LU505429B1 - Hierarchical diagnosis and early warning method, system and apparatus for faults of electrochemical energy storage system - Google Patents

Hierarchical diagnosis and early warning method, system and apparatus for faults of electrochemical energy storage system Download PDF

Info

Publication number
LU505429B1
LU505429B1 LU505429A LU505429A LU505429B1 LU 505429 B1 LU505429 B1 LU 505429B1 LU 505429 A LU505429 A LU 505429A LU 505429 A LU505429 A LU 505429A LU 505429 B1 LU505429 B1 LU 505429B1
Authority
LU
Luxembourg
Prior art keywords
early warning
battery cell
energy storage
faulty battery
diagnosis
Prior art date
Application number
LU505429A
Other languages
French (fr)
Inventor
Zhenkai Hu
Yuxuan Li
Wanzhou Sun
Peng Peng
Man Chen
Original Assignee
Csg Pgc Energy Storage Res Institute
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Csg Pgc Energy Storage Res Institute filed Critical Csg Pgc Energy Storage Res Institute
Application granted granted Critical
Publication of LU505429B1 publication Critical patent/LU505429B1/en

Links

Classifications

    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
    • H01M10/482—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte for several batteries or cells simultaneously or sequentially
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/392—Determining battery ageing or deterioration, e.g. state of health
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/396—Acquisition or processing of data for testing or for monitoring individual cells or groups of cells within a battery
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/18—Status alarms
    • G08B21/182—Level alarms, e.g. alarms responsive to variables exceeding a threshold
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/4207—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells for several batteries or cells simultaneously or sequentially
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
    • H01M10/486—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte for measuring temperature
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/425—Structural combination with electronic components, e.g. electronic circuits integrated to the outside of the casing
    • H01M2010/4271—Battery management systems including electronic circuits, e.g. control of current or voltage to keep battery in healthy state, cell balancing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Manufacturing & Machinery (AREA)
  • Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Electrochemistry (AREA)
  • General Chemical & Material Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Business, Economics & Management (AREA)
  • Emergency Management (AREA)
  • Theoretical Computer Science (AREA)
  • Microelectronics & Electronic Packaging (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Secondary Cells (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

Disclosed are hierarchical diagnosis and early warning method, system and apparatus for faults of an electrochemical energy storage system. The method includes: determining whether multidimensional detection information exceeds an information safety threshold; giving a first-level early warning signal; performing a second-level diagnosis and early warning by a big data analysis method, and recognizing a potential faulty battery cell; determining whether big data calculation information corresponding to the potential faulty battery cell exceeds a safety threshold; giving a second-level early warning signal according to the big data calculation information, and giving a serial number and position of the potential faulty battery cell; and performing a third-level diagnosis and early warning according to the multidimensional detection information corresponding to the potential faulty battery cell, determining a fault type, and giving a third-level early warning signal and a maintenance plan according to a fault diagnosis result.

Description

BL-5779
HIERARCHICAL DIAGNOSIS AND EARLY WARNING METHOD, SYSTEM 505429
AND APPARATUS FOR FAULTS OF ELECTROCHEMICAL ENERGY
STORAGE SYSTEM
[01] The present application claims the priority of Chinese Patent Application No. 202310649241.8, filed to the Chinese Patent Office on June 2, 2023, which is incorporated in its entirety herein by reference.
TECHNICAL FIELD
[02] The present application relates to the field of electrochemical energy storage, for example, which relates to a hierarchical diagnosis and early warning method, system and apparatus for faults of an electrochemical energy storage system.
BACKGROUND ART
[03] With development of new energy and a proposal of a smart power grid and a new power system, an electrochemical energy storage technology is playing an increasingly important role. Large-scale application of an electrochemical energy storage system puts forward new requirements for performance of the system, and especially in view of personnel and property loss and other problems that are caused by a fire and an explosion accident caused by electrochemical energy storage power station failures, safety of the electrochemical energy storage system has attracted more and more attention.
[04] In an actual operation process, since the electrochemical energy storage system includes a large number of energy storage battery cells, a power conversion system (PCS), a thermal management system, a battery management system (BMS) and other apparatuses, the number of apparatus components is large, and the amount of data in a system operation process is huge, it is difficult to effectively extract fault information, and a failure of each component will cause the system to fail to work normally.
Especially when a battery failure occurs, it is likely to cause a fire and an explosion of the energy storage system, and it is difficult to achieve an early warning of an accident 1
BL-5779 only by means of current, voltage and temperature information. In addition, for potential HUS05429 faults that occur slowly in the battery system, if the potential faults can be recognized at an early stage and maintenance measures are taken in advance, fault hazards will be greatly reduced.
[05] However, in current laboratory research and engineering application, a hierarchical diagnosis system for faults of a large-scale electrochemical energy storage system is not comprehensive. For example, Beijing Institute of Technology has developed a battery fault diagnosis method for real-time operation data of an electric vehicle, but which can only recognize abnormal battery cells and does not further diagnose a cause of a fault. Tsinghua University has developed a method for diagnosing an internal short circuit of a battery, but this method is implemented for battery cells and can not simultaneously diagnose all battery cells of the large-scale energy storage system in real time. The current research and application do not consider a hierarchical diagnosis and early warning method for faults of a large-scale electrochemical energy storage system, and a comprehensive fault diagnosis and early warning system has not been established.
SUMMARY
[06] The present application provides a hierarchical diagnosis and early warning method, system and apparatus for faults of an electrochemical energy storage system, which can accurately and rapidly achieve a hierarchical diagnosis and early warning for the faults of the electrochemical energy storage system, thereby improving safety of the large-scale electrochemical energy storage system.
[07] The present application provides the solutions as follows:
[08] A hierarchical diagnosis and early warning method for faults of an electrochemical energy storage system includes:
[09] acquiring multidimensional detection information during operation of the electrochemical energy storage system in real time according to a battery management system, where the multidimensional detection information includes a current, a voltage, a temperature, stress, sound, hydrogen, carbon monoxide, a volatile organic compound, methane and visible light; 2
BL-5779
[10] determining whether the real-time multidimensional detection information LU505429 exceeds an information safety threshold; giving a first-level early warning signal in response to the situation that the multidimensional detection information exceeds the information safety threshold; performing a second-level diagnosis and early warning by using a big data analysis method according to the temperature or voltage acquired in real time in response to the situation that the multidimensional detection information does not exceed the information safety threshold, and recognizing a potential faulty battery cell;
[11] determining whether big data calculation information corresponding to the potential faulty battery cell exceeds a safety threshold; determining that an operation state of the electrochemical energy storage system is normal in response to the situation that the big data calculation information corresponding to the potential faulty battery cell does not exceed the safety threshold; giving a second-level early warning signal according to the big data calculation information corresponding to the potential faulty battery cell in response to the situation that the big data calculation information corresponding to the potential faulty battery cell exceeds the safety threshold, and giving a serial number and position of the potential faulty battery cell; and
[12] performing a third-level diagnosis and early warning according to the multidimensional detection information corresponding to the potential faulty battery cell, determining a fault type, and giving a third-level early warning signal and a maintenance plan according to a fault diagnosis result.
[13] A hierarchical diagnosis and early warning system for faults of an electrochemical energy storage system includes:
[14] a multidimensional detection information acquisition module configured to acquire multidimensional detection information during operation of the electrochemical energy storage system in real time according to a battery management system, where the multidimensional detection information includes a current, a voltage, a temperature, stress, sound, hydrogen, carbon monoxide, a volatile organic compound, methane and visible light;
[15] a first determination module configured to determine whether the real-time multidimensional detection information exceeds an information safety threshold; give a first-level early warning signal in response to the situation that the multidimensional detection information exceeds the information safety threshold; perform a second-level diagnosis and early warning by using a big data analysis method according to the 3
BL-5779 temperature or voltage acquired in real time in response to the situation that the LU505429 multidimensional detection information does not exceed the information safety threshold, and recognize a potential faulty battery cell;
[16] a second determination module configured to determine whether big data calculation information corresponding to the potential faulty battery cell exceeds a safety threshold; determine that an operation state of the electrochemical energy storage system is normal in response to the situation that the big data calculation information corresponding to the potential faulty battery cell does not exceed the safety threshold; give a second-level early warning signal according to the big data calculation information corresponding to the potential faulty battery cell in response to the situation that the big data calculation information corresponding to the potential faulty battery cell exceeds the safety threshold, and give a serial number and position of the potential faulty battery cell; and
[17] a fault diagnosis result determination module configured to perform a third-level diagnosis and early warning according to the multidimensional detection information corresponding to the potential faulty battery cell, determine a fault type, and give a third-level early warning signal and a maintenance plan according to a fault diagnosis result.
[18] A hierarchical diagnosis and early warning apparatus for faults of an electrochemical energy storage system includes at least one processor, at least one memory, and a computer program instruction stored in the memory, and when the computer program instruction is executed by the processor, the above-mentioned method is implemented.
BRIEF DESCRIPTION OF THE DRAWINGS
[19] A brief introduction to the accompanying drawings required for the examples will be provided below, the accompanying drawings in the following description are merely some examples of the present application, and those of ordinary skill in the art would also have obtained other accompanying drawings according to these accompanying drawings without involving any creative effort.
[20] FIG. 1 is a schematic flowchart of a hierarchical diagnosis and early warning method for faults of an electrochemical energy storage system provided by the present application; and 4
BL-5779
[21] FIG. 2 is a schematic diagram for grading of a hierarchical early warning in a LU505420 second-level diagnosis and early warning.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[22] The following clearly and completely describes the technical solutions of the examples of the present application in conjunction with the accompanying drawings in the examples of the present application. Apparently, the examples described are merely some examples rather than all examples of the present application. Based on the examples of the present application, all other examples obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of the present application.
[23] The present application provides a hierarchical diagnosis and early warning method, system and apparatus for faults of an electrochemical energy storage system, which can accurately and rapidly achieve a hierarchical diagnosis and early warning for the faults of the electrochemical energy storage system, thereby improving safety of the large-scale electrochemical energy storage system.
[24] The present application will be described in detail below with reference to the accompanying drawings and particular embodiments.
[25] As shown in FIG. 1, a hierarchical diagnosis and early warning method for faults of an electrochemical energy storage system provided by the present application includes:
[26] S1. Acquire multidimensional detection information during operation of the electrochemical energy storage system in real time according to a battery management system, where the multidimensional detection information includes a current, a voltage, a temperature, stress, sound, hydrogen, carbon monoxide, a volatile organic compound, methane and visible light,
[27] that is, a first-level diagnosis and early warning in S2 is performed by acquiring the multidimensional detection information including electricity, heat, force, sound, a gas, light, etc. of the electrochemical energy storage system, and a first-level early warning signal is given.
[28] Specific acquisition information is shown in Table 1:
[29] Table I
BL-5779
Parameter Co ; LUS05429
Electricity | Heat Force Sound Gas Light type
Hydrogen
H
Current = 2 arbon ’ monoxide
CO Visible
Parameter Temperature | Stress Sound ( ) .
Volatile light name (M) (0) (5) . organic (L)
Voltage compound (U) (VOC)
Methane (CH4)
[30] S2. Determine whether the real-time multidimensional detection information exceeds an information safety threshold; give a first-level early warning signal in response to the situation that the multidimensional detection information exceeds the information safety threshold; and recognize a potential faulty battery cell by using a big data analysis method according to the current and voltage acquired in real time in response to the situation that the multidimensional detection information does not exceed the information safety threshold, and determine calculation information corresponding to the detection information of the battery cell, where the big data analysis method includes an information entropy method.
[31] Table2
Parameter CL ;
Electricity | Heat Force Sound Gas Light type
Hydrogen
H max
Current Cu ) arbon monoxide
CO max Visible
Parameter Temperature Stress Sound © 1 licht threshold (T max, T min) (6 max) (S max) 0 atl e 19 organic (L max)
Voltage
Una U compound =) (VOC mas) me Methane (CH, max)
[32] The information safety threshold is shown in Table 2: 6
BL-5779
[33] S3. Determine whether big data calculation information corresponding to the LU505429 potential faulty battery cell exceeds a safety threshold; determine that an operation state of the electrochemical energy storage system is normal in response to the situation that the big data calculation information corresponding to the potential faulty battery cell does not exceed the safety threshold; and give a second-level early warning signal according to the calculation information corresponding to the potential faulty battery cell in response to the situation that the big data calculation information corresponding to the potential faulty battery cell exceeds the safety threshold, and give a serial number and position of the potential faulty battery cell.
[34] S3 specifically includes:
[35] determine a hierarchical early warning threshold;
[36] determine a hierarchical early warning of a corresponding level according to the big data calculation information of the potential faulty battery cell, the hierarchical early warning threshold, and the safety threshold; and
[37] determine a serial number and position of the potential faulty battery cell.
[38] As shown in FIG. 2, hierarchical early warnings of levels I-III are given, where TV is a parameter value of the calculation information after normalization processing of a diagnosis result of the big data analysis method, ie., the multidimensional detection information corresponding to the potential faulty battery cell,
TV1 is the safety threshold, i.e., level I early warning threshold, and TV2 and TV3 are level II and level III early warning thresholds respectively.
[39] S4. Perform a third-level diagnosis and early warning according to the multidimensional detection information corresponding to the potential faulty battery cell, determine a fault type, and give a third-level early warning signal and a maintenance plan according to a fault diagnosis result.
[40] S4 specifically includes:
[41] acquire a model library, where the model library includes normal models, fault models and corresponding algorithms of various batteries and components;
[42] determine a fault type according to the multidimensional detection information and the model library corresponding to the potential faulty battery cell; and
[43] give an early warning signal and a maintenance plan according to the fault diagnosis result.
[44] According to the present application, a multi-level fault hierarchical diagnosis and early warning from a system to a battery cell can be performed in a daily operation 7
BL-5779 process of the large-scale electrochemical energy storage system. Based on an LU505429 established hierarchical diagnosis strategy, the faulty battery cell and potential faults in the large-scale electrochemical energy storage system can be recognized, and detailed diagnosis results and operation and maintenance suggestions can be given, such that safety performance of the energy storage system is greatly improved.
[45] Description is made below in combination with three examples:
[46] Example 1
[47] A large-scale electrochemical energy storage system in this example includes 10000 battery cells, an operating voltage range of the battery cells is 3.05 V to 3.55 V, and the maximum charge-discharge current is 140 A.
[48] Implementation steps of the hierarchical diagnosis and early warning method for faults of this example are as follows:
[49] (1) When the electrochemical energy storage system operates, the battery management system (BMS) acquires multidimensional detection information such as electricity, heat, force, sound, gas and light of the system in real time to monitor an operation state of the energy storage system. The information acquired at a certain time is shown in Table 3:
[50] Table3 ep Joe Js Jon Jt
Electricity | Heat Force Sound Gas Light type
Hydrogen (Z=187. Carbon 3A) monoxide
Parameter Temperature Stress Sound Visible name 5 (271,20 ours C0 qe)
Voltage compound (U=2.3V) (VOC=0) (CH4=0)
[51] (2) A first-level diagnosis and early warning is performed according to the acquired multidimensional detection information and each information safety threshold, and a first-level early warning signal is sent. According to the multidimensional detection information such as electricity, heat, force, sound, gas and light, and the 8
BL-5779 information safety threshold (as shown in Table 4), a current value exceeds the LU505429 maximum current threshold, and a voltage value is lower than the minimum voltage threshold. The battery cell is quite likely to have a serious failure. In this case, a first-level diagnosis and early warning and an operation and maintenance strategy shall be given, and the diagnosis ends. Table 4 is shown as follows:
[52] Table 4 ep tne Js Jon Jn
Electricity | Heat Force Sound Gas Light type
Hydrogen z
Current max=5ppm) (I Carbon max=150A) monoxide
Visible
Parameter fempeme es Sound (S ne PR) light name 5 max=30°C) _100Pa) max=10) 5,
Voltage compound ME (Umax (VOC =3.65V.U max=5ppm) min=3.00V) Methane (CH,
[53] Example 2
[54] A large-scale electrochemical energy storage power station in this example includes 10000 battery cells, an operating voltage range of the battery cells is 3.05 V to 3.55 V, and the maximum charge-discharge current is 140 A.
[55] Implementation steps of the hierarchical diagnosis and early warning method for faults of this example are as follows:
[56] (1) When the electrochemical energy storage system operates, the battery management system (BMS) acquires multidimensional detection information such as electricity, heat, force, sound, gas and light of the system in real time to monitor an operation state of the energy storage system. The information acquired at a certain time is shown in Table 5:
[57] Table 5 9
BL-5779
Hydrogen
I Carbon =70.05A) monoxide
Parameter Temperature Stress Sound (S Visible name m (T=27.2°C) (s -0) light =100Pa) organic (L=0)
Voltage compound (U=3.6V) (VOC=0) (CH4=0)
[58] A first-level diagnosis and early warning is performed according to the acquired multidimensional detection information and each information safety threshold.
Based on the multidimensional detection information such as electricity, heat, force, sound, gas and light, and each information safety threshold, all the battery cell signals in the energy storage system are within the safety threshold range. The information safety threshold is shown in Table 4. The system has no obvious fault and is in a normal operation state, and second-level diagnosis and early warning analysis is performed.
[59] The electricity (voltage) and heat (temperature) information acquired in real time is analyzed and normalized by using a big data analysis method (an information entropy method is used here). After recognition, calculation results of all the battery cells in the system do not exceed the safety threshold TV1, indicating that there is no potential faulty battery cell in the system and the system operates normally.
[60] Example 3
[61] A large-scale electrochemical energy storage power station in this example includes 10000 battery cells, an operating voltage range of the battery cells is 3.05 V to 3.55 V, and the maximum charge-discharge current is 140 A.
[62] Implementation steps of the hierarchical diagnosis and early warning method for faults of this example are as follows:
[63] (1) When the electrochemical energy storage system operates, the battery management system (BMS) acquires multidimensional detection information such as electricity, heat, force, sound, gas and light of the system in real time to monitor an operation state of the energy storage system. The information acquired at a certain time is shown in Table 6:
BL-5779
[64] Table 6 LU505429 eee | | Jon
Electricity | Heat Force Sound Gas Light type =
Current (H2=0)
I Carbon =70.05A) monoxide
Parameter Temperature Stress Sound (S Visible name (T=27.2°C) ope =0) qe)
Voltage compound (U=3.6V) (VOC=0) (CH4=0)
[65] (2) A first-level diagnosis and early warning is performed according to the acquired multidimensional detection information and each information safety threshold.
Based on the multidimensional detection information such as electricity, heat, force, sound, gas and light, and each information safety threshold, all the battery cell signals in the energy storage system are within the safety threshold range. The information safety threshold is shown in Table 4. The system has no obvious fault and is in a normal operation state, and second-level diagnosis and early warning analysis is performed.
[66] (3) Voltage and temperature analysis and normalization processing are performed on the electricity (voltage) information acquired in real time by using a big data analysis method (an information entropy method is used here) to obtain calculation information for recognition of a potential faulty battery cell in the system. After recognition, the calculation information corresponding to the voltage large data of the battery cell numbered 3601 is: TV1<TV3601<TV2, indicating that there is a potential fault in the battery cell numbered 3601. In this case, a level III early warning shall be given, and a detailed fault diagnosis shall be conducted at the same time.
[67] (4) After the multidimensional detection information of the faulty battery cell in the system is obtained by means of the second-level diagnosis and early warning, it is found that an actual faulty component is a voltage sensor of the battery numbered 3601 by means of a sensor fault diagnosis model based on a first-order equivalent circuit model and an extended Kalman filter algorithm in a model library. In this case, an output fault reason is a voltage sensor fault of the battery numbered 3601, and a specific 11
BL-5779 maintenance suggestion 1s to replace the voltage sensor of the battery cell. LU505429
[68] In an example, a hierarchical diagnosis and early warning system for faults of an electrochemical energy storage system provided by the present application includes:
[69] a multidimensional detection information acquisition module configured to acquire multidimensional detection information during operation of the electrochemical energy storage system in real time according to a battery management system, where the multidimensional detection information includes a current, a voltage, a temperature, stress, sound, hydrogen, carbon monoxide, a volatile organic compound, methane and visible light;
[70] a first determination module configured to determine whether the real-time multidimensional detection information exceeds an information safety threshold; give a first-level early warning signal in response to the situation that the multidimensional detection information exceeds the information safety threshold; perform a second-level diagnosis and early warning by using a big data analysis method according to the temperature or voltage acquired in real time in response to the situation that the multidimensional detection information does not exceed the information safety threshold, and recognize a potential faulty battery cell;
[71] a second determination module configured to determine whether big data calculation information corresponding to the potential faulty battery cell exceeds a safety threshold; determine that an operation state of the electrochemical energy storage system is normal in response to the situation that the big data calculation information corresponding to the potential faulty battery cell does not exceed the safety threshold; give a second-level early warning signal according to the big data calculation information corresponding to the potential faulty battery cell in response to the situation that the big data calculation information corresponding to the potential faulty battery cell exceeds the safety threshold, and give a serial number and position of the potential faulty battery cell; and
[72] a fault diagnosis result determination module configured to perform a third-level diagnosis and early warning according to the multidimensional detection information corresponding to the potential faulty battery cell, determine a fault type, and give a third-level early warning signal and a maintenance plan according to a fault diagnosis result.
[73] In order to implement the above-mentioned method so as to achieve corresponding functions and technical effects, the present application further provides a 12
BL-5779 hierarchical diagnosis and early warning apparatus for faults of an electrochemical LU505429 energy storage system, which includes at least one processor, at least one memory, and a computer program instruction stored in the memory, and when the computer program instruction is executed by the processor, the method is implemented.
[74] The memory is a computer-readable storage medium.
[75] According to particular examples provided by the present application, the technical effects disclosed in the present application are as follows:
[76] According to the hierarchical diagnosis and early warning method, system and apparatus for faults of an electrochemical energy storage system, the first-level diagnosis and early warning can be performed by acquiring the multidimensional detection information such as electricity (current and voltage), heat (temperature), force (stress), sound (sound), a gas (hydrogen, carbon monoxide, a volatile organic compound and methane) and light (visible light) during the operation process of the electrochemical energy storage system. If the multidimensional detection information does not exceed the threshold, big data analysis will be performed according to the electricity and heat information of all the battery cells in the system to recognize battery cells that may have potential faults in the system. Finally, detailed analysis is performed on the potential faulty battery cell by means of a battery model. According to the present application, the hierarchical early warning for faults of the energy storage system and the components is performed, such that a rapid alarm is given for the faults that have occurred, and potential faults of the system are recognized, thereby providing technical support for daily operation and maintenance of the energy storage system, and improving the safety of the large-scale electrochemical energy storage system.
[77] Based on the above description, the technical solution of the present application, in essence or from the view of part contributing to the related art, or part of the technical solution may be embodied in the form of a computer software product that is stored in a storage medium and includes a plurality of instructions configured to make one computer device (which may be a personal computer, a server or a network device, etc.) conduct all or part of the steps of the method in each of the examples of the present application. The foregoing storage medium includes various media which may store program codes, such as a universal serial bus flash drive, a mobile hard disk drive, a 13
BL-5779 read-only memory, a random access memory, a diskette and an optical disk. LU505429
[78] Various examples in the description are described in a progressive manner, differences between each example and other examples are mainly described, and the same and similar portions among various examples are seen from each other for reference. Since the system disclosed in the examples corresponds to the method disclosed in the examples, the description is simple, and reference can be made to the method description. 14

Claims (7)

  1. BL-5779 CLAIMS LU505429
    I. A hierarchical diagnosis and early warning method for faults of an electrochemical energy storage system, comprising: acquiring multidimensional detection information during operation of the electrochemical energy storage system in real time according to a battery management system, where the multidimensional detection information comprises a current, a voltage, a temperature, stress, sound, hydrogen, carbon monoxide, a volatile organic compound, methane and visible light; determining whether the real-time multidimensional detection information exceeds an information safety threshold; giving a first-level early warning signal in response to the situation that the multidimensional detection information exceeds the information safety threshold; performing a second-level diagnosis and early warning by using a big data analysis method according to the temperature or voltage acquired in real time in response to the situation that the multidimensional detection information does not exceed the information safety threshold, and recognizing a potential faulty battery cell; determining whether big data calculation information corresponding to the potential faulty battery cell exceeds a safety threshold; determining that an operation state of the electrochemical energy storage system is normal in response to the situation that the big data calculation information corresponding to the potential faulty battery cell does not exceed the safety threshold; giving a second-level early warning signal according to the big data calculation information corresponding to the potential faulty battery cell in response to the situation that the big data calculation information corresponding to the potential faulty battery cell exceeds the safety threshold, and giving a serial number and position of the potential faulty battery cell; and performing a third-level diagnosis and early warning according to the multidimensional detection information corresponding to the potential faulty battery cell, determining a fault type, and giving a third-level early warning signal and a maintenance plan according to a fault diagnosis result.
  2. 2. The hierarchical diagnosis and early warning method for faults of an electrochemical energy storage system according to claim 1, wherein the big data analysis method comprises an information entropy method.
  3. 3. The hierarchical diagnosis and early warning method for faults of an
    BL-5779 electrochemical energy storage system according to claim 1, wherein the giving a LU505429 second-level early warning signal according to the big data calculation information corresponding to the potential faulty battery cell in response to the situation that the big data calculation information corresponding to the potential faulty battery cell exceeds the safety threshold comprises: determining a hierarchical early warning threshold; determining a hierarchical early warning of a corresponding level according to the big data calculation information of the potential faulty battery cell and the hierarchical early warning threshold; and determining a serial number and position of the potential faulty battery cell.
  4. 4. The hierarchical diagnosis and early warning method for faults of an electrochemical energy storage system according to claim 1, wherein the performing third-level diagnosis and early warning according to multidimensional detection information corresponding to the potential faulty battery cell, determining a fault type, and giving a third-level early warning signal and a maintenance plan according to a fault diagnosis result comprises: acquiring a model library, wherein the model library comprises normal models, fault models and corresponding algorithms of various batteries and components; determining a fault type according to the multidimensional detection information and the model library corresponding to the potential faulty battery cell; and giving a maintenance plan according to the fault diagnosis result.
  5. 5. A hierarchical diagnosis and early warning system for faults of an electrochemical energy storage system, comprising: a multidimensional detection information acquisition module configured to acquire multidimensional detection information during operation of the electrochemical energy storage system in real time according to a battery management system, where the multidimensional detection information comprises a current, a voltage, a temperature, stress, sound, hydrogen, carbon monoxide, a volatile organic compound, methane and visible light; a first determination module configured to determine whether the real-time multidimensional detection information exceeds an information safety threshold; give a first-level early warning signal in response to the situation that the multidimensional detection information exceeds the information safety threshold; perform a second-level diagnosis and early warning by using a big data analysis method according to the 16
    BL-5779 temperature or voltage acquired in real time in response to the situation that the LU505429 multidimensional detection information does not exceed the information safety threshold, and recognize a potential faulty battery cell; a second determination module configured to determine whether big data calculation information corresponding to the potential faulty battery cell exceeds a safety threshold; determine that an operation state of the electrochemical energy storage system is normal in response to the situation that the big data calculation information corresponding to the potential faulty battery cell does not exceed the safety threshold; give a second-level early warning signal according to the big data calculation information corresponding to the potential faulty battery cell in response to the situation that the big data calculation information corresponding to the potential faulty battery cell exceeds the safety threshold, and give a serial number and position of the potential faulty battery cell; and a fault diagnosis result determination module configured to perform a third-level diagnosis and early warning according to the multidimensional detection information corresponding to the potential faulty battery cell, determine a fault type, and give a third-level early warning signal and a maintenance plan according to a fault diagnosis result.
  6. 6. A hierarchical diagnosis and early warning apparatus for faults of an electrochemical energy storage system, comprising: at least one processor, at least one memory, and a computer program instruction stored in the memory, wherein when the computer program instruction is executed by the processor, the method according to any one of claims 1-4 is implemented.
  7. 7. The hierarchical diagnosis and early warning apparatus for faults of an electrochemical energy storage system according to claim 6, wherein the memory is a computer-readable storage medium. 17
LU505429A 2023-06-02 2023-08-07 Hierarchical diagnosis and early warning method, system and apparatus for faults of electrochemical energy storage system LU505429B1 (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310649241.8A CN116565354A (en) 2023-06-02 2023-06-02 An electrochemical energy storage system fault classification diagnosis and early warning method, system and equipment

Publications (1)

Publication Number Publication Date
LU505429B1 true LU505429B1 (en) 2024-12-09

Family

ID=87503586

Family Applications (1)

Application Number Title Priority Date Filing Date
LU505429A LU505429B1 (en) 2023-06-02 2023-08-07 Hierarchical diagnosis and early warning method, system and apparatus for faults of electrochemical energy storage system

Country Status (3)

Country Link
CN (1) CN116565354A (en)
LU (1) LU505429B1 (en)
WO (1) WO2024244155A1 (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117013583B (en) * 2023-09-28 2024-01-16 烟台开发区德联软件有限责任公司 Fault early warning method and system for electrochemical energy storage power station
CN120354209B (en) * 2025-06-19 2025-09-12 山东大学 Lithium-ion battery multi-fault hierarchical diagnosis method and system based on large language model
CN121012849B (en) * 2025-08-11 2026-03-06 合肥安赛思半导体有限公司 A smart Internet of Things-based power storage system
CN120993220A (en) * 2025-08-28 2025-11-21 英大泰和财产保险股份有限公司 Electrochemical Energy Storage Power Station Operation Early Warning System Based on Battery Health Monitoring
CN121049778A (en) * 2025-10-31 2025-12-02 昆山正国新能源动力电池有限公司 A method, device and storage medium for intelligent fault detection of battery packs
CN121526346A (en) * 2026-01-13 2026-02-13 国网浙江省电力有限公司电力科学研究院 A risk early warning method and device for energy storage power stations

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110018425B (en) * 2019-04-10 2021-01-29 北京理工大学 Power battery fault diagnosis method and system
CN114839555A (en) * 2022-04-12 2022-08-02 安徽省国家电投和新电力技术研究有限公司 Early warning method and device for battery energy storage system, electronic equipment and storage medium
CN115327409B (en) * 2022-08-09 2025-08-01 阳光电源股份有限公司 Energy storage system and operation abnormality analysis method of energy storage battery
CN116106758B (en) * 2023-03-23 2024-01-30 华能新能源股份有限公司山西分公司 Data-driven battery fault diagnosis method and system

Also Published As

Publication number Publication date
CN116565354A (en) 2023-08-08
WO2024244155A1 (en) 2024-12-05

Similar Documents

Publication Publication Date Title
LU505429B1 (en) Hierarchical diagnosis and early warning method, system and apparatus for faults of electrochemical energy storage system
Sun et al. An online data-driven fault diagnosis and thermal runaway early warning for electric vehicle batteries
Yao et al. An intelligent fault diagnosis method for lithium-ion battery pack based on empirical mode decomposition and convolutional neural network
Fang et al. Early micro-short circuit fault diagnosis of lithium battery pack based on Pearson correlation coefficient and KPCA
CN119830144B (en) A method and system for intelligent battery fault diagnosis applicable to power grid energy storage systems
CN119561125A (en) Energy storage power station management system based on big data analysis
Hare et al. A review of faults and fault diagnosis in micro-grids electrical energy infrastructure
CN119355533A (en) A battery fault diagnosis method based on multi-dimensional feature fusion
Liu et al. A failure risk assessment method for lithium-ion batteries based on big data of after-sales vehicles
Li et al. Towards general and efficient fault diagnosis: A novel framework for multi-fault cross-domain diagnosis of lithium-ion batteries in real-world scenarios
CN118659448A (en) A method for analyzing the carrying capacity and grid connection of distributed power sources
CN116401625A (en) Fault diagnosis method and system for power-side energy storage equipment
Xia et al. Design of real-time monitoring and management system for digital power grid equipment based on intelligent analysis
CN118275816A (en) Cable system fault detection, classification and location method based on artificial neural network
CN115792636A (en) Battery pack tiny fault quantitative diagnosis method and system based on maximum voltage value
CN115079013A (en) Fault tree-based power battery system self-discharge root cause excavation method
CN113156232B (en) Excitation adjusting device network access detection system
Liu et al. Multifault Isolation and Quantification for Energy Storage Battery Module Based on Dynamic Time Warping
Chen et al. An Online Multi-Fault Diagnosis and Isolation Method for Lithium-Ion Battery System Subject to Multi-Point Fault Scenarios
CN114202134A (en) Battery replacement station fault diagnosis method, device and equipment
Xia A fault diagnosis method for electric vehicle charging circuit based on artificial intelligence recognition
Lv et al. Fault Detection and Identification of Lithium-Ion Battery Based on Improved Multiscale Fuzzy Distribution Entropy
Li et al. Fault Analysis of Intelligent Substation Secondary System Based on Improved Random Forest
Jiang et al. Adversarial Learning Based Battery Anomaly Detection in Electric Vehicle Charging Mode
CN120490848B (en) Intelligent battery state intelligent monitoring method and system based on intelligent cloud platform

Legal Events

Date Code Title Description
FG Patent granted

Effective date: 20241209