BaPoBs - Battery aging and Pareto-optimal operating strategy

Prof. Dr.-Ing. Oliver Bohlen
Prof. Dr. Herbert Palm
Department of Electrical Engineering and Information

Lithium-ion batteries have become indispensable in many everyday applications. In addition to their use in electro mobility, importance of battery storage systems is increasing in the areas of energy supply and distribution by providing ancillary system services: Battery storage systems may provide reactive power, reduce peak power or increase self-consumption rate by regenerative power generators. Costs of battery systems have to justify these advantages. In this respect, aging behaviour of energy storage systems represents a major challenge both in the planning and in their operation. While qualitative models do exist describing performance or storage capacity dependencies on factors such as charge-discharge cycles, temperature and charge state, the parameterization of these models has so far required very extensive and long measurements. At present, there is hardly any action taken on the aging behaviour during operation, since the operating strategy is usually just focussing on compliance with operating limits and the fulfillment of performance requirements.

The aim of the BaPoBs project is to develop fundamentally new methods and techniques for the efficient characterization of the aging of battery-electric storage devices and to demonstrate ways in which these results may be used for the multi-criteria optimized operation of systems with battery-electric storage devices - especially in the trade-off between technical and economic target values. The work ranges from theoretical preliminary studies to the experimental validation of the goals. In total, the project aims for a significant technical and economic contribution to the use of battery-electric storage devices over their life cycle in sustainable energy systems, tested in laboratory operation.

In terms of methodology, within the BaPoBs project framework for the first time the approach of a Hyper Space Exploration (HSE) based on a direct search strategy will be applied to battery storage systems. With a significantly reduced number of individual experiments, this approach promises to achieve a qualitatively increased, Pareto-optimal, multi-criterial optimization of both, battery aging data models and thereof derived operating strategies.

In this project, the Munich University of Applied Sciences closely works with its partners Siemens and Intilion bringing in many years of experience in the development and operation of large energy storage systems.