Hybrid TSA-TLBO optimization of an EF2 resonant DC-DC converter for energy-efficient fuel cell electric vehicles with reduced battery dependency

The fuel cell electric vehicles (FCEVs) with a lower battery dependency have a big problem in terms of the stability of voltage, high dynamic response and low torque ripple under different load conditions. To overcome these challenges, this paper presents a Tunicate Swarm Algorithm and Teaching–Learning-Based Optimization (TSA-TLBO) optimized hybrid Proportional-Integral-Derivative (PID) and Fractional-Order PID (PID-FOPID) control strategy to enhance the regulation of a PEMFC-based powertrain. This proposed controller operates at the converter and motor drive stage and it is coupled with an Elongation Factor 2 (EF 2 ) resonant DC-DC converter to increase voltage stabilization and power conversion efficiency. The hybrid optimization approach effectively tunes controller parameters to minimize speed and current tracking errors under dynamic operating conditions. Simulation results demonstrate that the proposed system achieves a maximum efficiency of 90.7% with an output power of 187 W. The harmonic analysis was performed at the three-phase inverter output supplying the PMSM. The measured voltage total harmonic distortion (THD) and current THD are 0.21% and 1.78%, respectively. The results also show enhanced dynamic performance and reduced torque fluctuations compared to conventional control methods. These results prove that the proposed solution enhances considerably control performance and allows working with FCEVs that have less auxiliary energy storage efficiently.