METHOD FOR PRODUCING AT LEAST ONE ELECTRODE FOR A BATTERY CELL
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).
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.