A Grey-box Model with Neural Ordinary Differential Equations for the Slow Voltage Dynamics of Lithium-ion Batteries: Model Development and Training
- Lithium-ion batteries exhibit slow voltage dynamics on the minute time scale that are usually associated with transport processes. We present a novel modelling approach toward these dynamics by combining physical and data-driven models into a Grey-box model. We use neural networks, in particular neural ordinary differential equations. The physical structure of the Grey-box model is borrowed fromLithium-ion batteries exhibit slow voltage dynamics on the minute time scale that are usually associated with transport processes. We present a novel modelling approach toward these dynamics by combining physical and data-driven models into a Grey-box model. We use neural networks, in particular neural ordinary differential equations. The physical structure of the Grey-box model is borrowed from the Fickian diffusion law, where the transport domain is discretized using finite volumes. Within this physical structure, unknown parameters (diffusion coefficient, diffusion length, discretization) and dependencies (state of charge, lithium concentration) are replaced by neural networks and learnable parameters. We perform model-to-model comparisons, using as training data (a) a Fickian diffusion process, (b) a Warburg element, and (c) a resistor-capacitor circuit. Voltage dynamics during constant-current operation and pulse tests as well as electrochemical impedance spectra are simulated. The slow dynamics of all three physical models in the order of ten to 30 min are well captured by the Grey-box model, demonstrating the flexibility of the present approach.…
Document Type: | Article (reviewed) |
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Zitierlink: | https://opus.hs-offenburg.de/8265 | Bibliografische Angaben |
Title (English): | A Grey-box Model with Neural Ordinary Differential Equations for the Slow Voltage Dynamics of Lithium-ion Batteries: Model Development and Training |
Author: | Jennifer BruckerStaff MemberORCiD, Wolfgang G. BesslerStaff MemberORCiDGND, Rainer GasperStaff MemberGND |
Year of Publication: | 2023 |
Date of first Publication: | 2023/12/28 |
Publisher: | IOP Publishing |
First Page: | 1 |
Last Page: | 19 |
Article Number: | 120537 |
Parent Title (English): | Journal of The Electrochemical Society |
Volume: | 170 |
Issue: | 12 |
ISSN: | 0013-4651 (Print) |
ISSN: | 1945-7111 (Elektronisch) |
DOI: | https://doi.org/10.1149/1945-7111/ad14cd |
URN: | https://urn:nbn:de:bsz:ofb1-opus4-82652 |
Language: | English | Inhaltliche Informationen |
Institutes: | Forschung / INES - Institut für nachhaltige Energiesysteme |
Fakultät Maschinenbau und Verfahrenstechnik (M+V) | |
Institutes: | Bibliografie |
Tag: | Diffusion; Grey-Box Model; Lithium-Ion Battery; Neural Ordinary Differential Equation; Warburg Element |
Funded by (selection): | Stiftungen |
Funded by (textarea): | Carl-Zeiss-Stiftung | Formale Angaben |
Relevance: | Wiss. Zeitschriftenartikel reviewed: Listung in Master Journal List |
Open Access: | Open Access |
Hybrid | |
Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |