Energy storage battery safety assessment method, system, device and program

Abstract

The present invention discloses a method, system, device, and program for evaluating the safety of energy storage batteries. The method comprises: collecting the imaginary part of the dielectric constant of the energy storage battery during the charge and discharge process; modulating the imaginary part of the dielectric constant based on the surface states of a topological insulator; performing quantum decoherence detection on the surface states of the topological insulator; constructing a relaxation time distribution function based on the modulated imaginary part of the dielectric constant when a quantum decoherence signal is detected; and determining whether an abnormal peak exists in the relaxation time distribution function. When an abnormal peak exists in the relaxation time distribution function, it is determined that the energy storage battery has a safety hazard. The processing scheme disclosed herein enables nanosecond-level evaluation of energy storage batteries and magnetic pulse blocking of safety hazards.

Description

Technical Field

[0001] The present invention relates to the technical field of battery safety assessment, and in particular to a method, system, device and program for energy storage battery safety assessment. Background Art

[0002] The safety assessment of energy storage batteries is of great significance to protecting the lives and property of personnel.

[0003] Existing assessment methods include electrochemical model simulation and real-time monitoring systems. Electrochemical model simulation, based on battery equivalent circuit models (such as the Thevenin model) or electrochemical P2D models, simulates changes in voltage, temperature, and SOC (state of charge) during battery charge and discharge, predicting the risks of overcharge, overdischarge, and thermal runaway. Real-time monitoring systems collect data from voltage, current, and temperature sensors in real time, incorporating threshold alarms (e.g., triggering an alarm if the temperature exceeds 60°C).

[0004] In addition, other existing assessment methods mostly rely on single sensor data such as voltage and temperature, and cannot capture hidden faults such as mechanical deformation and internal lithium deposition; traditional machine learning models cannot adapt to parameter drift caused by battery aging; existing methods mostly alarm after an abnormality occurs and lack the ability to predict risks.

[0005] Although the above method can evaluate energy storage batteries to a certain extent, it is found that there are still several shortcomings in its structure / method in actual use, and therefore it fails to achieve the best use effect. Its shortcomings can be summarized as follows:

[0006] 1) Limitations of electrochemical models: P2D models require significant computational resources and are difficult to embed into real-time control systems.

[0007] 2) Lag in real-time monitoring: The temperature sensor responds slowly and has a high false alarm rate (e.g., normal lithium deposition at low temperatures is mistakenly identified as a fault);

[0008] 3) Unable to detect intrinsic defects such as lattice distortion of electrode materials and cracking of solid electrolyte interface (SEI);

[0009] 4) Lack of risk prediction ability.

[0010] Therefore, the existing energy storage battery evaluation methods mentioned above still have inconveniences and defects in use and are in urgent need of further improvement. How to create a new energy storage battery safety evaluation method has become an urgent goal for improvement in the current industry. Summary of the Invention

[0011] In view of this, an embodiment of the present disclosure provides a method for evaluating the safety of an energy storage battery, which at least partially solves the problems existing in the prior art.

[0012] In a first aspect, an embodiment of the present disclosure provides a method for evaluating the safety of an energy storage battery, the method comprising the following steps:

[0013] Collect the imaginary part of the dielectric constant of the energy storage battery during the charging and discharging process;

[0014] Modulation of the imaginary part of the dielectric constant based on the surface states of topological insulators;

[0015] Quantum decoherence detection of topological insulator surface states;

[0016] When a quantum decoherence signal is detected, a relaxation time distribution function is constructed based on the imaginary part of the modulated dielectric constant;

[0017] Determine whether there is an abnormal peak in the relaxation time distribution function; wherein, when there is an abnormal peak in the relaxation time distribution function, it is determined that there is a safety hazard in the energy storage battery.

[0018] According to a specific implementation of an embodiment of the present disclosure, the modulation of the imaginary part of the dielectric constant based on the topological insulator surface state includes:

[0019] ;

[0020] in, is the imaginary part of the modulated dielectric constant; is the surface state carrier concentration; is the surface state conductivity; is the electron charge; is Planck's constant; is the dielectric constant of vacuum; is the angular frequency of the alternating electric field.

[0021] According to a specific implementation of the embodiment of the present disclosure, the quantum decoherence detection of the topological insulator surface state includes:

[0022] Obtain fractional derivatives of surface state conductivity based on Caputo;

[0023] Whether quantum decoherence occurs is determined based on the fractional derivative of the surface state conductivity.

[0024] According to a specific implementation of an embodiment of the present disclosure, obtaining the fractional derivative of surface state conductivity based on Caputo includes:

[0025] ;

[0026] in, is the fractional differential order, ∈(0,1); is the gamma function; is the time variable; is the surface state conductivity; is the derivative of the conductivity function; is the historical time variable;

[0027] The determining whether quantum decoherence occurs based on the fractional derivative of the surface state conductivity includes:

[0028] When the fractional derivative of the surface state conductivity is greater than three times the standard deviation of the conductivity baseline noise and the surface state conductivity decreases by more than 30%, quantum decoherence is determined to have occurred.

[0029] According to a specific implementation of the embodiment of the present disclosure, the method further includes: when the surface state conductivity decreases by 30%, increasing the frequency of collecting the imaginary part of the dielectric constant of the energy storage battery during the charging and discharging process by 10 times.

[0030] According to a specific implementation of the embodiment of the present disclosure, constructing a relaxation time distribution function based on the modulated imaginary part of the dielectric constant includes:

[0031] ;

[0032] in, is the relaxation time distribution function, satisfying ; is the imaginary part of the modulated dielectric constant; is the angular frequency of the alternating electric field; is the relaxation time; is pi.

[0033] According to a specific implementation of the embodiment of the present disclosure, the method further includes:

[0034] When there is a safety hazard in the energy storage battery, the location of the abnormal peak is obtained and the abnormality is blocked by magnetic pulses;

[0035] The magnetic pulse intensity required to block the anomaly is calculated based on the following formula:

[0036] ;

[0037] in, To block abnormal magnetic pulse intensity; is the critical relaxation time of the relaxation time distribution function; is the abnormal peak position of the relaxation time distribution function; is the reference magnetic field strength, and when hour, ;

[0038] In a second aspect, an embodiment of the present disclosure provides a system for evaluating the safety of an energy storage battery, the system comprising:

[0039] A collection module configured to collect the imaginary part of the dielectric constant of the energy storage battery during the charging and discharging process;

[0040] a modulation module configured to modulate an imaginary part of a dielectric constant based on a surface state of a topological insulator;

[0041] a detection module configured to perform quantum decoherence detection on a surface state of the topological insulator;

[0042] a function building module configured to build a relaxation time distribution function based on the modulated imaginary part of the dielectric constant when a quantum decoherence signal is detected;

[0043] The evaluation module is configured to determine whether there is an abnormal peak in the relaxation time distribution function; wherein, when there is an abnormal peak in the relaxation time distribution function, it is determined that there is a safety hazard in the energy storage battery.

[0044] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0045] at least one processor; and,

[0046] a memory communicatively connected to the at least one processor; wherein,

[0047] The memory stores instructions that can be executed by the at least one processor. When the instructions are executed by the at least one processor, the at least one processor performs the energy storage battery safety assessment method described in any one of the first aspect or any one of the implementations of the first aspect.

[0048] In a fourth aspect, an embodiment of the present disclosure further provides a non-transitory computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by at least one processor, the at least one processor executes the energy storage battery safety assessment method in the aforementioned first aspect or any implementation of the first aspect.

[0049] In a fifth aspect, an embodiment of the present disclosure further provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the energy storage battery safety assessment method in the aforementioned first aspect or any implementation of the first aspect.

[0050] The energy storage battery safety assessment method in the disclosed embodiment utilizes the ultrafast response characteristics of the boundary states of topological insulators and the analysis of dielectric spectrum relaxation peaks to achieve nanosecond-level quantitative diagnosis of SEI film microcracks and dendrites. Anomalies are blocked through precise high-magnetic field non-contact magnetic pulses, and the blocking efficiency is increased by 6 times compared to traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic flow chart of a method for evaluating the safety of an energy storage battery provided in an embodiment of the present disclosure;

[0052] Figure 2 A flow chart of a method for evaluating the safety of an energy storage battery provided in an embodiment of the present disclosure;

[0053] Figure 3 A schematic diagram of the structure of an energy storage battery safety assessment system provided in an embodiment of the present disclosure;

[0054] Figure 4 A schematic diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0055] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0056] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0057] It should be noted that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or functionality described herein is illustrative only. Based on this disclosure, those skilled in the art will appreciate that one aspect described herein may be implemented independently of any other aspect, and that two or more of these aspects may be combined in various ways. In addition, other structures and / or functionality other than one or more of the aspects described herein may be used to implement this apparatus and / or practice this method.

[0058] Additionally, in the following description, specific details are provided to provide a thorough understanding of the examples. However, one skilled in the art will appreciate that the aspects described can be practiced without these specific details.

[0059] An embodiment of the present invention provides a method for assessing the safety of energy storage batteries. By utilizing the ultrafast response characteristics of topological insulator boundary states and dielectric spectrum relaxation peak analysis, it achieves nanosecond-level quantitative diagnosis of SEI (Solid Electrolyte Interphase) film microcracks and dendrites, and performs magnetic pulse blocking of safety hazards, with a blocking efficiency six times higher than traditional methods.

[0060] Figure 1 A schematic diagram of the process of the energy storage battery safety assessment method provided in an embodiment of the present disclosure.

[0061] Figure 2 For Figure 1 Corresponding flow chart of the energy storage battery safety assessment method.

[0062] like Figure 1 As shown, at step S110, the imaginary part of the dielectric constant of the energy storage battery during the charging and discharging process is collected.

[0063] More specifically, during the charge and discharge process of the energy storage battery, 10 -3 ~10 2 Hz alternating electric field, measure the imaginary part of the dielectric constant.

[0064] The imaginary part of the dielectric constant (loss factor) can reflect the energy dissipation characteristics of the material in an alternating electric field. By scanning the angular frequency of the alternating electric field, the curve of the imaginary part of the dielectric constant can be obtained.

[0065] More specifically, the process proceeds to step S120.

[0066] At step S120 , the imaginary part of the dielectric constant is modulated based on the surface states of the topological insulator.

[0067] More specifically, a bismuth selenide (Bi2Se3) topological insulator film (thickness 50~200nm) was deposited on the surface of the electrode current collector, and the surface state electron mobility mutation was detected in real time.

[0068] In an embodiment of the present invention, the modulation of the imaginary part of the dielectric constant based on the topological insulator surface state includes:

[0069] ;

[0070] in, is the imaginary part of the modulated dielectric constant; is the surface state carrier concentration; is the surface state conductivity; is the electron charge; is Planck's constant; is the dielectric constant of vacuum; is the angular frequency of the alternating electric field, which is used to control the speed of the electric field change (10 -3 ~10 2 Hz correspondence =6.28×10 -3 ~6.28×10 2 rad / s).

[0071] Next, go to step S130.

[0072] In step S130 , quantum decoherence detection is performed on the surface state of the topological insulator.

[0073] More specifically, when dendrite growth occurs or the SEI film ruptures inside the energy storage battery, the topological surface state will undergo quantum decoherence, which is manifested as: a decrease in surface state conductivity (enhanced surface state electron scattering) and an increase in the spatial distribution inhomogeneity of the surface state carrier concentration.

[0074] When localized overheating (greater than 120°C) occurs within an energy storage battery, the topologically protected surface states of the Bi2Se3 film decohere due to lattice expansion, causing the surface state conductivity to drop sharply within 0.1ms. This decoherence indicates that the internal battery temperature exceeds 120°C. Fractional-order differentials can sensitively capture these non-stationary mutations, improving the signal-to-noise ratio by 4.2 times compared to conventional differentials.

[0075] In an embodiment of the present invention, the quantum decoherence detection of the surface state of the topological insulator includes: obtaining the fractional derivative of the surface state conductivity based on Caputo; and judging whether quantum decoherence occurs based on the fractional derivative of the surface state conductivity.

[0076] In an embodiment of the present invention, obtaining the fractional derivative of the surface state conductivity based on Caputo includes:

[0077] ;

[0078] in, is the fractional differential order, ∈(0,1); is the gamma function; is the time variable; is the surface state conductivity; is the derivative of the conductivity function; is the historical time variable;

[0079] The determining whether quantum decoherence occurs based on the fractional derivative of the surface state conductivity includes: determining that quantum decoherence occurs when the fractional derivative of the surface state conductivity is greater than three times the standard deviation of the conductivity baseline noise and the surface state conductivity decreases by more than 30%.

[0080] More specifically, the traditional integer-order derivatives ( =1) reflects only the instantaneous rate of change at the current moment and ignores historical information.

[0081] The fractional derivative of the present invention ( <1) Introducing the time integration kernel , giving the evaluation system a memory effect. For example, when =0.7, the system retains about 30% of the memory weight of the conductivity change in the past 1 second; when dendrite growth causes a sudden change in conductivity, the fractional-order derivative can more sensitively capture its non-stationarity.

[0082] In an embodiment of the present invention, the method further includes: when the surface state conductivity decreases by 30%, increasing the frequency of collecting the imaginary part of the dielectric constant of the energy storage battery during the charging and discharging process by 10 times, and pre-starting the magnetic pulse system.

[0083] Next, go to step S140.

[0084] At step S140 , when a quantum decoherence signal is detected, a relaxation time distribution function is constructed based on the modulated imaginary part of the dielectric constant.

[0085] In an embodiment of the present invention, constructing a relaxation time distribution function based on the modulated imaginary part of the dielectric constant includes:

[0086] ;

[0087] in, is the relaxation time distribution function, satisfying , characterizing ion dynamics processes at different time scales; is the imaginary part of the modulated dielectric constant; is the angular frequency of the alternating electric field; is the relaxation time, ; is pi.

[0088] More specifically, when the relaxation time distribution function is in the normal state, The main peak is located at =10 -2 ~10 -1 s (corresponding to the conventional lithium ion insertion and extraction); when <10 -3s (indicating rapid dendrite growth), and the relaxation time distribution function is in an abnormal state.

[0089] Further, When the half-height width of the main peak increases (greater than 0.8 eV), it indicates that the dispersion of the energy barrier for lithium ion migration increases, and it is determined that the SEI film has mechanically failed; when the peak position shifts to the left ( <10 -2 s), indicating that the proportion of low-energy barrier paths increases, which indicates the risk of dendrite growth; that is, microcracks occur at the electrode / electrolyte interface.

[0090] Next, go to step S150.

[0091] In step S150, it is determined whether there is an abnormal peak in the relaxation time distribution function; wherein, when there is an abnormal peak in the relaxation time distribution function, it is determined that there is a safety hazard in the energy storage battery.

[0092] In an embodiment of the present invention, the method further comprises: when there is a safety hazard in the energy storage battery, obtaining the position of the abnormal peak and blocking the abnormality by means of a magnetic pulse;

[0093] The magnetic pulse intensity required to block the anomaly is calculated based on the following formula:

[0094] ;

[0095] in, To block abnormal magnetic pulse intensity; is the critical relaxation time of the relaxation time distribution function; is the abnormal peak position of the relaxation time distribution function; is the reference magnetic field strength, and when = hour, , at this time the magnetic field can just suppress the critical growth of dendrites. is when the abnormal relaxation time Equal to the critical relaxation time The required magnetic pulse intensity is . It represents the minimum magnetic field intensity required in the critical state (i.e., the dendrite growth rate reaches the dangerous threshold) and is the design basis for the blocking system. < (Dendrite growth is accelerated), the magnetic field needs to be increased to To enhance the blocking effect.

[0096] Further, when =0.1ms (at the beginning of dendrite formation), =5T; when =0.01ms (dendrite penetrates the diaphragm), Needs to be upgraded to 15T (superconducting coil intervention).

[0097] More specifically, the location of abnormal peaks can be obtained through statistical tests, time-frequency domain joint analysis, or machine learning models. The following uses statistical tests as an example to illustrate:

[0098] Statistical test method:

[0099] Point-by-point inspection Whether it meets the normal distribution assumption and identifies outliers; if a Department If the peak exceeds the threshold, it is marked as a potential abnormal peak.

[0100] Perform variance analysis by moving the window, using the sliding window to calculate the local variance. When , it is determined to be an abnormal area, where is the local variance, is the global variance.

[0101] In an embodiment of the present invention, when the fractional derivative of the surface state conductivity is greater than five times the standard deviation of the conductivity baseline noise, the anomaly is blocked by a magnetic pulse.

[0102] Figure 3 The energy storage battery safety assessment system 300 provided by the present invention is shown, including an acquisition module 310 , a modulation module 320 , a detection module 330 , a function construction module 340 and an assessment module 350 .

[0103] The acquisition module 310 is used to collect the imaginary part of the dielectric constant of the energy storage battery during the charging and discharging process;

[0104] The modulation module 320 is used to modulate the imaginary part of the dielectric constant based on the surface state of the topological insulator;

[0105] The detection module 330 is used to perform quantum decoherence detection on the surface state of the topological insulator;

[0106] The function construction module 340 is used to construct a relaxation time distribution function based on the modulated imaginary part of the dielectric constant when a quantum decoherence signal is detected;

[0107] The evaluation module 350 is used to determine whether there is an abnormal peak in the relaxation time distribution function; wherein, when there is an abnormal peak in the relaxation time distribution function, it is determined that there is a safety hazard in the energy storage battery.

[0108] See also Figure 4 The present disclosure further provides an electronic device 40, which includes:

[0109] at least one processor; and,

[0110] a memory communicatively connected to the at least one processor; wherein,

[0111] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the energy storage battery safety assessment method in the aforementioned method embodiment.

[0112] An embodiment of the present disclosure further provides a non-transitory computer-readable storage medium, which stores computer instructions for causing the computer to execute the energy storage battery safety assessment method in the aforementioned method embodiment.

[0113] An embodiment of the present disclosure also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the energy storage battery safety assessment method in the aforementioned method embodiment.

[0114] Reference below Figure 4 , which shows a schematic structural diagram of an electronic device 40 suitable for implementing an embodiment of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0115] like Figure 4 As shown, electronic device 40 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 402 or programs loaded from a storage device 408 into a random access memory (RAM) 403. RAM 403 also stores various programs and data required for the operation of electronic device 40. Processing device 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to bus 404.

[0116] Typically, the following devices may be connected to the I / O interface 405: an input device 406 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; a storage device 408 including, for example, a magnetic tape, hard disk, etc.; and a communication device 409. The communication device 409 may allow the electronic device 40 to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows the electronic device 40 with various devices, it should be understood that not all of the devices shown are required to be implemented or present. More or fewer devices may alternatively be implemented or present.

[0117] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication device 409, or installed from the storage device 408, or installed from the ROM 402. When the computer program is executed by the processing device 401, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0118] It should be noted that the computer-readable medium described above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable storage media may include, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such a propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wire, optical cable, RF (radio frequency), or any suitable combination thereof.

[0119] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0120] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains at least two Internet Protocol addresses; sends a node evaluation request including the at least two Internet Protocol addresses to a node evaluation device, wherein the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; wherein the obtained Internet Protocol address indicates an edge node in a content distribution network.

[0121] Alternatively, the computer-readable medium carries one or more programs, which, when executed by the electronic device, causes the electronic device to: receive a node evaluation request including at least two Internet Protocol addresses; select an Internet Protocol address from the at least two Internet Protocol addresses; and return the selected Internet Protocol address; wherein the received Internet Protocol address indicates an edge node in a content distribution network.

[0122] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0123] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0124] The units involved in the embodiments described in this disclosure may be implemented in software or hardware. In some cases, the name of a unit does not limit the unit itself. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses."

[0125] It should be understood that various parts of the present disclosure can be implemented in hardware, software, firmware, or a combination thereof.

[0126] The above description is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with this technical field within the technical scope disclosed in this disclosure should be covered by the protection scope of the present disclosure.

Claims

1. A method for evaluating the safety of an energy storage battery, characterized in that: The method comprises the following steps: Collect the imaginary part of the dielectric constant of the energy storage battery during the charging and discharging process; Modulation of the imaginary part of the dielectric constant based on the surface states of topological insulators; Quantum decoherence detection of the topological insulator surface state is performed, and a topological insulator film is deposited on the surface of the electrode current collector; When a quantum decoherence signal is detected, a relaxation time distribution function is constructed based on the imaginary part of the modulated dielectric constant; Determine whether there is an abnormal peak in the relaxation time distribution function; wherein, when there is an abnormal peak in the relaxation time distribution function, it is determined that there is a safety hazard in the energy storage battery.

2. The energy storage battery safety assessment method according to claim 1, characterized in that: The method of modulating the imaginary part of the dielectric constant based on the surface state of the topological insulator includes: Where Δε″(ω) is the imaginary part of the modulated dielectric constant; n s is the surface state carrier concentration; σ is the surface state conductivity; e is the electron charge; h is the Planck constant; ε0 is the vacuum dielectric constant; ω is the angular frequency of the alternating electric field.

3. The energy storage battery safety assessment method according to claim 1, characterized in that: The quantum decoherence detection of the topological insulator surface state comprises: Obtain fractional derivatives of surface state conductivity based on Caputo; Whether quantum decoherence occurs is determined based on the fractional derivative of the surface state conductivity.

4. The energy storage battery safety assessment method according to claim 3, characterized in that: Caputo-based fractional derivatives of surface state conductivity are obtained, including: Where α is the fractional differential order, α∈(0,1); Γ is the gamma function; t is the time variable; σ is the surface state conductivity; σ′(u) is the derivative of the conductivity function; u is the historical time variable; The determining whether quantum decoherence occurs based on the fractional derivative of the surface state conductivity includes: When the fractional derivative of the surface state conductivity is greater than three times the standard deviation of the conductivity baseline noise and the surface state conductivity decreases by more than 30%, it is determined that quantum decoherence has occurred.

5. The energy storage battery safety assessment method according to claim 4, characterized in that: The method further comprises: when the surface state conductivity is reduced by 30%, increasing the frequency of collecting the imaginary part of the dielectric constant of the energy storage battery during the charging and discharging process by 10 times.

6. The energy storage battery safety assessment method according to claim 1, characterized in that: The step of constructing a relaxation time distribution function based on the modulated imaginary part of the dielectric constant comprises: Wherein, g(τ) is the relaxation time distribution function, satisfying ∫g(τ)dτ=1; Δε″(ω) is the imaginary part of the modulated dielectric constant; ω is the angular frequency of the alternating electric field; τ is the relaxation time; and π is pi.

7. The energy storage battery safety assessment method according to claim 1, characterized in that: The method further comprises: When there is a safety hazard in the energy storage battery, the location of the abnormal peak is obtained and the abnormality is blocked by magnetic pulses; The magnetic pulse intensity required to block the anomaly is calculated based on the following formula: Where B is the magnetic pulse intensity that blocks the anomaly; τ c is the critical relaxation time of the relaxation time distribution function; τ a is the abnormal peak position of the relaxation time distribution function; B0 is the reference magnetic field intensity, and when τ a =τ c When B=B0.

8. A storage battery safety assessment system, characterized in that: The system comprises: A collection module configured to collect the imaginary part of the dielectric constant of the energy storage battery during the charging and discharging process; a modulation module configured to modulate an imaginary part of a dielectric constant based on a surface state of a topological insulator; A detection module is configured to perform quantum decoherence detection on a topological insulator surface state, wherein a topological insulator thin film is deposited on a surface of an electrode current collector; a function building module configured to build a relaxation time distribution function based on the modulated imaginary part of the dielectric constant when a quantum decoherence signal is detected; The evaluation module is configured to determine whether there is an abnormal peak in the relaxation time distribution function; wherein, when there is an abnormal peak in the relaxation time distribution function, it is determined that there is a safety hazard in the energy storage battery.

9. An electronic device, characterized in that: The electronic device includes: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor. When the instructions are executed by the at least one processor, the at least one processor is caused to execute the energy storage battery safety assessment method according to any one of claims 1 to 7.

10. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer is caused to execute the energy storage battery safety assessment method according to any one of claims 1 to 7.

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