Method and system for electrode defect detection
The method and system for detecting electrode defects in lithium-ion batteries utilize an inductive stimulator and thermal imaging to identify defects early in the manufacturing process, enhancing detection efficiency and reducing waste by ensuring only functional electrodes are used in battery assembly.
Patent Information
- Application Number
- FR2022011572
- Authority / Receiving Office
- FR · FR
- Patent Type
- Patents
- Current Assignee / Owner
- Filing Date
- 2022-11-07
- Publication Date
- 2025-01-10
- Estimated Expiration
- 2042-11-07
AI Technical Summary
Existing methods for detecting electrode defects in lithium-ion batteries, such as those described in patent application JP20150022094, fail to identify defects in the electrodes during the manufacturing process and often result in discarding non-defective components, leading to inefficiencies and potential safety risks.
A method and system using an inductive stimulator, thermal imager, and computer program with an extraction algorithm to detect electrode defects by obtaining and analyzing thermographic data, allowing for early identification and sorting of defective electrodes without disrupting the manufacturing process.
Enables efficient and non-destructive detection of electrode defects, reducing waste and ensuring only functional electrodes are included in the battery, thereby improving safety and efficiency in the manufacturing process.
Smart Images
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Abstract
Description
Title of the invention: Method and system for detecting electrode faults Technical field of the invention
[0001] The invention relates to the technical field of electric batteries, in particular the invention relates to a method and a system for detecting a burr on an active electrode material during the manufacture of a lithium-ion cell. Prior art
[0002] During the manufacture of an electrode intended to be included in a battery, rough edges or ridges may form on the surface of the electrode. This occurs because cutting operations are carried out during the manufacture of the electrode. These rough edges or ridges are called burrs and have an impact on the quality and performance of the battery. A burr may pierce the separator and, consequently, bring two electrodes of opposite polarity (an anode and a cathode) into direct electrical contact. This results in a short circuit which may have serious consequences, such as the establishment of an electric arc and / or spontaneous combustion during use of the battery, or even a fire.
[0003] Due to the very nature of active materials and despite efforts to achieve good quality clean cuts, the presence of burrs is often unavoidable. Therefore, the identification of surface and subsurface burrs is crucial in order to better detect the electrode that has a defect so that it is not used in the manufacture of the battery.
[0004] Many attempts to detect the presence of a defect during battery manufacturing have been made. For example, patent application JP20150022094 discloses a method for inspecting the presence of a defect on an inner surface of a metal case for a lithium-ion battery in which, after an electrode assembly and an electrolyte are inserted into a metal case, a surface of the metal case is pressed with a binding device, and the lithium-ion battery to be inspected is then heated. In order to inspect the presence of at least one defect on the inner surface of the metal case, a temperature distribution of the metal case is observed for a set period of time until the temperature of the battery returns to a normal state.
[0005] However, the method described in patent application JP20150022094 only identifies the defect on the metal casing and does not detect the defect of the components inside the battery. In particular, the method described in patent application JP20150022094 does not help to detect any electrode defect inside the battery.
[0006] Furthermore, the method described in patent application JP20150022094 only detects the defect once the battery is assembled at a later stage of the manufacturing process, which means that if a defect is detected on the metal casing, the entire battery and the components inside must be scrapped even if these components are not themselves defective. General Description
[0007] The purpose of the present invention is to provide a defect detection method which can detect an electrode defect at an early stage of the manufacturing process without disrupting the battery manufacturing process while reducing the number of components in the battery likely to be discarded.
[0008] To this end, the present invention relates to a defect detection method for detecting a defect in an electrode intended to be included in a battery during the manufacture of the electrode, said detection method comprising the following steps: - placing the electrode on a primary conveyor and circulating it by means of this primary conveyor through an inductive stimulator and a field of vision of a thermal imager; - heating a surface of said electrode using the inductive stimulator; - obtaining thermographic data from different positions on the electrode surface using the thermal imager at different times as the electrode moves on the primary conveyor, in order to assign the electrode several thermal image sequences; - inserting the thermographic data into a computer program comprising an extraction algorithm, the extraction algorithm being configured to extract defect characteristics from the plurality of thermal image sequences; - sorting the electrodes according to the extracted fault characteristics, such as: if the extracted fault characteristics satisfy a predefined selection criterion, the electrode is deemed functional and the electrode remains on the primary conveyor in order to be included in the battery, if the extracted defect characteristics do not meet the predefined selection criterion, the electrode is considered defective and the electrode is transferred to at least one secondary conveyor.
[0009] According to one feature, the inductive stimulator is configured to deliver a current at a variable operating frequency depending on the extracted fault characteristics.
[0010] According to one characteristic, the fault detection method further comprises a step consisting of:
[0011] - adjust the operating frequency of the inductive stimulator according to the ca extracted defect characteristics to improve electrode defect detection on the electrode surface.
[0012] According to one characteristic, the inductive stimulator is an eddy current probe.
[0013] According to one characteristic, the fault detection method further comprises a step of: - formatting of thermographic data using a conditioning circuit prior to entering the thermographic data into the computer program.
[0014] According to one characteristic, the fault detection method comprises a step of adapting at least one parameter of the conditioning circuit as a function of the extracted fault characteristics.
[0015] According to one characteristic, the extraction algorithm is a machine learning algorithm, in particular an anomaly detection algorithm or a classification algorithm.
[0016] The invention further relates to a defect detection system for detecting an electrode defect during the manufacture of an electrode intended to be included in a battery, said defect detection system comprising: - an inductive stimulator heating a surface of the electrode; - a thermal imager; - a primary conveyor on which the electrode is arranged and by means of which the electrode circulates through an inductive stimulator and a field of vision of a thermal imager, the thermal imager generating thermographic data from different positions on the electrode surface at different times as the electrode moves on the primary conveyor, such that the electrode is assigned multiple thermal image sequences; - a computer program comprising an extraction algorithm, into which the thermographic data is inserted, the extraction algorithm being configured to extract defect characteristics from the thermal image sequences; - at least one secondary conveyor; the electrode being transferred to the at least one secondary conveyor if the extracted defect characteristics do not meet a predefined selection criterion and the electrode is deemed defective, And the electrode remaining on the primary conveyor to be included in the battery if the extracted defect characteristics meet the predefined selection criterion and the electrode is deemed functional.
[0017] According to one feature, the inductive stimulator is configured to deliver a current at a variable operating frequency depending on the extracted fault characteristics.
[0018] According to one characteristic, the inductive stimulator is an eddy current probe.
[0019] According to one feature, the defect detection system further comprises a conditioning circuit configured to format the thermographic data before inserting the thermographic data into the computer program. Brief description of the figures
[0020] The invention will now be described, by way of example only, with reference to the attached figure:
[0021] [Fig.l] [Fig.l] is a schematic representation of the fault detection system according to the invention.
[0022] In this figure, the same references are used to designate the same elements. For reasons of clarity, the figure is not necessarily reproduced to scale. Additional characteristics may emerge from the following description. Detailed description
[0023] The invention relates to a defect detection method for detecting a defect in an electrode 1 intended to be included in a battery, such as for example a lithium-ion battery, during the manufacture of the electrode 1.
[0024] More generally, the fault detection method described can be applied to any type of secondary cell.
[0025] The detection method comprises a first step which consists of placing the electrode 1 on a primary conveyor 5 and circulating it by means of this primary conveyor 5 through an inductive stimulator 2 and a field of vision of a thermal imager 3 as shown in [Fig.l].
[0026] An electrode sheet 1 can be continuously supplied by an electrode roll 1, and a cutter cuts the electrode sheet 1 to obtain the electrode 1 to be tested.
[0027] The cutter may have a speed of between 20 meters / minute and 100 meters / minute and preferably substantially equal to 80 meters / minute and an encoder may be used to measure the speed of the cutter.
[0028] The thermal imager 3 may for example be an infrared camera producing a two-dimensional image generated from the thermographic data.
[0029] The thermal imager 3 may have a resolution of between 100 and 2000 micrometers, and preferably substantially equal to 700 micrometers at a frame rate of 1700 images per second.
[0030] According to one possibility, the thermal imager 3 is configured such that the thermal imager 3 faces one side of the electrode 1 as shown in [Fig.l]. For example, the thermal imager 3 may be configured to face an upper face of the electrode 1 and another thermal imager may be configured to face a lower face of the electrode 1.
[0031] The inductive stimulator 2 can be configured to deliver a current at a variable operating frequency depending on the extracted fault characteristics.
[0032] The inductive stimulator 2 may be an eddy current probe.
[0033] Advantageously, the use of an eddy current probe in the described defect detection method allows non-destructive testing of the electrode 1.
[0034] The fault detection method may further comprise a step of adjusting the operating frequency of the inductive stimulator 2 as a function of the extracted fault characteristics in order to improve the detection of the electrode fault 1 on the surface of the electrode 1. The operating frequency may vary between 50Hz and 1000MHz for example.
[0035] Advantageously, adjusting the operating frequency of the inductive stimulator 2 makes it possible to refine the detection of defects and therefore to meticulously detect the defects of the electrodes.
[0036] Advantageously, having an inductive stimulator 2 with variable frequency makes it possible to refine the frequency of the current generated by the inductive stimulator 2.
[0037] The detection method also comprises a step which consists of heating a surface of said electrode 1 using the inductive stimulator 2.
[0038] Following heating of the electrode surface, thermographic data of different positions on the surface of the electrode 1 are obtained using the thermal imager 3 at different times as the electrode 1 moves on the primary conveyor 5, in order to assign to the electrode 1 several sequences of thermal images.
[0039] Then, a step is executed which consists of inserting the thermographic data into a computer program 4 comprising an extraction algorithm. The extraction algorithm is configured to extract defect characteristics from the several sequences of thermal images.
[0040] The extraction algorithm may be a machine learning algorithm, in particular an anomaly detection algorithm or a classification algorithm.
[0041] For example, the extraction algorithm may be a convolutional neural network CNN or a long short-term memory network LSTM.
[0042] Advantageously, the use of machine learning algorithms makes it possible to automatically detect defects quickly and efficiently.
[0043] For example, the following may be considered defects: - a first image defect derived above an aspect ratio of 1.1; - a 2nd derived image defect greater than 0.8 and - the 3rd derivative image defect greater than 0.7.
[0044] In order to detect the smear or anomaly, a derivative of an intensity value f(x,y) on the image made by the thermal imager is calculated in order to find a place where the derivative is maximum. The smear could then be located following detection of a strong intensity of the gradient in the image.
[0045] The components of the gradient are described by the following approximation:
[0046] [Math.l] df(x,y) _ a / (^AvL / IaO Za vv j
[0047] [Math.2]
[0048] Where the dx and dy graders measure a distance along the x and y axes of the image respectively.
[0049] In discrete images, dx and dy can be measured in terms of the number of pixels between two points. The point for which we have dx = dy = 1 is a point at which the pixel coordinates are called (i, j), so we have:
[0050] [Math.3] A j =
[0051] [Math.4] ay = 1)
[0052] In order to detect the presence of a gradient discontinuity, one could calculate the gradient change at the point with coordinates (i, j). This can be done by finding the following magnitude measure:
[0053] [Math.5] M = ln(\y AL + A y 2 )
[0054] and the direction of the gradient is given by:
[0055] [Math.6] 6= tan4^- Ax
[0056] Thus, for example, if M > 1.1, which represents a threshold value, then the anomaly is classified as a burr.
[0057] The aspect ratio is a ratio between the thickness of the material and the depth of the defect.
[0058] According to one possibility, the thermographic data are formatted using a conditioning circuit 6 prior to the entry of the thermographic data into the computer program 4. For example, the thermographic data can be sorted and formatted to be analyzed by the computer program 4.
[0059] Advantageously, the formatting of the thermographic data prior to the entry of the thermographic data into the computer program 4 allows better analysis of the thermographic data and reduces the risk of error during the analysis.
[0060] The conditioning circuit 6 may comprise an analog circuit or a digital circuit. For example, the conditioning circuit 6 may comprise a median filter used to remove salt and pepper noise or impulse noise from a thermal image.
[0061] Furthermore, the fault detection method may comprise a step of adapting at least one parameter of the conditioning circuit 6 as a function of the extracted fault characteristics.
[0062] The at least one parameter of the conditioning circuit 6 may be a kernel of the median filter which may be adaptively modified on the basis of a response of the inductive stimulator 2, and therefore as a function of the extracted fault characteristics.
[0063] Advantageously, making the at least one parameter of the conditioning circuit 6 depend on the extracted fault characteristics makes it possible to adapt the conditioning circuit 6 to the extracted fault characteristics in order to characterize the fault more precisely.
[0064] An electrode sorting step is then performed based on the extracted defect characteristics.
[0065] Thus, if the extracted fault characteristics satisfy a predefined selection criterion, the electrode 1 is deemed functional and the electrode 1 remains on the primary conveyor 5 in order to be included in the battery. If, on the contrary, the extracted fault characteristics do not satisfy the predefined selection criterion, the electrode 1 is considered defective and the electrode 1 is transferred to at least one secondary conveyor 5'.
[0066] The selection criterion may be a temperature-related parameter such as a logarithmic derivative of temperature.
[0067] According to one embodiment, the electrode 1 which is transferred onto the at least one secondary conveyor 5' can be scrapped in a scrapping machine where the defective electrodes are placed.
[0068] Advantageously, the described defect detection method can identify electrode defects 1 quickly and efficiently without interrupting the battery manufacturing process.
[0069] Advantageously, the defect detection method can be used at any stage of the battery manufacturing process to identify the surface and subsurface defects of electrodes 1.
[0070] The invention further relates to a defect detection system for detecting a defect in an electrode 1 during the manufacture of an electrode 1 intended to be included in a battery. This defect detection system is shown in [Fig.l].
[0071] The fault detection system comprises the predescribed inductive stimulator 2 which heats a surface of the electrode 1.
[0072] The inductive stimulator 2 can be configured to deliver a current at a variable operating frequency depending on the extracted fault characteristics.
[0073] Advantageously, having an inductive stimulator 2 with variable frequency makes it possible to refine the frequency of the current generated by the inductive stimulator 2.
[0074] The inductive stimulator 2 may be an eddy current probe.
[0075] Advantageously, the eddy current probe allows testing by eddy currents Foucault which uses the principle of electromagnetic induction to detect the defect of electrode 1 in a simple, economical and non-invasive way, which means that there is no break in the surface of electrode 1.
[0076] The fault detection system also comprises: - thermal imager 3; - the primary conveyor 5 on which the electrode 1 is arranged and by means of which the electrode 1 circulates through the inductive stimulator 2 and the field of vision of the thermal imager 3, the thermal imager 3 generating thermographic data from different positions on the surface of the electrode 1 at different times as the electrode 1 moves on the primary conveyor 5, so that the electrode 1 is assigned several sequences of thermal images; - the computer program 4 comprising the extraction algorithm, into which the thermographic data is inserted, the extraction algorithm being configured to extract defect characteristics from the thermal image sequences; - at least one 5' secondary conveyor.
[0077] The fault detection system may further comprise a conditioning circuit 6 configured to format the thermographic data before inserting the thermographic data into the computer program 4.
[0078] Advantageously, the conditioning circuit 6 processes the thermographic data in such a way that said thermographic data meets the requirements of the computer program 4 for carrying out an analysis of said thermographic data.
[0079] The conditioning circuit 6 may comprise a voltage and current limiting device and an anti-aliasing filter.
[0080] In accordance with what has been described previously, the electrode 1 is transferred to the at least one secondary conveyor 5' if the extracted fault characteristics do not meet a predefined selection criterion and the electrode 1 is deemed defective.
[0081] On the contrary, electrode 1 remains on the primary conveyor 5 in order to be included in the battery if the extracted fault characteristics satisfy the predefined selection criterion and electrode 1 is deemed functional.
[0082] Advantageously, the fault detection system allows the implementation of the fault detection method described previously.
[0083] The detection method and the detection system described above have an industrial application in the field of manufacturing secondary cells.
[0084] What applies in this detailed description to the detection method also applies to the detection system, and vice versa.
[0085] Although the invention has been described in relation to particular embodiments, it is obvious that it is in no way limited thereto and that it includes all technical equivalents of the means described as well as their combinations if these fall within the scope of the invention.
Claims
Claims
1. A defect detection method for detecting a defect in an electrode (1) intended to be included in a battery during the manufacture of the electrode (1), said detection method comprising the following steps: - arranging the electrode (1) on a primary conveyor (5) and circulating it by means of this primary conveyor (5) through an inductive stimulator (2) and a field of view of a thermal imager (3); - heating a surface of said electrode (1) using the inductive stimulator (2); - obtaining thermographic data of different positions on the surface of the electrode (1) using the thermal imager (3) at different times as the electrode (1) moves on the primary conveyor (5), in order to assign to the electrode (1) several sequences of thermal images;- inserting the thermographic data into a computer program (4) comprising an extraction algorithm, the extraction algorithm being configured to extract fault characteristics from the plurality of thermal image sequences; - sorting the electrodes according to the extracted fault characteristics, such that: if the extracted fault characteristics satisfy a predefined selection criterion, the electrode (1) is deemed functional and the electrode (1) remains on the primary conveyor (5) in order to be included in the battery, if the extracted fault characteristics do not satisfy the predefined selection criterion, the electrode (1) is considered defective and the electrode (1) is transferred to at least one secondary conveyor (5').;
2. A fault detection method according to claim 1, wherein the inductive stimulator (2) is configured to deliver a current at a variable operating frequency depending on the extracted fault characteristics.
3. A fault detection method according to claim 2, further comprising a step of: - adjusting the operating frequency of the inductive stimulator (2) according to the extracted fault characteristics in order to improve the detection of the electrode defect (1) on the surface of the electrode (1).
4. A fault detection method according to any one of claims 1 to 3, wherein the inductive stimulator (2) is an eddy current probe.
5. A method of detecting defects according to any one of claims 1 to 4, further comprising a step of: - shaping the thermographic data using a conditioning circuit (6) comprising a median filter, the step of shaping the thermographic data comprising the removal, by the median filter, of impulse noise from said several sequences of thermal images prior to the entry of the thermographic data into the computer program (4).
6. A fault detection method according to claim 5, comprising a step of adapting at least one parameter of the conditioning circuit (6) as a function of the extracted fault characteristics, said parameter of the conditioning circuit (6) being a kernel of the median filter capable of being modified adaptively on the basis of a response of the inductive stimulator (2).
7. A fault detection method according to any one of claims 1 to 6, wherein the extraction algorithm is a convolutional neural network CNN or a long short-term memory network LSTM.
8. A defect detection system for detecting a defect in an electrode (1) during the manufacture of an electrode (1) intended to be included in a battery, said defect detection system comprising: - an inductive stimulator (2) heating a surface of the electrode (1); - a thermal imager (3); - a primary conveyor (5) on which the electrode (1) is arranged and by means of which the electrode (1) circulates through an inductive stimulator (2) and a field of view of a thermal imager (3), the thermal imager (3) generating thermographic data from different positions on the surface of the electrode (1) at different times as the electrode (1) moves on the primary conveyor (5), so that the electrode (1) is assigned several sequences of thermal images;- a computer program (4) comprising an extraction algorithm, into which the thermographic data are inserted, the extraction algorithm being configured to extract characteristics; fault characteristics of the thermal image sequences; - at least one secondary conveyor (5'); the electrode (1) being transferred to the at least one secondary conveyor (5') if the extracted fault characteristics do not meet a predefined selection criterion and the electrode (1) is deemed defective, and the electrode (1) remaining on the primary conveyor (5) in order to be included in the battery if the extracted fault characteristics meet the predefined selection criterion and the electrode (1) is deemed functional.
9. A fault detection system according to claim 8, wherein the inductive stimulator (2) is configured to deliver a current at a variable operating frequency according to the extracted fault characteristics.
10. A fault detection system according to any one of claims 8 and 9, wherein the inductive stimulator (2) is an eddy current probe.
11. A fault detection system according to any one of claims 8 to 10, further comprising a conditioning circuit (6) comprising a median filter configured to remove impulse noise from the thermal images, so as to format the thermographic data before inserting the thermographic data into the computer program (4).