Lithium-ion battery degradation poses a significant obstacle to the widespread adoption of battery-powered technologies. Accurate aging models, relying on parameters derived from experiments, are essential to predict battery lifespan. However, traditional experimental methods are often time-consuming and expensive. This study proposes a novel two-stage experimental characterization method to efficiently estimate parameters for a semiempirical battery aging model.

In the initial stage, a Latin hypercube sampling method establishes baseline aging behavior. Subsequently, the piOptimized stage leverages this knowledge to compute a parameter-individual optimal experimental design (pi-OED). This poster presents the Design of Experiments (DoE) of both stages, laying the foundation for future validation of the pi-OED approach. This approach holds the potential to streamline battery aging studies, reducing experimental effort while enhancing model accuracy for future battery technologies.