Robert Boschgmbh

Christoph Kroener

Christian Simonis

Jens Becker

Christoph Woll

Triantafyllos Zafiridis

Tobias Huelsing

Olivier Cois

Christel Sarfert

Joerg Poehler

Vincent Scharff

Florian Stief

Aleksandar Aleksandrov

Melwin Pascal Watterott

Marco Stroebel

Peter Birke

Felix Kleinheinz

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.

Method for operating a user interface for selecting a charging strategy for a device battery and for operating the device battery as well as a user interface

The invention relates to a method for estimating the state of an energy store comprising at least one electrochemical battery cell () using a battery management system (BMS) which comprises an impedance spectroscopy chip, having at least the following steps: a) determining the frequency-dependent impedance of the at least one electrochemical battery cell () using a data set recording taken in real-time, b) training an artificial neural network () with temperature-based training spectra as the input and a specification for temperature values belonging to each training spectrum as the output, c) taking into consideration a battery cell-to-battery cell variance () between the electrochemical battery cells () when testing the artificial neural network () using weighting functions ascertained during step b) and test spectra and estimating the temperature values belonging to the test spectra according to the weighting functions ascertained in step b), and d) estimating at least one internal state (SoC, SoH, T) of the at least one electrochemical battery cell () of the energy store using the trained artificial neural network ().

Method for estimating the state of an energy store

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.

Method and apparatus for determining a state of health of an electrical energy store of unknown type by using machine learning methods

A method for controlling a charging current limiting value for a battery management system. In one example, the method includes determining, for a measured temperature and a prescribed state of charge, reference currents for various time intervals; calculating a corresponding reference time constant for each reference current by using a model for the calculation of a mean value of a charging current based on a continuous current; constituting a diagram for the relationship between the reference time constant and the reference current; determining a predictive time constant by the comparison of a measured value of a charging current with the reference currents; calculating a predictive limiting mean value of the charging current; and calculating a first predictive limiting value ifor a short predictive time t, a second predictive limiting value ifor a long predictive time t, and a third predictive limiting value ifor a continuous predictive time.

Method for controlling a charging current limiting value for a battery management system, battery management system

A method, device, and computer-readable medium for operating an electrical energy store is provided. The electrical energy store includes a storage cell for storing electrical energy and a control unit. State variables of the electrical energy store are detected. The state variables are transmitted to a computation unit outside of the electrical energy store. The electrical energy store is operated based on operating parameters provided by the computation unit.

Method for operating an electrical energy store

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.

Method and apparatus for operating a system for providing predicted states of health of electrical energy stores for a device using machine learning methods

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.

Method and device for improving the service life of an equipment battery in a battery-operated machine in a machine-specific manner

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.

Method and apparatus for device-specific determination of an internal-resistance based state of health of a battery

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.

Method and apparatus for parameterizing an electrochemical battery model for a battery based on data from a plurality of batteries

Method for balancing states of charge of an electrical energy store with a plurality of battery cells.

Method for balancing states of charge of an electrical energy store

Method for checking the plausibility of an electronic circuit for time measurement of an electrochemical energy storage system by means of a cooling behavior of at least one electrochemical energy store during non-use of the electrochemical energy store.

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Rosstal, Germany

Christoph Kroener