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Izenburua
Review of computational parameter estimation methods for electrochemical models
Egilea
Miguel, Eduardo
Oca, Laura
IRAOLA, UNAI
Egilea (beste erakunde batekoa)
Plett, Gregory L.
Trimboli, M. Scott
Bekaert, Emilie
Ikerketa taldea
Almacenamiento de energía
Beste instituzio
University of Colorado Colorado Springs (UCCS)
CIC energiGUNE
Bertsioa
Berrikusten dagoen preprinta
Eskubideak
© 2021 Elsevier Ltd
Sarbidea
Sarbide irekia
URI
https://hdl.handle.net/20.500.11984/5799
Argitaratzailearen bertsioa
https://doi.org/10.1016/j.est.2021.103388
Non argitaratua
Journal of Energy Storage. Vol. 44. Part B. Article 103388  December, 2021
Argitaratzailea
Elsevier Ltd
Gako-hitzak
Battery
Electrochemical model
Parameter estimation
Parameterization ... [+]
Battery
Electrochemical model
Parameter estimation
Parameterization
Li-ion battery
Battery modeling [-]
Laburpena
Electrochemical models are an incipient technique for estimation of battery cells internal variables, useful for cells design or state of function optimization. One of the non-trivial procedures that ... [+]
Electrochemical models are an incipient technique for estimation of battery cells internal variables, useful for cells design or state of function optimization. One of the non-trivial procedures that allow the use of this type of models is the estimation of model parameter values. This paper presents a review of the existing computational parameter estimation methods for rocking chair batteries electrochemical models, a crucial step for real case implementation. Physics-based models cannot reach accurate predictions if the parameters are not properly estimated, what highlights the necessity of reviewing the validity of these protocols, that are not extensively treated within literature. The gathered methods are explained and analyzed taking into account the accuracy and extent of the presented results, to give the most objective overview of their applicability within real case scenarios. The methods are classified into two different groups: single optimization analysis (using only one optimization procedure to estimate parameters) and multiple optimization analysis (methods using multiple optimizations). In addition, the need for at least some amount of physico-chemical characterization is analyzed as a common procedure for all the parameter estimation methods. The accuracy of each method is determined, taking as reference the best achievements found in literature. The results show that it is possible to estimate parameters with a high accuracy using non-invasive parameter estimation methods. Finally the potential of mixed (non invasive and physico-chemical based) methodologies is presented. These type of estimation procedures can potentially increase the accuracy of the procedures by lightening up the optimizations involved in the processes, and increasing the ability to estimate values for insensitive parameters. These mixed methods could achieve faster and cheaper estimation protocols, making them more efficient in general. [-]
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