Manufacturing process and system for battery storage

Description

DESCRIPTION INTRODUCTION

[0001] The invention relates to a method for producing an electrode layer of a battery storage device according to the preamble of patent claim 1.

[0002] Furthermore, the invention relates to a manufacturing plant for producing an electrode layer of a battery storage device according to the preamble of patent claim 11. STATE OF THE ART

[0003] Lithium-ion accumulators, also referred to as lithium-ion batteries, are used as energy storage devices in mobile and stationary applications due to their high power density and energy density.

[0004] A lithium-ion battery typically comprises multiple battery cells. A battery cell, especially a lithium-ion battery cell, comprises a plurality of layers. These layers typically include anodes, cathodes, separators, and other elements. These layers can be configured as stacks or as windings.

[0005] The electrodes typically comprise metal foils, particularly copper and / or aluminum, coated with an active material. A paste containing lithium compounds or carbon, called a slurry, is typically applied as the active material. The foils and coating each have a thickness of several dozen micrometers. Even a deviation of just a few micrometers in the thickness of the coating, the porosity, or the material composition can have a negative impact on the quality of the electrode. The disadvantage of irregular coating is that battery cells of inferior quality are produced. Furthermore, safe operation of the battery cell cannot be guaranteed.

[0006] Detecting various defects (e.g., contamination, air bubble formation) online during the coating process remains a challenge. Sometimes they only become apparent after the entire battery cell production process has been completed, during a so-called end-of-line test. In some cases, defective coatings are only discovered after the battery cell has been in operation for several years.

[0007] The disadvantage is that a large proportion of defective battery cells are generated during battery production. The production process therefore requires a large amount of material and energy to produce a sufficient quantity of high-quality battery cells.

[0008] DE 10 2012 224264 A1 discloses a method for producing an electrode layer using an electrode layer paste (slurry) for lithium-ion batteries. The layer thickness is measured as a system parameter and compared with a target value. If the desired layer thickness is not achieved, the pressure in the extrusion head is automatically corrected. Other possible corrections include the distance of the extrusion head from the substrate, the transport speed, the geometry of the nozzles used in the extrusion head, the composition of the slurry, and the viscosity.

[0009] CN 107 958 988 A discloses a method for producing an electrode layer for a lithium-ion battery, in which an electrode layer paste is applied to a carrier material. The method comprises the process steps of recording the system parameters, comparing them with error clusters, creating correction models, and adjusting the system parameters.

[0010] It is therefore an object of the present invention to provide a method for producing an electrode layer of a battery storage device and a production plant for producing an electrode layer of a battery storage device, which enable rapid and immediate intervention in the production process and thus reduce the reject rate in battery production. SOLUTION TO THE TASK

[0011] This object is achieved according to the invention with a method for producing an electrode layer of a battery storage device according to claim 1 and a production plant for producing an electrode layer of a battery storage device according to claim 12. DESCRIPTION OF THE INVENTION

[0012] The method according to the invention relates to the production of an electrode layer of a battery storage device in at least one system using an electrode layer paste. The method comprises several steps. First, system parameters associated with the production of the electrode layer are recorded. Furthermore, one or more measured values of at least two measured variables of the electrode layer paste and / or the electrode layer are recorded. Furthermore, a correction value is determined from a comparison of the recorded measured values of the electrode layer with a defined target value range for the measured variables. The system parameters are adjusted depending on the determined correction value.

[0013] According to the invention, the battery storage device is a lithium-ion battery.

[0014] According to the invention, at least one spatially and / or temporally resolved porosity of a layer thickness of the electrode layer is recorded as a measurement variable of the electrode layer.

[0015] The manufacturing plant according to the invention for producing an electrode layer of a battery storage device is designed to carry out the method according to the invention.

[0016] According to the invention, a plant is understood in particular to be a facility in which one or more of the following process steps take place: Manufacturing, processing and / or conveying the electrode layer paste, coating the electrode with the electrode layer paste, drying the coating and / or calendering the electrode layer.

[0017] Electrode layer paste (slurry) is the raw substance used to produce the electrode layer that is applied to the electrodes.

[0018] According to the invention, system parameters are understood to be one or more parameters that can be adjusted within the system. In particular, controllable environmental influences are also understood to be system parameters. System parameters include, for example, Mass flow of the electrode layer paste, roller speed of the rollers during calendering, properties of the nozzles, in particular deposits and / or blockages, temperature, in particular fluctuations in the temperature e.g. of the electrode layer paste, viscosity of the electrode layer paste, and / or contamination in particular of the electrode layer paste, for example from the environment.

[0019] Depending on the respective process step being examined, the measured variable refers to the electrode layer paste and / or the electrode layer produced from the electrode layer paste. Parameters measured for the electrode layer paste include, for example, temperature, viscosity, particle size distribution, degree of dispersion, environmental contamination, and similar. Parameters measured for the electrode layer include, for example, the surface topology and defects, porosity, composition, or conductivity of the electrode layer, for which the measured values are determined, for example, using eddy current measurements. According to the invention, at least the porosity is used.

[0020] According to the invention, the correction value is / are to be understood as one or more changes in the system parameters in order to achieve a desired change in the properties of the electrode layer paste or the electrode layer in the further production process.

[0021] The correction value is determined in particular by comparing the measured values with defined target value and error value ranges, whereby in the case of measured variables outside the target value range, specific correction values are defined for the error value ranges.

[0022] Advantages and embodiments of the invention, which can be used individually or in combination with one another, are the subject of the dependent claims.

[0023] In an advantageous embodiment of the invention, the electrode layer paste comprises a material containing lithium. Lithium, especially the lithium-containing materials used in lithium-ion batteries, have a particularly high energy density.

[0024] Furthermore, it is advantageous that the measured values of the following parameters are also measured: one or more surface features, such as the occurrence of agglomerates, depressions and / or holes, scratches, frayed edges and / or roughnesses, layer thickness, in particular temporal fluctuations in the layer thickness, and / or spatial gradients in the layer thickness, and / or one or more spatial inhomogeneities in the electrical conductivity, in particular spatial and / or temporal fluctuations in the inhomogeneity of the electrical conductivity.

[0025] In a further embodiment of the method according to the invention, the following additional steps are carried out: Comparing one or more measured values with defined error clusters, selecting the error cluster and setting the system parameters based on a correction value defined for the error cluster.

[0026] According to the invention, an error cluster is understood to be a collection of surface phenomena, i.e., combinations of measured values of one or more measured variables that lie outside the setpoint range. In particular, the combination of several conspicuous measured variables with measured values outside a specified setpoint range can constitute an error cluster that is appropriate for a current situation in the system. One or more system parameters can be adjusted using a correction value, whereby the correction value is specified for the respective error cluster. The correction value can comprise several individual correction values that relate to different system parameters. In particular, the correction value is transmitted via control signals to a system controller for adjusting the system parameters.

[0027] A further embodiment of the method according to the invention comprises a further method step, namely the creation of correction models that map the relationship between measured values of various measurands, particularly for measured values outside a target value range, and system parameters. Correction models can be created, for example, on the basis of laboratory tests that map the relationship between measured variables, particularly surface features, or defect clusters, and the causative physical causes of defects. These models can be used in so-called soft sensors to determine the necessary correction values for the causative system parameters from variables measurable in the production plant. The correction models contain, in particular, these relationships and correction values for regaining the target values during the further course of the manufacturing process.

[0028] In another variant, a supervised or unsupervised machine learning method, combined with knowledge-based models and / or physical models, creates the correction models. This advantageously allows the insights gained from laboratory tests and ongoing plant operation regarding the relationships between the correction values and the effects on the measured variables to be analyzed and the correction models to be adapted.

[0029] In an advantageous embodiment, the machine learning method uses neural networks, deep learning methods, clustering methods or physically informed neural networks to adapt the correction models.

[0030] The correction models created in this way or as described above by means of laboratory tests can also be used as so-called soft sensors in running production lines of the plant like real sensors, although the effects to be determined are only indirectly derived from other measured variables and plant parameters.

[0031] In an advantageous embodiment of the method, the system parameters are adjusted continuously during the operation of the system by means of a feedback loop.

[0032] In a further embodiment, the correction models are adapted in-situ and iteratively by analyzing the effects of the changes made in the feedback loop by implementing the correction value and using them in a machine learning process in combination with knowledge-based models to improve the correction models or correction values.

[0033] In a further embodiment of the invention, a soft sensor takes over the steps of detecting one or more measured values of a measured variable, determining the correction value and setting the system parameters.

[0034] In a further embodiment, the changes in the measured values of various measured variables are tracked following adjustment of the system parameters using the correction value.

[0035] The invention further comprises a manufacturing plant for producing an electrode layer of a battery storage device, which is designed to carry out a method according to the preceding claims. FIGURE DESCRIPTION

[0036] In the following, the invention is described with reference to the Figure 1 The illustrated embodiment is described and explained in more detail.

[0037] In the exemplary embodiment and the figure, identical or similarly functioning elements may be provided with the same reference numerals. The illustrated elements and their relative sizes are generally not to scale; rather, individual elements may be shown larger in proportion for clarity and / or clarity.

[0038] Figure 1 shows a manufacturing plant 1. The manufacturing plant 1 comprises an electrode layer manufacturing device 8. The electrode layer manufacturing device 8 comprises two measuring devices: a laser scanning device 5 as a first measuring device 5, and a porosity measuring device 9 as a second measuring device. Both measuring devices are connected to a computing unit 100 via data cables.

[0039] The electrode layer production device 8 comprises a carrier substrate 3, onto which an electrode layer 4 made of an electrode layer paste 2 (slurry) is applied. The electrode layer paste 2 is homogenized in a container by means of an agitator 7. The agitator 7 and the substrate transport are also connected to the computing unit 100 via a data cable.

[0040] In this example, the laser scanning device 5 records at least one image of the electrode layer 4 and a piece of location information E1 as a second measurement variable. Furthermore, the porosity measuring device 9 determines a first measurement variable, namely a porosity and / or an electrode layer thickness of the electrode layer 4. In this example, the analysis takes place at the same location on the electrode layer 4 after the substrate with the electrode layer 4 has been transported further, now marked with the location information E1'. The porosity analysis takes place during the production of the electrode layer 4. The porosity measuring device is then designed, in particular, as an ultrasound measuring unit, an X-ray absorption unit, or a computer tomography scanner.

[0041] In this example, the measured values determined by the porosity measuring device 9 (first measured variable) and the laser scanning device 5 (second measured variable) are transmitted to the computing unit 100. In the computing unit 100, the measured values—here, the topological properties—of the electrode layer 4 are then determined based on at least one image from the laser scanning device 5 as a function of the location information E1. In the computing unit 100, the transmitted values are compared with the target value ranges defined for the respective measured variables.

[0042] A storage unit 110 stores correction models determined based on laboratory tests. These models depict the relationship between surface features or clusters of features and the causes of defects, particularly physical ones. Furthermore, the correction models include correction values for plant parameters of the production plant, which can eliminate the causes of defects during plant operation. Examples of correction values can relate to the viscosity or temperature of the electrode layer paste, the conveying speed of the electrode layer paste, particle size distribution, particle dispersion in the slurry, the roller speed of the electrode foils (substrates), the condition and settings of coating nozzles and slots, their gap size, etc.

[0043] The computing unit 100 transmits the correction values to the control of the electrode layer production device 8, so that the system parameters are adjusted during ongoing operation of the production plant 1.

[0044] The correction models created in this way are also called soft sensors because they can be used in the running production lines like real sensors, although the effects to be determined are only indirectly derived from other measurement and control variables.

[0045] The magnitude of the correction value can either be learned from measurement data or iteratively adjusted in-situ through the effect of a feedback loop. The use of an AI engine for adapting, improving, and training the correction models is advantageously suitable. The AI unit is connected to the computing unit 100 via a data line. The AI unit is connected to the data transmitted by the laser scanning device 5 and the porosity measuring device 9, as well as to the first control signals 101 and second control signals 102 transmitted to the electrode layer production device 8 via the control signal 101. In other embodiments, the AI unit could be connected to optical cameras in combination with image processing methods, hyperspectral cameras, and / or eddy current measuring devices.

[0046] The AI unit thus continuously evaluates the effects of the correction models and improves the correction models stored in the storage unit 110 during ongoing operation of the production plant 1. CONCLUSION

[0047] Although the invention has been illustrated and described in detail by the preferred embodiment, the invention is not limited to the disclosed examples. Variations may be derived by those skilled in the art without departing from the scope of the invention as defined by the following claims.

Claims

1. Method for producing an electrode layer of a battery store by means of at least one system, in which an electrode layer paste is used, comprising the following steps: - acquiring system parameters of at least one system associated with the production of the electrode layer, - acquiring one or more measured values of a measured variable of the electrode layer paste and / or the electrode layer, - calculating / determining a correction value from a comparison of the acquired measured value of the electrode layer with a defined target value range and - setting the system parameters as a function of the calculated correction value.

2. Method according to claim 1, characterised in that a material comprising lithium and / or carbon is used as the electrode layer paste.

3. Method according to one of the preceding claims, characterised in that when the one or more measured values is acquired - one or more surface features, - a layer thickness, - a gradient of the layer thickness, - a porosity of the layer thickness and / or - one or more spatial inhomogeneities of the electrical conductance are acquired.

4. Method according to one of the preceding claims, comprising the following steps: - comparing the one or more measured values with defined error clusters, - selecting the relevant error cluster and - setting the system parameters with the aid of a correction value defined for the error cluster.

5. Method according to one of the preceding claims, comprising the following step: - creating correction models, which map the relationship between measured values of different measured variables and system parameters.

6. Method according to one of the preceding claims, wherein a supervised or unsupervised machine learning method creates the correction models in combination with knowledge-based models and / or physical models.

7. Method according to one of the preceding claims, wherein the machine learning method uses neural networks, deep-learning methods, cluster methods or physically informed neural networks.

8. Method according to one of the preceding claims, wherein the system parameters are set by means of a feedback loop.

9. Method according to one of the preceding claims, wherein an adjustment of the correction models is carried out iteratively in situ by the effect of the feedback loop on the measured values.

10. Method according to one of the preceding claims, wherein a soft sensor carries out the steps: acquiring the one or more measured values of a measured variable, determining the correction value and setting the system parameters.

11. Method according to one of the preceding claims, comprising the following step: - acquiring the changes to the measured values of different measured variables after setting the system parameters with the aid of the correction value.

12. Production system for producing an electrode layer of a battery store, characterised in that it is embodied to carry out a method according to one of the preceding claims.