Health-aware battery fast charging technique with zonal battery model using pulsed amplitude-width modulation and multi-step constant current constant voltage

This article presents an innovative method for rapidly charging batteries while considering health factors. The strategy utilizes a closed-loop control approach based on a mathematical model. Initially, an intricate thermo-electric aging cell model (TEACM) was constructed based on electrochemical aging study. The model’s parameters are continuously updated in real-time by pulling data from the battery pack while it is being charged. The TEACM algorithm analyzes the parameter values and calculates different battery statuses. These estimations are then used to improve the charging process. The charge optimization phase optimizes the precharging and boosts charging processes individually. Several pulse precharging approaches have been analyzed, and by calculating the diffusion coefficient, the most effective precharging method has been identified. The use of multistage constant current technique is utilized to optimize the battery charging process. The optimization is accomplished by implementing the non-linear model predictive controller. Ultimately, the two refined charging methods are combined and sent to the charger. The hypothesis was validated using a cell cycling test bench. The acquired findings were compared with other recognized benchmark charging techniques, demonstrating that the suggested approach charges the cell in half the time compared to the 1C-CCCV charging methodology while also increasing the cycle life by 6.67%.