We are excited to announce the launch of our joint research project BaetterAI! The project is funded by the German Federal Ministry for Research, Technology and Space (BMFTR) (FKZ: 03XPB020) and managed by Projektträger Jülich.
The Challenge
Battery development faces several fundamental problems: Innovation cycles take years because aging tests are time-consuming and modern analysis methods are lacking. Test data from different test stands come in varying formats – data exchange between research and industry is cumbersome. And although Electrochemical Impedance Spectroscopy (EIS) provides valuable insights into aging processes, EIS-capable battery management systems for field deployment are still missing.
The consequences: Long development times, untapped data potential, and a lack of insights from real-world operation. The use of Artificial Intelligence (AI) is a key lever to effectively address these challenges. However, developing reliable and highly accurate AI models requires the availability of high-quality, systematically preprocessed data – one of the biggest hurdles to date for the broad, effective application of AI methods.
Our Solution
With BaetterAI, we are developing a Universal Data Processing (UDP) Tool – a software module that enables heterogeneous battery test data at cell and module level to be automatically preprocessed for AI-based applications.
Fraunhofer IFAM creates standardized templates for the systematic acquisition of machine-readable data and develops a pipeline for automated outlier and anomaly treatment, as well as for extracting aging-relevant characteristics, known as “features.”
MAP and Batene provide the experimental data foundation through comprehensive aging tests on state-of-the-art and next-generation lithium-ion cells with innovative 3D current collectors.
Heimdalytics extends this with EIS measurements and develops a hardware component for battery management systems that captures operational data directly from the field.
Batalyse integrates all these data streams into a central platform, implements quality control and automated feature extraction methods, and creates secure interfaces between laboratory, BMS, and database.
Based on the UDP, a foundation will be created for the first time to systematically consolidate and utilize battery data for AI applications – capable of processing heterogeneous battery data from development, testing, and operational environments. The UDP empowers users to prepare their data quickly, with quality assurance, and without manual effort for specific AI use cases – such as shortening development cycles or optimizing cell control during operation.
Through standardized and automated data preprocessing and integration processes, AI becomes simple, reproducible, and scalable to use, enabling individual goals to be achieved efficiently and with high result quality. The platform is open to international standards and makes research results comparable across institutional boundaries.
Our Team
🔧 Batalyse – Provides the central software platform and implements methods for automated data processing and feature extraction, including correlation analyses and intelligent visualization.
🔬 Fraunhofer IFAM – Brings deep expertise in electrochemical characterization, data science, and data-driven modeling, developing tailored processing procedures for automated quality assessment and feature extraction of battery test data.
âš¡ Batene – A spin-off of the Max Planck Society, provides the experimental foundation: aging tests on novel lithium-ion cells using three-dimensional current collectors made of metal fibers instead of conventional copper and aluminum foils. The measurement data forms the empirical basis for the consortium’s AI-supported diagnostic development.
🔌 Heimdalytics – Connects BMS diagnostic systems with integrated impedance spectroscopy directly to the research database, bringing real-world operational field data into the analysis for the first time.
🧪 MAP-Test – Develops innovative testing and analysis methods for improved and faster aging diagnostics on state-of-the-art and next-generation lithium-ion cells.
More information will follow in the coming months! 🚀
