Avl List Gmbh

Stefan Jakubek

Christoph Hametner

Oliver König

Günter Prochart

Felix Pfister

Kurt Gschweitl

Christoph Kügele

János Kancsár

Nico Didcock

Amra Suljanovic

Nikolaus Keuth

Markus Stadlbauer

Andreas Fleck

Andreas Rainer

Maxime Deregnaucourt

Herbert Lanschützer

Karl Zettel

Martin Batliner

Stefan Geneder

Gregor Gregorcic

Wolfgang Falmbigl

Thomas Winsel

Christian Mayr

Horst Pflügl

Alexander Eder

Hans-Michael Kögeler

Markus Dohr

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.

Method and battery management system for ascertaining a state of health of a secondary battery

An apparatus is provided for conditioning at least one process gas which is supplied to at least one electrochemical converter, via at least one process gas supply. The process gas supply has a humidifying unit configured and arranged to introduce a humidifying agent into the process gas. Water in a supercritical state can be introduced as the humidifying agent.

PEM fuel cell

Various embodiments of the present disclosure are directed to methods and systems for determining at least one actual value of a controlled variable of a conditioning unit for a reactant of a fuel cell. In one example embodiment, the method steps for determining the at least one actual value of a controlled variable includes: measuring a measured value of the actual value of the at least one controlled variable, calculating a model value of the at least one controlled variable using a model of the conditioning unit, calculating a model value of the actual value of the at least one controlled variable using a sensor model, calculating a correction value for the at least one controlled variable, and calculating the actual value of the at least one controlled variable as the sum of the correction value and of the model value of the at least one controlled variable.

Regulation of a controlled variable of a conditioning unit of a reactant of a fuel cell with ascertainment of an actual value of the controlled variable

Various aspects of the present disclosure are directed to energy accumulator emulators. In one embodiment, an energy accumulator emulator is disclosed including a DC-to-DC converter having a number of power switches, a control unit that calculates a reference current from electrical variables of the DC-to-DC converter, and a battery model connected to the control unit. The battery model receives and processes the reference current and communicates a referenced voltage to the control unit. The control unit includes a voltage controller that processes the reference voltage and controls a current, on the basis of which the control unit controls the power switches via switching pulses to control an output voltage. The energy accumulator emulator further includes a PPPC unit that is connected to the voltage controller. The PPPC unit provides a number of pulse patterns, selects a pulse pattern, and controls the power switches according to the selected pulse pattern.

Energy accumulator emulator and method for emulation of an energy accumulator

A method for generating a model ensemble that estimates at least one output variable of a physical process as a function of at least one input variable, the model ensemble being formed from a sum of model outputs from a plurality of models that have been weighted with a weighting factor.

Method for generating a model ensemble for calibrating a control device

The aim of the invention is to allow a precise and quick regulation of the influencing variables in the operation of a fuel cell. A regulator is used for the regulation, said regulator being designed on the basis of a model of the gas conditioning unit in the form of a coupled nonlinear multivariable system in that the coupled nonlinear multivariable system is decoupled and linearized using Lie derivatives, and the regulator is designed for the decoupled linear multivariable system. The regulator calculates the control variables (u, u, u, Q) for at least three present actuators of the influencing variables of the gas conditioning unit at each sampling time of the regulation using specified target variables (y), and at least the three actuators of the gas conditioning unit adjust the calculated control variables (u, u, u, Q) at each sampling time of the regulation.

Controlled gas conditioning for a reaction gas of a fuel cell

To subject a test object during a test run on a test bench to real environmental and/or surrounding conditions, particularly thermal conditions, it is provided that at least one temperature is measured at a measurement point as a measured variable during the test run on the test bench. At least one test object component of the test object is subdivided in a number of segments. The thermal interaction of at least one segment with the environment of the vehicle is simulated during the test run by a thermal simulation model of the simulation model. The thermal simulation model calculates the segment heat flow that is supplied to or dissipated from the at least one segment. This segment heat flow is adjusted as a function of the measured temperature at the test bench on at least one segment by means of a number of heat flow actuators.

Method and test object for carrying out a test run with a test object

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

Method for designing a non-linear controller for non-linear processes

In a test run, in order to easily provide a high-quality propulsion torque of a torque generator based on the partially low-quality measured variables available on the test bench, it is foreseen that an inner torque (M) of the torque generator (D) is measured and based on the measured inner torque (M), from an equation of motion, including the measured inner torque (M), a dynamic torque (M) and a shaft torque (M) measured on the output shaft of the torque generator (D), a correction torque ({circumflex over (M)}) is estimated, and from the estimated correction torque ({circumflex over (M)}) and the measured inner torque (M), the propulsion torque (M) according to the relation M={circumflex over (M)}+Mis computed.

Method and a device for determining the propulsion torque

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 (LM) of the model network determined in this manner, a local observer is then determined. The control observer () for estimating the SoC then results from a linear combination of the local observers.

Method for determining a control observer for the SoC

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 (u). A distance-based selection criterion is used for the selection of the input quantity vectors (u).

Method for determining a model of an output quantity of a technical system

A method for testing electrical energy storage systems for driving vehicles provides that the load current of the energy storage system traces by means of a control loop, if possible without delay, a reference current that varies over time according to predetermined test cycles. The control loop is created by means of a model-based controller design method in which a model of the impedance of the energy storage system is integrated in the model of the controlled system.

Method and device for testing electrical energy storing systems for driving vehicles

In order to reduce the excitation of vibrations and resonances in a test bed for a real component and a virtual component, one of the following method steps is provided: a) determining a first correction value (K) from the measured variable (M), wherein the first correction value (K) is added to the measured variable (M) and the sum is communicated as a corrected measured variable (M*) to the virtual component for calculating the control variable (S), b) determining a second correction value (K) from the calculated control variable (S), wherein the second correction value (K) is added to the calculated control variable (S) and the sum is transferred as a corrected control variable (S*) to the actuator, c) determining a third correction value (K) from the measured variable (M), wherein the third correction value (K) is used to modify a parameter (P) of the equation of movement.

Method for reducing vibrations in a test bed

For an easily implementable method for model predictive control of a DC/DC converter, and a corresponding controller, with which the optimization problem of the model predictive control can also be solved sufficiently quickly with large prediction horizons, the optimization problem is divided into two optimization problems by a model predictive output variable control and a model predictive choke current control being implemented in the control unit (), wherein: the strands of the multiphase DC/DC converter () for the output variable control are combined into a single strand; a time-discrete state space model is produced therefrom; and the output variable control predicts the input voltage (u) of the next sampling step (k+1) for this single strand on the basis of a first cost function (J) of the optimization problem of the output variable control, said input voltage being given to the choke current control as a setpoint and the choke current control determining therefrom the necessary switch positions of the switches (S, S, S, S, S, S) of the strands of the multiphase DC/DC converter () for the next sampling step (k+1) on the basis of a second cost function (J) of the optimization problem of the choke current control.

Method and controller for model predictive control of a multi-phase DC/DC converter

In a process for testing the powertrain of vehicles that are at least in part electrically driven, the voltage supplied to the powertrain is controlled by a controller coupled with a simulation system for the energy storage system in a way that the voltage acts dynamically as for a real energy storage system. The controller is designed with a model based controller design method, with a load model of the powertrain being used in the model of the controlled system.

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Vienna, Austria

Stefan Jakubek