Lithium-Ion batteries, whose invention and subsequent refinement earned the 2019 Nobel Prize in Chemistry for John B. Goodenough, M. Stanley Whittingham, and Akira Yoshino, represent a cornerstone in the transition to a "rechargeable" and increasingly sustainable world.
Essential for the widespread adoption of portable electronic devices, they also play a key role in the electric vehicle sector. However, despite their high potential, lithium-ion batteries are affected by various degradation processes that negatively impact their performance and lifespan, especially when subjected to demanding operating conditions, such as those required in the transport sector (e.g., extreme temperatures, rapid charge cycles, etc.).
Through the LIBDEMO project, funded by the Swiss Innovation Agency (Innosuisse), the research teams at the Computational Materials Science Laboratory (CMS) and the Energy Storage Research Centre aim to refine the modeling and prediction of the lifecycle of lithium-ion batteries. The goal is to develop advanced algorithms for optimal management and usage, extending their lifespan and improving their efficiency.
The project adopts an innovative approach by combining two modeling levels at different scales to deepen the understanding and prediction of degradation phenomena. Molecular models developed at the CMS Lab provide precise microscopic descriptions (approximately 10^-10 m) of the molecular dynamics of key battery components, such as the electrolyte and the electrode-electrolyte interface. Claudio Perego, project leader, explains, “Molecular models are crucial for understanding and predicting complex phenomena such as lithium-ion battery degradation.”
In addition to enabling detailed characterization of battery degradation processes, the simulation results have been integrated into a physical model developed by BFH, operating at a higher scale and representing the entire electrochemical cell. This model allows the prediction of battery performance under real operating conditions, analyzing its evolution under the effects of degradation.
“The combined approach used in LIBDEMO enhances the prediction of degradation effects in physical battery models,” comments Priscilla Caliandro, head of the Energy Storage Research Centre at BFH.
Looking ahead, the LIBDEMO project aims to leverage the results to develop operational management strategies for commercially available batteries, optimizing their performance and extending their average lifespan.