At InES, we research Lithium-ion batteries and next generation batteries. The target is to achieve detailed understanding of physical and chemical processes to optimize batteries with respect to capacity, performance, life time and safety. With the aid of mathematical modeling in combination with dynamic measurement methods,  phenomena and fundamental causes can be correlated, which enables a knowledge based development of batteries. As a member of the Battery LabFactory Braunschweig (BLB) the group plays an active role in improving of batteries and their manufacturing processes.

Characterization of Dynamic Processes

battery_group_website_fig1We study and develop dynamic measurement methods and their application to batteries. Methods are cyclovoltammetry, electrochemical impedance spectroscopy and nonlinear frequency response analysis. These methods facilitate advanced analysis due to separation of time constants to distinctively analyze particular processes, e.g. solid diffusion, ion transport or electrochemical reactions. This is used to identify changes of cell chemistry, electrode structure due to the production process, or aging. With these non destructive methods it is possible to realize a fast, cheap and precise insitu characterization and state estimation of cells.

Model Development

battery_group_website_fig2Mathematical models connect phenomena and physical and chemical causes. In our models we consider transport processes as well as chemical and electrochemical reaction kinetics to simulate performance and degradation. Further, the development of innovative methods for coupling of models enables simulation of multi-physics (thermal, electrical and chemical) and multi-scale (atomistic and macroscopic) systems. On the basis of these models we investigate physical processes and their interaction in batteries.

Simulation Based Analysis

battery_group_website_fig3While experimental methods often do not provide a distinct physical insight into a battery system, theoretical models often do not quantitatively describe the reality. Therefore, it is essential to combine both to enable a thorough analysis and optimization of batteries. This allows to quantify the dynamic behavior and identify reaction kinetics, degradation processes and the impact of production processes. This can be used in advanced diagnosis systems, which enable the identification, online state estimation or mathematical optimization of cell design.

  last changed 21.03.2019
TU_Icon_E_Mail_1_17x17_RGB pagetop