Abstract: A method for determining an internal-resistance based state-of-health of a battery in a device includes determining the internal-resistance based state-of-health of the battery using a hybrid state-of-health model including a physical ageing model and a correction model by (i) determining, based on an electrochemical battery model, a physical state-of-health according to one or more operating parameters of the battery using the physical ageing model, (ii) mapping operating features derived from the one or more operating parameters onto a correction parameter using the correction model that includes a data-based configuration, and (iii) applying the correction parameter to the physical state of health to determine the internal-resistance based state-of-health. The method further includes determining a modeled battery voltage, and determining a characteristic of the modeled battery voltage using an adaptation model.

Abstract: A method for operating a system including a battery-operated machine having a battery, the method includes continuously providing operating variables of the battery, ascertaining operating features for a current evaluation period depending on characteristics of the provided operating variables, the operating features characterizing a use of the battery in the current evaluation period, ascertaining at least one stress-maximizing operating feature of the ascertained operating features that is associated with a stress factor for the battery using a state of health model, the stress factor having a greatest influence on aging of the battery, and signaling a measure in the battery-operated machine depending on the stress factor.

Abstract: A computer-implemented method for ascertaining a state of health trajectory of an electrical energy store of a device, in particular an electrically driveable motor vehicle. The method including continually providing characteristics of operating variables that characterize operation of the electric energy store, ascertaining states of health based on the operating variables using an empirical state of health model at multiple times, parameterizing a trajectory function for describing the state of health trajectory based on multiple state of health points that each indicate one of the states of health of the ascertained states of health based on a time-dependent reference variable, and providing the trajectory function.

Abstract: A method for operating a system for ascertaining a state of charge of a battery of a motor vehicle includes providing operating variables of the battery, providing a calculated state of charge in the motor vehicle using a state-of-charge model that indicates a calculated state of charge depending on at least one battery model parameter of the state-of-charge model for the battery, and ascertaining a reference state of charge using a reference state-of-charge model depending on the operating variables. The reference state-of-charge model is trained to indicate the reference state of charge depending on the operating variables and/or a predefined aging state. The method further includes performing a correction of the at least one battery model parameter depending on a difference between the reference state of charge and the calculated state of charge in order to adapt the calculated state of charge to the reference state of charge.

Abstract: A method for ascertaining an approximation and/or a prognosis for the true degradation state of a rechargeable battery. The method includes: providing a time sequence of values of the degradation state ascertained using measuring technology for past points in time; providing a trained HMM, which indicates, as a function of the true degradation state, at which probability during the ascertainment using measuring technology which particular value of the degradation state is monitored, and at which probability the true degradation state is maintained for what length of time, and/or at which probability this true degradation state transitions to which worse degradation state in the next time step; from the monitored time sequence and the HMM, the most probable characteristic of the true degradation state in the past that is in agreement with the monitored time sequence is ascertained; the desired approximation and/or prognosis is evaluated based on the most probable characteristic.

Abstract: A computer-implemented method for operating a motor vehicle, in particular an electrically drivable motor vehicle, depending on a predicted state of health of an electrical energy store, in particular a vehicle battery. The method includes: providing vehicle parameters which influence the state of health of the electrical energy store; predicting the vehicle parameters at a prediction point in time; ascertaining the predicted state of health depending on the predicted vehicle parameters with the aid of a data-based state of health model which is trained to output a state of health of the electrical energy store depending on the vehicle parameters; and signaling the predicted state of health.

Abstract: A method for determining at least one aging state of a first plurality of electrical energy store units. The method includes: a) ascertaining the number of balancing operations already carried out between the first plurality of electrical energy store units within a predefined first time interval and/or the total converted energy quantity during a balancing operation between the first plurality of electrical energy store units and/or a first measure of dispersion of a particular state of charge-characterizing parameter of the first plurality of electrical energy store units; b) determining the at least one aging state of the first plurality of electrical energy store units as a function of the number of balancing operations already carried out and/or as a function of the total converted energy quantity during a balancing operation and/or as a function of the first measure of dispersion.