[1] Zou, C., Manzie, C., & Nešić, D., 2016. A framework for simplification of PDE-based lithium-ion battery models. IEEE Transactions on Control Systems Technology, 24(5), pp.1594-1609.  [PDF]

[3] Li, Y., Karunathilake, D., Vilathgamuwa, D. M., Mishra, Y., Farrell, T. W., & Zou, C. (2022). Model order reduction techniques for physics-based lithium-ion battery management: A survey. IEEE Industrial Electronics Magazine, 16(3), 36-51. [PDF]

[4] Huang, Y., Zou, C. Li, Y., & Wik, T. (2023). MINN: Learning the dynamics of differential-algebraic equations and application to battery modeling. arXiv:2304.14422. Received the Best Presentation Award of the 2022 International Conference on Energy and AI. [PDF]

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.