Organisation Introduction

The University of Bayreuth is one of the most successful young universities in Germany. The university is ranked 45th out of the world’s top 475 universities younger than 50 in the “Times Higher Education (THE) Young University Ranking”. Interdisciplinary research and teaching is the main feature of our more than 186 degree programmes offered at seven faculties in the natural sciences, food sciences, engineering, law and economics, as well as language, literature and cultural studies. The University of Bayreuth has about 12,500 students, 1,601 academic staff (274 of them professors) and 1016 non-academic employees on the campus in Bayreuth and at satellite campus in Kulmbach. This makes it one of the largest employers in the region. Research at the engineering faculty of uni bayreuth includes process and energy technology, materials science, bio- and environmental technology, mechatronics, design theory, materials technology, electrical engineering, as well as production and processing technology.

Why: our participation to AccCellBaT

Battery modelling will play a significant role in battery development as it can significantly reduce physical testing and therefore development time and cost. However, models require valid parameters to simulate cell behavior with sufficient confidence. Identifying or directly measuring these parameters can be difficult and time consuming, so new methods are needed. Finally, existing physico-chemical models may not capture novel phenomena that occur in next-generation cells. We aim to address this challenge with modern data-driven methods. In summary, we are contributing our expertise to the project by developing and parameterising battery models that can predict electrochemical, thermal, and ageing behavior.

What: our objectives in AccCellBaT

UBT’s goal in this project is twofold. Firstly, together with the project partners, we aim to develop a reliable and fast parameterisation procedure for electrochemical battery models. Secondly, we aim to develop new methods for simulating the degradation of next-generation batteries that do not require in-depth knowledge of the internal mechanisms of the cell. The ageing models will be embedded in a hybrid modelling platform combining physics-based and data-driven models.

Statement

The multidisciplinary and collaborative nature of AccCellBat enables the creation of predictive battery models that are key to reducing development costs and time.