TY  - CPAPER
U1  - Konferenzveröffentlichung
A1  - Brucker, Jennifer
A1  - Bessler, Wolfgang G.
A1  - Gasper, Rainer
ED  - Weidlich, Anke
ED  - Neumann, Dirk
ED  - Gust, Gunther
ED  - Staudt, Philipp
ED  - Schäfer, Mirko
T1  - Grey-box modelling of lithium-ion batteries using neural ordinary differential equations
T2  - Proceedings of the 10th DACH+ Conference on Energy Informatics
T2  - Energy Informatics
N2  - 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.
Y1  - 2021
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:ofb1-opus4-50730
UR  - https://energyinformatics.springeropen.com/articles/10.1186/s42162-021-00170-8
SN  - 2520-8942
SS  - 2520-8942
U6  - https://doi.org/10.1186/s42162-021-00170-8
DO  - https://doi.org/10.1186/s42162-021-00170-8
VL  - 4
IS  - Suppl 3.
SP  - 1
EP  - 13
S1  - 13
PB  - SpringerOpen
CY  - Cham
ER  -