Optimizing Battery Charging in Wireless Sensor Networks: Performance Assessment of MPPT Algorithms in Different Environmental Settings

Background: Photovoltaic (PV)-based energy harvesting systems are crucial for ensuring the sustainability and long-term operation of wireless sensor networks (WSNs), especially in remote or infrastructure-less environments. Given the critical role of battery performance in WSN reliability, efficient energy management through Maximum Power Point Tracking (MPPT) algorithms is essential to adapt to variable environmental conditions such as solar irradiance and ambient temperature.Objective: This study aims to comparatively assess the performance of four widely adopted MPPT algorithms-Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic (FL), and Particle Swarm Optimization (PSO)-in enhancing battery charging efficiency in PV-powered WSNs under dynamic environmental conditions.Methods: A simulation-based evaluation framework was developed using MATLAB/Simulink to model a PV-powered WSN system. Each MPPT algorithm was implemented and tested using the same simulation conditions, with key performance metrics including voltage and current overshoot, response time, energy transfer efficiency, and adaptability to fluctuating irradiance and temperature profiles. A Proportional-Integral (PI) controller was also used to manage the battery charging process, and environmental profiles were varied across simulation periods to assess algorithm robustness.Results: The PSO algorithm achieved superior performance across all metrics, demonstrating the fastest response time (0.1 s), lowest overshoot (14.8 V, 25 mA), and highest energy transfer efficiency. IC and FL methods showed balanced adaptability and performance, while P&O lagged in both responsiveness and efficiency. The simulation results also confirmed that environmental conditions significantly affect PV panel output and battery State of Charge (SoC), highlighting the necessity for adaptive MPPT solutions.Conclusion: This study provides a unified and realistic comparative analysis of major MPPT algorithms for PV-powered WSNs. The PSO algorithm emerges as the most effective, though its computational complexity may limit its application in low-power systems. IC and FL serve as promising alternatives for scenarios with resource constraints. The findings contribute to the design of environmentally adaptive and energy-efficient WSNs, paving the way for their robust deployment in real-world settings.