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11th IEEE International Conference on systems and control, ICSC 2023

作者： Rauh, Andreas Lahme, Marit Carl von Ossietzky Universität Oldenburg Group: Distributed Control in Interconnected Systems Department of Computing Science OldenburgD-26111 Germany

ISBN: (纸本)9798350304886

Equivalent circuit models for fuel cell stacks as well as for the series connection of battery cells are characterized by multiple series-connected RC sub-networks. In practical applications, for example, when aiming at the aging detection and monitoring of the individual cells in a fuel cell stack, it is desired to estimate the individual state variables of each of these series-connected cells. The same holds true for battery management systems that are dedicated to the state of charge (resp., voltage) equalization in a series connection of multiple battery cells. However, the pure knowledge of the current through this series connection as well as the sum over all terminal voltages is insufficient to make the overall system model fully observable. To efficiently observe the state variables in each of the series-connected subsystems, we aim at avoiding the measurement of the voltages of each individual fuel (resp., battery) cell. Instead, this paper presents a systematic sensor switching strategy which allows for estimating the individual state variables of all series-connected subsystems in an Unscented Kalman Filter framework. Simulation results are presented to compare the achievable estimation accuracy with a scenario in which all cell (resp., terminal) voltages were measured simultaneously. © 2023 IEEE.

关键词： Battery management systems

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A Finite Memory Approach Applied to Verified Pseudo State Estimation of Fractional Models of Lithium-Ion Batteries

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IFAC-PapersOnLine 2024年第12期58卷 185-190页

作者： Andreas Rauh Marit Lahme Carl von Ossietzky Universität Oldenburg Department of Computing Science Group: Distributed Control in Interconnected Systems D-26111 Oldenburg Germany

Fractional equivalent circuit models have been used in numerous publications to represent long-term memory phenomena of the charging/discharging behavior of Lithium-ion batteries. However, despite the fact that fractional models allow for capturing (infinite horizon) memory properties, the same feature also complicates the numerical evaluation. This is especially critical in cases, in which state estimation procedures are implemented that need to be executed in real time. For this type of applications, it is necessary that the execution time in each time step is bounded a-priori and that it does not grow over time. For that purpose, classical simulation approaches exploit, for example, Grünwald-Letnikov schemes with a finite length of previous state information. However, truncating the memory window without considering the arising errors may lead to wrong state estimates. This is especially critical if interval observers are designed which are meant to include the sets of reachable states with certainty.

关键词： Interval observer design fractional differential equations Grünwald-Letnikov discretization quantification of temporal truncation errors

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Online Identification of the Open-Circuit Voltage Characteristic of Lithium-Ion Batteries with a Contractor-Based Procedure

Online Identification of the Open-Circuit Voltage Characteri...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Marit Lahme Andreas Rauh Department of Computing Science Distributed Control in Interconnected Systems Carl von Ossietzky Universität Oldenburg Oldenburg Germany

An accurate estimation of the dynamic behavior of lithium-ion batteries is important for a lot of applications, for example, in battery management systems to efficiently and safely charge and discharge the battery cells. The dynamic behavior of lithium-ion batteries is influenced by aging and temperature effects. In order to precisely estimate the dynamic behavior, these effects have to be considered. This is possible for example with a set-valued online identification approach, combining an interval state estimation with the identification of state-dependent characteristics. A drawback of this approach is that the estimation uncertainty increases over time, so that the identification accuracy is limited. In this paper, we investigate if a contractor-based procedure can be used to reduce the estimation uncertainty. The contractor-based procedure was successfully integrated into the set-valued identification approach and leads to a significant improvement of the identification result. The extended identification approach is evaluated with a numerical simulation.

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Analysis of a Sensor Switching Approach for State Estimation with Applications to Electrochemical Power Converters and Energy Storage systems

Analysis of a Sensor Switching Approach for State Estimation...

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International Conference on systems and control (ICSC)

作者： Andreas Rauh Marit Lahme Department of Computing Science Group: Distributed Control in Interconnected Systems Carl von Ossietzky Universität Oldenburg Oldenburg Germany

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Combination of Stochastic State Estimation with Online Identification of the Open-Circuit Voltage of Lithium-Ion Batteries

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IFAC-PapersOnLine 2022年第40期55卷 97-102页

作者： Marit Lahme Andreas Rauh Carl von Ossietzky Universität Oldenburg Department of Computing Science Group: Distributed Control in Interconnected Systems D-26111 Oldenburg Germany

Typical equivalent circuit models for Lithium-ion batteries are represented in the form of the so-called Thévenin form. These models comprise a state of charge (SOC) controlled voltage source as well as lumped elements such as series resistances as well as at least one RC subnetwork to describe dynamic effects by the terminal current and voltage as input and output of the battery. In classical state estimation approaches, these characteristics are assumed to be identified beforehand, while aging of battery cells is related to a loss of the total capacity, changes in the charging/discharging efficiency, an increase of the Ohmic cell resistance, and/or changes of the time constants of the above-mentioned RC networks. Such variations can be estimated by means of additional parameters that are included in the system's state vector. However, the typically applied approaches do not allow for a direct identification of the nonlinear dependencies of the circuit elements on the SOC and other influence factors such as currents or the cell temperature. Therefore, this paper proposes a real-time capable, two-stage identification of these dependencies on the example of the open-circuit voltage by means of an Unscented Kalman Filter (UKF) approach. In the first stage, the state variables of the dynamic system model are estimated together with a lumped disturbance term (an additive correction of the open-circuit voltage). In the second stage, this disturbance term is used to correct the a-priori knowledge of the open-circuit voltage and to simultaneously express the identification quality in terms of covariances for the coefficients of its piecewise polynomial approximation in each temporal discretization step. The efficiency of the proposed methodology is demonstrated in simulations for an experimentally validated system model.

关键词： Online identification stochastic state estimation uncertainty quantification Unscented Kalman Filter Lithium-ion batteries

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Experimental Validation of an Ellipsoidal State Estimation Procedure for a Magnetic Levitation System

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IFAC-PapersOnLine 2023年第2期56卷 8494-8499页

作者： Andreas Rauh Jonas Soueidan Simon Rohou Luc Jaulin Carl von Ossietzky Universität Oldenburg Department of Computing Science Group: Distributed Control in Interconnected Systems D-26111 Oldenburg Germany ENSTA Bretagne Lab-STICC Robex 29806 Brest France

Set-based state estimation procedures have the advantage of enclosing all possible system states under the assumption of bounded measurement uncertainty, the structural correctness of dynamic systems models, and the representation of external disturbances and imperfectly known parameters by finitely large sets. In contrast to stochastic counterparts, often employing one of the available variants of Kalman filters, set-based approaches are less widely used. The reason for this observation is the fact that naive implementations often suffer from a non-negligible degree of overestimation and that (unless certain monotonicity properties are satisfied) set-based computations come with a notable increase of the computational complexity, resulting among others from required interval splitting procedures. This paper tries to resolve both issues by means of an ellipsoidal implementation of a discrete-time set-valued state estimation procedure that is validated experimentally and compared with an Unscented Kalman Filter (UKF) for a laboratory-scale magnetic levitation system.

关键词： Set-based state estimation ellipsoidal state enclosures bounded uncertainty nonlinear system models magnetic levitation systems

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Robust Iterative Learning Observers Based on a Combination of Stochastic Estimation Schemes and Ellipsoidal Calculus 25

Robust Iterative Learning Observers Based on a Combination o...

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25th International Conference on Information Fusion, FUSION 2022

作者： Rauh, Andreas Chevet, Thomas Dinh, Thach Ngoc Marzat, Julien Raissi, Tarek Carl von Ossietzky Universität Oldenburg Department of Computing Science Distributed Control in Interconnected Systems Oldenburg Germany Université Paris-Saclay Dtis Onera Palaiseau France Cedric-Laetitia Conservatoire National des Arts et Métiers Paris France

ISBN: (纸本)9781737749721

The state estimation of repetitive processes with periodically repeated trajectories can be interpreted as the dual task of iterative learning control design. While the latter has been widely investigated over the last two decades, only few approaches exist for the design of iterative learning observers. However, the exploitation of the knowledge about periodically repeated trajectories, which occur among others in pick and place tasks in robotics as well as in charging and discharging of batteries, offers the opportunity to enhance the estimation accuracy from one execution of the control task to the next. In this paper, we generalize a linear stochastic approach for iterative learning state estimation, inspired by the Kalman filter in terms of a minimization of the estimation error covariance, to the class of models with bounded parameter uncertainty and to nonlinear ones that can be represented by means of quasi-linear discrete-time state-space representations. To solve this task, a novel combination of set-valued ellipsoidal state enclosure techniques with the aforementioned stochastic iterative learning state estimator is presented and visualized for a quasi-linear model of the charging/discharging dynamics of Lithium-Ion batteries. © 2022 International Society of Information Fusion.

关键词： State estimation

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A Zonotopic State Estimation Approach for Nonlinear Uncertain systems with Bounded Uncertainty in the Measurement Time Instants

A Zonotopic State Estimation Approach for Nonlinear Uncertai...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Andreas Rauh Sara Ifqir Marit Lahme Department of Computing Science Group: Distributed Control in Interconnected Systems Carl von Ossietzky Universität Oldenburg Oldenburg Germany CRIStAL UMR CNRS 9189 Centrale Lille Institut Villeneuve d’Ascq Cedex France

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

In recent years, the use of zonotopes has gained in popularity for the implementation of state estimation procedures for systems with bounded uncertainty due to their capability to better capture correlations between individual state variables in comparison with more simple shapes such as axis-aligned interval boxes. However, many zonotopic state estimation procedures are limited to linear (uncertain) dynamics or to polytopic overapproximations of nonlinear system models to make observer structures applicable that perform the correction step with the help of a linear feedback of the output error. In this work, linearity assumptions are removed by implementing a predictor-corrector technique which - as a generalization of classical assumptions - does also not suppose that the measurement time instants are precisely known. To cope with such scenarios and to allow for a refinement of a-priori knowledge about the uncertainty of the measurement time instant in a model-based framework, this work makes use of an augmented state-space representation in which time is interpreted as a further state variable. Two simulation case studies conclude this paper and demonstrate the effectiveness of the proposed estimation technique.

关键词： Uncertain systems Uncertainty Correlation Shape Measurement uncertainty Linearity Observers

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Interval Observer Design for an Uncertain Time-Varying Quasi-Linear System Model of Lithium-Ion Batteries

Interval Observer Design for an Uncertain Time-Varying Quasi...

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European control Conference (ECC)

作者： Marit Lahme Andreas Rauh Guillaume Defresne Department of Computing Science Group: Distributed Control in Interconnected Systems Carl von Ossietzky Universität Oldenburg Oldenburg Germany Institut Supérieur de l’Aéronautique et de l'Espace (ISAE-SUPAERO) Toulouse France

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Lithium-ion batteries are currently used in numerous applications, for example in electric vehicles and energy storage systems. Accurately estimating the dynamic behavior is crucial to safely and efficiently charge and discharge the battery cells. This can be done with the help of stochastic or interval based identification routines which require an accurate state estimation. In previous works, we used an interval observer based on a Luenberger observer structure to estimate the lower and upper bounds of the state variables. However, with this approach it was necessary to use specified input current profiles to charge or discharge the battery cells, otherwise, the estimation uncertainty increased over time. In this paper, we aim to estimate the state variables during system operation without using specified input current profiles. A promising approach is a TNL observer, which was introduced for uncertain time-invariant systems in the literature, that provides multiple design degrees of freedom. The TNL observer is extended to an uncertain time-varying system model of lithium-ion batteries in this work. The time-varying part is herein considered with two different approaches. At first, a nominal system model is considered where the time-varying part is treated as a measurement uncertainty and secondly it is considered using a polytopic representation of the system matrix. The TNL observer was successfully extended to the time-varying system model of a lithium-ion battery. The polytopic representation of the system matrix leads to more accurate estimation results in comparison to the nominal approach.

关键词： Lithium-ion batteries Analytical models Uncertain systems Accuracy Uncertainty Upper bound Observers

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A Stochastic Design Approach for Iterative Learning Observers for the Estimation of Periodically Recurring Trajectories and Disturbances

A Stochastic Design Approach for Iterative Learning Observer...

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2022 European control Conference, ECC 2022

作者： Rauh, Andreas Chevet, Thomas Dinh, Thach Ngoc Marzat, Julien Raïssi, Tarek Carl von Ossietzky Universität Oldenburg Department of Computing Science Group: Distributed Control in Interconnected Systems OldenburgD-26111 Germany DTIS ONERA Université Paris-Saclay Palaiseau91123 France Cedric-Laetitia 292 Rue St-Martin ParisCedex 03 75141 France

ISBN: (纸本)9783907144077

Many repetitive control problems are characterized by the fact that disturbances have the same effect in each successive execution of the same control task. Such disturbances comprise the lumped representation of unmodeled parts of the open-loop system dynamics, a systematic model-mismatch or, more generally, deterministic yet unknown uncertainty. In such cases, well-known strategies for iterative learning control are based on enhancing the system behavior not only by exploiting data gathered during a single execution of the task but also using information from previous executions. The corresponding dual problem, namely, iterative learning state and disturbance estimation has not yet received the same amount of attention. However, it is obvious that improved estimates for the aforementioned states and disturbances which periodically occur in each execution will be a means to achieve an improved accuracy and, therefore, in future work also to optimize the control accuracy. In this paper, we present a joint design procedure for observer gains in two independent dimensions, a gain for processing information in the temporal domain during a single execution of the task (also named trial) and a gain for learning in the iteration domain (i.e., from trial to trial). © 2022 EUCA.

关键词： Stochastic systems

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