Parametrized physics-informed deep operator networks for design of experiments applied to lithium-ion-battery cells

Brendel, Philipp (author), Mele, Igor (author), Rosskopf, Andreas (author), Katrašnik, Tomaž (author), Lorentz, Vincent (author)

Model-based state estimation of lithium-ion batteries relies on a robust, yet efficient parametrization of the underlying model under different conditions, which can be analyzed and improved through the lenses of Design-of-Experiments (DoE) methodologies. This paper presents parametrized physics-informed deep operator networks (PI-DeepONets) trained without any measured or synthetic data to predict solutions of a Single-Particle-Model for varying current profiles and electrode-specific diffusivity values. The prediction accuracy is evaluated based on three use cases representing three sets of current profiles featuring constant, smoothly time-dependent and non-smooth pulse profiles. After training PI-DeepONets, lithium concentration profiles are predicted within milliseconds achieving normalized percentage errors on the particle surfaces below 0.3% for constant or smoothly time-dependent current profiles and below 2% for non-smooth pulse profiles. The fast approximation of Fisher-Information-Matrices (FIMs) based on the trained PI-DeepONets offers additional speed-up potentials for DoE methodologies and yields a speed-up factor of 30 in the considered use case when compared to classical FIM approximation via finite differences on numerical reference solutions.

• Language: English
• Keywords: Li-ion batteries, modelling, design of experiment DoE, machine learning, physics informed neural networks
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: 14 str.
• Numbering: Vol. 128, art. 117055
• PID: 20.500.12556/RUL-169531
• UDC: 621.35
• ISSN on article: 2352-152X
• DOI: 10.1016/j.est.2025.117055
• COBISS.SI-ID: 237949187
• Publication date in RUL: 02.06.2025

Record is a part of a journal

• Title: Journal of energy storage
• Publisher: Elsevier
• ISSN: 2352-152X
• COBISS.SI-ID: 526511129

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Projects

• Funder: EC - European Commission
• Project number: 101103755
• Name: Fast-track hybrid testing platform for the development of battery systems
• Acronym: FASTEST

• Funder: UKRI - UK Research and Innovation
• Project number: 10078013
• Name: Fast-track hybrid testing platform for the development of battery systems
• Acronym: FASTEST