A lithium-ion battery welding anomaly detection method and storage medium

Abstract

The present invention discloses a lithium-ion battery welding anomaly detection method and a storage medium, wherein the lithium-ion battery welding anomaly detection method comprises: selecting an energy mode as a welding mode, setting detection welding parameters under the energy mode; inputting a welding qualified interval, wherein the welding qualified interval is a qualified interval of a peak power P or a qualified interval of a welding time t; welding a tab and a battery cell, collecting the peak power P during the welding process, i Or welding time t i ; According to the peak power P i The qualified interval of peak power P can be used to judge whether the battery core is abnormally welded; or the welding time t i The qualified interval of welding time t is used to judge whether the battery core has abnormal welding. The present invention can realize 100% non-destructive online detection, improve the detection efficiency, eliminate the subjectivity and uncontrollability of manual detection, and the damage to the battery core during the detection process; reduce the risk of poorly welded battery cores flowing out, reduce the safety risk of battery cores, and improve production efficiency and product quality.

Description

Technical Field

[0001] The present invention relates to the technical field of battery welding detection, and in particular to a lithium-ion battery welding anomaly detection method and a storage medium. Background Art

[0002] Ultrasonic metal welding is a solid-phase connection method widely used in the lithium battery industry. The connection between the foil and the foil, and between the foil and the tab of the single cell requires the use of ultrasonic metal welding technology. Ultrasonic metal welding technology uses the mechanical vibration energy generated by ultrasound. Under the action of pressure, the welding head transmits energy to the interface of the workpieces through high-frequency vibration, causing high-frequency friction between the workpiece interfaces, which is converted into friction work, deformation energy and temperature rise between the workpiece interfaces, thereby destroying the oxide layer on the surface of the workpiece and causing plastic deformation of the interface of the workpiece to be welded, thereby producing pure metal bonding and atomic diffusion, and finally achieving solid-phase connection of metal workpieces.

[0003] After the lithium-ion battery is welded, it is necessary to test whether the welding part is qualified. The existing detection methods include three: tensile test, fix the tab sheet and foil connected by the pole ear welding on the upper and lower jaws of the tensile machine respectively, and perform tensile test on the welding part; manual inspection, manually move the pole ear welding position up and down or left and right to observe whether the welding part is detached; manual visual inspection, manually observe whether there is a gap between the two-phase welding edges, and whether the weld mark is too shallow. The above three detection methods have the following defects: 1. Tensile test of battery cell welding can effectively detect whether the battery cell pole ear welding is abnormal, but this detection method is a destructive test, which cannot perform 100% detection on the battery cell, and this test method is an offline test, which affects production efficiency; 2. Manual inspection creates external force by artificial means and applies it to the welding part. The size and angle of the external force affect the welding effect, and the strength is not easy to control, which is easy to miss or misdetect, and is greatly affected by human subjectivity; 3. Manual visual inspection has no unified quantitative standard, which is easy to miss or misdetect, and the production line needs to be stopped during visual inspection, which affects production capacity. Summary of the invention

[0004] To solve the above problems, the technical solution provided by the present invention is:

[0005] A method for detecting abnormal welding of a lithium-ion battery, comprising:

[0006] Select energy mode as welding mode and set the detection welding parameters in energy mode;

[0007] Input welding qualified interval, the welding qualified interval is the qualified interval of peak power P or the qualified interval of welding time t;

[0008] Weld the tabs and cells and collect the peak power P during welding. i Or welding time ti ;

[0009] According to the peak power P i The qualified interval of peak power P is used to judge whether the battery core is abnormally welded. If the peak power P i If the peak power P is within the qualified range, the welding is considered normal. i If it does not belong to the qualified range of peak power P, the welding is judged to be abnormal; or according to the welding time t i The qualified interval of welding time t is used to judge whether the battery core is welded abnormally. If the welding time t i If the welding time t is within the qualified range, the welding is considered normal. i If the welding time does not fall within the qualified range of welding time t, the welding is judged to be abnormal.

[0010] The present invention is further configured such that the qualified interval of the peak power P is obtained in the following manner:

[0011] In energy mode, set the sample welding parameters and collect the peak power P of N welding points. s , the N peak powers P s Generate a scatter plot of peak power and analyze the peak power P of the scatter plots by visual inspection or disassembly. s Perform the welding point qualification test from large to small until a qualified welding point is found and record the peak power of the welding point as the maximum value P of the qualified interval of the peak power P. MAX ; The peak power P of the scattered points can be measured by visual inspection or disassembly s Perform qualified welding point tests from small to large until a qualified welding point is found and the peak power of the welding point is recorded as the minimum value P of the qualified interval of the peak power P. MIN ; The qualified interval of peak power P is [P MIN , P MAX ].

[0012] The present invention is further configured such that the qualified interval of the welding time t is obtained in the following manner:

[0013] In the energy mode, set the sample welding parameters and collect the welding time t of N welding points. s , N welding times t s Generate a scatter plot of welding time, and analyze the welding time t of the scattered points by visual inspection or disassembly. s Perform the welding point qualification test from large to small until a qualified welding point is found and record the welding time of the welding point as the maximum value t of the qualified interval of welding time t MAX ; Welding time t of scattered points by visual inspection or disassembly sPerform qualified welding point tests from small to large until a qualified welding point is found and record the welding time of the welding point as the minimum value t of the qualified interval of welding time t MIN ; The qualified interval of welding time t is [t MIN , t MAX ].

[0014] The present invention is further configured such that the sample welding parameters are the same as the detection welding parameters.

[0015] The present invention is further configured such that both the sample welding parameters and the detection welding parameters include welding amplitude, welding energy and welding pressure.

[0016] A method for detecting abnormal welding of a lithium-ion battery, comprising:

[0017] Select energy mode as welding mode and set the detection welding parameters in energy mode;

[0018] Input welding qualified interval, the welding qualified interval is the qualified interval of the ratio k of peak power P and welding time t;

[0019] Weld the tabs and cells and collect the peak power P during welding. i and welding time t i , and calculate the ratio k i , ratio k i =Peak power P i / Welding time t i ;

[0020] According to the ratio k i The qualified interval of the ratio k is used to judge whether the battery core is abnormally welded. If the ratio k i If the ratio k is within the qualified range, the welding is judged to be normal. i If the ratio k does not fall within the qualified range, the welding is judged to be abnormal.

[0021] The present invention is further configured such that the qualified interval of the ratio k is obtained in the following manner:

[0022] In energy mode, set the sample welding parameters and collect the peak power P of N welding points. s and welding time t s , calculate the peak power P of each welding point s With welding time t s The ratio k s ; N ratios k s Generate a scatter plot of the ratios, and analyze the ratios k of the scatter plots by visual inspection or disassembly sPerform the qualified test of the welding points from large to small until a qualified welding point is found and record the ratio of the welding point as the maximum value k of the qualified interval of the ratio k MAX ; The ratio k of the scattered points by visual inspection or disassembly s Perform qualified welding point tests from small to large until a qualified welding point is found and record the ratio of the welding point as the minimum value k of the qualified interval of the ratio k. MIN ; The qualified interval of ratio k is [k MIN , k MAX ].

[0023] The present invention is further configured such that the sample welding parameters are the same as the detection welding parameters.

[0024] The present invention is further configured such that both the sample welding parameters and the detection welding parameters include welding amplitude, welding energy and welding pressure.

[0025] A storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned lithium-ion battery welding anomaly detection method.

[0026] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0027] The technical solution collects the peak power or welding time or the ratio of the peak power to the welding time of the battery cell welding point in real time online under the energy mode welding mode, and determines whether the peak power or welding time or the ratio of the peak power to the welding time of the battery cell welding point is within the qualified range of the peak power or the qualified range of the welding time or the qualified range of the ratio of the peak power to the welding time. It quantitatively analyzes and determines whether the battery cell welding point is abnormal, thereby achieving 100% non-destructive online detection, improving detection efficiency, eliminating the subjectivity and uncontrollability of manual detection, and damage to the battery cell during the detection process; reducing the risk of poorly welded battery cells flowing out, reducing the safety risk of the battery cells, and improving production efficiency and product quality.

[0028] In the technical solution, the ratio of peak power to welding time is used as a judgment factor. According to the energy mode, the welding energy is certain. When the peak power is smaller, the welding time is relatively longer. The ratio of peak power to welding time amplifies the discrete distribution of scattered points in the process of obtaining the qualified interval, which is convenient for obtaining a more accurate interval range for judging the qualified welding points, and further improves the detection accuracy of abnormal battery cells. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of Example 1 of the present invention.

[0030] Figure 2 This is a flow chart of Example 2 of the present invention.

[0031] Figure 3 This is a scatter plot of the peak power of Example 1 of the present invention.

[0032] Figure 4 This is a scatter plot of welding power in Example 1 of the present invention.

[0033] Figure 5 This is a scatter plot of the ratio k in Example 2 of the present invention. DETAILED DESCRIPTION

[0034] In order to further understand the content of the present invention, the present invention is described in detail in conjunction with the accompanying drawings and embodiments.

[0035] In the description of the present invention, it should be noted that, unless otherwise clearly stipulated and limited, the terms "installation", "connection" and "connection" should be understood in a broad sense. For example, it can be a fixed connection, an integral connection, or a detachable connection; it can be a mechanical connection or an electrical connection, or it can be the internal connection of two elements; it can be a direct connection, or it can be indirectly connected through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to the specific circumstances.

[0036] Example 1

[0037] Combined with Figure 1 , 3 4. The technical solution of the present invention is a method for detecting abnormal welding of a lithium-ion battery, comprising:

[0038] Select energy mode as welding mode and set the detection welding parameters in energy mode;

[0039] Input welding qualified interval, the welding qualified interval is the qualified interval of peak power P or the qualified interval of welding time t;

[0040] Weld the tabs and cells and collect the peak power P during welding. i Or welding time t i ;

[0041] According to the peak power P i The qualified interval of peak power P is used to judge whether the battery core is abnormally welded. If the peak power P i If the peak power P is within the qualified range, the welding is considered normal. i If it does not belong to the qualified range of peak power P, the welding is judged to be abnormal; or according to the welding time t i The qualified interval of welding time t is used to judge whether the battery core is welded abnormally. If the welding time t i If the welding time t is within the qualified range, the welding is considered normal. iIf the welding time does not fall within the qualified range of welding time t, the welding is judged to be abnormal.

[0042] In this embodiment, the qualified interval of the peak power P is obtained in the following manner:

[0043] In energy mode, set the sample welding parameters and collect the peak power P of N welding points. s , the N peak powers P s Generate a scatter plot of peak power and analyze the peak power P of the scatter plots by visual inspection or disassembly. s Perform the qualified test of the welding points from large to small until a qualified welding point is found and the peak power of the welding point is recorded as the maximum value P of the qualified interval of the peak power P. MAX ; The peak power P of the scattered points can be measured by visual inspection or disassembly s Perform qualified welding point tests from small to large until a qualified welding point is found and the peak power of the welding point is recorded as the minimum value P of the qualified interval of the peak power P. MIN ; The qualified interval of peak power P is [P MIN , P MAX ].

[0044] In this embodiment, if Figure 3 As shown, through the scatter distribution diagram of peak power, welding points with large peak power deviation can be found more quickly, and qualified inspections can be carried out on welding points in order of deviation to quickly find the upper and lower limits of the qualified range of peak power P, which provides great help when the N value in the sample is large.

[0045] In this embodiment, the qualified interval of the welding time t is obtained in the following manner:

[0046] In the energy mode, set the sample welding parameters and collect the welding time t of N welding points. s , N welding times t s Generate a scatter plot of welding time, and analyze the welding time t of the scattered points by visual inspection or disassembly. s Perform the welding point qualification test from large to small until a qualified welding point is found and record the welding time of the welding point as the maximum value t of the qualified interval of welding time t MAX ; Welding time t of scattered points by visual inspection or disassembly s Perform qualified welding point tests from small to large until a qualified welding point is found and record the welding time of the welding point as the minimum value t of the qualified interval of welding time t MIN ; The qualified interval of welding time t is [t MIN , t MAX ].

[0047] In this embodiment, if Figure 4 As shown, through the scatter distribution diagram of welding time, welding points with large welding time deviation can be found more quickly, and qualified inspections can be carried out on welding points in order of deviation to quickly find the upper and lower limits of the qualified interval of welding time t, which provides greater help when the N value in the sample is large.

[0048] In this embodiment, the sample welding parameters and the detection welding parameters are the same, and both the sample welding parameters and the detection welding parameters include welding amplitude, welding energy and welding pressure.

[0049] In this embodiment, a SCHUNK welding machine may be used to monitor the peak power and welding time of a welding point; in other embodiments, other welding methods may also monitor the peak power and welding time.

[0050] The technical solution of the present invention is based on welding the tab and the battery cell in an energy mode under certain welding amplitude, welding energy and welding pressure conditions, and collecting the peak power P of the welding point. s Or welding time t s , and generate the corresponding scatter distribution diagram, quickly find the qualified interval of peak power P or the qualified interval of welding time t, and based on the qualified interval of peak power P or the qualified interval of welding time t, weld in energy mode under the same welding amplitude, welding energy and welding pressure conditions, and detect the peak power P of the welding point in real time. i Or welding time t i Whether the welding point is abnormal is determined by whether it is within the qualified range of peak power P or the qualified range of welding time t, achieving 100% online detection, greatly improving the detection efficiency, eliminating the subjectivity and uncontrollability of manual detection, and the damage to the battery cell during the detection process; reducing the risk of poorly welded battery cells flowing out, reducing the safety risk of battery cells, and improving production efficiency and product quality.

[0051] Example 2

[0052] Combined with Figure 2 and attached Figure 5 The technical solution of the present invention is a method for detecting abnormal welding of a lithium-ion battery, comprising:

[0053] Select energy mode as welding mode and set the detection welding parameters in energy mode;

[0054] Input welding qualified interval, the welding qualified interval is the qualified interval of the ratio k of peak power P and welding time t;

[0055] Weld the tabs and cells and collect the peak power P during welding. i and welding time t i , and calculate the ratio ki , ratio k i =Peak power P i / Welding time t i ;

[0056] According to the ratio k i The qualified interval of the ratio k is used to judge whether the battery core is abnormally welded. If the ratio k i If the ratio k is within the qualified range, the welding is judged to be normal. i If the ratio k does not fall within the qualified range, the welding is judged to be abnormal.

[0057] In this embodiment, the qualified interval of the ratio k is obtained as follows:

[0058] In energy mode, set the sample welding parameters and collect the peak power P of N welding points. s and welding time t s , calculate the peak power P of each welding point s With welding time t s The ratio k s ; N ratios k s Generate a scatter plot of the ratios, and analyze the ratios k of the scatter plots by visual inspection or disassembly s Perform the qualified test of the welding points from large to small until a qualified welding point is found and record the ratio of the welding point as the maximum value k of the qualified interval of the ratio k MAX ; The ratio k of the scattered points by visual inspection or disassembly s Perform qualified welding point tests from small to large until a qualified welding point is found and record the ratio of the welding point as the minimum value k of the qualified interval of the ratio k. MIN ; The qualified interval of ratio k is [k MIN , k MAX ].

[0059] In this embodiment, the sample welding parameters and the detection welding parameters are the same, and both the sample welding parameters and the detection welding parameters include welding amplitude, welding energy and welding pressure.

[0060] In this embodiment, if Figure 5 As shown, through the scatter distribution diagram of the ratio k, welding points with large deviations of the ratio k can be found more quickly, and qualified inspections can be carried out on the welding points in order of the deviations to quickly find the upper and lower limits of the qualified interval of the ratio k, which provides great help when the N value in the sample is large.

[0061] In this embodiment, the ratio of peak power to welding time is used as the judgment factor. According to the energy mode, the welding energy is certain. When the peak power is smaller, the welding time is relatively longer. The ratio of peak power to welding time amplifies the discrete distribution of scattered points in the process of obtaining the qualified interval, which is convenient for obtaining a more accurate interval range for judging whether the welding point is qualified, and further improves the detection accuracy of abnormal battery cells.

[0062] The technical solution of the present invention is based on welding the tab and the battery cell in an energy mode under certain welding amplitude, welding energy and welding pressure conditions, and collecting the peak power P of the welding point. s and welding time t s , calculate the peak power P s and welding time t s The ratio k of s The corresponding scatter distribution diagram is used to quickly find the qualified interval of the ratio k, and based on the qualified interval of the ratio k, welding is performed in energy mode under the same welding amplitude, welding energy and welding pressure conditions, and the peak power P of the welding point is detected in real time. i and welding time t i Calculate the real-time ratio k i , according to the ratio k i Whether the welding point is abnormal is judged by whether it is within the qualified range of the ratio k, and 100% online detection is achieved, which greatly improves the detection efficiency, eliminates the subjectivity and uncontrollability of manual detection, and the damage to the battery cell during the detection process; reduces the risk of poorly welded battery cells flowing out, reduces the safety risk of battery cells, and improves production efficiency and product quality.

[0063] Example 3

[0064] A storage medium stores a computer program, which, when executed by a processor, implements the steps of the lithium-ion battery welding anomaly detection method described in Embodiments 1 and 2.

[0065] The present invention and its embodiments are described schematically above, and the description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. Therefore, if a person skilled in the art is inspired by it and designs a structural method and an embodiment similar to the technical solution without creativity without departing from the purpose of the invention, they shall all fall within the protection scope of the present invention.

Claims

1. A method for detecting abnormal welding of a lithium-ion battery, characterized in that: include: Select energy mode as welding mode and set the detection welding parameters in energy mode; Input welding qualified interval, the welding qualified interval is the qualified interval of the ratio k of peak power P and welding time t; Weld the tab and the cell, collect the peak power Pi and welding time ti during the welding process, and calculate the ratio ki, which is the peak power Pi / welding time ti. Whether the welding of the battery cell is abnormal is judged according to the qualified interval of the ratio ki and the ratio k. If the ratio ki belongs to the qualified interval of the ratio k, the welding is judged to be normal. If the ratio ki does not belong to the qualified interval of the ratio k, the welding is judged to be abnormal.

2. A lithium-ion battery welding anomaly detection method according to claim 1, characterized in that: The qualified interval of the ratio k is obtained as follows: In energy mode, set sample welding parameters, collect peak power Ps and welding time ts of N welding points, calculate the ratio ks of peak power Ps to welding time ts of each welding point; generate a scatter distribution diagram of the ratios ks for the N ratios ks, perform welding point qualification tests on the scatter ratios ks from large to small by visual inspection or disassembly, until a qualified welding point is found and the ratio of the welding point is recorded as the maximum value kMAX of the qualified interval of the ratio k; perform welding point qualification tests on the scatter ratios ks from small to large by visual inspection or disassembly, until a qualified welding point is found and the ratio of the welding point is recorded as the minimum value kMIN of the qualified interval of the ratio k; the qualified interval of the ratio k is [kMIN, kMAX].

3. A lithium-ion battery welding anomaly detection method according to claim 2, characterized in that: The sample welding parameters are the same as the detection welding parameters.

4. A lithium-ion battery welding anomaly detection method according to claim 3, characterized in that: The sample welding parameters and the detection welding parameters both include welding amplitude, welding energy and welding pressure.

5. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 4 are implemented.

Images