Increasing efficiency of the photovoltaic system of mobile robotic platforms for military application and exploration

For the unmanned military robotic platforms and exploration vehicles, the frequent external charging of energy storage devices (batteries) are not feasible. In such cases photovoltaic are preferred as backup for the energy storage devices, which charge these devices onboard. But the low energy conversion efficiency of photovoltaic is much of a concern. Hence every attempt of extracting the maximum output from photovoltaic (PV) is greatly appreciated, especially in military platforms where size and weight of the PV array is constrained. Furthermore the mobility of these platforms adds to the inefficiencies of the array by partial shading and dynamic irradiance. Few methodologies proposed for maximizing the output and increase efficiency includes Maximum Power Point Tracking using Adaptive Resonance Theory (ART2) Artificial Neural Network algorithm and unsupervised learning using improved incremental conductance algorithm, which tracks the maximum power point of the photovoltaic array that fluctuates along with the fluctuations in irradiance of the sun using an efficient neural network. Also determining the angle of sun with the MEMS digital sun sensors and aligning the modules accordingly, increases the input irradiance received by the panel. The techniques for bypassing other sources for inefficiencies like shading effect, thermal effect is also presented. The integration of various modular systems together ensures the effective utilization of the available solar energy hence increasing the efficiency. The simulation confirms the facts and illustrates the increase in output efficiency of the PV module for onboard backup charging of the energy storage device on a military mobile platform.