Method for carrying out a closing operation, closing device, server apparatus and communication terminal for carrying out a method of this type
A method for carrying out a closing operation of a closing part device of a vehicle driven by an electric motor drive unit is disclosed. The method has the following steps: ascertaining friction force data that represent a current stiffness of the closing device using a machine learning method that has been trained with reference friction force data from past closing operations as input data, wherein the input data are transmitted to a server apparatus that is configured to ascertain the friction force data using the machine learning method, ascertaining target drive power data, taking the friction force data into consideration, for the drive unit, and operating the drive unit based on the ascertained target drive power data to carry out the closing operation in at least one subsequent closing operation.
1 . A method for carrying out a closing operation of a closing part of a closing device of a vehicle driven by an electric motor drive unit, wherein the method comprises the following steps:
ascertaining friction force data representing a current stiffness of the closing device in a control unit using a model,
ascertaining target drive power data for the drive unit that take the friction force data into consideration, and
operating the drive unit based on the ascertained target drive power data to perform the closing operation during at least one subsequent closing operation,
wherein the model was generated by a machine learning method on an external server device, wherein the machine learning method is trained with frictional force reference data as input data from previous closure operations, wherein the input data is transmitted from the control unit to the external server device to update the model, and where the model is updated based on current input data in response to a trigger event in the external server device and transferred to the control unit in an updated form.
2 . The method as claimed in claim 1 , wherein the reference friction force data are ascertained based on closing-position-dependent load torque measurements at an electric motor of the drive unit.
3 . The method as claimed in claim 1 , wherein the input data also furthermore comprise data that represent at least one of an ambient temperature of the vehicle, an ambient air humidity of the vehicle and a positioning speed of the closing part.
4 . The method as claimed in claim 1 , wherein the friction force data representing a current stiffness are used as additional input data for training the machine learning method.
5 . A closing device for a vehicle, with:
a closing part for closing an opening of the vehicle,
an electric motor drive unit for driving the closing part and
a control device that is configured for carrying out a method as claimed in claim 1 .
6 . A communication terminal that is configured to carry out the machine learning method as claimed in claim 1 , wherein the communication terminal is external to the vehicle and is mobile in design.
7 . The communication terminal as claimed in claim 6 , wherein the reference friction force data are ascertained based on closing-position-dependent load torque measurements at an electric motor of the drive unit.
8 . The communication terminal as claimed in claim 7 , wherein the input data also furthermore comprise data that represent at least one of an ambient temperature of the vehicle, an ambient air humidity of the vehicle and a positioning speed of the closing part.
9 . The communication terminal as claimed in claim 8 , wherein the friction force data representing a current stiffness are used as additional input data for training the machine learning method.
10 . The communication terminal as claimed in claim 7 , wherein the friction force data representing a current stiffness are used as additional input data for training the machine learning method.
11 . The communication terminal as claimed in claim 6 , wherein the input data also furthermore comprise data that represent at least one of an ambient temperature of the vehicle, an ambient air humidity of the vehicle and a positioning speed of the closing part.
12 . The communication terminal as claimed in claim 11 , wherein the friction force data representing a current stiffness are used as additional input data for training the machine learning method.
13 . The communication terminal as claimed in claim 6 , wherein the friction force data representing a current stiffness are used as additional input data for training the machine learning method.
14 . The method as claimed in claim 3 , wherein the friction force data representing a current stiffness are used as additional input data for training the machine learning method.
15 . The method as claimed in claim 2 , wherein the input data also furthermore comprise data that represent at least one of an ambient temperature of the vehicle, an ambient air humidity of the vehicle and a positioning speed of the closing part.
16 . The method as claimed in claim 2 , wherein the friction force data representing a current stiffness are used as additional input data for training the machine learning method.