Non-destructive evaluation and modeling of degradation mechanisms in Lithium-ion battery modules.

The PhD project focuses on addressing the challenges associated with re-using lithium-ion batteries (LIBs) in second-life applications. With a growing abundance of LIBs reaching end-of-life conditions from the transport and mobility sectors, maximizing the value of critical raw materials present in each battery becomes crucial. However, the re-use of batteries is complex due to their different aging patterns and degradation mechanisms. The project aims to develop methods to evaluate the state of health (SoH) of LIBs, enabling their second-life application. It involves the use of electrochemical impedance spectroscopy (EIS) and post-mortem analysis to assess degradation phenomena, along with charge/discharge data analysis. The research will scrutinize the optimal combination of physical models and data-driven models to achieve accurate SoH estimation while optimizing computational resources. By advancing the understanding of battery aging and developing practical techniques for SoH evaluation, the project contributes to the efficient utilization of LIBs and the sustainable deployment of energy storage solutions.

The PhD project focuses on addressing the challenges associated with re-using lithium-ion batteries (LIBs) in second-life applications. With a growing abundance of LIBs reaching end-of-life conditions from the transport and mobility sectors, maximizing the value of critical raw materials present in each battery becomes crucial. However, the re-use of batteries is complex due to their different aging patterns and degradation mechanisms. The project aims to develop methods to evaluate the state of health (SoH) of LIBs, enabling their second-life application. It involves the use of electrochemical impedance spectroscopy (EIS) and post-mortem analysis to assess degradation phenomena, along with charge/discharge data analysis. The research will scrutinize the optimal combination of physical models and data-driven models to achieve accurate SoH estimation while optimizing computational resources. By advancing the understanding of battery aging and developing practical techniques for SoH evaluation, the project contributes to the efficient utilization of LIBs and the sustainable deployment of energy storage solutions.