Development of a fast-charging protocol considering degradation using high-fidelity lithium-ion batteries

As demand for electric vehicles and grid stability increases, optimizing lithium-ion batteries (LIB) usage has become crucial. To address this, advanced battery management systems are spotlight as continuously monitor battery internal phenomena. Fast charging of LIB is one of the challenges for advanced battery management. Applying high current can reduce charging time, but it also accelerates battery degradation. Therefore, development of fast-charging protocol considering degradation condition is crucial. Porous electrode theory (PET) models can suggest high fidelity battery model. However, due to the wide design space and numerous complex degradation mechanisms, limitations exist using PET model. Using model-free approach, such as genetic algorithm (GA), can handle this problem. In this study, we applied GA for parameter identification and fastcharging protocol development. Identified parameters are applied to the PET model and we employed GA to minimize charging time while considering voltage and temperature constraints. Using the optimal charging protocol achieved by model-free approach, charging performance can be compared with experiments to identify the improvement.