Daniel Ritzberger
Vienna University of Technology
18 Papers
31 Citations
Daniel Ritzberger is an academic researcher from Vienna University of Technology. The author has contributed to research in topics: Proton exchange membrane fuel cell & Model predictive control. The author has an hindex of 6, co-authored 18 publications. Previous affiliations of Daniel Ritzberger include AVL.
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Papers
Thermodynamically consistent reduced dimensionality electrochemical model for proton exchange membrane fuel cell performance modelling and control
Andraz̆ Kravos,Daniel Ritzberger,Gregor Tavčar,Christoph Hametner,Stefan Jakubek,Tomaz Katrasnik +5 more
TL;DR: The derivation of a zero-dimensional thermodynamically consistent electrochemical model for proton exchange membrane fuel cells performance modelling and control is provided, further extended to accommodate the transport of gaseous species along the channel and through gas diffusion layer, yielding a quasi-one-dimensional Electrochemical model.
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Constrained extended Kalman filter design and application for on-line state estimation of high-order polymer electrolyte membrane fuel cell systems
TL;DR: The proposed method provides state estimates for challenging operating conditions such as shut-down and start-up of the fuel cell for which the unconstrained EKF fails, and is compared with the unscented Kalman filter.
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Methodology for efficient parametrisation of electrochemical PEMFC model for virtual observers: Model based optimal design of experiments supported by parameter sensitivity analysis
TL;DR: Results reveal that application of D-optimal DoE enables enhancement of calibration parameters information resulting in up to order of magnitude lower relative standard errors on smaller data-sets and increased information and thus identifiability, inherently leads to improved robustness of the FC electrochemical model.
19
A Real-Time Dynamic Fuel Cell System Simulation for Model-Based Diagnostics and Control: Validation on Real Driving Data
TL;DR: In this work, a real time stack model is presented and its experimental parameterization is discussed and its integrated in a system simulation, where the compressor dynamics, the feedback controls for the hydrogen injection and back-pressure valve actuation, and the purging strategy are considered.
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Data-driven parameterization of polymer electrolyte membrane fuel cell models via simultaneous local linear structured state space identification
TL;DR: A parameterization scheme based on the simultaneous estimation of multiple structured state space models, obtained by analytic linearization of a candidate fuel cell stack model, is proposed, which has the advantage of high computational efficiency and regaining the desired flexibility required for the typically iterative task of model parameterization.
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