Adaptive improved particle swarm optimization-based maximum power point tracking and energy smoothing for photovoltaic hybrid battery–supercapacitor storage systems

The rapid development of photovoltaic (PV) systems has made them an important component of the global clean energy strategy. However, the intermittency and non-linear characteristics of photovoltaic (PV) output remain major challenges for stable renewable energy utilization. This study proposes an adaptive improved particle swarm optimization (IPSO)-based maximum power point tracking (MPPT) strategy integrated with hybrid energy storage coordination for photovoltaic systems. The IPSO introduces adaptive inertia adjustment, velocity clamping, and stagnation reinitialization, which improve the convergence robustness under dynamic irradiance and temperature conditions. The algorithm was benchmarked against Perturb & Observe (P&O), Incremental Conductance (INC), and standard PSO using convergence speed, ripple, bus voltage stability, and battery stress as performance indicators. The results show that IPSO significantly reduces settling time compared with conventional PSO, minimizes steady-state oscillations, and enables coordinated battery–supercapacitor operation, which is expected to mitigate battery stress under dynamic conditions. This demonstrates IPSOs' potential as a multi-objective optimization tool for PV–HESS systems, offering practical insights for intelligent energy management in microgrids and renewable networks.