Abstract: Various aspects of the present disclosure are directed to methods for calibrating a technical system with respect to stochastic influences during real operation of the technical system. In one example embodiment of the present disclosure, the method includes the steps of: determining the values of a number of control variables, carrying out the calibration on the basis of a load cycle which results in a sequence of a number of operating points, executing the load cycle multiple times under the influence of at least one random influencing variable, with each realization of the load cycle resulting in a random sequence of the number i of operating points, defining a risk measure of the probability distribution, with which the probability distribution is mapped to a scalar variable, and optimizing the risk measure by varying the number of control variables in order to obtain optimal control variables for calibration.

Abstract: The invention relates to a computer-aided method for generating a drive cycle for a vehicle which is suitable for simulating a driving operation, in particular a real driving operation. The computer-aided method comprises establishing a state vector of the drive cycle for a current time interval from a past speed curve, providing an acceleration prediction model, determining an acceleration value in consideration of probabilities resulting from the acceleration prediction model and the state vector, integrating the determined acceleration value over the current time interval in order to obtain a predicted speed value for a next future time interval, and appending the predicted speed value to the past speed curve in order to generate the drive cycle.

Abstract: Various aspects of the present disclosure are directed to methods for calibrating a technical system with respect to stochastic influences during real operation of the technical system. In one example embodiment of the present disclosure, the method includes the steps of: determining the values of a number of control variables, carrying out the calibration on the basis of a load cycle which results in a sequence of a number of operating points, executing the load cycle multiple times under the influence of at least one random influencing variable, with each realization of the load cycle resulting in a random sequence of the number i of operating points, defining a risk measure of the probability distribution, with which the probability distribution is mapped to a scalar variable, and optimizing the risk measure by varying the number of control variables in order to obtain optimal control variables for calibration.

Abstract: Various aspects of the present disclosure are directed to a method for ascertaining the state of health of a secondary battery. In one example embodiment, the method includes making a first estimation for the state of health by an observer, using an aging prediction model to ascertain a second estimation for the state of health, the aging prediction model being parameterized on the basis of the first estimation for the state of health. The first or second estimation of the state of health, or a combination of the first and second estimation of the state of health, is used as the state of health of the secondary battery.

Abstract: For the determination of a non-linear controller for a non-linear system it is proposed that a parameter set (KPID(k)) of the controller (1) is determined by means of an optimization using a multi-criteria evolutionary algorithm, in which algorithm a plurality of parameter sets (KPID(k)), which each represent a possible solution of the optimization, are determined in each evolution step and at least two quality values (fi) are determined for each parameter set (KPID(k)) and the quality values (fi) are optimized by the multi-criteria evolutionary algorithm.

Abstract: In order to create a control observer for any battery type in a structured and at least partially automated manner, first, a nonlinear model of the battery, in form of a local model network including a number of local, linear, time-invariant, and dynamic models, which each have validity in specific ranges of the input variables, is determined from the measuring data of a previously ascertained, optimized experimental design via a data-based modeling method. For each local model (LMi) of the model network determined in this manner, a local observer is then determined. The control observer (13) for estimating the SoC then results from a linear combination of the local observers.

Abstract: To determine a model for an output quantity (y) of a technical system that is dependent in a nonlinear manner on a number of input quantities in the form of an input quantity vector (u), a target output quantity range (COR) is defined and a model-based experimental design is determined with which the model is parameterized in the target output quantity range (COR) through the selection of associated input quantity vectors (ucand,COR). A distance-based selection criterion is used for the selection of the input quantity vectors (ucand,COR).

Abstract: A method for obtaining data from a nonlinear dynamic real system during a test run, for instance an internal combustion engine, a drivetrain, or parts thereof, a sequence of dynamic excitation signals is generated according to an initial design and a system output is measured. To enable the quick and precise generation of experimental designs for global measurement, modeling, and optimization of a nonlinear dynamic real system, a sequence of dynamic excitation signals was generated by generating a design with a sequence of excitation signals, obtaining output data by feeding said sequence into a model for the real system, determining a criterion for the information content of the complete design of experiment sequence, varying the sequence, obtaining new output data by feeding said modified sequence into the model, determining again said criterion, iterating until said criterion improves, and using the improved sequence of excitation signals for the real system.

Abstract: To determine a model for an output quantity (y) of a technical system that is dependent in a nonlinear manner on a number of input quantities in the form of an input quantity vector (u), a target output quantity range (COR) is defined and a model-based experimental design is determined with which the model is parameterized in the target output quantity range (COR) through the selection of associated input quantity vectors (Ucand,COR).

Abstract: For the determination of a non-linear controller for a non-linear system it is proposed that a parameter set (KPID(k)) of the controller (1) is determined by means of an optimization using a multi-criteria evolutionary algorithm, in which algorithm a plurality of parameter sets (KPIS(k)), which each represent a possible solution of the optimization, are determined in each evolution step and at least two quality values (fi) are determined for each parameter set (KPID(k)) and the quality values (fi) are optimized by the multi-criteria evolutionary algorithm.

Abstract: In order to create a control observer for any battery type in a structured and at least partially automated manner, first, a nonlinear model of the battery, in form of a local model network including a number of local, linear, time-invariant, and dynamic models, which each have validity in specific ranges of the input variables, is determined from the measuring data of a previously ascertained, optimized experimental design via a data-based modeling method. For each local model (LMi) of the model network determined in this manner, a local observer is then determined. The control observer (13) for estimating the SoC then results from a linear combination of the local observers.

Abstract: In a machine-implemented method for obtaining data from a nonlinear dynamic real system during a test run, for instance an internal combustion engine, a drive-train or parts thereof, of a sequence of dynamic excitation signals for at least one measurement channel is generated according to a previously generated design of experiment for said test run and the system output of at least one output channel is measured.