Abstract: The disclosure concerns a method for determining a predicted state of health of a device battery in a battery-operated machine. A state of health characteristic model for a state of health time characteristic is provided using a number of linked parameterizable characteristic functions that each indicate a state of health over a time period of ageing times, the characteristic functions being initially parameterized. A number of data points are captured that each indicate a state of health and an ageing time. A parameter of one of the characteristic functions is adapted based on the data points in the respective time periods of the characteristic functions. A parameter of another of the characteristic functions, for which there is no data point available, is also adapted. The state of health characteristic model is provided based on the adapted characteristic functions.

Abstract: A method for determining a state of health of at least one electrochemical energy store includes bringing the electrochemical energy store to a predefined voltage state, after which the electrochemical energy store is discharged for a predefined time period and using predefined electric currents, and determining a rate of voltage change during the discharge, on the basis of which the state of health is determined.

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 method for determining a state of health trajectory of a device battery is based on state of health values of device batteries of an identical battery type. The method includes providing time characteristics of operating variables of a multiplicity of device batteries of battery-operated machines in a central processing unit, and determining one or more state of health values of one or more of the multiplicity of device batteries by evaluating a respective characteristic of the operating variables within an evaluation period. The one or more state of health values with the applicable aging times each indicate a data point for the relevant device battery. The method further includes eliminating data points from the determined data points to obtain a set of cleaned-up data points, and ascertaining the state of health trajectory with an accuracy statement for each trajectory point based on the set of cleaned-up data points.

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 determining a current sensor offset of a current sensor (47) in a device battery (41) of a technical device (4). In one example, the method includes measuring the battery current using the current sensor (47); providing (S1) a temporal profile of the battery current; recording (S2) a charge balance of the electrical charge amount flowing into the device battery (41) and out of the device battery (41) over time depending on the temporal profile of the battery current; creating (S2) or adjusting a charge balance model depending on a time-accumulated charge amount; and determining the current sensor offset depending on a local slope of the charge balance model at a specified time.

Abstract: A monitoring method includes ascertaining from a temporal operating variable curve of operating variables of a battery an operating feature point characterizing a battery state and/or an operating history in a time period between a most recent and a second most recent change point time in the curve, providing a trace graph model comprising nodes with respective characteristic operating feature points connected via directed transitions with respective transition probabilities. One node is an anomaly node associated with an operating feature point corresponding to a particular fault of the battery. The ascertained operating feature point is assigned to one of the nodes as a monitoring node. An overall probability of occurrence of a fault is ascertained as a sum of all path probabilities from the monitoring node to the anomaly node. The path probability is a product of the respective transition probabilities along the nodes of the respective path.

Abstract: A method for determining a trajectory function to represent an aging state trajectory for at least one electrical energy storage unit of one or more devices includes determining uncertainty-containing model values of aging states of the at least one electrical energy storage unit, cleaning error-containing model values of the determined uncertainty-containing model values, determining the aging state trajectory based on the cleaned model values, and determining the trajectory function based on the determined aging state trajectory.

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: A method of monitoring a device battery for predictively detecting a fault in the device battery in a technical device includes providing a historical temporal operating variable curve of several operating variables of a specific device battery, and providing a predicted temporal operating variable curve dependent on a usage pattern model, which is dependent on a usage behavior characterizing a type of use of the device battery. The method further includes determining a time series of input variable vectors each with elements which comprise one or more operating variables and/or one or more variables derived therefrom for a time step. A time series includes time steps from the historical and predicted operating variable curves. The method further includes evaluating a data-based anomaly prediction model comprising a data-based time series transformer model and a data-based prediction model.

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.

Abstract: A method for determining a predicted state of health of an electrical energy storage unit in a machine includes providing a data-based or hybrid state of health model, the data-based state of health model is trained, depending on operating variables of the electrical energy storage unit and/or operating features derived from the operating variables, to indicate a state of health and to indicate a model uncertainty of the indicated state of health, ascertaining a state of health characteristic and the associated model uncertainty of the energy storage unit based on the operating variables using the state of health model, and generating at least one random constructed state of health characteristic candidate that corresponds to constructed state of health characteristics within characteristic of confidence intervals, the characteristic of confidence intervals defined by the model uncertainties of the states of health of the ascertained state of health characteristic.

Abstract: A method for initially preparing an at least partially data-based aging state model for an electrical energy storage means is disclosed. The method includes providing a number of energy storage means on a test bench for measurement based on a respective load profile, wherein the load profiles are different and characterize a chronological trend of at least one load-imposing operational variable for the energy storage means. The method also includes operating the number of energy storage means having the respective associated load profile and recording chronological operational variable trends. Further, the method includes at a predetermined evaluation timepoint, determining an aging state of a subset of the energy storage means as a label based on an input vector, and generating a training data set, which includes the operational variable trends and the determined label, for each energy storage means of the subset of the energy storage means.

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 monitoring cell voltage sensors (31) of a battery system (1) comprising a device battery (2) having a plurality of battery cells (21), comprising the following steps: continuous monitoring (Si) of the device battery (2) to detect a faulty cell voltage sensor (31) with a voltage offset above a predetermined threshold; upon detecting a faulty cell voltage sensor (31) (S2), detecting a plurality of voltage offsets at a plurality of evaluation time points; creation of (S4, S5, S6) a sensor drift model based on the multiple voltage offsets to determine or predict a voltage offset depending on the calendrical age of the device battery (2); wherein the voltage offset of a faulty cell voltage sensor (31) is detected by a deviation from a reference cell voltage, wherein the reference cell voltage is determined depending on the sensor drift model.

Abstract: A method for detecting a self-discharge fault of a device battery of a technical device and its criticality includes (a) providing at least one operating variable curve of at least one operating variable of the device battery, (b) determining at least one operational feature based on the at least one operating variable curve, (c) detecting a self-discharge fault based on fault criteria depending on the at least one operating feature, (d) determining a criticality of the self-discharge fault depending on which of the fault criteria are met, and (e) signaling a self-discharge fault depending on its criticality.

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 assigning a device type of a battery powered technical device having a device battery and belonging to a plurality of various device types to a user.

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: A computer-implemented method for determining an anomaly 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, and determining at least one feature from the sensed operating variable profile of the at least one operating variable of the electrical energy store. The method further includes evaluating an anomaly detection model using an autoencoder with a supplied input vector that includes or depends on the determined at least one feature, in order to determine a reconstructed input vector, and signaling an error based on a reconstruction error between the reconstructed input vector and the supplied input vector.