A method for detecting false welding of a lithium ion battery module

Abstract

The present application belongs to the technical field of power battery module detection, and particularly relates to a virtual welding detection method for a lithium ion battery module, comprising: obtaining voltage values of each battery cell of the lithium ion battery module in the whole charging and discharging process; generating a voltage sequence of each battery cell and a battery cell average voltage sequence according to the voltage values of the battery cell in the whole charging and discharging process; calculating voltage Euclidean distances of the voltage sequence of each battery cell and the battery cell average voltage sequence; calculating difference values of the voltage Euclidean distances of each battery cell and the voltage Euclidean distance of a normal battery cell; comparing each difference value with a preset threshold value; and when the difference value is greater than the preset threshold value, the battery cell corresponding to the difference value is a virtual welding battery cell. The present application utilizes the fact that when the welding position has a defect, the contact resistance becomes larger, thereby causing abnormal changes in the battery cell terminal voltage, and calculates the voltage Euclidean distance to achieve effective detection of virtual welding of the laser welding of the power battery module.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of power battery module detection, and particularly relates to a virtual welding detection method for a lithium ion battery module. BACKGROUND

[0002] With the development of new energy vehicles and electric bicycles and other transportation tools, power batteries have been widely used in the field of automobiles. In order to achieve the required use voltage or current, it is necessary to use a connecting assembly to series or parallel weld the power battery cells, thereby forming a power battery pack with high energy density and long service life. In modern factories, laser welding is generally used to weld the bus bar and the cell pole when manufacturing the battery module, thereby realizing the function of series and parallel connection of the power battery. The quality of laser welding directly relates to the overall performance, yield rate and service life of the battery. When welding the cell pole and the bus bar of the power battery, due to the existence of foreign matters or process parameters, the cell pole may be virtually welded with the bus bar during the welding process. Once virtual welding occurs, it will seriously affect the use of the battery pack, which may affect the charging and discharging of the module, or even cause a thermal runaway safety risk, and even product recall, so it is necessary to detect the defects of the laser welding of the power battery pack.

[0003] The existing welding quality detection technologies include visual inspection, tensile test, X-ray detection, automatic optical detection, ultra-high-speed profile detection, infrared non-destructive testing, three-dimensional scanning, electron microscope scanning, energy spectrometer, acoustic scanning microscopic detection, and laser ultrasonic scanning. However, the existing technologies have the problems of damaging the battery structure, high equipment cost, slow detection speed, inability to be online, single physical quantity, low accuracy, weak mechanism support, and low fusion degree with the production line equipment. In view of the problem that the existing common welding quality detection technologies cannot effectively detect the virtual welding defects of the battery module laser welding, a new detection method is needed to quickly and accurately detect the quality of the battery module laser welding. SUMMARY

[0004] In view of the above-mentioned shortcomings of the prior art, the purpose of the present application is to provide a virtual welding detection method for a lithium ion battery module, which can not damage the battery structure, has fast detection speed, high precision, and low cost.

[0005] To achieve the above object and other related objects, the present application provides a virtual welding detection method for a lithium ion battery module, comprising: obtaining voltage values of each battery cell of the lithium ion battery module in a full charging and discharging process; generating a voltage sequence of each battery cell and a battery cell average voltage sequence according to the voltage values of the battery cell in the full charging and discharging process; calculating a voltage Euclidean distance of the voltage sequence of each battery cell and the battery cell average voltage sequence; calculating a difference value between the voltage Euclidean distance of each battery cell and a voltage Euclidean distance of a normal battery cell; comparing each difference value with a preset threshold value; and when the difference value is greater than the preset threshold value, the battery cell corresponding to the difference value is a virtual welding battery cell.

[0006] According to an embodiment of the present application, the full charging and discharging process comprises: a constant current pre-charging phase, a constant current pre-discharging phase, a constant current charging phase and a constant current discharging phase.

[0007] According to an embodiment of the present application, the step of generating a voltage sequence of each battery cell and a battery cell average voltage sequence according to the voltage values of the battery cell in the full charging and discharging process comprises: the voltage values of the battery cell in the full charging and discharging process are voltage values collected at preset time intervals, the voltage values of the battery cell are arranged in time sequence to form the voltage sequence of the battery cell; and the voltage sequences of all the battery cells are summed and averaged to obtain the battery cell average voltage sequence.

[0008] According to an embodiment of the present application, the step of calculating a voltage Euclidean distance of the voltage sequence of each battery cell and the battery cell average voltage sequence comprises: a global voltage Euclidean distance of the voltage sequence of the battery cell and the battery cell average voltage sequence is:

[0009]

[0010] wherein, the voltage sequence of the battery cell is Q={q1...q n}; and the battery cell average voltage sequence is C={c1...c n}.

[0011] According to an embodiment of the present application, the difference value between the voltage Euclidean distance of the battery cell and the voltage Euclidean distance of the normal battery cell is a difference value between a global voltage Euclidean distance of the battery cell and a global voltage Euclidean distance of the normal battery cell.

[0012] According to an embodiment of the present application, the step of calculating a voltage Euclidean distance of the voltage sequence of each battery cell and the battery cell average voltage sequence in the discharging phase comprises: extracting the voltage sequence of each battery cell in the discharging phase and the battery cell average voltage sequence in the discharging phase; and calculating a voltage Euclidean distance of the voltage sequence of each battery cell and the battery cell average voltage sequence in the discharging phase:

[0013]

[0014] wherein the voltage sequence of the battery cell during the discharging phase {q i ...q j} and the average voltage sequence of the battery cell during the discharging phase {c i ...c j}.

[0015] According to an embodiment of the present application, the difference between the voltage Euclidean distance of the battery cell and the voltage Euclidean distance of the normal battery cell is the difference between the voltage Euclidean distance of the battery cell during the discharging phase and the voltage Euclidean distance of the normal battery cell during the discharging phase.

[0016] A virtual welding detection system of a lithium ion battery module, comprising: an information acquisition module, configured to acquire voltage values of each battery cell of the lithium ion battery module during the whole charging and discharging process; an information extraction module, configured to generate a voltage sequence of the battery cell and an average voltage sequence of the battery cell according to the voltage values of the battery cell during the whole charging and discharging process; a first information calculation module, configured to calculate voltage Euclidean distances of the voltage sequence of each battery cell and the average voltage sequence of the battery cell; a second information calculation module, configured to calculate differences between the voltage Euclidean distance of each battery cell and the voltage Euclidean distance of a normal battery cell; and an information diagnosis module, configured to compare each difference with a preset threshold value, and when the difference is greater than the preset threshold value, the battery cell corresponding to the difference is a virtual welding battery cell.

[0017] A virtual welding detection device of a lithium ion battery module, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method according to any one of the preceding embodiments.

[0018] A computer readable medium having instructions stored thereon, the instructions being loaded and executed by a processor to implement the method according to any one of the preceding embodiments.

[0019] The technical effect of the present application is that when there is a defect in the welding of the battery cell, the defect will reduce the area of the welding contact surface and cause the contact resistance to become larger, which in turn leads to abnormal changes in the battery cell terminal voltage and the battery cell temperature. The temperature further affects the voltage through the electro-thermal coupling relationship of the battery, so the characterization of the final welding defect is based on the battery voltage. The performance on the charge-discharge curve is the deviation of the battery cell voltage curve. The virtual welding detection method of the lithium battery module of the present application obtains the voltage sequence of the battery cell and the voltage sequence of the normal battery cell through the charging and discharging of the lithium ion battery module, thereby obtaining the global voltage Euclidean distance and the voltage Euclidean distance during the discharging phase. If the value is greater than the preset threshold value, the battery cell deviating from the normal voltage curve is a virtual welding battery cell. Therefore, the detection can be performed quickly and conveniently without affecting the state of the lithium battery module, and the detection does not damage the battery module. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 A flowchart of a virtual soldering detection method of a lithium ion battery module provided by the present application;

[0021] Figure 2 A flowchart of obtaining a voltage value of a battery module cell by a specific embodiment provided by the present application;

[0022] Figure 3 A charge-discharge current change curve diagram of a specific embodiment provided by the present application;

[0023] Figure 4 A voltage Euclidean distance diagram of a cell of a specific embodiment provided by the present application;

[0024] Figure 5 A voltage curve diagram of a cell discharge stage of a specific embodiment provided by the present application;

[0025] Figure 6 A flowchart of a virtual soldering detection system of a lithium ion battery module provided by the present application;

[0026] Figure 7 A structural diagram of a virtual soldering detection device of a lithium ion battery module provided by the present application. DETAILED DESCRIPTION

[0027] The advantages and effects of the present application can be easily understood by the skilled in the art from the above description of specific embodiments. The present application can also be implemented or applied by different specific embodiments, and the details in the description can be modified or changed based on different views and applications without departing from the spirit of the present application.

[0028] Reference will now be made to the drawings Figures 1-7 It should be noted that the diagrams provided in the embodiments only schematically illustrate the basic concept of the present application, and the diagrams only show the components related to the present application, not the number, shape and size of the components in actual implementation. The shape, number and proportion of the components in actual implementation can be arbitrarily changed, and the layout pattern of the components can be more complex.

[0029] The embodiment of the present application can detect virtual welding of a lithium power battery module based on the Euclidean distance. In mathematics, the Euclidean distance or Euclidean metric is the "ordinary" (i.e. straight-line) distance between two points in Euclidean space. Using this distance, Euclidean space becomes a metric space. The associated norm is called the Euclidean norm. Earlier literature refers to it as the Pythagorean metric. The Euclidean metric (also called Euclidean distance) is a commonly adopted distance definition, which refers to the real distance between two points in m-dimensional space, or the natural length of a vector (i.e. the distance of the point to the origin). The Euclidean distance in two-dimensional and three-dimensional space is the actual distance between two points.

[0030] The lithium power battery virtual welding detection method is applied to one or more electronic devices, which is a device capable of automatically performing numerical calculation and / or information processing according to pre-set or stored instructions. The hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0031] The electronic device can be any electronic product that can interact with the user, such as a personal computer, a tablet computer, a smart phone, a personal digital assistant (PDA), a game console, an interactive Internet Protocol Television (IPTV), a smart wearable device, etc.

[0032] The electronic device can also include network devices and / or user devices. The network devices include, but are not limited to, a single network server, a server group composed of multiple network servers, or a cloud composed of a large number of hosts or network servers based on cloud computing.

[0033] The network in which the electronic device is located includes but is not limited to the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), etc.

[0034] Embodiment 1

[0035] Please refer to Figure 1 The flowchart of the lithium battery virtual welding detection method is shown in the figure. A virtual welding detection method for a lithium ion battery module includes:

[0036] Step S10, asFigure 2 As shown in the figure, the step of acquiring the voltage values of each cell of the lithium ion battery module in the whole charging and discharging process includes:

[0037] In the lithium battery module, the rated capacity of 20 cells is 6 Ah, the rated voltage is 3.7 V, the charging cutoff voltage is 4.2 V, and the discharging cutoff voltage is 3 V.

[0038] Step S11, charging and discharging the lithium battery module:

[0039] The charging and discharging mode is:

[0040] In the constant current pre-charging and discharging stage, first charge at 50 A for 5 s, then discharge at 50 A for 5 s, and finally stand for 15 s.

[0041] In the constant current charging stage, first charge at 150 A for 70 s, and then stand for 60 s.

[0042] In the constant current discharging stage, first discharge at 340 A for 30 s, and then stand for 20 s.

[0043] The current change in the charging and discharging process is as shown in the figure. Figure 3

[0044] Step S12, collecting the voltage values in the whole charging and discharging process:

[0045] The data sampling frequency of the whole charging and discharging process is 50 Hz, and the voltage data of the constant current charging and discharging stage of the lithium battery module is extracted from the whole process.

[0046] Step S20, generating the voltage sequence of the cell and the cell average voltage sequence according to the voltage values of the cell in the whole charging and discharging process includes:

[0047] Step S21, the voltage values of the cell in the whole charging and discharging process are voltage values collected at a preset time interval, and the voltage values of the cell are arranged in time sequence to form the voltage sequence of the cell;

[0048] Step S22, summing and averaging the voltage sequences of all cells to obtain the cell average voltage sequence

[0049]

[0050] Wherein, T represents the number of cells in the lithium battery module, V t is the voltage sequence of the tth cell. In an embodiment of the present application, T = 20.

[0051] Step S30, calculating the voltage Euclidean distance of each cell voltage sequence and the cell average voltage sequence includes: ​

[0052] Step S31, calculate the global voltage Euclidean distance of each cell voltage sequence and the cell average voltage sequence, assuming that the voltage sequence of the cell is Q={q1...q n} and the cell average voltage sequence is C={c1...c n}, then the global voltage Euclidean distance is as follows:

[0053]

[0054] Step S32, calculate the discharge stage voltage Euclidean distance of each cell voltage sequence and the cell average voltage sequence,

[0055] Step S321, extract the discharge stage voltage sequence of each cell and the discharge stage cell average voltage sequence, then the discharge stage cell voltage sequence{q i ...q j} and the discharge stage cell average voltage sequence{c i ...c j}.

[0056] Step S322, the discharge stage voltage Euclidean distance is as follows

[0057]

[0058] Step S40, calculate the difference between the voltage Euclidean distance of each cell and the voltage Euclidean distance of the normal cell, including:

[0059] Calculate the difference between the global voltage Euclidean distance of the cell and the global voltage Euclidean distance of the normal cell, and the difference between the discharge stage voltage Euclidean distance of the cell and the discharge stage voltage Euclidean distance of the normal cell.

[0060] Step S50, compare each of the differences with a preset threshold value: when the difference is greater than the preset threshold value, the cell corresponding to the difference is a virtual welding cell, and the specific performance is as shown in Figure 4 、 5 The 18th cell deviates from other cells, so the virtual welding cell of the battery module is the 18th battery.

[0061] In the battery manufacturing process, it is found that the 18th cell of the battery module has manufacturing defects, and after detection, it is found that the welding strength is not enough, and there are gaps, shrinkage and other defects in the welding place.

[0062] In summary, the virtual welding cell determined by calculating the voltage Euclidean distance is consistent with the virtual welding cell detected in the battery manufacturing process, therefore, the voltage Euclidean distance can be used as an index to determine whether the laser welding of the battery module has virtual welding.

[0063] It should be noted that, in the present application, in order to ensure the security of the data, the data involved can be deployed in the blockchain to prevent malicious tampering of the data.

[0064] It should be noted that the step division of the above methods is only for the purpose of clear description, and in implementation, one step can be combined or some steps can be split and decomposed into multiple steps, as long as the same logical relationship is included, and all are within the protection scope of the present patent; adding insignificant modifications or introducing insignificant designs in the algorithm or process, but not changing the core design of the algorithm and process, are within the protection scope of the present patent.

[0065] Embodiment 2

[0066] As shown in Figure 6 The present embodiment discloses a virtual welding detection system of a lithium ion battery module, comprising:

[0067] An information acquisition module is configured to acquire voltage values of a full charging and discharging process of each battery cell of the lithium ion battery module.

[0068] An information extraction module is configured to generate a voltage sequence of the battery cell and a battery cell average voltage sequence according to the voltage values of the full charging and discharging process of the battery cell.

[0069] A first information calculation module is configured to calculate a voltage Euclidean distance of the voltage sequence of each battery cell and the battery cell average voltage sequence.

[0070] A second information calculation module is configured to calculate a difference value between the voltage Euclidean distance of each battery cell and the voltage Euclidean distance of a normal battery cell.

[0071] An information diagnosis module is configured to compare each difference value with a preset threshold value: when the difference value is greater than the preset threshold value, the battery cell corresponding to the difference value is a virtual welding battery cell.

[0072] It should be noted that the above functional modules can be integrated into one physical entity in whole or in part, or can be physically separated. These modules can all be implemented in the form of software through a processing element; or all can be implemented in the form of hardware; or some modules can be implemented in the form of software through a processing element, and some modules can be implemented in the form of hardware. In addition, all or part of these modules can be integrated together, or can be independently implemented. The processing element mentioned herein can be an integrated circuit having a signal processing capability. In the implementation process, some or all steps of the above method, or each functional module of the above can be completed by the integrated logic circuit of hardware in the processing element or the instructions in the form of software.

[0073] Embodiment 3

[0074] AsFigure 7 The embodiment shown discloses a virtual welding detection device for a lithium ion battery module, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the method according to any one of the above embodiments when executing the computer program.

[0075] The electronic device can include a memory, a processor, and a bus, and can further include a computer program stored in the memory and executable on the processor.

[0076] The memory includes at least one type of readable storage medium, such as a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory can be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory can also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory can include both an internal storage unit and an external storage device of the electronic device. The memory can be used to store application software and various data installed in the electronic device, and can also be used to temporarily store data that has been output or will be output.

[0077] The processor can be composed of an integrated circuit in some embodiments, such as a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more combinations of a central processing unit (CPU), a microprocessor, a digital processing chip, a graphics processor, and various control chips, etc. The processor is the control unit of the electronic device, and connects various components of the electronic device through various interfaces and lines. The processor executes or runs programs or modules stored in the memory, and calls data stored in the memory, to perform various functions and process data of the electronic device.

[0078] The processor executes an operating system of the electronic device and various installed application programs. The processor executes the application programs to implement the steps in each of the above lithium power battery virtual welding detection method embodiments.

[0079] For example, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present application. The one or more modules can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the electronic device.

[0080] The integrated unit in the form of the software function module described above can be stored in a computer readable storage medium. The software function module described above is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a computer device, or a network device, etc.) or a processor to execute part of the functions of the lithium battery virtual welding detection method of various embodiments of the present application.

[0081] In summary, the technical effect of the present application is that when there is a defect in the welding of the battery cell, the defect will reduce the area of the welding contact surface and cause the contact resistance to increase, which in turn leads to abnormal changes in the cell terminal voltage and the cell temperature. The temperature further affects the voltage through the electro-thermal coupling relationship of the battery, so the characterization of the welding defect ultimately focuses on the battery voltage. The performance on the charge-discharge curve is the deviation of the cell voltage curve. The lithium battery module virtual welding detection method of the present application obtains the voltage sequence of the battery cell and the voltage sequence of the normal battery cell by charging and discharging the lithium ion battery module, thereby obtaining the global voltage Euclidean distance and the voltage Euclidean distance in the discharge stage. Greater than the preset threshold, the battery cell deviating from the normal voltage curve is the virtual welding battery cell, so that the detection is fast and convenient without affecting the state of the lithium battery module, and the detection does not damage the battery module.

[0082] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0083] The above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for detecting poor solder joints in a lithium-ion battery module, characterized in that, include: Obtain the voltage value of each cell in the lithium-ion battery module throughout the entire charging and discharging process; The process involves generating a voltage sequence and an average voltage sequence for each battery cell based on the voltage values ​​during the entire charging and discharging process. The steps include: for each battery cell, collecting multiple voltage values ​​at preset time intervals during its charging and discharging process, and arranging the multiple voltage values ​​in chronological order to obtain the voltage sequence of the battery cell; summing and averaging the voltage sequences of all batteries cell to obtain the average voltage sequence of the battery cells in the lithium-ion battery module. Calculate the voltage Euclidean distance between the voltage sequence of each of the battery cells and the average voltage sequence of the battery cells; Calculate the difference between the voltage Euclidean distance of each of the stated cells and the voltage Euclidean distance of a normal cell; Each of the differences is compared with a preset threshold: When the difference is greater than the preset threshold, the cell corresponding to the difference is a poorly soldered cell.

2. The method for detecting cold solder joints according to claim 1, characterized in that, The entire charging and discharging process includes: constant current pre-charging stage, constant current pre-discharging stage, constant current charging stage, and constant current discharging stage.

3. The method for detecting cold solder joints according to claim 1, characterized in that, The calculation of the voltage Euclidean distance between the voltage sequence of each of the battery cells and the average voltage sequence of the battery cells includes: The global voltage Euclidean distance between the voltage sequence of the battery cell and the average voltage sequence of the battery cell is: The voltage sequence of the battery cell is Q = {q1...q}. n The average cell voltage sequence is C = {c1...c...} n } 4. The method for detecting cold solder joints according to claim 3, characterized in that, The difference between the voltage Euclidean distance of the battery cell and the voltage Euclidean distance of a normal battery cell is the difference between the global voltage Euclidean distance of the battery cell and the global voltage Euclidean distance of a normal battery cell.

5. The method for detecting cold solder joints according to claim 4, characterized in that, The calculation of the voltage Euclidean distance during the discharge phase of the voltage sequence of each of the battery cells and the average voltage sequence of the battery cells includes: Extract the voltage sequence of each cell during the discharge phase, and the average voltage sequence of the cell during the discharge phase; Calculate the voltage Euclidean distance during the discharge phase of the voltage sequence for each of the battery cells and the average voltage sequence of the battery cells: Among them, the voltage sequence {q} of the cell during the discharge phase i ...q j The cell average voltage sequence {c} during the discharge phase i ...c j } 6. The method for detecting cold solder joints according to claim 5, characterized in that, The difference between the voltage Euclidean distance of the battery cell and the voltage Euclidean distance of a normal battery cell is the difference between the voltage Euclidean distance during the discharge phase of the battery cell and the voltage Euclidean distance during the discharge phase of a normal battery cell.

7. A system for detecting poor solder joints in lithium-ion battery modules, characterized in that, include: The information acquisition module is used to acquire the voltage value of each cell in the lithium-ion battery module throughout the entire charging and discharging process; The information extraction module is used to generate a voltage sequence and an average voltage sequence of the battery cell based on the voltage values ​​during the entire charging and discharging process of the battery cell. The steps include: for each battery cell, collecting multiple voltage values ​​at preset time intervals during its charging and discharging process, arranging the multiple voltage values ​​in chronological order to obtain the voltage sequence of the battery cell; summing and averaging the voltage sequences of all battery cells to obtain the average voltage sequence of the battery cells of the lithium-ion battery module. The first information calculation module is used to calculate the voltage Euclidean distance between the voltage sequence of each of the battery cells and the average voltage sequence of the battery cells. The second information calculation module is used to calculate the difference between the voltage Euclidean distance of each of the battery cells and the voltage Euclidean distance of a normal battery cell. The information diagnosis module is used to compare each difference with a preset threshold: when the difference is greater than the preset threshold, the cell corresponding to the difference is a poorly soldered cell.

8. A faulty solder joint detection device for lithium-ion battery modules, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable medium, characterized in that, It stores instructions that are loaded by a processor and executed as described in any one of claims 1 to 6.

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