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Media type:
E-Article
Title:
Grey-box modelling of lithium-ion batteries using neural ordinary differential equations
Contributor:
Brucker, Jennifer;
Bessler, Wolfgang G.;
Gasper, Rainer
imprint:
Springer Science and Business Media LLC, 2021
Published in:Energy Informatics
Language:
English
DOI:
10.1186/s42162-021-00170-8
ISSN:
2520-8942
Origination:
Footnote:
Description:
<jats:title>Abstract</jats:title><jats:p>Grey-box modelling combines physical and data-driven models to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling, as differential equations given by physical laws and neural networks can be combined in a single modelling framework. This simplifies the simulation and optimization and allows to consider irregularly-sampled data during training and evaluation of the model. We demonstrate this approach using two levels of model complexity; first, a simple parallel resistor-capacitor circuit; and second, an equivalent circuit model of a lithium-ion battery cell, where the change of the voltage drop over the resistor-capacitor circuit including its dependence on current and State-of-Charge is implemented as NODE. After training, both models show good agreement with analytical solutions respectively with experimental data.</jats:p>