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A Flexible Approach To Parameter Estimation Of Physics-Based Battery Models With Bayesian Optimization

Kuhn, Yannick and Horstmann, Birger and Latz, Arnulf (2020) A Flexible Approach To Parameter Estimation Of Physics-Based Battery Models With Bayesian Optimization. Oxford Battery Modeling Symposium | OBMS 2020, 2020-03-16 - 2020-03-17, Oxford, Großbritannien / Virtuell.

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A wealth of measurement techniques are available for determining transport or thermodynamic properties of batteries. Some examples are GITT, pulse experiments or least squares with impedance spectroscopy, which are excellent at automatically retrieving a subset of model parameters with relatively low computational cost. 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 study the interactions and effects of multiple model parameters at once, since various measurement techniques would need to be combined intoone so-called cost functional. This cost functional is a function of the measurement data and the battery model parameters and can be used to determine the parameters that best describe the data. The automated computation for this usually leverages the derivative/gradient of the cost functional by the parameters. Beyond some degree of complexity, this gradient would become intractable or unfeasible to analytically calculate, making the computation expensive and/or numerically unstable.We want to eliminate the need for those gradients. The goal is to enable automated material screening with flexible selection of various measurements. Our approach uses the existing specialized techniques as a starting point and refines their results by performing simulations with randomized parameters and comparing them with allavailable measurements. This Monte Carlo approach is made computationally feasible by utilizing Bayesian Optimization for sparse sampling and preprocessing the measurements into intuitive features. The latter also enables us to use measurements that are most often fitted by hand, e.g. discharge curves. Additionally, we make use of a single particlemodel with electrolyte from and/or PyBaMM to make the simulations as efficientas possible, but any 1D+1D model can be used thanks to the sparse sampling approach.

Item URL in elib:https://elib.dlr.de/139357/
Document Type:Conference or Workshop Item (Poster)
Title:A Flexible Approach To Parameter Estimation Of Physics-Based Battery Models With Bayesian Optimization
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Kuhn, YannickUNSPECIFIEDhttps://orcid.org/0000-0002-9019-2290UNSPECIFIED
Horstmann, BirgerUNSPECIFIEDhttps://orcid.org/0000-0002-1500-0578UNSPECIFIED
Latz, ArnulfUNSPECIFIEDhttps://orcid.org/0000-0003-1449-8172UNSPECIFIED
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Keywords:Parameter Optimization, Bayesian Optimization
Event Title:Oxford Battery Modeling Symposium | OBMS 2020
Event Location:Oxford, Großbritannien / Virtuell
Event Type:international Conference
Event Start Date:16 March 2020
Event End Date:17 March 2020
HGF - Research field:Energy
HGF - Program:Storage and Cross-linked Infrastructures
HGF - Program Themes:Electrochemical Energy Storage
DLR - Research area:Energy
DLR - Program:E SP - Energy Storage
DLR - Research theme (Project):E - Electrochemical Prcesses (Batteries) (old)
Location: Stuttgart
Institutes and Institutions:Institute of Engineering Thermodynamics > Computational Electrochemistry
Deposited By: Bolay, Linda
Deposited On:11 Dec 2020 16:39
Last Modified:24 Apr 2024 20:40

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