Flexible Inverse Design of Carnot-Batteries with Fluid Mixtures: A combined theoretical-experimental approach

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Carnot batteries (CBs) store electrical energy as thermal exergy. Along discharging it is aimed to restore the electrical energy. For high efficiencies each part of the system must approach thermodynamic reversibility. Beside efficiency, also costs and further criteria will be important in future energy markets. In this project an inverse design of CBs using multi-component mixtures will be investigated and mathematically optimized, using detailed models for machines, heat exchangers, storages, and fluids. Selected points will be investigated experimentally, aiming to prove the validity of the models and their predictions. The project heavily relies on co-operations within the priority program. Criteria for the inverse design will come from projects from energy systems analysis, and detailed features and correlations of the parts will be obtained from thermodynamics, heat transfer and termo-fluid machine projects. The main hypothesis is that relatively simple cycles with co-optimized working fluid mixtures, and working conditions lead to a high enough degree in flexibility for an inverse design approach and meet the criteria formulated from energy systems analysis, like round trip efficiency per investment costs and given storage to power ratio for different temporal application-profiles, to meet the market needs and to evaluate the limits of Carnot batteries. The second main hypothesis is that actual thermodynamic models of CBs neglect too many details, like local properties, fluid dependent efficiencies, time dependencies, and the coupling effects so that their predictions suffer from large uncertainties, especially due to a lacking comparison with experiments. Here, it is expected that the results of this project will allow to assess the level of detail needed in models for good accuracy predictions and reliable inverse design. At the end of this funding period, it is expected to have a CB model, which can be used for inverse engineering, and is experimentally validated at selected operation points.