Neural Ordinary Differential Equations for Grey-Box Modelling of Lithium-Ion Batteries on the Basis of an Equivalent Circuit Model
- 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 newLithium-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.…
Author: | Jennifer BruckerORCiD, René BehmannORCiD, Wolfgang G. BesslerORCiDGND, Rainer GasperGND |
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Publisher: | MDPI AG |
Year of Publication: | 2022 |
Language: | English |
Tag: | equivalent circuit model; grey-box model; lithium-ion batteries; neural ordinary differential equations |
Parent Title (English): | Energies |
Volume: | 15 |
Issue: | 7 |
ISSN: | 1996-1073 |
First Page: | 1 |
Last Page: | 20 |
Document Type: | Article (reviewed) |
Open Access: | Ja |
Institutes: | Bibliografie |
Release Date: | 2022/04/11 |
Licence (German): | ![]() |
URN: | urn:nbn:de:bsz:ofb1-opus4-56326 |
DOI: | https://doi.org/10.3390/en15072661 |