A non-invasive lithium ion battery internal micro-short circuit fault diagnosis method and system

Abstract

The application discloses a non-invasive lithium ion battery internal micro-short circuit fault diagnosis method and system, the method comprises the following steps: collecting the magnetic field distribution data of the space above the lithium ion battery in the working state by using a magnetic field scanning device; based on the Biot-Savart law, a mathematical relationship model of the current element and the magnetic induction intensity is constructed, and the least square method is used for inversion solution to obtain the current distribution characteristics inside the lithium ion battery; according to the current distribution characteristics, the abnormal area of the current distribution is identified, and early diagnosis and positioning of the internal micro-short circuit fault of the battery are realized accordingly. The application breaks through the technical limitations of traditional detection methods which need to disassemble the battery or can only evaluate the overall state, has the significant advantages of non-destructive detection, accurate positioning and automatic operation, and provides reliable technical support for the safe operation and early fault warning of the lithium ion battery, and has a wide application prospect in the fields of power battery production and manufacturing, operation and maintenance and step utilization.

Description

Technical Field

[0001] This invention relates to the field of lithium-ion battery fault detection, and in particular to a non-invasive method and system for diagnosing internal micro-short circuit faults in lithium-ion batteries. Background Technology

[0002] In recent years, with the development of battery energy storage technology, battery energy density and the scale of energy storage systems have gradually increased. During use, lithium-ion batteries often exhibit uneven current distribution due to manufacturing defects, material aging, and changes in the external environment. This leads to significantly higher temperatures in localized areas compared to other parts of the battery, accelerating material degradation in those areas and reducing overall battery performance. Lithium-ion batteries are mainly composed of flammable electrolytes and active electrode materials. Under high-temperature abuse or accidental conditions, they may trigger a series of safety issues such as thermal runaway, posing a direct threat to users. Therefore, real-time monitoring and fault diagnosis of battery operating status are imperative.

[0003] Traditional non-destructive testing methods for batteries primarily rely on measured external electrical parameters. Measuring battery voltage and current values ​​provides some insight into the battery's operating state. However, early faults such as internal short circuits and micro-short circuits exhibit minimal voltage fluctuations and insignificant temperature changes in their initial stages. Traditional monitoring methods based on voltage or temperature thresholds struggle to detect these anomalies promptly. Current mainstream lithium battery fault diagnosis algorithms mainly include model-based and data-driven approaches. Model-based prediction methods suffer from cumulative errors, require long periods of static storage, and exhibit nonlinear time-varying parameters in the equivalent circuit model. Data-driven diagnostic methods, on the other hand, focus primarily on data processing, neglecting the analysis of the battery's internal mechanisms. Summary of the Invention

[0004] This invention provides a non-invasive method and system for diagnosing internal micro-short circuit faults in lithium-ion batteries, in order to solve the problem that existing technologies cannot quickly and accurately identify internal micro-short circuit faults in batteries through external parameters, which leads to potential safety hazards in battery systems.

[0005] According to a first aspect of this specification, a non-invasive method for diagnosing internal micro-short circuit faults in lithium-ion batteries is provided, the method comprising the following steps:

[0006] S1. Magnetic field data acquisition: The magnetic field distribution data of the space above the lithium-ion battery during operation is acquired using a magnetic field scanning device. Three single-axis magnetic sensors are used to measure the magnetic induction intensity components in the x, y, and z directions respectively.

[0007] S2. Current Distribution Calculation: The lithium-ion battery is divided into several units and equivalent to a finite-length straight conductor. Based on the Biot-Savart law, a linear mapping relationship between the current element and the magnetic induction intensity component is constructed. The linear coefficient is equal to the product of the product term and the angle difference. The angle difference is the difference of the cosine values ​​of the azimuth angles generated by the connection between the two ends of the straight conductor and the sensor. The product term is determined by the corresponding spatial parameters and the square of the perpendicular distance from the sensor to the straight conductor. Based on this, a calculation model is constructed and the least squares method is used for inversion to obtain the internal current distribution characteristics of the lithium-ion battery.

[0008] S3. Construct a three-dimensional electromagnetic coupling finite element simulation model to obtain magnetic field distribution data under micro short circuit faults; based on the current distribution characteristics obtained by inversion solution, compare with the reference current distribution map under normal operating conditions to identify abnormal current distribution areas, thereby realizing early diagnosis and location of micro short circuit faults inside the battery.

[0009] Furthermore, the magnetic field scanning device includes a magnetic sensor, a three-axis linear motion platform, and a servo motor driver; the magnetic sensor is mounted on the three-axis linear motion platform; the servo motor driver controls the three-axis linear motion platform to collect three-dimensional magnetic field distribution data of the space above the lithium-ion battery according to a preset bow-shaped path.

[0010] Furthermore, step S2 specifically includes:

[0011] The upper surface of the battery is set as the calculation reference plane, and a spatial rectangular coordinate system is established with the width of the battery as the x-axis and the height as the y-axis. Based on the finite element discretization method, the lithium-ion battery is divided into several square battery units along its width and height. The current element in each battery unit is equivalent to a finite-length straight wire element and is decomposed into x and y direction components.

[0012] Based on the Biot-Savart law, a linear mapping relationship is established between the current component of each battery cell and the magnetic induction intensity component at the magnetic field measurement point, and a calculation model is constructed with the current components of all battery cells as unknowns; based on the Biot-Savart law, the external magnetic field generated by the battery current element can be expressed as:

[0013]

[0014] Where B is the magnetic flux density value. The permeability of free space, The current element is within the integration path C. It is the position vector pointing from the current element to the magnetic field measurement point.

[0015] The calculation model, with all battery cell current components as unknowns, uses three-dimensional magnetic induction intensity. With battery cell current The linear relationship between them can be expressed as:

[0016]

[0017] in, , , These represent the three-dimensional magnetic induction intensity components at the magnetic field measurement point. , These represent the current components of the battery cell in the x and y directions, respectively. The mapping coefficients between the magnetic flux density component in the x-direction and the current component in the y-direction are given. The mapping coefficients between the magnetic flux density component in the y-direction and the current component in the x-direction are given. The mapping coefficients between the magnetic flux density component in the z-direction and the current component in the x-direction are given. is the mapping coefficient between the magnetic flux density component in the z-direction and the current component in the y-direction.

[0018] The calculation model is inverted and solved using the least squares method to obtain the current distribution results of each battery cell, thereby reconstructing the overall current distribution characteristics inside the lithium-ion battery.

[0019] Furthermore, any magnetic induction intensity component generated by a wire in any battery cell at any measurement point is linearly related to the current magnitude of the wire, and the corresponding linear coefficient is equal to the product of the product term and the angle difference.

[0020] Each angle difference is the difference between the cosine values ​​of two azimuth angles generated by the line connecting the magnetic sensor that collects the magnetic induction intensity component and the two endpoints of the corresponding straight conductor; each product term is generated by... / 4 Multiply by the corresponding spatial parameter and then divide by the square of the perpendicular distance from the magnetic sensor to the corresponding straight wire.

[0021] The spatial parameters are defined as follows, depending on the different conductors and magnetic induction component directions:

[0022] For the y-direction magnetic induction intensity component generated by the x-direction conductor, the spatial parameter is the z-coordinate of the corresponding y-direction magnetic sensor;

[0023] For the z-direction magnetic induction intensity component generated by the x-direction conductor, the spatial parameter is the coordinate difference between the corresponding z-direction magnetic sensor and the x-direction conductor in the y-axis direction;

[0024] For the x-direction magnetic induction intensity component generated by the y-direction conductor, the spatial parameter is the z-coordinate of the corresponding x-direction magnetic sensor;

[0025] For the z-direction magnetic induction intensity component generated by the y-direction conductor, the spatial parameter is the coordinate difference between the corresponding z-direction magnetic sensor and the y-direction conductor in the x-axis direction.

[0026] Further, in step S2, the inversion solution using the least squares method includes: using the current components in the x and y directions within each battery cell as decision variables, constructing an objective function to minimize the sum of squares of the differences between the measured magnetic induction intensity values ​​and the calculated magnetic induction intensity values ​​at all measurement points; and using the linear mapping relationship between the current components of each battery cell and the magnetic induction intensity components at the magnetic field measurement points as a constraint condition.

[0027] Further, step S3 specifically involves: constructing a three-dimensional electromagnetic coupling finite element simulation model; obtaining magnetic field distribution data under micro-short circuit faults by combining experiments and simulations; constructing a current distribution map under the current operating condition based on the battery internal current distribution characteristics obtained by the inversion solution; comparing and analyzing the current distribution map with the reference current distribution map under normal operating conditions; and realizing early diagnosis and location of micro-short circuit faults by identifying abnormal current distribution areas.

[0028] According to a second aspect of this specification, a non-invasive lithium-ion battery internal micro-short circuit fault diagnosis system is provided, comprising a magnetic field scanning device for collecting magnetic field distribution data of the space above the lithium-ion battery in its working state; and a processor, communicatively connected to the magnetic field scanning device, for executing a computer program to implement the non-invasive lithium-ion battery internal micro-short circuit fault diagnosis method as described in the first aspect.

[0029] According to a third aspect of this specification, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the non-invasive method for diagnosing internal micro-short circuit faults in lithium-ion batteries as described in the first aspect.

[0030] According to a fourth aspect of this specification, a computer program product is provided, comprising a computer program / instructions that, when executed by a processor, implement the non-invasive lithium-ion battery internal micro-short circuit fault diagnosis method as described in the first aspect.

[0031] Compared with the prior art, the present invention has the following advantages and technical effects:

[0032] 1. This invention acquires external magnetic field data of the battery through a magnetic sensor array and combines it with an electromagnetic field inversion algorithm to realize internal micro-short circuit fault diagnosis. It does not require disassembling the battery, thus maintaining the battery's integrity and sealing. It is suitable for online detection of in-service batteries and significantly improves the safety and engineering applicability of battery fault diagnosis.

[0033] 2. This invention employs the finite element discretization method and establishes a linear mapping relationship between current elements and magnetic induction intensity. Through electromagnetic field inversion algorithm, a two-dimensional current distribution map with high spatial resolution is obtained, which solves the technical bottleneck of existing electrical parameter detection that can only judge the overall state and cannot locate the spatial location of faults. It realizes the early identification and accurate location of micro short-circuit faults and provides a reliable basis for preventive maintenance.

[0034] 3. This invention constructs an automated magnetic field scanning device consisting of a three-axis linear motion platform, a servo drive system, and a magnetic field sensing module. It adopts a "bow"-shaped scanning path to achieve efficient data acquisition. Combined with a stable inversion algorithm, it forms a complete hardware-software integrated diagnostic system with advantages such as high automation, high detection efficiency, and good result repeatability. It is suitable for power battery production line testing and operation and maintenance scenarios. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention;

[0037] Figure 2 This is a schematic diagram of the various parts and the overall scheme of the magnetic field scanning device according to an embodiment of the present invention;

[0038] Figure 3 This is a schematic diagram of the battery magnetic field data measurement point location and scanning path according to an embodiment of the present invention;

[0039] Figure 4 This is a schematic diagram of a battery simulation model under normal operating conditions and micro-short circuit conditions according to an embodiment of the present invention;

[0040] Figure 5 This is a comparison chart of the battery internal current distribution under normal operating conditions and micro-short circuit conditions according to an embodiment of the present invention. Detailed Implementation

[0041] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0042] like Figure 1As shown, this embodiment provides a non-invasive method for diagnosing internal micro-short circuit faults in lithium-ion batteries. For ease of explanation, the following detailed description uses a lithium-ion pouch battery (model: NMC34) with tabs arranged on the same side as the battery tabs as the research object, specifically including the following steps:

[0043] S1. Magnetic field data acquisition: Using a magnetic field scanning device, acquire magnetic field distribution data of the space above the lithium-ion battery when it is in operation.

[0044] In this embodiment, the magnetic field distribution of the lithium-ion battery under 20A constant current charging state without faults is collected.

[0045] Specifically, the magnetic field scanning device includes a magnetic sensor, a signal amplification circuit, a data acquisition card, a three-axis linear motion platform, and a servo motor driver, such as... Figure 2 As shown, this embodiment employs a TMR sensor; the TMR sensor, signal amplification circuit, and data acquisition card constitute the magnetic field sensing module of the device, which is mounted on a three-axis linear motion platform; the three-axis linear motion platform and servo motor driver constitute the motion control system of the device. The host computer interacts with the motion control system via the industrial communication protocol (Modbus-TCP). The servo motor driver controls the three-axis linear motion platform to collect three-dimensional magnetic field distribution data of the space above the lithium-ion battery according to a preset "bow" shaped path to reduce sampling path repetition. In this embodiment, the measurement area is a 40*28 grid array, with a total of 1120 measurement points, as shown... Figure 3 As shown.

[0046] Specifically, the magnetic field scanning area plane is parallel to the mounting reference plane of the lithium-ion battery under test and is 20mm away from the positive current collector of the lithium-ion battery. At each measurement point, the system continuously collects 100 sets of magnetic field data at a high sampling frequency to improve the signal-to-noise ratio. The magnetic field data of the corresponding measurement point is collected and transmitted to the data acquisition card. The data acquisition card communicates with the host computer and stores the magnetic field data.

[0047] Furthermore, to eliminate background magnetic fields and systemic interference, a differential measurement method was employed: a complete three-dimensional scanning process was performed with the battery in a static, uncharged state, recording the background magnetic field data at each measurement point. If necessary, electromagnetic shielding materials could be placed around the measurement area to reduce external electromagnetic interference. Subsequently, under the same environmental conditions, measurement parameters, and sensor calibration status, a second scanning measurement was performed on the battery under a 20A constant current charge. The magnetic sensor was periodically calibrated using a standard magnetic field source to eliminate measurement errors caused by factors such as temperature drift and zero-point offset.

[0048] Furthermore, by subtracting the magnetic field data at the corresponding measurement points obtained from the two scans, common-mode interference such as the geomagnetic field and environmental stray magnetic fields can be eliminated, and magnetic field data generated by the battery's internal current under 20A constant current charging conditions can be obtained.

[0049] S2. Current distribution calculation: Based on the Biot-Savart law, a mathematical relationship model between the current element and the magnetic induction intensity is constructed, and the least squares method is used for inversion to obtain the current distribution characteristics inside the lithium-ion battery.

[0050] Furthermore, the upper surface of the battery is set as the calculation reference plane, and a spatial rectangular coordinate system is established with the battery width as the x-axis and the height as the y-axis. Based on the finite element discretization method, the lithium-ion battery is divided into several square battery cells along its width and height. The current element in each battery cell is equivalent to a finite-length straight wire element, which is decomposed into mutually orthogonal x-direction and y-direction components. The spatial position of the wire element is determined according to the center coordinates and dimensions of the corresponding battery cell. The starting position coordinates of the finite-length straight wire in the x-direction within the i-th battery cell (i=1,2,3,…,n) are... The coordinates of the termination position are The starting position coordinates of the finite-length straight conductor in the y-direction are: The coordinates of the termination position are .

[0051] Furthermore, to accurately acquire three-dimensional magnetic field data, three uniaxial magnetic sensors are used. By adjusting the sensitive axis direction of the sensors, the magnetic induction intensity components in the x, y, and z directions can be obtained respectively. Let the total number of magnetic field measurement points be m, then the position coordinates of the magnetic sensors in the x, y, and z directions at the j-th measurement point (j=1,2,3,…,m) are respectively... , , .

[0052] Furthermore, according to the Biot-Savart law, when current flows along the x-axis, the magnetic field lines it generates will form a closed loop in the yz plane centered on the conductor. Therefore, the y-direction magnetic induction component generated by the finite-length straight conductor in the x-direction within the i-th battery cell at the j-th magnetic field measurement point... and the z-direction magnetic induction component They are respectively:

[0053]

[0054]

[0055] in, The permeability of free space, Let be the current in the finite-length straight wire in the x-direction within the i-th battery cell.

[0056] For ease of subsequent writing, it can be simplified to:

[0057]

[0058]

[0059] The parameters are as follows:

[0060]

[0061]

[0062]

[0063]

[0064] Furthermore, the magnetic field generated by the current in the y-direction lies in the xz plane, and the magnetic induction intensity component in the x-direction generated by the finite-length straight wire in the y-direction within the i-th battery cell at the j-th magnetic field measurement point is... and the z-direction magnetic induction component They are respectively:

[0065]

[0066]

[0067] in, Let be the current in the finite-length straight wire in the y-direction within the i-th battery cell.

[0068] Furthermore, for ease of subsequent writing, it can be simplified to:

[0069]

[0070]

[0071] The parameters are as follows:

[0072]

[0073]

[0074]

[0075]

[0076] Furthermore, by superimposing the magnetic induction intensity components generated by all battery cells at each measurement point, a calculation model can be obtained with the current components of all battery cells as unknowns:

[0077]

[0078]

[0079]

[0080] in, Let be the magnetic flux density component in the x-direction at the j-th measurement point. Let be the magnetic flux density component in the y-direction at the j-th measurement point. Let be the magnetic induction intensity component in the z-direction of the j-th measurement point.

[0081] Specifically, the current components in the x and y directions within each battery cell are used as decision variables; the objective function is to minimize the sum of squares of the differences between the magnetic flux density calculated by the computational model and the measured magnetic field data obtained in step S1; and the linear relationship between magnetic flux density and battery cell current is used as the equality constraint. In this embodiment, the grid size of the inversion calculation plane is 11*8, i.e., the number of battery cells n=88, while the number of magnetic field measurement points m=1120. The number of observations in the model is much greater than the number of parameters, which means that there is enough observation data to constrain the model parameters. The specific least squares model is as follows:

[0082]

[0083] in, These are the calculated values ​​of the y-direction magnetic flux density component and the z-direction magnetic flux density component generated by the x-direction current element in the i-th battery cell at the j-th magnetic field measurement point, respectively. These are the calculated values ​​of the x-direction magnetic flux density component and the z-direction magnetic flux density component generated by the y-direction current element in the i-th battery cell at the j-th magnetic field measurement point, respectively.

[0084] This embodiment uses the Python-based open-source optimization modeling framework Pyomo to construct the solution model for the above least squares problem. The current inversion results are as follows: Figure 5 As shown, Figure 5 (a) in the figure represents the current distribution inverted under measured normal operating conditions.

[0085] Furthermore, based on the COMSOL Multiphysics® 6.3 multiphysics simulation platform, a three-dimensional electromagnetic coupling finite element simulation model was constructed to simulate the magnetic field distribution under normal operating conditions and micro-short circuit conditions. Figure 4(a) shows the three-dimensional electromagnetic coupling finite element simulation model under normal operating conditions. This model couples an electrochemical module and a magnetic field module. The electrochemical module uses a pseudo-two-dimensional (P2D) model, while the magnetic field module is solved based on Maxwell's equations. The model's geometry includes seven key components: a positive current collector, a positive electrode tab, a positive electrode, a diaphragm, a negative electrode, a negative current collector, and a negative electrode tab. An air domain is also set outside the model to provide the physical space for the natural decay of the magnetic field. The main geometrical parameters of the model are shown in Table 1, and the material properties of each component are shown in Table 2.

[0086] Table 1 Geometric Dimensions of Lithium-ion Battery Model

[0087] 16 22 4.5 2.35 60 30 60 10

[0088] Table 2 Lithium-ion battery element materials

[0089] NMC <![CDATA[LiPF6]]> <![CDATA[Li x C6]]> Al Al Cu Cu Air

[0090] Furthermore, a cylindrical conductive region is set in the diaphragm region of the three-dimensional electromagnetic coupling finite element simulation model under normal operating conditions to form a micro-short-circuit fault simulation model, such as... Figure 4 As shown in (b), this is used to simulate a micro-short-circuit fault caused by lithium dendrite growth. In this embodiment, the cylindrical region extends through the thickness of the separator, and its conductivity is set to the conductivity of lithium metal. Based on this fault model, the magnetic field distribution of a battery with a micro-short-circuit fault under a 20A constant current charging state is simulated.

[0091] Furthermore, by extracting the magnetic induction intensity values ​​at each magnetic field measurement point from the micro-short-circuit fault simulation model and the three-dimensional electromagnetic coupling finite element simulation model under normal operating conditions, and calculating the difference, the change in magnetic field caused by lithium dendrites can be obtained. Since directly conducting charge-discharge experiments on faulty batteries poses safety risks such as thermal runaway, combustion, and even explosion, this embodiment uses a combination of simulation and experimentation to obtain simulated magnetic field data under micro-short-circuit conditions. This ensures that the magnetic field data is as close as possible to the actual measurement results, while effectively avoiding experimental safety hazards.

[0092] Furthermore, the simulated changes in the magnetic field under micro-short-circuit fault conditions are superimposed onto the measured magnetic field data under normal operating conditions obtained in step S1 to construct synthetic magnetic field distribution data under micro-short-circuit fault conditions. This synthetic magnetic field distribution data retains both the environmental characteristics and noise features of actual measurements and includes the magnetic field anomaly information of micro-short-circuit faults. Based on the least squares model, the current distribution under simulated micro-short-circuit fault conditions is calculated by inversion, such as... Figure 5 As shown in (b) of the diagram.

[0093] S3. Identify abnormal current distribution areas based on current distribution characteristics, thereby enabling early diagnosis and location of micro short-circuit faults inside the battery.

[0094] Specifically, by Figure 5 The comparison of current distribution under normal operating conditions and micro-short circuit faults shows that the current density at the preset lithium dendrite location is significantly increased, and the current distribution exhibits obvious local concentration characteristics. Based on this, the location of the micro-short circuit fault can be effectively identified, verifying the accuracy and effectiveness of the method of the present invention in fault location.

[0095] Accordingly, this application also provides a non-invasive lithium-ion battery internal micro-short circuit fault diagnosis system, including a magnetic field scanning device for collecting magnetic field distribution data of the space above the lithium-ion battery in the working state; and a processor, which is communicatively connected to the magnetic field scanning device for executing a computer program to implement the above-mentioned non-invasive lithium-ion battery internal micro-short circuit fault diagnosis method.

[0096] Accordingly, this application also provides a computer-readable storage medium storing computer instructions thereon, which, when executed by a processor, implement the non-invasive lithium-ion battery internal micro-short circuit fault diagnosis method described above. The computer-readable storage medium can be an internal storage unit of any data-processing device as described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data-processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data-processing device, and can also be used to temporarily store data that has been output or will be output.

[0097] Accordingly, this application also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the above-described non-invasive lithium-ion battery internal micro-short circuit fault diagnosis method.

[0098] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only.

[0099] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

[0100] The above description is merely a preferred embodiment of the present invention. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make many possible variations and modifications to the technical solutions of the present invention using the methods and techniques disclosed above, or modify them into equivalent embodiments with equivalent changes, without departing from the scope of the technical solutions of the present invention. Therefore, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solutions of the present invention shall still fall within the protection scope of the technical solutions of the present invention.

Claims

1. A non-invasive lithium-ion battery internal micro-short circuit fault diagnosis method, characterized in that, Includes the following steps: S1. Magnetic Field Data Acquisition: A magnetic field scanning device is used to acquire the magnetic field distribution data of the space above the lithium-ion battery during operation. Three uniaxial magnetic sensors are used to measure the magnetic field distribution. x , y , z directional magnetic flux density component; S2. Current Distribution Calculation: The lithium-ion battery is divided into several units and equivalent to a finite-length straight conductor. Based on the Biot-Savart law, a linear mapping relationship between the current element and the magnetic induction intensity component is constructed. The linear coefficient is equal to the product of the product term and the angle difference. The angle difference is the difference of the cosine values ​​of the azimuth angles generated by the connection between the two ends of the straight conductor and the sensor. The product term is determined by the corresponding spatial parameters and the square of the perpendicular distance from the sensor to the straight conductor. Based on this, a calculation model is constructed and the least squares method is used for inversion to obtain the internal current distribution characteristics of the lithium-ion battery. The process of obtaining the internal current distribution characteristics of a lithium-ion battery includes: setting the upper surface of the battery as the calculation reference plane, and using the battery width as... x axis, height is y A spatial rectangular coordinate system is established using the axis. Based on the finite element discretization method, the lithium-ion battery is divided into several battery cells along its width and height directions. The current element in each battery cell is equivalent to a finite-length straight wire element, and is decomposed into... x, y Directional components; based on the Biot-Savart law, a linear mapping relationship is established between the current components of each battery cell and the magnetic induction intensity components at the magnetic field measurement point, and a calculation model with the current components of the battery cells as unknowns is constructed; the calculation model is inverted and solved based on the least squares method to obtain the current distribution results of each battery cell, and then the overall current distribution characteristics inside the lithium-ion battery are reconstructed. The construction of the linear mapping relationship includes: any magnetic induction intensity component generated by a wire within any battery cell at any measurement point is linearly related to the current magnitude of the wire, and the corresponding linear coefficient is equal to the product of the product term and the angle difference; each angle difference is the difference between the cosine values ​​of two azimuth angles generated by the line connecting the magnetic sensor that collects the magnetic induction intensity component and the two endpoints of the corresponding straight wire; each product term is composed of... μ 0 / 4π multiplied by the corresponding spatial parameter, and then divided by the square of the perpendicular distance from the magnetic sensor to the corresponding straight conductor, yields the result. μ 0 represents the permeability of free space; The inversion solution using the least squares method includes: using the values ​​within each battery cell... x direction and y The current component in the direction is used as a decision variable to construct an objective function that minimizes the sum of squares of the differences between the measured and calculated magnetic induction values ​​at all measurement points; the linear mapping relationship between the current component of each battery cell and the magnetic induction component at the magnetic field measurement point is used as a constraint condition. S3. Construct a three-dimensional electromagnetic coupling finite element simulation model to obtain magnetic field distribution data under micro short circuit faults; based on the current distribution characteristics obtained by inversion solution, compare with the reference current distribution map under normal operating conditions to identify abnormal current distribution areas, thereby realizing early diagnosis and location of micro short circuit faults inside the battery.

2. The non-invasive lithium-ion battery internal micro-short fault diagnosis method of claim 1, wherein, The magnetic field scanning device includes a magnetic sensor, a three-axis linear motion platform, and a servo motor driver; the magnetic sensor is mounted on the three-axis linear motion platform; the servo motor driver controls the three-axis linear motion platform to collect magnetic field distribution data of the space above the lithium-ion battery according to a preset bow-shaped path.

3. The non-invasive lithium-ion battery internal micro-short fault diagnosis method of claim 1, wherein, The spatial parameters are as follows, depending on the different conductors and the directions of the magnetic induction components: For x directional field lines generated by y a directional magnetic induction component, the spatial parameter being a corresponding y directional magnetic sensor z coordinate; for x Directional guide generated z directional magnetic induction intensity component, the spatial parameter being the corresponding z Directional magnetic sensor and this x Directional guide wires at y Coordinate difference along the axis; For y The direction of the magnetic field generated by the x The direction of the magnetic field generated by the x The direction of the magnetic field generated by the z The direction of the magnetic field generated by the For y the direction of the direction wire z the direction of the magnetic induction component, the spatial parameter is the corresponding z direction of the magnetic sensor and the y direction of the direction wire in x the coordinate difference of the axis direction.

4. The non-invasive lithium-ion battery internal micro-short fault diagnosis method of claim 1, wherein, Step S3 is as follows: A three-dimensional electromagnetic coupling finite element simulation model is constructed. The magnetic field distribution data under micro short circuit faults are obtained by combining experiments and simulations. Based on the battery internal current distribution characteristics obtained by the inversion solution, a current distribution map under the current operating condition is constructed and compared with the reference current distribution map under normal operating conditions to achieve early diagnosis and location of micro short circuit faults.

5. A non-invasive lithium-ion battery internal micro-short circuit fault diagnosis system, characterized in that, The method includes a magnetic field scanning device for collecting magnetic field distribution data of the space above the lithium-ion battery when it is in operation; and a processor, which is communicatively connected to the magnetic field scanning device and is used to execute a computer program to implement the non-invasive lithium-ion battery internal micro short circuit fault diagnosis method as described in any one of claims 1-4.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the program is executed by the processor, it implements the non-invasive lithium-ion battery internal micro-short circuit fault diagnosis method as described in any one of claims 1-4.

7. A computer program product comprising computer programs / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the non-invasive method for diagnosing internal micro-short circuit faults in lithium-ion batteries as described in any one of claims 1-4.

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