Bayesian parameter estimation applied to the Li-ion battery single particle model with electrolyte dynamics
This paper presents a Bayesian parameter estimation approach and identifiability analysis for a lithium-ion battery model, to determine the uniqueness, evaluate the sensitivity and quantify the uncertainty of a subset of the model parameters. The analysis was based on the single particle model with...
| Main Authors: | , , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2021
|
| _version_ | 1826303627262689280 |
|---|---|
| author | Aitio, A Marquis, SG Ascencio, P Howey, D |
| author_facet | Aitio, A Marquis, SG Ascencio, P Howey, D |
| author_sort | Aitio, A |
| collection | OXFORD |
| description | This paper presents a Bayesian parameter estimation approach and identifiability analysis for a lithium-ion battery model, to determine the uniqueness, evaluate the sensitivity and quantify the uncertainty of a subset of the model parameters. The analysis was based on the single particle model with electrolyte dynamics, rigorously derived from the Doyle-Fuller-Newman model using asymptotic analysis including electrode-average terms. The Bayesian approach allows complex target distributions to be estimated, which enables a global analysis of the parameter space. The analysis focuses on the identification problem (i) locally, under a set of discrete quasi-steady states of charge, and in comparison (ii) globally with a continuous excursion of state of charge. The performance of the methodology was evaluated using synthetic data from multiple numerical simulations under diverse types of current excitation. We show that various diffusivities as well as the transference number may be estimated with small variances in the global case, but with much larger uncertainty in the local estimation case. This also has significant implications for estimation where parameters might vary as a function of state of charge or other latent variables. |
| first_indexed | 2024-03-07T06:05:35Z |
| format | Journal article |
| id | oxford-uuid:edb8a9e9-7d4d-4736-b13c-23e2994959cc |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T06:05:35Z |
| publishDate | 2021 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:edb8a9e9-7d4d-4736-b13c-23e2994959cc2022-03-27T11:27:25ZBayesian parameter estimation applied to the Li-ion battery single particle model with electrolyte dynamicsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:edb8a9e9-7d4d-4736-b13c-23e2994959ccEnglishSymplectic ElementsElsevier2021Aitio, AMarquis, SGAscencio, PHowey, DThis paper presents a Bayesian parameter estimation approach and identifiability analysis for a lithium-ion battery model, to determine the uniqueness, evaluate the sensitivity and quantify the uncertainty of a subset of the model parameters. The analysis was based on the single particle model with electrolyte dynamics, rigorously derived from the Doyle-Fuller-Newman model using asymptotic analysis including electrode-average terms. The Bayesian approach allows complex target distributions to be estimated, which enables a global analysis of the parameter space. The analysis focuses on the identification problem (i) locally, under a set of discrete quasi-steady states of charge, and in comparison (ii) globally with a continuous excursion of state of charge. The performance of the methodology was evaluated using synthetic data from multiple numerical simulations under diverse types of current excitation. We show that various diffusivities as well as the transference number may be estimated with small variances in the global case, but with much larger uncertainty in the local estimation case. This also has significant implications for estimation where parameters might vary as a function of state of charge or other latent variables. |
| spellingShingle | Aitio, A Marquis, SG Ascencio, P Howey, D Bayesian parameter estimation applied to the Li-ion battery single particle model with electrolyte dynamics |
| title | Bayesian parameter estimation applied to the Li-ion battery single particle model with electrolyte dynamics |
| title_full | Bayesian parameter estimation applied to the Li-ion battery single particle model with electrolyte dynamics |
| title_fullStr | Bayesian parameter estimation applied to the Li-ion battery single particle model with electrolyte dynamics |
| title_full_unstemmed | Bayesian parameter estimation applied to the Li-ion battery single particle model with electrolyte dynamics |
| title_short | Bayesian parameter estimation applied to the Li-ion battery single particle model with electrolyte dynamics |
| title_sort | bayesian parameter estimation applied to the li ion battery single particle model with electrolyte dynamics |
| work_keys_str_mv | AT aitioa bayesianparameterestimationappliedtotheliionbatterysingleparticlemodelwithelectrolytedynamics AT marquissg bayesianparameterestimationappliedtotheliionbatterysingleparticlemodelwithelectrolytedynamics AT ascenciop bayesianparameterestimationappliedtotheliionbatterysingleparticlemodelwithelectrolytedynamics AT howeyd bayesianparameterestimationappliedtotheliionbatterysingleparticlemodelwithelectrolytedynamics |