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Parameterisation of Physics-Based Battery Models From Few Noisy Measurements

Kuhn, Yannick and Horstmann, Birger and Latz, Arnulf (2021) Parameterisation of Physics-Based Battery Models From Few Noisy Measurements. COMPDYN-UNCECOMP-EUROGEN 2021, 28.-30. Juni 2021, Athen, Griechenland.

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A wealth of measurement techniques are available for determining the transport or thermodynamic properties of batteries. Some examples are the Galvanostatic Intermittent Titration Technique, nuclear magnetic resonance imaging or impedance spectroscopy, which are excellent at retrieving a subset of battery model parameters. They achieve this at the cost of accuracy and compatibility since each employs a different approximation in order to obtain analytic expressions. So it remains a challenge to obtain a complete and consistent parameter set that is useful for running simulations that can accurately predict future battery behaviour. Exacerbating this challenge is the long runtime and/or high cost of any battery measurement, which means that in practice only a few measurements of varying type with considerable noise are available and that the parameters might change between measurements due to battery ageing. Due to the complexity of the widely used Doyle-Fuller�Newman model and its simplifications, their parameters are not directly observable in normal battery operation. Thus, some measurements involve the destruction of the battery, which make the parallel parameterisation of "identical" batteries with slightly different manufacturing defects necessary. The goal is to enable automated material screening with a flexible selection of various measurements. The issues described above necessitate that an inverse parameter identification algorithm for this task is aware of the uncertainties in the parameters and the measurements and can quantify the uncertainties of the estimated parameters. These uncertainties are most certainly intractable, so we decided on a Bayesian approach where the likelihood is substituted by a simulator, realised with Expectation Propagation and Bayesian Optimisation. We will discuss the results of their application.

Item URL in elib:https://elib.dlr.de/147393/
Document Type:Conference or Workshop Item (Poster)
Title:Parameterisation of Physics-Based Battery Models From Few Noisy Measurements
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Kuhn, Yannickyannick.kuhn (at) dlr.dehttps://orcid.org/0000-0002-9019-2290
Horstmann, Birgerbirger.horstmann (at) dlr.dehttps://orcid.org/0000-0002-1500-0578
Latz, Arnulfarnulf.latz (at) dlr.dehttps://orcid.org/0000-0003-1449-8172
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Keywords:battery modelling, uncertainty quantification, bayesian optimisiation
Event Location:Athen, Griechenland
Event Type:international Conference
Event Dates:28.-30. Juni 2021
HGF - Research field:Energy
HGF - Program:Materials and Technologies for the Energy Transition
HGF - Program Themes:Electrochemical Energy Storage
DLR - Research area:Energy
DLR - Program:E SP - Energy Storage
DLR - Research theme (Project):E - Electrochemical Storage
Location: Ulm
Institutes and Institutions:Institute of Engineering Thermodynamics > Computational Electrochemistry
Deposited By: Werres, Martin Alexander
Deposited On:23 Dec 2021 10:01
Last Modified:23 Dec 2021 10:01

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