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 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: 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 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 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: A computer-implemented method for operating a user interface for specifying a charging strategy for charging a device battery of a battery-operated device includes detecting a historical usage pattern of an operation of the device battery, and specifying a plurality of different charging strategies. Each charging strategy having a corresponding charging curve for charging of the device battery. The method further includes determining an aging indication for each of the charging strategies based on (i) the historical usage pattern, and (ii) an assumption that all future charging processes for charging the device battery are performed using a relevant charging strategy of the plurality of different charging strategies. The method also includes informing a user about a relevant characteristic variable for each of the charging strategies and the respectively associated aging indication, and specifying one of the charging strategies for future charging processes corresponding to a received user selection.

Abstract: A computer-implemented method for providing a residual service life based on a diagnosis of components of an electric drive system in a vehicle, includes recording distributions of a plurality of operating parameters comprising at least one sensor parameter and/or at least one control parameter, in the vehicle; using a plurality of diagnostic models for a plurality of fault types, each of which is configured to detect a specific fault type in one of the components based on at least some of the plurality of operating parameters and to signal corresponding fault information associated with the fault type; determining the residual service life using a residual usage model depending on the signaled corresponding fault information, the residual usage model configured to indicate the residual service life depending on the corresponding fault information from the plurality of diagnostic models; and signaling the residual service life.

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 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 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: Devices, data structure, and computer-implemented methods for machine learning. A method for machine learning includes providing a data structure of a database, which data structure includes a set of nodes and a set of relations, and a set of tuples. Each respective tuple includes at least two nodes, and at least one relation. The method includes predicting a plurality of tuples depending on the data structure, wherein each respective tuple includes at least two nodes and at least one relation, predicting, whether the respective tuples of the plurality of tuples classifies as a member of the set of tuples, selecting a tuple from the plurality of tuples depending on the uncertainties predicted for the respective tuples, acquiring a label that indicates whether the selected tuple classifies as a member of the set of tuples, and adding the selected tuple to the set of tuples based on the label.

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.