Data-driven parameterization of polymer electrolyte membrane fuel cell models via simultaneous local linear structured state space identification

Ritzberger, Daniel; Höflinger, Johannes; Du, Zhang Peng; Hametner, Christoph; Jakubek, Stefan

doi:10.1016/j.ijhydene.2021.01.037

Cited by 18 publications

(21 citation statements)

References 35 publications

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“…Ritzberger et al [10] developed the model, and it is an adapted version of the Pukrushpan et al [8] model. The difference is that the adapted model has the property of analytical differentiability, which is the reason for its selection.…”

Section: Model Descriptionmentioning

confidence: 99%

“…The model equations and their derivations are not the focus of this work. More information in this regard is available in [10].…”

Section: Model Descriptionmentioning

confidence: 99%

“…Therefore the combination of both approaches, grey-box models, provides the needed structure and fidelity for FC control. Control-oriented grey-box PEMFC models aim to have a low spatial dimension to be numerically efficient for real-time applications [8][9][10][11][12]. Parametrizing grey-box PEMFC models is not straightforward because they are nonlinear, numerically stiff, and have numerous unknown parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Their proposed parameterization method optimizes each analytically linearized model for each operating point dependent on the same parameter set simultaneously. In [10], they additionally conducted a parameter sensitivity analysis based on the analytically linearized models. This approach's disadvantage is that it is limited to data with small excitations because of the linearization.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, the two-step parameterization method is demonstrated with the model developed by Ritzberger et al [10] because of its advantageous property of analytical differentiability. The model is adapted to reduce its stiffness for more efficient numerical evaluations.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Two-Step Parametrization of a Control-Oriented Zero-Dimensional Polymer Electrolyte Membrane Fuel Cell Model Based on Measured Stack Data

Steindl

Jakubek

2021

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This paper proposes a new efficient two-step method for parametrizing control-oriented zero-dimensional physical polymer electrolyte membrane fuel cell (PEMFC) models with measured stack data. Parametrizations of these models are computationally intensive due to the numerous unknown parameters and the typically nonlinear, stiff model properties. This work reduces an existing model to decrease its stiffness for accelerated numerical simulations. Subdividing the parametrization into two consecutive subproblems (thermodynamic and electrochemical ones) reduces the solution space significantly. A parameter sensitivity analysis further reduces each sub-solution space by excluding non-significant parameters. The method results in an efficient parametrization process. The two-step approach minimizes each sub-solution space’s dimension by two-thirds, respectively three-fourths, compared to the global one. An achieved R2 value between simulation and measurement of 91% on average provides the required accuracy for control-oriented models.

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Section: Model Descriptionmentioning

confidence: 99%

“…The model equations and their derivations are not the focus of this work. More information in this regard is available in [10].…”

Section: Model Descriptionmentioning

confidence: 99%