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

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

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.

• Jezik: Angleški jezik
• Ključne besede: Li-ion batteries, modelling, design of experiment DoE, machine learning, physics informed neural networks
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FS - Fakulteta za strojništvo
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2025
• Št. strani: 14 str.
• Številčenje: Vol. 128, art. 117055
• PID: 20.500.12556/RUL-169531
• UDK: 621.35
• ISSN pri članku: 2352-152X
• DOI: 10.1016/j.est.2025.117055
• COBISS.SI-ID: 237949187
• Datum objave v RUL: 02.06.2025

Gradivo je del revije

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

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Projekti

• Financer: EC - European Commission
• Številka projekta: 101103755
• Naslov: Fast-track hybrid testing platform for the development of battery systems
• Akronim: FASTEST

• Financer: UKRI - UK Research and Innovation
• Številka projekta: 10078013
• Naslov: Fast-track hybrid testing platform for the development of battery systems
• Akronim: FASTEST