Abstract: The present disclosure relates to an adhesive tape and to a method for jacketing an elongated item, more particularly cable sets. The adhesive tape comprises a tapelike carrier provided on at least one side with an adhesive layer which consists of a self-adhesive, pressure-sensitive adhesive, characterized in that the self-adhesive pressure-sensitive adhesive is a UV-curable composition comprising, based on the total weight of the composition: 15 to 50 parts by weight of matrix polymer; 50 to 85 parts by weight of epoxy resin; 0.1 to 3 parts by weight of photoinitiator, with the matrix polymer forming a self-supporting film in which epoxy resin and photoinitiator are embedded.

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.

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.