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Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature

Adachi, Masaki and Kuhn, Yannick and Horstmann, Birger and Latz, Arnulf and Osborne, Michael A. and Howey, David A. (2023) Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature. In: IFAC World Congress 2023, pp. 1-11. IFAC-PapersOnLine. IFAC World Congress 2023, 9.-14. Jul. 2023, Yokohama, Japan. doi: 10.48550/arXiv.2210.17299. (Submitted)

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Official URL: https://arxiv.org/abs/2210.17299

Abstract

A wide variety of battery models are available, and it is not always obvious which model `best' describes a dataset. This paper presents a Bayesian model selection approach using Bayesian quadrature. The model evidence is adopted as the selection metric, choosing the simplest model that describes the data, in the spirit of Occam's razor. However, estimating this requires integral computations over parameter space, which is usually prohibitively expensive. Bayesian quadrature offers sample-efficient integration via model-based inference that minimises the number of battery model evaluations. The posterior distribution of model parameters can also be inferred as a byproduct without further computation. Here, the simplest lithium-ion battery models, equivalent circuit models, were used to analyse the sensitivity of the selection criterion to given different datasets and model configurations. We show that popular model selection criteria, such as root-mean-square error and Bayesian information criterion, can fail to select a parsimonious model in the case of a multimodal posterior. The model evidence can spot the optimal model in such cases, simultaneously providing the variance of the evidence inference itself as an indication of confidence. We also show that Bayesian quadrature can compute the evidence faster than popular Monte Carlo based solvers.

Item URL in elib:https://elib.dlr.de/192920/
Document Type:Conference or Workshop Item (Other)
Title:Bayesian Model Selection of Lithium-Ion Battery Models via Bayesian Quadrature
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Adachi, MasakiUNSPECIFIEDhttps://orcid.org/0000-0003-2580-2280UNSPECIFIED
Kuhn, YannickUNSPECIFIEDhttps://orcid.org/0000-0002-9019-2290UNSPECIFIED
Horstmann, BirgerUNSPECIFIEDhttps://orcid.org/0000-0002-1500-0578UNSPECIFIED
Latz, ArnulfUNSPECIFIEDhttps://orcid.org/0000-0003-1449-8172UNSPECIFIED
Osborne, Michael A.University of Oxfordhttps://orcid.org/0000-0003-1959-012XUNSPECIFIED
Howey, David A.The Faraday Institutionhttps://orcid.org/0000-0002-0620-3955UNSPECIFIED
Date:2023
Journal or Publication Title:IFAC World Congress 2023
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
DOI:10.48550/arXiv.2210.17299
Page Range:pp. 1-11
Publisher:IFAC-PapersOnLine
Status:Submitted
Keywords:Methodology (stat.ME), Machine Learning (cs.LG), Systems and Control (eess.SY), Chemical Physics (physics.chem-ph), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Physical sciences, FOS: Physical sciences, 62C10, 62F15
Event Title:IFAC World Congress 2023
Event Location:Yokohama, Japan
Event Type:international Conference
Event Dates:9.-14. Jul. 2023
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, E - Electrochemical Processes
Location: Ulm
Institutes and Institutions:Institute of Engineering Thermodynamics > Computational Electrochemistry
Deposited By: Kuhn, Yannick
Deposited On:05 Jan 2023 15:09
Last Modified:05 Jan 2023 15:09

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