Developing digital twin solutions for electrode fabrication in electrochemical devices manufacturing

​Grant Funding for Young Researchers 2025-2027

This project focuses on the development of a digital twin model for electrode fabrication in electrochemical device manufacturing. Electrode materials are critical components in energy storage devices such as batteries, fuel cells, and supercapacitors, where their performance is directly linked to fabrication processes. However, current manufacturing methods often lack the ability to predict and optimize the impact of process variations on the final electrode performance. The core objective of the project is to create a virtual model that simulates the entire electrode fabrication process, predicts material properties based on data, and provides insights into process optimization, quality control, and scalability.
The primary problem addressed by this research is the lack of predictive capabilities in current electrode manufacturing methods. Existing processes often fail to account for variations in manufacturing conditions and their impact on the final electrode performance, leading to inefficiencies and inconsistent product quality. This project aims to overcome these limitations by developing a digital twin—an advanced virtual model integrated with machine learning algorithms that will simulate key stages of electrode fabrication, such as slurry preparation, drying, and calendaring.
The research methodology combines advanced deep learning algorithms, synthetic data generation, and computational modeling to replicate and optimize the complex relationships between electrode microstructure and electrochemical performance. The project will generate high-resolution 3D reconstructions of electrode microstructures using open-source and synthetic datasets, which will then be segmented and analyzed to uncover correlations between structural parameters and functional outcomes. Simulation-driven experimental phases, utilizing physics-based tools like finite element analysis (FEA) and lattice Boltzmann methods, will predict how microstructural features influence electrochemical devices performance. These simulations will guide the optimization of electrode properties, providing the foundation for scalable and efficient electrode production processes.
The project is expected to have significant scientific and practical implications. Scientifically, it will push the boundaries of materials science and manufacturing technology by developing a predictive framework for electrode production. It will also contribute to advancing the integration of AI and machine learning in manufacturing processes, a field that remains underexplored in electrode production. The results will be of great value to both local and global manufacturers, offering a pathway for the commercialization of digital twin technologies in the manufacturing of energy storage devices. By improving the efficiency of electrode production, the project will directly contribute to reducing the cost and increasing the performance of energy storage devices, addressing the growing demand for renewable energy solutions, electric vehicles, and portable electronics.
In conclusion, this project offers a novel, data-driven approach to optimizing electrode fabrication for electrochemical devices, providing both scientific advancements and practical solutions to key challenges in the manufacturing of energy storage technologies. The expected outcomes will contribute to the scientific and technological development of Kazakhstan while positioning its industrial sector to compete in the global market.