries in energy storage stations, the capacity assessment method is the dynamic periodic capacity weighted average method.

[0017] In one embodiment of the above-described method for identifying abnormal energy storage batteries in energy storage stations, the dynamic periodic capacity-weighted average method further includes the following steps:

[0018] 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;

[0019] 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 n is an integer greater than 1;

[0020] For each charging process m, obtain the charging capacity sequence Cc1,...,Ccm, with the formula: Capacity = Charging Amount / abs(End SOC - Start SOC). Take the weighted average of the charging capacity sequence, with the formula: Cc = sum(i / m*Cci) / sum(i / m), 1≤i≤m, to obtain the charging capacity Cc, where abs is the formula for calculating the absolute value, sum is the summation formula, SOC is the state of charge, and i is an integer.

[0021] For each discharge process n, obtain the discharge capacity sequence Cd1,...,Cdn, with the formula: Capacity = Charge Amount / abs(End SOC - Initial SOC). Take the weighted average of the discharge capacity sequence with the formula: Cd = sum(j / n*Cdj) / sum(j / n), 1≤j≤n, to obtain the discharge capacity Cd, where abs is the formula for calculating the absolute value, sum is the summation formula, SOC is the state of charge, and j is an integer.

[0022] In one embodiment of the above-mentioned method for identifying abnormal energy storage batteries in energy storage stations, the method for calculating the charging efficiency, discharging efficiency, and conversion efficiency of retired power batteries is the nominal efficiency calculation method.

[0023] In one embodiment of the above-mentioned method for identifying abnormal energy storage batteries in energy storage stations, the nominal efficiency calculation method further includes:

[0024] The charging efficiency is calculated based on the charging capacity Cc, using the formula: Charging efficiency = Cc / Cs.

[0025] The discharge efficiency is calculated based on the discharge capacity Cd, using the formula: discharge efficiency = Cd / Cs.

[0026] The conversion efficiency is calculated based on the charging capacity Cc and the discharging capacity Cd, using the formula: Conversion efficiency = Cd / Cc.

[0027] In one embodiment of the above-mentioned method for identifying abnormal energy storage batteries in energy storage stations, the capacity scoring method is either the minimum value method or the average value method.

[0028] In one embodiment of the above-mentioned method for identifying abnormal energy storage batteries in energy storage stations, the formula for the minimum value method or the average value method is: capacity score = min(charging efficiency, discharging efficiency, conversion efficiency) or capacity score = avg(charging efficiency, discharging efficiency, conversion efficiency). If the capacity score is less than a preset parameter threshold, the energy storage battery is judged to be abnormal, where min is the formula for finding the minimum value and avg is the formula for finding the average value.

[0029] 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:

[0030] The data acquisition module is used to acquire the charge / discharge status data and nominal capacity Cs of the energy storage battery;

[0031] The capacity assessment module is used to assess the charging capacity Cc and discharging capacity Cd of the energy storage battery based on the state of charge and discharge data and using capacity assessment methods.

[0032] The efficiency calculation module is used to calculate the charging efficiency, discharging efficiency, and conversion efficiency of the energy storage battery based on the charging capacity Cc, discharging capacity Cd, and nominal capacity Cs.

[0033] The capacity rating module is used to rate the capacity of energy storage batteries based on charging efficiency, discharging efficiency, and conversion efficiency.

[0034] The status identification module is used to identify energy storage batteries that are malfunctioning based on capacity scores.

[0035] 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.

[0036] In one embodiment of the above-mentioned energy storage battery anomaly identification system for energy storage stations, the capacity assessment method is the dynamic periodic capacity weighted average method.

[0037] In one embodiment of the above-mentioned energy storage battery anomaly identification system for energy storage stations, the dynamic periodic capacity-weighted average method further includes the following steps:

[0038] 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;

[0039] 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 n is an integer greater than 1;

[0040] For each charging process m, obtain the charging capacity sequence Cc1,...,Ccm, with the formula: Capacity = Charging Amount / abs(End SOC - Start SOC). Take the weighted average of the charging capacity sequence, with the formula: Cc = sum(i / m*Cci) / sum(i / m), 1≤i≤m”, to obtain the charging capacity Cc, where abs is the formula for calculating the absolute value, sum is the summation formula, SOC is the state of charge, and i is an integer;

[0041] For each discharge process n, obtain the discharge capacity sequence Cd1,...,Cdn, with the formula: Capacity = Charge Amount / abs(End SOC - Initial SOC). Take the weighted average of the discharge capacity sequence with the formula: Cd = sum(j / n*Cdj) / sum(j / n), 1≤j≤n, to obtain the discharge capacity Cd, where abs is the formula for calculating the absolute value, sum is the summation formula, SOC is the state of charge, and j is an integer.

[0042] In one embodiment of the above-mentioned energy storage battery anomaly identification system for energy storage stations, the method for calculating the charging efficiency, discharging efficiency, and conversion efficiency of the energy storage battery is the nominal efficiency calculation method.

[0043] In one embodiment of the above-mentioned energy storage battery anomaly identification system for energy storage stations, the nominal efficiency calculation method further includes:

[0044] The charging efficiency is calculated based on the charging capacity Cc, using the formula: Charging efficiency = Cc / Cs.

[0045] The discharge efficiency is calculated based on the discharge capacity Cd, using the formula: discharge efficiency = Cd / Cs.

[0046] The conversion efficiency is calculated based on the charging capacity Cc and the discharging capacity Cd, using the formula: Conversion efficiency = Cd / Cc.

[0047] In one embodiment of the above-mentioned energy storage battery anomaly identification system for energy storage stations, the capacity scoring method is either the minimum value method or the average value method.

[0048] In one embodiment of the above-mentioned abnormal identification system for energy storage batteries in energy storage stations, the formula for the minimum value method or the average value method is: capacity score = min(charging efficiency, discharging efficiency, conversion efficiency) or capacity score = avg(charging efficiency, discharging efficiency, conversion efficiency). If the capacity score is less than a preset parameter threshold, the energy storage battery is judged to be abnormal, where min is the formula for finding the minimum value and avg is the formula for finding the average value.

[0049] 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;

[0050] The local module is used to collect the capacity score of the energy storage batteries in the energy storage station, and uploads the collected capacity score to the energy storage battery database through the data interaction module in a periodic or real-time transmission manner.

[0051] An energy storage battery database is used to store capacity ratings for multiple energy storage stations;

[0052] The data processing module is used to analyze the capacity scores in the energy storage battery database to determine preset parameter thresholds.

[0053] 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 above-described energy storage battery anomaly identification method when running.

[0054] 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 above-described method for identifying abnormal energy storage batteries.

[0055] To better achieve the purpose of the invention, the present invention also provides an energy storage station equipped with, or connected via a network, the aforementioned energy storage battery anomaly identification system.

[0056] 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.

[0057] 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.

[0058] 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

[0059] 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.

[0060] Figure 2 This is a flowchart illustrating the steps of a dynamic periodic capacity-weighted average method according to an embodiment of the present invention.

[0061] Figure 3 This is a structural block diagram of an energy storage battery anomaly identification system according to an embodiment of the present invention.

[0062] Figure 4 This is a structural block diagram of an energy storage battery anomaly identification system according to another embodiment of the present invention.

[0063] Figure 5 This is a schematic diagram of an energy storage station according to an embodiment of the present invention.

[0064] Figure 6 This is a schematic diagram of an energy storage station according to yet another embodiment of the present invention.

[0065] In the attached figures, the following labels are used:

[0066] Steps of the S1~S5 Energy Storage Battery Anomaly Identification Method

[0067] Steps of the S21~S24-Dynamic Periodic Capacity Weighted Average Method

[0068] Cs - Nominal Capacity

[0069] Cc - Charging capacity

[0070] Cd - Discharge Capacity

[0071] abs - calculates absolute value

[0072] sum - to calculate the summation

[0073] SOC - State of Charge

[0074] min - find the minimum value

[0075] avg - calculate the average value

[0076] 1, 1', 2 - Energy Storage Battery Anomaly Identification System

[0077] 11, 21 - Data Acquisition Module

[0078] 12, 22 - Capacity Assessment Module

[0079] 13, 23 - Efficiency Calculation Module

[0080] 14, 24 - Capacity Scoring Module

[0081] 15, 25 - Status Recognition Module

[0082] 16, 26 - Local Modules

[0083] 17, 27 - Data Interaction Module

[0084] 18, 28 - Data Processing Module

[0085] D-Energy Storage Battery Database

[0086] 3, 3'-Energy Storage Station

[0087] 4, 4', 4" - Energy Storage Battery

[0088] T, m, n, i, j - integers Detailed Implementation

[0089] 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.

[0090] The core of this invention is to provide a method, system, device, and energy storage station for identifying abnormalities in energy storage batteries. The method identifies abnormalities in energy storage batteries based on capacity, and the identification method balances accuracy and computational complexity. It can quickly, accurately, and timely reflect the safety status of energy storage batteries, improve the safety of energy storage batteries during use, and is easy to apply.

[0091] 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:

[0092] S1: Obtain the charge / discharge status data and nominal capacity Cs of the energy storage battery.

[0093] 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).

[0094] For dedicated energy storage battery packs, these are new batteries specifically designed for energy storage stations 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.

[0095] 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.

[0096] S2: Based on charge and discharge data, use capacity evaluation methods to evaluate the charging capacity Cc and discharging capacity Cd of the energy storage battery.

[0097] Please refer to the following: Figure 2 Preferably, the capacity assessment method is a dynamic periodic capacity weighted average method, which further includes the following steps:

[0098] 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 duration filtering and SOC (State of Charge) change magnitude filtering, but this 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 11 charging processes. Each charging process includes data such as charging amount, initial SOC, and final SOC. The initial SOC and final SOC are the SOC data at the start and end of charging, respectively. Validity filtering is performed on data with a charging duration of less than 15 minutes and an SOC change value of less than 30%.

[0099] 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 duration filtering and SOC (State of Charge) change magnitude 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 13 discharge processes. Each discharge process includes data such as charge amount, initial SOC, and final SOC. The initial SOC and final SOC are the SOC data at the start and end of the discharge, respectively. Validity filtering is performed on data with a discharge duration of less than 15 minutes and an SOC change value of less than 30%.

[0100] S23: Based on the results of S21, for each charging process m, obtain the charging capacity sequence Cc1,...,Ccm, with the formula being Capacity = Charging Amount / abs(End SOC - Start SOC) (1). Take the weighted average of the charging capacity sequence, with the formula being Cc = sum(i / m*Cci) / sum(i / m), 1≤i≤m (2), to obtain the charging capacity Cc, where abs is the formula for calculating the absolute value, 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 is obtained, which includes 11 charging processes. The charging capacity sequence Cc1,Cc2,Cc3...,Cc11 is obtained through formula (1), and then the charging capacity sequence is taken as a weighted average through formula (2) to obtain the charging capacity Cc, which is 52.36 amp-hours. The specific data is shown in the table below:

[0101] 11 20.8 30.2 69.9 52.3929471 10 20.94 30.1 69.9 52.61306533 9 20.79 30.2 69.9 52.36775819 8 20.8 30.1 69.9 52.26130653 7 20.75 30.2 69.9 52.26700252 6 20.92 30.1 69.9 52.56281407 8 20.73 30.2 69.9 52.21662469 4 20.66 30.1 69.9 51.90954774 3 20.83 30.1 69.9 52.33668342 2 20.88 30.2 69.9 52.59445844 1 20.59 30.1 69.9 51.73366834

[0102] S24: Based on the results of S22, for each discharge process n, obtain the discharge capacity sequence Cd1,...,Cdn, with the formula being Capacity = Charge Amount / abs(End SOC - Start SOC) (3). Take the weighted average of the discharge capacity sequence, with the formula being Cd = sum(j / n*Cdj) / sum(j / n), 1≤j≤n (4), to obtain the discharge capacity Cd, where abs is the formula for calculating the absolute value, 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 13 discharge processes. The discharge capacity sequence Cd1,Cd2,Cd3...,Cd13 is obtained through formula (3), and then the discharge capacity sequence is taken as a weighted average through formula (4) to obtain the discharge capacity Cd of 50.54 amp-hours. The specific data is shown in the table below:

[0103] 13 20.16 70 30.1 50.52631579 12 20.21 70 30.1 50.65162907 11 20.13 70 30.1 50.45112782 10 20.35 70 30.1 51.00250627 9 20.12 70 30.1 50.42606516 8 20.18 70 30.1 50.5764411 7 20.1 70 30.1 50.37593985 6 20.27 70 30.1 50.80200501 5 20.08 70 30.1 50.32581454 4 19.94 70 30.1 49.97493734 3 19.9 70 30.1 49.87468672 2 20.24 70 30.1 50.72681704 1 20.19 70 30.1 50.60150376

[0104] The dynamic periodic capacity weighted average method used to evaluate capacity 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.

[0105] Furthermore, by filtering the collected data for validity, the accuracy of capacity assessment was further improved.

[0106] Furthermore, this invention performs a weighted average of the charging capacity sequence and the discharging capacity sequence, calculating each data point according to reasonable weights, to further comprehensively evaluate the charging capacity and discharging capacity.

[0107] As shown in the table above, calculating charging and discharging capacity only requires obtaining charging amount, initial SOC, and final SOC data. Therefore, even with a large amount of data, calculations can still be performed quickly and accurately to obtain charging and discharging capacity data.

[0108] The dynamic periodic capacity weighted average method is fast, timely, and highly accurate, and it can be applied to various energy storage batteries mentioned above, solving the consistency problem between different energy storage batteries.

[0109] S3: Calculate the charging efficiency, discharging efficiency, and conversion efficiency of the energy storage battery based on the charging capacity Cc, discharging capacity Cd, and nominal capacity Cs.

[0110] Preferably, the method for calculating the charging efficiency, discharging efficiency, and conversion efficiency is the nominal efficiency calculation method, further including the following steps: calculating the charging efficiency based on the charging capacity Cc, using the formula: charging efficiency = Cc / Cs; calculating the discharging efficiency based on the discharging capacity Cd, using the formula: discharging efficiency = Cd / Cs; and calculating the conversion efficiency based on the charging capacity Cc and the discharging capacity Cd, using the formula: conversion efficiency = Cd / Cc. In a specific embodiment, based on the above method and data, the charging capacity Cc of the energy storage battery is obtained as 52.36 AH, the discharging capacity Cd as 50.54 AH, and the nominal capacity Cs as 54 AH, and the charging efficiency, discharging efficiency, and conversion efficiency are calculated to be 0.97, 0.94, and 0.97, respectively.

[0111] The nominal efficiency calculation method in this embodiment of the invention is based on the aforementioned charging capacity, discharging capacity, and nominal capacity. It does not require additional data and can save the calculation process while ensuring the accuracy of the calculation results, thereby quickly obtaining the charging efficiency, discharging efficiency, and conversion efficiency.

[0112] S4: The capacity of the energy storage battery is rated based on charging efficiency, discharging efficiency and conversion efficiency.

[0113] Preferably, the capacity scoring method is the minimum value method or the mean value method, specifically including the following steps:

[0114] Capacity score = min(charging efficiency, discharging efficiency, conversion efficiency) (5) or capacity score = avg(charging efficiency, discharging efficiency, conversion efficiency) (6), where min is the formula for finding the minimum value and avg is the formula for finding the average value. Other methods can also be used to calculate the capacity score, and this invention is not limited to these. In one specific embodiment, the charging efficiency of the energy storage battery is 0.97, the discharging efficiency is 0.94, and the conversion efficiency is 0.97. According to formula (5), the capacity score of the energy storage battery can be calculated as 0.94. In other embodiments, the capacity scores obtained according to the above method are shown in the following table:

[0115]

[0116]

[0117] The capacity score in this embodiment of the invention can be obtained using either the minimum value method or the average value method. As shown in the table above, the minimum value among the charging efficiency, discharging efficiency, and conversion efficiency is obtained as the capacity score. The minimum value in the data represents the capacity score of the energy storage battery. This process does not require cumbersome calculations and ensures accuracy, further optimizing the speed of energy storage battery anomaly identification.

[0118] S5: Identify energy storage batteries that exhibit abnormalities based on capacity rating.

[0119] Specifically, if the capacity score is less than a preset parameter threshold, the energy storage battery is determined to be abnormal. In one specific embodiment, the energy storage battery's capacity score is 0.36, and the preset parameter threshold is 0.40, therefore the energy storage battery is determined to be abnormal. The preset parameter threshold is obtained through statistical analysis of historical data of the energy storage battery, and can also be flexibly set and adjusted as needed.

[0120] For dedicated energy storage battery packs, all capacity data during use can be obtained, and a preset parameter threshold can be determined by combining the state analysis during use.

[0121] 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. This means that when determining the preset parameter thresholds, not only is the capacity data of the secondary-use power batteries used in energy storage stations analyzed, but also the raw data collected and accumulated by the charging network on the big data platform during their long-term charging and discharging 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 parameter thresholds 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.

[0122] The present invention provides a method for identifying anomalies in energy storage batteries for energy storage stations. This method compares the capacity of the energy storage battery in both charging and discharging states based on charging and discharging data, and then compares these values ​​again with the battery's nominal capacity to obtain a capacity score. Based on this, the present invention's method for identifying anomalies in energy storage batteries is implemented by analyzing the battery's capacity in these states. In contrast, existing technologies for identifying anomalies in energy storage batteries employ methods such as integral methods, model-based evaluation methods, and machine learning, which are complex and involve large amounts of data computation. Therefore, the present invention can quickly, timely, and accurately identify anomaly-prone 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.

[0123] Please see Figure 3 , Figure 3 A structural block diagram of an energy storage battery anomaly identification system 1 according to an embodiment of the present invention is shown, comprising:

[0124] The data acquisition module 11 is used to acquire the charge / discharge state data and nominal capacity Cs 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).

[0125] For dedicated energy storage battery packs, these are new batteries specifically designed for energy storage applications from the outset. Furthermore, energy storage batteries specifically 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.

[0126] 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.

[0127] The capacity assessment module 12 is used to assess the charging capacity Cc and discharging capacity Cd of the energy storage battery based on charge / discharge state data and using a capacity assessment method. Preferably, the capacity assessment method used in the capacity assessment module 12 is the dynamic periodic capacity weighted average method, the calculation method and specific implementation of which are as described in step S2 above, and will not be repeated here.

[0128] The efficiency calculation module 13 is used to calculate the charging efficiency, discharging efficiency, and conversion efficiency of the energy storage battery based on the charging capacity Cc, discharging capacity Cd, and nominal capacity Cs. Preferably, the method for calculating the charging efficiency, discharging efficiency, and conversion efficiency in the efficiency calculation module 13 is the nominal efficiency calculation method, and its calculation method and specific implementation are as described in step S3 above, and will not be repeated here.

[0129] The capacity scoring module 14 is used to score the capacity of the energy storage battery based on charging efficiency, discharging efficiency, and conversion efficiency. Preferably, the method for calculating the capacity score in the capacity scoring module 14 is the minimum value method or the average value method, and the calculation method and specific implementation are as described in step S4 above, and will not be repeated here.

[0130] The status identification module 15 is used to identify energy storage batteries that have malfunctioned based on capacity rating. Preferably, the identification method and specific implementation of the status identification module 15 are as described in step S5 above, and will not be repeated here.

[0131] Furthermore, please refer to Figure 4 , Figure 4 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 17, a local module 16, and / or a data processing module 18, wherein:

[0132] The local module 16 is used to collect the capacity score of the energy storage battery in the energy storage station, and upload the collected capacity score to the energy storage battery database D through the data interaction module 17 in a periodic or real-time transmission manner.

[0133] The energy storage battery database D is used to store the capacity ratings of multiple energy storage stations;

[0134] The data processing module 18 is used to analyze the capacity score in the energy storage battery database D to determine the preset parameter threshold.

[0135] 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 capacity scores of the energy storage batteries at their respective local energy storage stations and upload them to the energy storage battery database D via data interaction modules 17 and 27 in a periodic or real-time transmission manner. Therefore, the energy storage battery database D stores the capacity scores of all energy storage batteries from multiple energy storage stations with different specifications, types, and locations. Data processing modules 18 and 28 can retrieve all capacity scores from the local energy storage stations and the energy storage battery database D through data interaction modules 17 and 27 and perform big data analysis to determine a preset parameter threshold for judging whether an energy storage battery is abnormal, thereby making the anomaly detection more accurate.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Please see Figure 5 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 capacity rating data in the energy storage battery database D locally or in the cloud to determine the preset parameter threshold.

[0143] 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.

[0144] Please refer to the following: Figure 6 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 capacity score data in the energy storage battery database D to determine the preset parameter threshold.

[0145] 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.

[0146] 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 degree of functional scalability, supporting secondary development of capacity-based energy storage battery anomaly identification systems. For example, other influencing factors can be incorporated into the anomaly identification method, facilitating further optimization of the system.

[0147] In summary, the energy storage battery anomaly identification method provided by this invention can accurately, quickly, 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.

[0148] 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.

[0149] 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.

[0150] 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: Obtain the charge / discharge status data and nominal capacity Cs of the energy storage battery; Based on the charge and discharge state data, the charging capacity Cc and discharging capacity Cd of the energy storage battery are evaluated using a capacity evaluation method. Based on the charging capacity Cc, the discharging capacity Cd, and the nominal capacity Cs, calculate the charging efficiency, discharging efficiency, and conversion efficiency of the energy storage battery. The capacity of the energy storage battery is scored based on the charging efficiency, the discharging efficiency, and the conversion efficiency. Based on the capacity score, abnormal energy storage batteries are identified; wherein, The capacity assessment method is a dynamic periodic capacity weighted average method, including: 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, containing n discharge processes, and perform validity filtering, where n is an integer greater than 1; and, The method for calculating the charging efficiency, discharging efficiency, and conversion efficiency of the energy storage battery is the nominal efficiency calculation method, which further includes: The charging efficiency is calculated based on the charging capacity Cc, and the formula is: charging efficiency = Cc / Cs; The discharge efficiency is calculated based on the discharge capacity Cd, using the formula: discharge efficiency = Cd / Cs; The conversion efficiency is calculated based on the charging capacity Cc and the discharging capacity Cd, and the formula is conversion efficiency = Cd / Cc.

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 dynamic periodic capacity-weighted average method further includes the following steps: For each charging process m, a charging capacity sequence Cc1,...,Ccm is obtained, with the formula being Capacity = Charging Amount / abs(End SOC - Start SOC). The charging capacity sequence is then weighted and averaged, with the formula Cc = sum(i / m*Cci) / sum(i / m), where 1≤i≤m, to obtain the charging capacity Cc. Here, abs is the formula for calculating the absolute value, sum is the summation formula, SOC is the state of charge, and i is an integer. For each discharge process n, a discharge capacity sequence Cd1,...,Cdn is obtained, with the formula: Capacity = Charge Amount / abs(End SOC - Start SOC). The discharge capacity sequence is then weighted and averaged, with the formula: Cd = sum(j / n*Cdj) / sum(j / n), where 1≤j≤n, to obtain the discharge capacity Cd. Here, abs is the formula for calculating the absolute value, sum is the summation formula, SOC is the state of charge, and j is an integer.

4. The method for identifying abnormal energy storage batteries in an energy storage station according to claim 1 or 2, characterized in that, The capacity scoring method is either the minimum value method or the mean value method.

5. The method for identifying abnormal energy storage batteries in an energy storage station according to claim 4, characterized in that, The formula for the minimum or average method is capacity score = min(charging efficiency, discharging efficiency, conversion efficiency) or capacity score = avg(charging efficiency, discharging efficiency, conversion efficiency). If the capacity score is less than a preset parameter threshold, the energy storage battery is judged to be abnormal, where min is the formula for finding the minimum value and avg is the formula for finding the average value.

6. An abnormal battery identification system for an energy storage station, characterized in that, include: The data acquisition module is used to acquire the charge and discharge status data and nominal capacity Cs of the energy storage battery; The capacity assessment module is used to assess the charging capacity Cc and discharging capacity Cd of the energy storage battery based on the charge and discharge state data and using a capacity assessment method. An efficiency calculation module is used to calculate the charging efficiency, discharging efficiency, and conversion efficiency of the energy storage battery based on the charging capacity Cc, the discharging capacity Cd, and the nominal capacity Cs. A capacity rating module is used to rate the capacity of the energy storage battery based on the charging efficiency, the discharging efficiency, and the conversion efficiency. The status identification module is used to identify energy storage batteries that have malfunctioned based on the capacity score; wherein, The capacity assessment method is a dynamic periodic capacity weighted average method, including: 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, containing n discharge processes, and perform validity filtering, where n is an integer greater than 1; and, The method for calculating the charging efficiency, discharging efficiency, and conversion efficiency of the energy storage battery is the nominal efficiency calculation method, which further includes: The charging efficiency is calculated based on the charging capacity Cc, and the formula is: charging efficiency = Cc / Cs; The discharge efficiency is calculated based on the discharge capacity Cd, using the formula: discharge efficiency = Cd / Cs; The conversion efficiency is calculated based on the charging capacity Cc and the discharging capacity Cd, and the formula is conversion efficiency = Cd / Cc.

7. The energy storage battery anomaly identification system for energy storage stations according to claim 6, 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.

8. The energy storage battery anomaly identification system for an energy storage station according to claim 6 or 7, characterized in that, The dynamic periodic capacity-weighted average method further includes the following steps: For each charging process m, a charging capacity sequence Cc1,...,Ccm is obtained, with the formula being Capacity = Charging Amount / abs(End SOC - Start SOC). The charging capacity sequence is then weighted and averaged, with the formula Cc = sum(i / m*Cci) / sum(i / m), where 1≤i≤m, to obtain the charging capacity Cc. Here, abs is the formula for calculating the absolute value, sum is the summation formula, SOC is the state of charge, and i is an integer. For each discharge process n, a discharge capacity sequence Cd1,...,Cdn is obtained, with the formula: Capacity = Charge Amount / abs(End SOC - Start SOC). The discharge capacity sequence is then weighted and averaged, with the formula: Cd = sum(j / n*Cdj) / sum(j / n), where 1≤j≤n, to obtain the discharge capacity Cd. Here, abs is the formula for calculating the absolute value, sum is the summation formula, SOC is the state of charge, and j is an integer.

9. The energy storage battery anomaly identification system for an energy storage station according to claim 6 or 7, characterized in that, The capacity scoring method is either the minimum value method or the mean value method.

10. The energy storage battery anomaly identification system for an energy storage station according to claim 9, characterized in that, The formula for the minimum or average method is: capacity score = min(charging efficiency, discharging efficiency, conversion efficiency) or capacity score = avg(charging efficiency, discharging efficiency, conversion efficiency). If the capacity score is less than a preset parameter threshold, the energy storage battery is judged to be abnormal, where min is the formula for finding the minimum value and avg is the formula for finding the average value.

11. The energy storage battery anomaly identification system for an energy storage station according to claim 10, characterized in that, It also includes an energy storage battery database, a data interaction module, a local module, and / or a data processing module; among which, The local module is used to collect the capacity score of the energy storage battery in the energy storage station, and upload the collected capacity score 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 capacity ratings of multiple energy storage stations; The data processing module is used to analyze the capacity score in the energy storage battery database to determine the preset parameter threshold.

12. 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 1-5 when it is run.

13. 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 1-5.

14. 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 6-11.

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