Modelling of solid electrolyte interphase growth using neural ordinary differential equations

Title data

Ramasubramanian, Srivatsan ; Schomburg, Felix ; Röder, Fridolin:
Modelling of solid electrolyte interphase growth using neural ordinary differential equations.
In: Electrochimica Acta. Vol. 473 (2024) . - 143479.
ISSN 0013-4686
DOI: https://doi.org/10.1016/j.electacta.2023.143479

Official URL:

Abstract in another language

In this work, neural ordinary differential equations (NODE) are used to identify phenomenological growth rate functions to model the solid electrolyte interphase (SEI) growth during formation. To analyse the capabilities of this approach in a controlled setting, synthetic SEI thickness data is generated using a model that uses a mechanistic growth rate function. Several possible implementations and extensions of the NODE are investigated, including physical constraints and data augmentation. All the investigated variants agree well with the training data, but significant differences are observed for the validation data. The results show that the growth rate functions learnt by the baseline implementation without further constraints significantly differs from the growth rate function given by the mechanistic model. However, it is shown that the use of appropriate data augmentation or physical constraints provides a significant improvement, and low errors can be achieved within the validation data sets. It is concluded that NODE can reveal growth rate functions, but careful consideration is needed to achieve functions that are phenomenologically consistent with the underlying mechanisms.

Further data

Item Type:	Article in a journal
Refereed:	Yes
Keywords:	Solid electrolyte interphase; Neural ordinary differential equations; Scientific machine learning
Institutions of the University:	Faculties > Faculty of Engineering Science > Junior Professor Methods of Managing Batteries > Junior Professor Methods of Managing Batteries - Juniorprof. Dr.-Ing. Fridolin Röder Research Institutions > Central research institutes > Bayerisches Zentrum für Batterietechnik - BayBatt
Result of work at the UBT:	Yes
DDC Subjects:	600 Technology, medicine, applied sciences > 620 Engineering
Date Deposited:	12 Mar 2024 10:14
Last Modified:	12 Mar 2024 10:14
URI:	https://eref.uni-bayreuth.de/id/eprint/88867