This paper addresses the challenges of integrating renewable energy sources (RES) into DC microgrids (DCM) for fast DC charging in electric vehicles (EVs). The study proposes a hybrid Firefly Algorithm-Particle Swarm Optimization (FA-PSO) approach to tune Takagi-Sugeno Fuzzy Inference Systems (TSFIS), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Fractional Order Proportional-Integral-Derivative (FO-PID) controllers. This strategy optimizes power management within the DCM, ensuring faster convergence, superior accuracy, and reduced topological constraints. A comprehensive Small Signal Stability Analysis (SSSA) evaluates the impact of the proposed hybrid optimization techniques on DC microgrid stability. The effectiveness of these strategies is validated through a hardware prototype under real-world uncertainties, such as varying wind speed and solar insolation, demonstrating their practical feasibility for DC microgrid applications with integrated EV charging. The paper also discusses the detailed modeling and control design of each subsystem, including PV cells, wind systems, electrolyzers, battery energy storage systems, and fuel cells, along with the unified controller design methodology using FO-PID and the FA-PSO optimization algorithm. The stability of the test microgrid is confirmed through frequency domain analysis, and the results are validated through three distinct test scenarios: variations in solar irradiance and wind speed, fluctuations in EV charging demand, and unpredictability in supply and demand.This paper addresses the challenges of integrating renewable energy sources (RES) into DC microgrids (DCM) for fast DC charging in electric vehicles (EVs). The study proposes a hybrid Firefly Algorithm-Particle Swarm Optimization (FA-PSO) approach to tune Takagi-Sugeno Fuzzy Inference Systems (TSFIS), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and Fractional Order Proportional-Integral-Derivative (FO-PID) controllers. This strategy optimizes power management within the DCM, ensuring faster convergence, superior accuracy, and reduced topological constraints. A comprehensive Small Signal Stability Analysis (SSSA) evaluates the impact of the proposed hybrid optimization techniques on DC microgrid stability. The effectiveness of these strategies is validated through a hardware prototype under real-world uncertainties, such as varying wind speed and solar insolation, demonstrating their practical feasibility for DC microgrid applications with integrated EV charging. The paper also discusses the detailed modeling and control design of each subsystem, including PV cells, wind systems, electrolyzers, battery energy storage systems, and fuel cells, along with the unified controller design methodology using FO-PID and the FA-PSO optimization algorithm. The stability of the test microgrid is confirmed through frequency domain analysis, and the results are validated through three distinct test scenarios: variations in solar irradiance and wind speed, fluctuations in EV charging demand, and unpredictability in supply and demand.