TY  - JOUR
U1  - Wissenschaftlicher Artikel
A1  - Brucker, Jennifer
A1  - Behmann, René
A1  - Bessler, Wolfgang G.
A1  - Gasper, Rainer
T1  - Neural Ordinary Differential Equations for Grey-Box Modelling of Lithium-Ion Batteries on the Basis of an Equivalent Circuit Model
JF  - Energies
N2  - Lithium-ion batteries exhibit a dynamic voltage behaviour depending nonlinearly on current and state of charge. The modelling of lithium-ion batteries is therefore complicated and model parametrisation is often time demanding. Grey-box models combine physical and data-driven modelling to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling. Differential equations given by physical laws and NODEs can be combined in a single modelling framework. Here we demonstrate the use of NODEs for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis and represents the physical part of the model. The voltage drop over the resistor–capacitor circuit, including its dependency on current and state of charge, is implemented as a NODE. After training, the grey-box model shows good agreement with experimental full-cycle data and pulse tests on a lithium iron phosphate cell. We test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model.
KW  - neural ordinary differential equations
KW  - grey-box model
KW  - equivalent circuit model
KW  - lithium-ion batteries
Y1  - 2022
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:ofb1-opus4-56326
SN  - 1996-1073
SS  - 1996-1073
U6  - https://doi.org/10.3390/en15072661
DO  - https://doi.org/10.3390/en15072661
VL  - 15
IS  - 7
SP  - 1
EP  - 20
PB  - MDPI AG
ER  -