Abstract: A method for predicting an aging state of a device battery with at least one electrochemical unit includes providing a temporal operating variable profile of an operating variable of a device battery, determining successive cycles from the temporal operating variable profile, respectively assigning the determined cycles to predetermined cycle profiles so that a sequence of cycle profiles is obtained, determining a frequency distribution of transitions in the obtained sequence of cycle profiles in the form of a hidden Markov model, creating a predicted sequence of cycle profiles by successively, randomly selecting cycle profiles according to the frequency distribution of the transitions, assigning profile operating variable profiles assigned to the cycle profiles to the predicted sequence of cycle profiles to obtain a predicted operating variable profile, and determining a predicted aging state or a predicted aging state profile based on the predicted operating variable profile using a predetermined aging stat

Abstract: A computer-implemented method, for associating a user with a device type of a battery powered technical device having a device battery and belonging to a plurality of various device types. In one example, the method includes providing a usage behavior of the user; associating a usage category with the user's usage behavior; determining a predicted usage parameter profile of at least one usage parameter according to the usage category, wherein the at least one usage parameter is indicative of a mode of operation of the technical device affecting a load on the device battery; simulating a predicted ageing state profile for the predicted usage parameter profile for a predetermined duration for each type of device belonging to a plurality of device types to determine a predicted ageing state at a predetermined end of a useful life period; and selecting a device type depending on the predicted ageing state.

Abstract: A method for monitoring a device battery for the presence of an anomaly in the battery operation. In some examples, the method includes providing temporal operational variable profiles of operational variables for the device battery; determining an input dataset of operational characteristics for a historical evaluation period as a function of the temporal operational variable profiles; and using an anomaly detection model with an encoder/decoder model to determine a reconstruction error for the input dataset. The encoder/decoder model is trained with input datasets of device batteries of normal function to map an input dataset onto an input dataset that is reconstructed as identically as possible. When a reconstruction error is found, a rule-based criticality value is determined as a function of one or more characteristic-based criticality values. An error type is also signaled.

Abstract: A method for operating a central processing unit which is communicatively connected to a plurality of devices having electrical energy stores, includes determining a data-based state of health model which is trained to assign a state of health to an operating feature point which characterizes operation of a corresponding electrical energy store of the electrical energy stores of the plurality of devices and results from a plurality of operating features, determining operating feature points for the electrical energy stores of the plurality of devices, and selecting at least one of the operating feature points based on state uncertainties of the states of health of all operating feature points of the corresponding electrical energy store, such that the at least one operating feature point has a highest relevance for improving the state uncertainties of the operating feature points determined.

Abstract: The disclosure relates to a method for monitoring battery cells and continuously providing cell aging states of battery cells of a device battery. The method includes providing temporal cell-operating value profiles of operating values for each of the battery cells and determining cell aging states of each battery cell based on one or more aging state models, respectively, depending on cell-operating value profiles provided with a high temporal resolution, after a respective evaluation period. The method includes selecting a portion of the battery cells for continuously capturing the cell-operating value profiles with the high temporal resolution in the respective next evaluation period, and continuously providing the cell- operating value curves of the selected battery cells with the high temporal resolution in the next evaluation period, while for remaining battery cells of the device battery no operating values or cell-operating value curves with low temporal resolution are provided.

Abstract: A computer-implemented method is introduced for predicting a residual service life of vehicle batteries of a fleet of electric vehicles. In the method, parameters of the vehicle batteries are measured during the operation of the electric vehicles and transmitted to a server; a conditional probability is determined that the residual service life of a specific vehicle battery undershoots a predefined limit value at a point in time lying in the past; and the residual service life of vehicle batteries of the fleet is predicted as a function of the conditional probability.

Abstract: The disclosure relates to a method for providing an aging state of a device battery of a battery-operated device including detecting curves of operating variables of the device battery and providing at least one load factor at a determination time, providing a correction model that maps a correction variable depending on the at least one load factor, and ascertaining an aging state by evaluating the curves of the operating variables with the aid of an aging state model or an aging state observer or an aging state measurement and depending on the correction variable resulting from the at least one load factor of the device battery of the battery-operated device at the determination time.

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 parameterizing an electrochemical battery model of a specific battery of a device, includes providing a current operating feature point of the specific battery and operating feature points of further batteries of a plurality of further devices for various evaluation periods. The operating feature points of the further batteries characterize an operation of a respective further battery within one or more of the evaluation periods based on several operating features. The several operating features are produced depending on characteristics of operating variables of the respective further battery. The method further includes selecting operating feature points of the further batteries that are similar to the current operating feature point of the specific battery, and providing model parameters of the electrochemical battery model that are associated with the current operating feature point and the selected operating feature points.

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 computer-implemented method for determining an error of a behavior of an electrical energy store in a technical device includes sensing an operating variable profile of at least one operating variable of the electrical energy store, determining at least one operating feature from the operating variable profile of the at least one operating variable of the electrical energy store, and evaluating a variational autoencoder with a supplied input variable vector, which includes an operating feature point from the at least one operating feature, in order to determine a degree of deviation from a distribution in a latent state space or a reconstruction error. The method further includes detecting the error of the behavior depending on (i) the degree of deviation or the reconstruction error, and (ii) a predetermined error threshold.

Abstract: A method for determining a state of health of at least one electrochemical energy store.

Abstract: A computer-implemented method predicts a modeled state of health of an electrical energy store having at least one electrochemical unit in a technical device. The method includes providing a data-based state of health model, based on a characteristic of at least one operating variable of the electrical energy store up to a time, to assign the electrical energy store a corresponding state of health for the time and to indicate a corresponding modeling uncertainty, and predicting the characteristic of the at least one operating variable starting from a present time into the future based on a usage pattern model that is determined by a user-specific or usage-specific usage pattern. The method further includes predicting a characteristic of the state of health based on the data-based state of health model and the predicted characteristic, generated in a model-based manner, of the at least one operating variable.

Abstract: A method and to a device for ascertaining a state of health of a battery for a transportation device. The method includes: charging the battery until a predefined target voltage is reached in a first charging phase of the battery, ascertaining a first voltage value of the battery at a first predefined point in time after the predefined target voltage has been reached in a relaxation phase of the battery, ascertaining a second voltage value of the battery at a second predefined point in time, deviating from the first predefined point in time, in the relaxation phase of the battery, ascertaining a piece of information about the state of health of the battery based on a change of the second voltage value with respect to the first voltage value, and using the piece of information in the transportation device and/or in an external server.

Abstract: A computer-implemented method provides an electrochemical battery model and a state of health model for a device battery. The electrochemical battery model is based on a system of differential equations, models an equilibrium state, and reports a dependence between operating parameters of the device battery and a state of charge of the device battery. The state of health model includes at least one physical aging model based on a further system of differential equations, and models the state of health depending on progressions of the operating parameters of the device battery.

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.

Abstract: A method for predicting an aging state of a battery. A vehicle is also provided that includes at least one battery, the aging state of which is predited using the method and/or the aging behavior of which is improved based on the aging state prediction. A prediction system that is configured to carry out the method is also provided.

Abstract: The disclosure relates to a computer-implemented method for operating a system comprising a central processing unit, which is in communication with a multiplicity of battery-operated machines that each have an equipment battery, and for providing an action for extending the service life of the equipment battery of the machine in a machine-specific manner.

Abstract: The disclosure relates to a method for determining a predicted state-of-health curve of device batteries in battery-powered machines. The method comprises: (i) providing data points of a state-of-health curve/trajectory of a device battery, the data points indicating a state of health via an aging time point of the device battery, the state-of-health curve/trajectory indicating a progression of a state of health up to a current state of health; (ii) determining a database of a plurality of data points within a time period which ends at the current aging time point, the database being determined such that a residual between the model function and the data points is minimized by fitting the model function; (iii) extrapolating the plurality of data points of the database by parameterizing the model function; and (iv) determining a predicted state of health using the parameterized model function at a predetermined prediction time point.

Abstract: The invention relates to a computer-implemented method for predicting a modeled state of health of an electrical energy store having at least one electrochemical unit, in particular a battery cell, having the following steps: providing a data-based state of health model trained to assign a modeled state of health to the electrochemical energy store on the basis of characteristics of operating variables of the energy store; providing time characteristics of the operating variables that characterize operation of the electrical energy store; and determining a present or predicted modeled state of health on the basis of the generated characteristics of the operating variables using the state of health model, wherein data gaps in the time characteristics of the operating variables owing to a phase of inactivity are completed based on a characteristic of a temperature of the energy store that is derived from at least one provided ambient condition.