Robustness of advanced optimization assisted maximum power point tracking for cascaded power converters in solar-powered electric vehicles

Introduction Maximization of photovoltaic energy extraction in solar-powered electric vehicles (EVs) has become a key priority for improving system efficiency and autonomy. Methods This paper presents an Improved Tunicate Swarm Algorithm (ITSA)-based MPPT strategy interfaced with an Enhanced Cascaded Converter (ECC) for Switched Reluctance Motor (SRM) drives. The proposed ITSA integrates Lévy flight-based global exploration to avoid local optima and improve convergence speed under dynamic irradiance conditions. ITSA performs continuous tuning of the converter’s PI controller parameters to maintain real-time adaptation to the maximum power region of the PV module. The ECC topology provides high voltage gain along with continuous input current, which further stabilizes the power delivery to the EV drivetrain. Results ITSA achieves 98.41% MPPT efficiency compared to 94.68% for conventional P&O and tracks the maximum power point in 0.12s versus 0.25s for P&O. Statistical validation across 50 independent simulation runs confirms significance in all pairwise comparisons (p < 0.05), with practical effect sizes of r = 0.71–0.95. Discussion The proposed ITSA–ECC framework presents a robust and intelligent MPPT solution for solar-powered EV systems, with significant improvements in renewable energy utilization, reduced torque ripple in the SRM drive and validated suitability for sustainable electric mobility.