We propose a hybrid modeling and control framework that combines physical interpretability with data-driven intelligence. The Random Forest–enhanced electro-thermal-degradation model accurately captures nonlinear electro-thermal behaviors, while a constraint-aware Bayesian Optimization scheme optimizes charging speed and degradation suppression under safety limits. Implemented through a dual-loop structure with temperature and current regulation, the strategy enables secure, efficient, and real-time charging on embedded microcontrollers with minimal computational cost. Experimental results show substantial improvements in charging time, thermal safety, and cycle life over conventional CC-CV and temperature-regulated methods. This work has been published in the IEEE Transactions on Power Electronics under the title “A Secure-Sustainable-Fast Charging Strategy for Lithium-ion Batteries based on a Random Forest-Enhanced Electro-Thermal-Degradation Model”. A concise conference version of this work was presented here.