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Next leap in the battery development is expected to be the lithium solid-state batteries (SSB), allowing the replacement of the flammable organic electrolyte in Li-ion batteries with a safer and a more compact solid electrolyte. Within the framework of the Swedish governmental battery initiative COMPEL, aimed to support competence and excellence in battery research and education for the transport sector, KTH join forces with a coordinated initiative on composite SSB with seven postdoctoral positions in several disciplines, focusing on various aspects along the battery value chain, ranging from synthesis, cell assembly, characterization, modeling to scaled-up manufacturing. The 2-year postdoctoral project Machine Learning-based Electro-Chemo-Mechanical Estimation and Control of Solid-State Batteries will be hosted by the Department of Decision and Control Systems, EECS. The project will focus on the development of reduced-order electro-chemo-mechanical models, state estimation methods and predictive control strategies for solid-state batteries using data from parallel postdoctoral projects within the initiative. The research aims to improve the understanding of how operating conditions, including charging protocols, temperature and mechanical loading, influence battery performance, degradation and lifetime. The work combines physics-based modelling with machine learning for parameter estimation, degradation prediction, and analysis of electrochemical and structural characterization data, including X-ray CT image reconstruction, segmentation and quantitative microstructure analysis, with the long-term goal of enabling health-aware battery management and optimized charging strategies.
Requirements
Preferred qualifications
Great emphasis will be placed on personal skills.
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