Optimization of DC Fast Charging in CHAdeMO Systems Using Thunderstorm Algorithm with Thermal and Health Constraints

The significant increase in the use of electric vehicles (EVs) demands the development of fast charging systems that are not only efficient but also maintain battery integrity. One of the primary challenges in direct current (DC) charging is balancing speed with minimizing degradation caused by thermal stress. This study proposes a charging optimization model based on the Thunderstorm Optimization Algorithm (TA) for CHAdeMO-based DC systems. A lithium-ion equivalent circuit battery model was used to simulate electrochemical and thermal dynamics. The model introduces an adaptive charging current profile designed with a dynamic boundary configuration, defined here as the iterative adjustment of current limits according to real-time thermal and health constraints. Compared to conventional constant current–constant voltage (CC–CV) methods, TA considers maximum temperature, State of Health (SoH), and target State of Charge (SoC) simultaneously. The simulation (180 minutes, passive cooling, Python-based) showed that TA reduced SoH degradation to 1.3% and battery life usage to 18.4%—the latter defined as cumulative stress energy normalized to initial capacity—compared to 2.9% and 22.5% for CC–CV. Additionally, TA achieved a higher average charging power (26.1 kW vs. 24.8 kW) without exceeding 50 °C. Although the algorithm requires more computational effort than CC–CV, its moderate complexity suggests feasibility for real-time integration in battery management systems. These findings highlight TA as a promising adaptive and sustainability-oriented charging strategy.