Artificial Intelligence for Batteries
The junior research group “Artificial Intelligence for Batteries”, led by Dr.-Ing. Weihan Li develops technologies that integrate physics-based models and machine learning within battery research, empowering digitalized and intelligent approaches to battery production, testing and real-world application.
Our research encompasses two core domains: At the material and component level, we are committed to enhancing image-based battery data analysis through deep learning. Our emphasis is on automating segmentation and detecting anomalies, ensuring the utmost accuracy and dependability. At the cell and system level, our expertise shines in battery modeling, parameterization, online monitoring, and control technologies, all aimed at ensuring the safe and reliable use of batteries.
A special focus of our work is the fast characterization of the performance of batteries from the production lines with machine learning, accelerating battery production evaluation and optimization.
- Automated segmentation for battery analysis
- Denoising and enhancing battery CT scans
- Battery digital twin and cloud-based battery management
- Accelerated ageing test with machine learning
- Data-driven and non-invasive parameterization
- State estimation, control and optimization
- Ageing diagnosis and prognosis
- Safety monitoring and thermal runaway prediction
- Fast charging strategy and operation strategy
- Battery field data analysis
- Fast screening for second-life application