MINN: Learning the dynamics of differential-algebraic equations and application to battery modeling
Journal article, 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

Author

Yicun Huang

Chalmers, Electrical Engineering, Systems and control

Changfu Zou

Chalmers, Electrical Engineering, Systems and control

Yang Li

Chalmers, Electrical Engineering, Systems and control

Torsten Wik

Chalmers, Electrical Engineering, Systems and control

IEEE Transactions on Pattern Analysis and Machine Intelligence

0162-8828 (ISSN) 19393539 (eISSN)

Vol. In Press

Modelling plating morphology in lithium-ion batteries for enhanced safety

European Commission (EC) (101068764), 2022-07-05 -- 2024-07-04.

Driving Forces

Sustainable development

Areas of Advance

Energy

Subject Categories

Control Engineering

DOI

10.1109/TPAMI.2024.3456475

More information

Latest update

9/24/2024