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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.show moreshow less

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Metadaten
Author:Jennifer BruckerORCiD, René BehmannORCiD, Wolfgang G. BesslerORCiDGND, Rainer GasperGND
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):License LogoCreative Commons - CC BY - Namensnennung 4.0 International
URN:urn:nbn:de:bsz:ofb1-opus4-56326
DOI:https://doi.org/10.3390/en15072661