METHOD FOR PRODUCING AT LEAST ONE ELECTRODE FOR A BATTERY CELL

Abstract

The invention relates to a method for producing at least one electrode for a battery cell, the method comprising at least the following steps: a) providing a suspension for creating the at least one electrode, at least one target process parameter (1) being specifiable for providing the suspension; b) capturing at least one actual process parameter (2) while providing the suspension in step a); c) performing a prediction of at least one property (4) of the suspension by means of a machine-learned prediction algorithm (5), which estimates the at least one property (4) of the suspension depending on the at least one actual process parameter (2) and taking into account information on previously provided suspensions; d) defining at least one target process parameter (1) for providing the suspension in step a) depending on the prediction results from step c).

Claims

1. A method for producing at least one electrode for a battery cell, the method comprising at least the following steps: a) providing a suspension for creating the at least one electrode, at least one target process parameter being specifiable for providing the suspension; b) capturing at least one actual process parameter while providing the suspension in step a); c) performing a prediction of at least one property of the suspension by means of a machine-learned prediction algorithm, which estimates the at least one property of the suspension depending on the at least one actual process parameter and taking into account information on previously provided suspensions; d) defining at least one target process parameter for providing the suspension in step a) depending on the prediction results from step c).

2. The method according to claim 1, wherein the method is performed for producing at least one anode or cathode for a lithium-ion cell.

3. The method according to claim 1, wherein the step of providing the suspension takes place by extruding the suspension into a container.

4. The method according to claim 1, wherein the machine-learned prediction algorithm is formed by means of an artificial neural network.

5. The method according to claim 1, wherein the machine-learned prediction algorithm is formed by means of a long short-term memory network.

6. The method according to claim 1, wherein at least one property of the suspension is measured in order to validate a prediction that has been performed.

7. A training method for a machine-learning-capable prediction algorithm, the method comprising at least the following steps: i) reading in training input data for the prediction algorithm, which comprise actual process parameters of a large number of provision processes for providing a suspension for creating an electrode for a battery cell; ii) reading in training output data for the prediction algorithm, which comprise properties of the suspension provided by means of the corresponding provision process; and iii) adapting elements of the prediction algorithm in order to map the training input data that have been read in as precisely as possible to the training output data that have been read in.

8. The training method according to claim 7, wherein a gradient descent method is used to adapt elements of the prediction algorithm.

9. A computer program for performing a method according to claim 7.

10. A machine-readable storage medium on which the computer program according to claim 9 is stored.

11. A computer program for performing a method according to claim 1.

12. A machine-readable storage medium on which the computer program according to claim 11 is stored.

Description

[0039] The solution presented herein and its technical field are explained in more detail below with reference to the drawings. It should be pointed out that the invention is not intended to be limited by the embodiments shown. In particular, unless explicitly stated otherwise, it is also possible to extract partial aspects of the facts explained in or in connection with the drawings and to combine them with other components, and/or findings from other drawings, and/or the present description. Schematically, in the drawings:

[0040] FIG. 1: shows a flowchart to illustrate an embodiment of the method described herein;

[0041] FIG. 2: shows an illustration of an embodiment of the prediction algorithm described herein;

[0042] FIG. 3: shows an illustration of possible prediction results of the prediction algorithm; and

[0043] FIG. 4: shows a flowchart to illustrate an embodiment of the training method described herein.

[0044] FIG. 1 schematically shows a flowchart to illustrate an embodiment of the method described herein for the production of at least one electrode for a battery cell. The sequence of steps a), b), c), and d) represented by blocks 110, 120, 130, and 140 is an example and can thus occur, for example, in a regular sequence of the method.

[0045] In block 110 and according to step a), providing of a suspension takes place for creating the at least one electrode, at least one target process parameter 1 being specifiable for providing the suspension. In block 120 and according to step b), capturing of at least one actual process parameter 2 and, for example, at least one ambient condition 3 takes place during the provision in step a). In block 130 and according to step c), performing of a prediction of at least one property 4 of the suspension by means of a machine-learned prediction algorithm 5 takes place, which estimates the at least one property 4 of the suspension depending on the at least one actual process parameter 2 and, for example, on the at least one ambient condition 3, and taking into account information on previously provided suspensions. In block 140 and according to step d), defining of at least one target process parameter 1 takes place for providing the suspension in step a) depending on the prediction results from step c).

[0046] The method can be performed, for example, for producing at least one anode or cathode for a lithium-ion cell. In this context, provision can also be made for the suspension to be provided by extruding the suspension into a container. The extruder usually produces battery suspension continuously. During the extrusion, a machine prediction of, for example, property 4 of the suspension just introduced into the container and/or possibly even of the finished suspension can be made in fixed time intervals (e.g., as a forecast of the properties that are set when the container is filled to a predeterminable degree of filling and/or the suspension has cooled down). In particular, the method can even allow a prediction of the properties 4 that is as accurate as possible, which prediction is or will be expected to result in the battery suspension at the end of the extrusion process (i.e, when the container is filled to a predeterminable degree of filling and/or the suspension has cooled down). In a particularly advantageous manner, this allows for an (inline) adjustment of the target process parameters during the still ongoing extrusion process if the predicted properties 4 do not correspond to the desired or specifiable target values.

[0047] FIG. 2 schematically shows an illustration of an embodiment of the prediction algorithm described herein. The reference signs are used consistently so that reference can be made to the previous explanations.

[0048] The machine-learned prediction algorithm 5 can be formed by means of an artificial neural network, for example. In this context, FIG. 2 shows, by way of example, a prediction algorithm that is formed by means of a Long Short-Term Memory network (abbreviated as LSTM network). The variables “x” show the input data or the input vector, the variables “h” show the (possibly hidden) output data or the (possibly hidden) output vector, the variables “c” show the cell states of the LSTM network, the subscripts “t−1,” “t,” “t+1,” etc. show different time steps, the subscripts “1 . . . N” show the different input data at a corresponding point in time, the subscripts “1 . . . M” show the different output data at a corresponding point in time.

[0049] As an example, the course of the input data “x” is mapped by time-variant signals. The input data “x” comprise in this context at the corresponding point in time the actual process parameters 2, possibly the ambient conditions 3, and possibly also the (already specified) target process parameters 1, and/or possibly also known raw material properties 8. In the embodiment shown, modeling by means of a Long Short-Term Memory (LSTM) network describes the relationship between the input data mentioned and the output data “h.” In this context, the output data “h” comprised the predicted properties 4 of the battery suspension (depending on the process time) at the corresponding point in time. In other words, this can also be described in such a way that a feature vector “x” that contains all (actual) process parameters and possibly ambient conditions at a discrete point in time is given as an input vector in the LSTM network. The LSTM network is set up to approximate the properties 4 of the battery suspension, i.e., the target vector, at this point in time.

[0050] During production, the quality of the suspension or the (actual) property 6 of the suspension (for comparison with the predicted property 4) can optionally and additionally be measured manually, for example at fixed time intervals, in particular until the container is filled up to the predeterminable degree of filling (or is filled completely). These manually measured properties 6 can contribute advantageously to validating the forecasts of the prediction algorithm 5. These properties 6 are described in FIG. 2 with the variable “h′.” This represents an example of the fact that, and possibly how, the at least one property 6 of the suspension can be measured in order to validate a prediction that has been performed.

[0051] If the measured properties 6 are also present, an error “L” can be determined, which can be used (inline or after the actual or initial training) to further train or improve the prediction algorithm. For example, the LSTM network can be implemented over time in this context. Furthermore, prediction errors “L” and gradients can be calculated for each time step. In addition, the gradients can be averaged over all time steps. Elements 7 of the algorithm 5 can then be adapted on this basis. Alternatively or cumulatively, a so-called Truncated Backpropagation Through Time method (abbreviated as TBPTT method) can also be used to improve the (already initially trained) network.

[0052] FIG. 3 schematically shows an illustration of possible prediction results of the prediction algorithm 5. The reference signs are used consistently so that reference can be made to the previous explanations.

[0053] It can be seen in particular that a time-variant modeling of (continuous) process parameters 2, possibly ambient conditions 3 and possibly time-invariant (raw) material properties 8, can take place using an algorithm 5 in the form of a Long Short-Term Memory network.

[0054] FIG. 3, at the top left, shows an example of input data 2, 3, 8, and FIG. 3, at the top right, shows an example of an illustration of output data 4 of the algorithm 5. It can be seen that the individual time steps are represented by successive cells of the algorithm 5. The algorithm 5 can generally input data 2, 3, 8 at a corresponding time step—for example comprising actual process parameters 2, ambient conditions 3, and possibly raw material properties 8 (and possibly also target process parameters 1, cf. FIG. 2)—as well as historical data or information about previously provided suspensions. The historical data or information about previously provided suspensions generally comprises at least such information that was learned during the training phase from the training data records which are usually based on historical data or information about previously provided suspensions. This information learned during the (initial) training phase can be represented, for example, by appropriate configurations and/or linkages of the elements 7 of the algorithm 5.

[0055] FIG. 3 also shows that the historical data or information about previously provided suspensions can also comprise information from a(n) (immediately) preceding time step of the algorithm 5 (in this case, for example, an upstream cell of the network). In addition, measured, actual properties 6 of the suspension, in particular for validation purposes, can flow into the algorithm 5 as an input. These properties 6 can also contribute to further training and/or improving the algorithm 5 (“inline”, i.e., during its regular operation or the ongoing process).

[0056] FIG. 3 also shows that the prediction can be made not only for a specific point in time in the future (in this case t_x+0.2, for example), but possibly also for a large number of points in time in the future, which may possibly be validated over time by the properties 6. In this way, for example, (continuous) product properties 4 can be estimated at the corresponding point in time t. In particular (as an alternative or cumulatively to a prediction of a large number of future points in time), a prediction can also be made for a final point in time (herein “T,” by way of example). In other words, this can also be described in such a way that the prediction can also take place in such a way that it estimates at least one property 4 of the finished suspension or at least one (discrete) product property 4 at the final point in time T.

[0057] This at least one property 4 of the finished suspension can also be referred to herein as at least one “final” property 9 (symbol T) and can be monitored, for example, according to the illustration at the bottom in FIG. 3. The final property 9 of the suspension predicted at the respective points in time or time steps during the process is plotted herein over time. From this illustration, it can be determined, for example, whether the prediction of the final property 9 of the suspension during the process remains or has remained within a tolerance band 10 or possibly for what period(s) 11 this property 9 is or was outside of the tolerance band 10. For example, it can be provided that, for example, the target process parameters are adjusted by a person responsible for the process or are automatically adjusted depending on the prediction and/or a resulting error if the prediction results in a violation of tolerance limits.

[0058] FIG. 4 shows a schematic flowchart to illustrate an embodiment of the training method described herein for a machine-learning-capable prediction algorithm 5 (cf. FIGS. 2 and 3). The reference signs are used consistently so that reference can be made to the previous explanations. The sequence of steps i), ii), and iii) represented by blocks 210, 220, and 230 is an example and can thus occur, for example, in a regular sequence of the method.

[0059] In block 210 and according to step i), reading in of training input data for the prediction algorithm 5 takes place, which comprise actual process parameters 2 and optionally ambient conditions 3 of a large number of provision processes for providing a suspension for creating an electrode for a battery cell. In block 220 and according to step ii), reading in of training output data for the prediction algorithm 5 takes place, which comprise properties 6 of the suspension provided by means of the corresponding provision process. In block 230 and according to step iii), adapting of elements 7 of the prediction algorithm 5 takes place, in order to map the training input data that has been read in as precisely as possible to the training output data that has been read in. For example, a gradient descent method can be used to adapt elements 7 of the prediction algorithm 5.

[0060] With the help of artificial intelligence (AI) methods, it is advantageously possible to predict the results of future measurements on the basis of a data set of historical (extrusion) processes and measurement(s) of the battery suspension, preferably at a large number of points in time or, if possible, at any point in time of the (corresponding) process. In this context, it is particularly advantageous to record process parameters 1, 2, ambient conditions 3, and raw material properties 8 and to continue to record them in a traceable manner. This database can serve as an example training set for the (AI) algorithm 5.

[0061] During the training, the algorithm 5 (in this case the LSTM network, as an example) is presented with a large number of training examples, usually consisting of a feature vector and a target vector. Furthermore, randomly initialized model parameters (shown herein by way of example by elements 7) of the LSTM network can ensure an inaccurate prediction at the beginning of the training. During the training, the model parameters can be adjusted by means of the gradient descent method, for example, until the prediction error is minimal across all training examples.

[0062] After training the model or algorithm for predicting the quality of the battery suspension, it can be used to make predictions for unknown combinations of process parameters 1, 2, possibly ambient conditions 3 and possibly material properties 8 during the (current) process. The additional measurement of suspension properties 6 can help validate the predictions and/or improve prediction accuracy at later points in time. Due to the statistical significance of large amounts of data, the prediction of the algorithm 5 is advantageously more accurate than that of a person skilled in the art and of physical models.

[0063] A method for producing at least one electrode for a battery cell and a training method for a machine-learning-capable prediction algorithm are thus specified, which methods at least partially solve the problems described in connection with the prior art. In particular, a method for producing at least one electrode for a battery cell and a training method for a machine-learning-capable prediction algorithm are specified, which methods allow a mechanical, inline-capable, and as reliable as possible prediction of at least one property of the suspension for creating the electrode. In addition, the prediction can be made with as little expenditure of time and/or as cost-effectively as possible.

LIST OF REFERENCE SIGNS

[0064] 1 Target process parameters [0065] 2 Actual process parameters [0066] 3 Ambient condition [0067] 4 Property [0068] 5 Prediction algorithm [0069] 6 Property [0070] 7 Element [0071] 8 Raw material properties [0072] 9 Property [0073] 10 Tolerance band [0074] 11 Time window