Robert Boschgmbh

Robert Bosch Gmbh

Christian Simonis

Christoph Woll

Christoph Kroener

Stefan Schindler

Andras Gabor Kupcsik

Csaba Domokos

Tobias Huelsing

Reinhardt Klein

Volker Doege

Tunan Shen

Maurice Caspar

Kirill Gorelik

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.

Method for predicting a residual service life of vehicle batteries of a fleet of electric vehicles

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.

Method and apparatus for determining a residual service life based on a predictive diagnosis of components of an electric drive system using artificial intelligence techniques

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

A method for monitoring cell voltage sensors () of a battery system () comprising a device battery () having a plurality of battery cells (), comprising the following steps:

Method and apparatus for diagnosing voltage sensors of a device battery having a plurality of battery cells

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.

Method and apparatus for operating a system for providing predicted confidence intervals for states of health of electrical energy stores for at least one device with the aid of machine learning methods

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.

Method and apparatus for operating an early warning system for robustly detecting an error of an electrical energy store for a device by means of machine learning methods

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.

Method and apparatus for predicting a state of health of a device battery in a battery-operated device

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.

Method for determining a state of health of at least one electrochemical energy store

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.

Cost-effective yet still precise ascertainment of the degradation state of a rechargeable battery

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.

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 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.

Method and apparatus for determining and fusing an aging state trajectory of an electrical energy storage unit based on statistical, model-based, and machine-learning methods

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.

Method for predicting an aging state of a battery

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

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.

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Leonberg, Germany

Christian Simonis