In the ever-evolving landscape of agricultural technology, a groundbreaking development has emerged from the Electrical Engineering Study Program at Universitas Muhammadiyah Surakarta, Indonesia. Lead author Doan Perdana and his team have introduced an innovative dual-battery system with intelligent auto-switching capabilities, designed to revolutionize soil nutrient monitoring in remote IoT applications. This advancement promises to address the critical challenge of energy reliability in agricultural sensing systems, particularly in environments with unpredictable energy availability.
The novel architecture integrates Lithium-Sulphur (Li-S) and Lithium-Ion (Li-Ion) batteries, coupled with advanced switching algorithms—Dynamic Load Balancing–Power Allocation Optimisation (DLB–PAO) and Dynamic Load Balancing–Genetic Algorithm (DLB–GA). These algorithms are tailored to maximize the operational longevity of sensors, ensuring uninterrupted data collection and analysis. “Our system goes beyond conventional single-battery or passive redundancy approaches by introducing active redundancy, adaptive energy management, and fault tolerance,” explains Perdana. “This significantly improves operational continuity, which is crucial for sustainable agriculture and remote sensing applications.”
The practical implications of this research are substantial. Traditional single-battery systems often fall short in maintaining consistent performance, especially in remote areas where energy sources can be unreliable. The dual-battery system, however, offers a robust solution that extends operational uptime and enhances energy reliability. “By optimizing the dual-battery configuration for real-world deployment, we have created a practical foundation for sustainable IoT agricultural systems in areas with limited resources,” Perdana adds.
The experimental validation of the system involved a functional prototype tested under realistic load profiles, demonstrating seamless battery switching, extended uptime, and enhanced energy reliability. To further assess long-term performance, a simulation framework was developed in MATLAB/Simulink, incorporating battery degradation models and empirical sensor load profiles. The results were impressive: the DLB–PAO algorithm elevated average reliability to 91.7% over 120 hours, while the DLB–GA algorithm achieved near-perfect reliability (99.9%) for over 170 hours, with minimal variability.
This research, published in the Journal of Sensor and Actuator Networks (translated as “Jurnal Sensor dan Jaringan Aktuator”), highlights the potential for significant advancements in the energy sector. The integration of intelligent auto-switching mechanisms and metaheuristic optimization algorithms marks a pivotal step forward in energy management for agricultural technologies. As the demand for sustainable and efficient energy solutions grows, this innovation could pave the way for more reliable and long-lasting energy storage systems, not just in agriculture but across various industries.
The commercial impacts of this research are far-reaching. For the energy sector, the dual-battery system with intelligent auto-switching offers a scalable and adaptable solution that can be tailored to different applications. This could lead to the development of more resilient energy systems, reducing downtime and improving overall efficiency. Moreover, the enhanced reliability and energy efficiency of soil nutrient monitoring systems can support precision agriculture, enabling farmers to make data-driven decisions that optimize crop yields and resource usage.
As the world continues to grapple with the challenges of climate change and resource scarcity, innovations like this dual-battery system are crucial. They not only address immediate practical needs but also lay the groundwork for a more sustainable and technologically advanced future. The research by Doan Perdana and his team exemplifies the power of interdisciplinary collaboration and the potential for technological advancements to drive positive change in the agricultural and energy sectors.