Lithium ion battery early short circuit fault diagnosis method and system

Abstract

The method comprises the following steps: calculating diffusion coefficients according to an impedance spectrum of a low-frequency region of a lithium ion battery to be diagnosed, carrying out linear fitting on a plurality of diffusion coefficients to obtain a linear slope, and determining whether the lithium ion battery to be diagnosed has a short-circuit fault or not according to the positive and negative linear slopes. According to the method, the problem that the fault is difficult to recognize due to weak changes of electrical and temperature signals in the early stage of the battery short-circuit fault can be effectively solved, and the requirements for accuracy, high efficiency and real-time performance in actual application scenes are met.

Description

Technical Field

[0001] The present invention belongs to the technical field related to battery fault diagnosis, and in particular relates to a method and system for diagnosing early short-circuit faults of lithium-ion batteries. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Internal short circuit is a common cause of thermal runaway of lithium-ion batteries. In the early stage of internal short circuit fault, the short circuit degree is small and the battery parameters are almost unchanged, which is difficult to be detected by general fault diagnosis algorithms.

[0004] Current methods for early short-circuit failure of lithium-ion batteries mainly include model-based methods and data-driven methods.

[0005] Model-based methods usually obtain a residual signal by comparing the measurable signal with the signal generated by the model, and then compare the residual with a predetermined value to determine the diagnosis result. Due to the deep understanding of the dynamics of the battery system, the model-based method can not only detect faults, but also locate the faults and estimate their intensity.

[0006] With the development of machine learning and artificial intelligence in recent years, data-driven methods have received extensive attention in lithium-ion battery fault diagnosis. The data-driven method can be regarded as a "black box" that directly analyzes and processes operating data to detect faults without relying on accurate models and expert experience. It overcomes the complex problems of modeling and parameter identification, does not consider complex fault mechanisms and system structures, especially faults such as thermal runaway and accelerated battery aging that are affected by various unclear and coupled factors, and thus simplifies the fault detection process.

[0007] However, model-based methods require high model accuracy and are affected by interference and noise in practical applications, while some data-driven methods are computationally intensive. In addition, both methods rely on laboratory-generated data and therefore require high-quality and accurate data. For practical scenarios, such as electric vehicles, their complex operating conditions and poor data quality, such as low sampling accuracy and frequency, make existing fault diagnosis methods difficult to apply or ineffective in practice. Summary of the invention

[0008] In order to overcome the deficiencies of the above-mentioned prior art, the present invention provides a method and system for diagnosing early short-circuit faults of lithium-ion batteries, which can effectively deal with the problem that faults are difficult to identify in the early stage of battery short-circuit faults due to slight changes in electrical and temperature signals, and meet the requirements of accuracy, efficiency and real-time performance in practical applications.

[0009] In order to achieve the above object, the present invention adopts the following technical solution:

[0010] In a first aspect, the present invention provides a method for diagnosing an early short circuit fault of a lithium ion battery, comprising:

[0011] Calculate the impedance spectrum of the low-frequency region of the lithium-ion battery to be diagnosed in batches;

[0012] According to the impedance spectrum of the lithium-ion battery to be diagnosed in the low-frequency region, the diffusion coefficient corresponding to the lithium-ion battery to be diagnosed is calculated respectively;

[0013] Performing curve fitting on a plurality of continuous diffusion coefficients of the lithium-ion battery to be diagnosed to obtain a corresponding slope;

[0014] According to the positive or negative value of the slope obtained by fitting, it is determined whether the lithium-ion battery to be diagnosed has a short circuit fault.

[0015] In a second aspect, the present invention provides a lithium-ion battery early short circuit fault diagnosis system, comprising:

[0016] The first calculation module is used to calculate the impedance spectrum of the lithium-ion battery to be diagnosed in the low-frequency region in batches;

[0017] The second calculation module is used to calculate the diffusion coefficients corresponding to the lithium-ion batteries to be diagnosed according to the impedance spectrum of the lithium-ion batteries to be diagnosed in the low-frequency region;

[0018] A linear fitting module is used to perform curve fitting on a plurality of continuous diffusion coefficients of the lithium-ion battery to be diagnosed to obtain a corresponding slope;

[0019] The fault diagnosis module is used to determine whether a short circuit fault occurs in the lithium-ion battery to be diagnosed according to the positive or negative slope obtained by fitting.

[0020] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in the first aspect is performed.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0022] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements the method described in the first aspect.

[0023] One or more of the above technical solutions have the following beneficial effects:

[0024] In the present invention, the diffusion coefficient is calculated based on the impedance spectrum of the lithium-ion battery to be diagnosed in the low-frequency region, and multiple diffusion coefficients are linearly fitted to obtain the slope of the straight line. According to the positive or negative slope of the straight line, the result of whether the lithium-ion battery to be diagnosed has a short circuit fault is determined. The method of the present invention can effectively deal with the problem that the fault is difficult to identify in the early stage of the battery short circuit fault due to the weak changes in electrical and temperature signals, and meets the requirements of accuracy, efficiency and real-time in actual application scenarios.

[0025] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0027] Figure 1 This is a flow chart of a method for diagnosing an early short circuit fault of a lithium-ion battery in Embodiment 1 of the present invention;

[0028] Figure 2 It is a structural diagram of an impedance equivalent model in Embodiment 1 of the present invention;

[0029] Figure 3 This is an example diagram of the impedance spectrum results of a lithium-ion battery in Example 1 of the present invention;

[0030] Figure 4 This is an example diagram of the fitting results of the diffusion coefficient of a lithium-ion battery in Example 1 of the present invention. DETAILED DESCRIPTION

[0031] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0032] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0033] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0034] Embodiment 1

[0035] This embodiment discloses a method for diagnosing an early short circuit fault of a lithium-ion battery, comprising:

[0036] Step 1: Calculate the impedance spectrum of the lithium-ion battery to be diagnosed in the low-frequency region in batches;

[0037] Step 2: Calculate the diffusion coefficients corresponding to the lithium-ion batteries to be diagnosed according to the impedance spectrum of the lithium-ion batteries to be diagnosed in the low-frequency region;

[0038] Step 3: linearly fit multiple diffusion coefficients of the lithium-ion battery to be diagnosed to obtain the corresponding straight line slope;

[0039] Step 4: Determine whether a short circuit fault occurs in the lithium-ion battery to be diagnosed based on the positive or negative sign of the obtained straight line slope.

[0040] In this embodiment, the diffusion coefficient is calculated based on the impedance spectrum of the lithium-ion battery to be diagnosed in the low-frequency region, a plurality of diffusion coefficients are linearly fitted to obtain a straight line slope, and whether a short circuit fault occurs in the lithium-ion battery to be diagnosed is determined based on the positive or negative sign of the straight line slope.

[0041] Early short-circuit faults have the characteristics of slow development, weak signal changes, and are easily submerged in the natural aging process and noise of the battery. The method of threshold judgment based only on a single diffusion coefficient measurement result is prone to false alarms and missed reports due to inaccurate threshold setting. This embodiment uses the combined results of multiple diffusion coefficients to judge early short-circuit faults, taking into account the characteristics of the large time scale of early short-circuit faults, which can effectively reduce the false alarm rate and missed report rate. And compared with the method of using deep learning for battery fault diagnosis, this embodiment has a simple calculation process, does not require a large amount of high-quality data and computing resources for training, has a small amount of calculation, has low hardware performance requirements, is easy to implement online, and meets the requirements of accuracy, efficiency, and real-time performance in actual application scenarios.

[0042] Combine the following Figure 1 A lithium-ion battery early short-circuit fault diagnosis method proposed in this embodiment is described in detail.

[0043] Step 1: Calculate the impedance spectrum of the lithium-ion battery to be diagnosed in the low-frequency region in batches.

[0044] Determine a multi-sinusoidal excitation signal for the impedance spectrum of the low-frequency region of the lithium-ion battery to be diagnosed. The specific form of the multi-sinusoidal excitation signal is:

[0045]

[0046] Where N is the number of sinusoidal signals with different frequencies, A is the same amplitude for each sinusoidal signal component, and f k , Φ k They are different frequencies and phase angles for each sinusoidal signal component.

[0047] The frequency of each sinusoidal signal component is determined according to formula (2):

[0048] Φ k =Φ1-πk 2 / N,k∈2,...,N (2)

[0049] Where Φ1 is the phase angle of the first sinusoidal signal component.

[0050] Exemplarily, this embodiment adopts voltage signal excitation, and the signal form is:

[0051]

[0052] Each sinusoidal voltage signal component uses a quasi-logarithmic frequency interval. When four frequency points are used in each decade, the frequencies are f k =[0.1Hz, 0.2Hz, 0.4Hz, 1Hz, 2Hz, 4Hz, 10Hz, 20Hz, 40Hz, 100Hz, 200Hz, 400Hz, 1000Hz], N=13.

[0053] The common voltage excitation signal amplitude is 5-10 mV. In this embodiment, the voltage excitation signal amplitude is selected as A=10 mV.

[0054] A multi-sinusoidal excitation signal is applied to the lithium-ion battery to be diagnosed, and the applied multi-sinusoidal excitation signal and the response signal of the lithium-ion battery to be diagnosed are processed by Fourier fast transform to calculate the impedance of the lithium-ion battery to be diagnosed in the low-frequency region. The impedance equivalent model structure is as follows: Figure 2 The impedance spectrum results are shown in Figure 3 shown.

[0055] Specific:

[0056]

[0057] Where V is voltage, I is current, j is imaginary unit, ω is angular velocity, ω=2πf, R0 is ohmic internal resistance, R1 and C1 are polarization internal resistance and polarization capacitance respectively, σ W is the Warburg element coefficient.

[0058] Step 2: Calculate the diffusion coefficients corresponding to the lithium-ion batteries to be diagnosed based on the impedance spectrum of the lithium-ion batteries to be diagnosed in the low-frequency region.

[0059] By fitting the Warburg element coefficient σ in step 1 W , calculate the diffusion coefficient D of lithium-ion batteries, specifically:

[0060]

[0061] Where R is the molar gas constant, R = 8.314 J / (mol·K), T is temperature, F is Faraday constant, F = 96485.332C / mol, n is the number of transferred electrons, and C0 is the lithium ion concentration, all of which are determined by the properties of the electrode material itself.

[0062] Step 3: Linearly fit multiple diffusion coefficients of the lithium-ion battery to be diagnosed to obtain the corresponding straight line slope.

[0063] Specifically, the diffusion coefficient of the lithium-ion battery to be diagnosed under the same SOC under the charging state or the discharging state is screened, and the diffusion coefficient obtained by the screening is linearly fitted to calculate the slope b of the fitting line y=mx+b. The calculation method is:

[0064]

[0065] Where N is the number of points, y i is the diffusion coefficient value, i=1,…,N.

[0066] For example, this embodiment selects five consecutive diffusion coefficient historical data when SOC=50% during battery charging, performs linear fitting on these five historical data, and calculates the slope b of the fitting line y=mx+b. The result is as follows: Figure 4 shown.

[0067] Step 4: Determine whether a short circuit fault occurs in the lithium-ion battery to be diagnosed based on the positive or negative sign of the obtained straight line slope.

[0068] Fault diagnosis is performed based on the changing trend of the diffusion coefficient within the time window. When the diffusion coefficient shows an upward trend during several consecutive charging or discharging processes, that is, the straight line slope of the fitting curve is greater than 0, it is considered that a short circuit fault has occurred. When the fitting slope is less than 0, it is determined that the lithium-ion battery to be diagnosed has not suffered a short circuit fault.

[0069] Embodiment 2

[0070] The purpose of this embodiment is to provide a lithium-ion battery early short circuit fault diagnosis system, comprising:

[0071] The first calculation module is used to calculate the impedance spectrum of the lithium-ion battery to be diagnosed in the low-frequency region in batches;

[0072] The second calculation module is used to calculate the diffusion coefficients corresponding to the lithium-ion batteries to be diagnosed according to the impedance spectrum of the lithium-ion batteries to be diagnosed in the low-frequency region;

[0073] A linear fitting module is used to perform curve fitting on a plurality of continuous diffusion coefficients of the lithium-ion battery to be diagnosed to obtain a corresponding slope;

[0074] The fault diagnosis module is used to determine whether a short circuit fault occurs in the lithium-ion battery to be diagnosed according to the positive or negative slope obtained by fitting.

[0075] In further embodiments, there is also provided:

[0076] An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in Embodiment 1 is performed. For the sake of brevity, no further description is given here.

[0077] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0078] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0079] A computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the method described in embodiment 1 is completed.

[0080] The method in the first embodiment can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0081] A computer program product includes a computer program, and when the computer program is executed by a processor, the method described in the first embodiment is implemented.

[0082] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer executable instructions, such as instructions included in a program module, which are executed in a device on a real or virtual processor of the target to perform the process / method as described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or divided between program modules as needed. Machine executable instructions for program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0083] The computer program code for implementing the method of the present invention can be written in one or more programming languages. These computer program codes can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the computer or other programmable data processing device, causes the function / operation specified in the flow chart and / or block diagram to be implemented. The program code can be executed completely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer or completely on a remote computer or server.

[0084] In the context of the present invention, computer program codes or related data may be carried by any appropriate carrier to enable a device, apparatus or processor to perform the various processes and operations described above. Examples of carriers include signals, computer readable media, etc. Examples of signals may include electrical, optical, radio, acoustic or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0085] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0086] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for diagnosing an early short circuit fault of a lithium-ion battery, characterized in that: include: Calculate the impedance spectrum of the low-frequency region of the lithium-ion battery to be diagnosed in batches; According to the impedance spectrum of the lithium-ion battery to be diagnosed in the low-frequency region, the diffusion coefficient corresponding to the lithium-ion battery to be diagnosed is calculated respectively; Performing curve fitting on a plurality of continuous diffusion coefficients of the lithium-ion battery to be diagnosed to obtain a corresponding slope; According to the positive or negative value of the slope obtained by fitting, it is determined whether the lithium-ion battery to be diagnosed has a short circuit fault.

2. A method for diagnosing an early short circuit fault of a lithium-ion battery as claimed in claim 1, characterized in that: The impedance spectrum of the low-frequency region of the lithium-ion battery to be diagnosed is calculated in batches, specifically: An excitation signal is applied to the lithium-ion battery to be diagnosed, and the applied excitation signal and the response signal of the lithium-ion battery to be diagnosed are processed by Fourier fast transform to obtain an impedance spectrum in a low-frequency region of the lithium-ion battery to be diagnosed.

3. A lithium-ion battery early short circuit fault diagnosis method as claimed in claim 2, characterized in that: A multi-sinusoidal excitation signal is applied to the lithium-ion battery to be diagnosed, so as to determine the impedance spectrum of the lithium-ion battery to be diagnosed in the low-frequency region.

4. A lithium-ion battery early short circuit fault diagnosis method as claimed in claim 1, characterized in that: According to the impedance spectrum of the lithium-ion battery to be diagnosed in the low-frequency region, the diffusion coefficient corresponding to the lithium-ion battery to be diagnosed is calculated respectively, specifically: According to the impedance spectrum of the lithium-ion battery to be diagnosed in the low-frequency region, the Warburg element coefficient is obtained by fitting; The diffusion coefficient of the lithium-ion battery to be diagnosed is calculated based on the Warburg element coefficient.

5. A lithium-ion battery early short circuit fault diagnosis method as claimed in claim 1, characterized in that: The multiple continuous diffusion coefficients of the lithium-ion battery to be diagnosed are curve-fitted to obtain the corresponding slope, which is specifically: Multiple diffusion coefficients of the lithium-ion battery to be diagnosed under the same state and the same SOC are screened for curve fitting to obtain the corresponding slope.

6. A lithium ion battery early short circuit fault diagnosis method as claimed in claim 1 or 5, characterized in that: According to the positive or negative slope obtained by fitting, the result of determining whether the lithium-ion battery to be diagnosed has a short circuit fault is as follows: If the slope obtained by fitting is positive, it means that the lithium-ion battery to be diagnosed has a short circuit fault; If the slope obtained by fitting is negative, it means that the lithium-ion battery to be diagnosed does not have a short circuit fault.

7. A lithium-ion battery early short circuit fault diagnosis system, characterized in that: include: The first calculation module is used to calculate the impedance spectrum of the lithium-ion battery to be diagnosed in the low-frequency region in batches; The second calculation module is used to calculate the diffusion coefficients corresponding to the lithium-ion batteries to be diagnosed according to the impedance spectrum of the lithium-ion batteries to be diagnosed in the low-frequency region; A linear fitting module is used to perform curve fitting on a plurality of continuous diffusion coefficients of the lithium-ion battery to be diagnosed to obtain a corresponding slope; The fault diagnosis module is used to determine whether a short circuit fault occurs in the lithium-ion battery to be diagnosed according to the positive or negative slope obtained by fitting.

8. An electronic device, characterized in that: The method comprises a memory and a processor and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 6 is completed.

9. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the method described in any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

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