MINN: Learning the dynamics of differential-algebraic equations and application to battery modeling
Artikel i vetenskaplig tidskrift, 2024

The concept of integrating physics-based and data-driven approaches has become popular for modeling sustainable energy systems. However, the existing literature mainly focuses on the data-driven surrogates generated to replace physics-based models. These models often trade accuracy for speed but lack the generalisability, adaptability, and interpretability inherent in physics-based models, which are often indispensable in the modeling of real-world dynamic systems for optimization and control purposes. In this work, we propose a novel architecture for generating model-integrated neural networks (MINN) to allow integration on the level of learning physics-based dynamics of the system. The obtained hybrid model solves an unsettled research problem in control-oriented modeling, i.e., how to obtain an optimally simplified model that is physically insightful, numerically accurate, and computationally tractable simultaneously. We apply the proposed neural network architecture to model the electrochemical dynamics of lithium-ion batteries and show that MINN is extremely data-efficient to train while being sufficiently generalizable to previously unseen input data, owing to its underlying physical invariants. The MINN battery model has an accuracy comparable to the first principle-based model in predicting both the system outputs and any locally distributed electrochemical behaviors but achieves two orders of magnitude reduction in the solution time.

Energy storage systems

Physics based machine learning

Lithium ion battery

Författare

Yicun Huang

Chalmers, Elektroteknik, System- och reglerteknik

Changfu Zou

Chalmers, Elektroteknik, System- och reglerteknik

Yang Li

Chalmers, Elektroteknik, System- och reglerteknik

Torsten Wik

Chalmers, Elektroteknik, System- och reglerteknik

IEEE Transactions on Pattern Analysis and Machine Intelligence

0162-8828 (ISSN) 19393539 (eISSN)

Vol. In Press

Modellering av pläteringsmorfologi i litiumjonbatterier för ökad säkerhet

Europeiska kommissionen (EU) (101068764), 2022-07-05 -- 2024-07-04.

Drivkrafter

Hållbar utveckling

Styrkeområden

Energi

Ämneskategorier

Reglerteknik

DOI

10.1109/TPAMI.2024.3456475

Mer information

Senast uppdaterat

2024-09-24