From Digital Models to Digital Twins: Battery Manufacturing Optimization in the Digital Era

Alejandro A. Franco^a

^aUniversité de Picardie Jules Verne

Proceedings of MATSUS Spring 2026 Conference (MATSUSSpring26)

F3 Processing and manufacturing of next generation batteries

Barcelona, Spain, 2026 March 23rd - 27th

Organizer: Sergio Pinilla

Invited Speaker, Alejandro A. Franco, presentation 586
Publication date: 15th December 2025

In this lecture, I present our latest research on the development of digital tools to optimize battery manufacturing processes. I discuss our digital models based on physics-based numerical simulations, artificial intelligence, and hybrid approaches capable of predicting how manufacturing parameters impact the properties and performance of electrodes and cells.[1-4,7]

These models are coupled with multi-objective optimization algorithms to predict the manufacturing recipes required to reach specific property and performance targets. This inverse design concept paves the way toward digital twins for battery manufacturing.[1,4]

I provide illustrations of application of our concept for lithium ion, sodium ion, solid state and redox flow batteries.

Furthermore, I present our recent work on the interaction of the human operators with the digital models by leveraging Virtual Reality (VR) and Mixed Reality (MR). These immersive tools are also utilized to train students, as illustrated by examples from the Erasmus+ Master's programme i-MESC.[5,6]

Finally, I will discuss our recently created spin-off company dedicated to transferring our modeling and digital technology to the industrial sector.

References:

[1] Troncoso, J. F., Zanotto, F. M., Galvez‐Aranda, D. E., Zapata Dominguez, D., Denisart, L., & Franco, A. A. (2025). The ARTISTIC battery manufacturing digitalization initiative: from fundamental research to industrialization. Batteries & Supercaps, 8(1), e202400385.

[2] Galvez‐Aranda, D. E., Dinh, T. L., Vijay, U., Zanotto, F. M., & Franco, A. A. (2024). Time‐Dependent Deep Learning Manufacturing Process Model for Battery Electrode Microstructure Prediction. Advanced Energy Materials, 14(15), 2400376.

[3] Galvez-Aranda, D. E., Fernandez, F., & Franco, A. A. (2025). Physics-Assisted Machine Learning for the Simulation of the Slurry Drying in the Manufacturing Process of Battery Electrodes: A Hybrid Time-Dependent VGG16-DEM Model. ACS Applied Materials & Interfaces, 17(22), 32150-32162.

[4] Duquesnoy, M., Liu, C., Dominguez, D. Z., Kumar, V., Ayerbe, E., & Franco, A. A. (2023). Machine learning-assisted multi-objective optimization of battery manufacturing from synthetic data generated by physics-based simulations. Energy Storage Materials, 56, 50-61.

[5] Galvez‐Aranda, D. E., Saravanan, S., & Franco, A. A. (2025). Human‐Centered Holographic Assistant using Computer Vision to Interact with Battery Manufacturing Machinery in a Predictive Manner. Batteries & Supercaps, 2500292.

[6] Denisart, L., Zapata‐Dominguez, D., David, X., Leclere, A., Lelong, R., Liu, C., Xu, J., Loup-Escande, E., & Franco, A. A. (2024). Combining virtual reality with mixed reality for efficient training in battery manufacturing. Batteries & Supercaps, 7(1), e202300268.

[7] Fernandez, F., Saravanan, S., Omongos, R.L. et al. Transfer learning assessment of small datasets relating manufacturing parameters with electrochemical energy cell component properties. npj Adv. Manuf. 2, 14 (2025).

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