Singh, Soumya ; Ebongue, Yvonne Eboumbou ; Rezaei, Shahed ; Birke, Kai Peter (2023)
Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation.
In: Batteries, 9 (6)
doi: 10.3390/batteries9060301
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
Accurate forecasting of the lifetime and degradation mechanisms of lithium-ion batteries is crucial for their optimization, management, and safety while preventing latent failures. However, the typical state estimations are challenging due to complex and dynamic cell parameters and wide variations in usage conditions. Physics-based models need a tradeoff between accuracy and complexity due to vast parameter requirements, while machine-learning models require large training datasets and may fail when generalized to unseen scenarios. To address this issue, this paper aims to integrate the physics-based battery model and the machine learning model to leverage their respective strengths. This is achieved by applying the deep learning framework called physics-informed neural networks (PINN) to electrochemical battery modeling. The state of charge and state of health of lithium-ion cells are predicted by integrating the partial differential equation of Fick’s law of diffusion from a single particle model into the neural network training process. The results indicate that PINN can estimate the state of charge with a root mean square error in the range of 0.014% to 0.2%, while the state of health has a range of 1.1% to 2.3%, even with limited training data. Compared to conventional approaches, PINN is less complex while still incorporating the laws of physics into the training process, resulting in adequate predictions, even for unseen situations.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Singh, Soumya ; Ebongue, Yvonne Eboumbou ; Rezaei, Shahed ; Birke, Kai Peter |
| Art des Eintrags: | Bibliographie |
| Titel: | Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation |
| Sprache: | Englisch |
| Publikationsjahr: | 2023 |
| Ort: | Darmstadt |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Batteries |
| Jahrgang/Volume einer Zeitschrift: | 9 |
| (Heft-)Nummer: | 6 |
| Kollation: | 19 Seiten |
| DOI: | 10.3390/batteries9060301 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Accurate forecasting of the lifetime and degradation mechanisms of lithium-ion batteries is crucial for their optimization, management, and safety while preventing latent failures. However, the typical state estimations are challenging due to complex and dynamic cell parameters and wide variations in usage conditions. Physics-based models need a tradeoff between accuracy and complexity due to vast parameter requirements, while machine-learning models require large training datasets and may fail when generalized to unseen scenarios. To address this issue, this paper aims to integrate the physics-based battery model and the machine learning model to leverage their respective strengths. This is achieved by applying the deep learning framework called physics-informed neural networks (PINN) to electrochemical battery modeling. The state of charge and state of health of lithium-ion cells are predicted by integrating the partial differential equation of Fick’s law of diffusion from a single particle model into the neural network training process. The results indicate that PINN can estimate the state of charge with a root mean square error in the range of 0.014% to 0.2%, while the state of health has a range of 1.1% to 2.3%, even with limited training data. Compared to conventional approaches, PINN is less complex while still incorporating the laws of physics into the training process, resulting in adequate predictions, even for unseen situations. |
| Freie Schlagworte: | Li-ion battery, battery modeling, state estimation, state of health (SOH), state of charge (SOC), hybrid modeling, physics-informed neural network (PINN), single-particle model (SPM) |
| Zusätzliche Informationen: | This article belongs to the Special Issue The Precise Battery—towards Digital Twins for Advanced Batteries |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau 600 Technik, Medizin, angewandte Wissenschaften > 660 Technische Chemie |
| Fachbereich(e)/-gebiet(e): | 11 Fachbereich Material- und Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft 11 Fachbereich Material- und Geowissenschaften > Materialwissenschaft > Fachgebiet Mechanik Funktionaler Materialien |
| Hinterlegungsdatum: | 02 Aug 2024 12:52 |
| Letzte Änderung: | 02 Aug 2024 12:52 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
-
Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation. (deposited 19 Jun 2023 13:14)
- Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation. (deposited 02 Aug 2024 12:52) [Gegenwärtig angezeigt]
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum