Optimized Dual-Battery System for Reliable Soil Nutrient Monitoring in Remote IoT Applications

Strategic Insights -

Researchers from Universitas Muhammadiyah Surakarta have developed a novel dual-battery architecture with intelligent auto-switching control, designed to ensure uninterrupted operation of agricultural sensing systems in environments with unpredictable energy availability. The proposed system integrates Lithium-Sulphur (Li-S) and Lithium-Ion (Li-Ion) batteries with advanced switching algorithms, tailored to maximize sensor operational longevity. Experimental results reveal distinct performance improvements, with the integration of intelligent auto-switching mechanisms and metaheuristic optimization algorithms demonstrating a marked enhancement in both reliability and energy efficiency for soil nutrient monitoring systems.

Key Takeaways:

  • The proposed dual-battery architecture with intelligent auto-switching control ensures uninterrupted operation of agricultural sensing systems in environments with unpredictable energy availability.
  • The system integrates Lithium-Sulphur (Li-S) and Lithium-Ion (Li-Ion) batteries with advanced switching algorithms, specifically the Dynamic Load Balancing-Power Allocation Optimisation (DLB-PAO) and Dynamic Load Balancing-Genetic Algorithm (DLB-GA).
  • Experimental results reveal that the baseline single-battery system sustains 28 hours of operation with 31.2% average reliability, compared to a conventional dual-battery configuration that extends operation to 45 hours with 42.6% reliability.
  • Implementing the DLB-PAO algorithm elevates the average reliability to 91.7% over 120 hours, while the DLB-GA algorithm achieves near-perfect reliability (99.9%) for over 170 hours, exhibiting minimal variability (standard deviation: 0.9%).
  • The research concludes that this method extends the lifespan of electronic devices while ensuring reliable energy storage over time, creating a practical foundation for sustainable IoT agricultural systems in areas with limited resources.
  • Authors of the research include Doan Perdana, Indonesian researchers, and Pascal Lorenz from the University of Haute-Alsace, France.
  • Funders for this research include Universitas Muhammadiyah Surakarta.
  • The research was published in the Journal of Sensor and Actuator Networks, volume 14, issue 3, page 53.

Statistics:

  • 28 hours: baseline single-battery system operation time with 31.2% average reliability.
  • 45 hours: conventional dual-battery configuration operation time with 42.6% reliability.
  • 120 hours: DLB-PAO algorithm-achievable operation time with 91.7% reliability.
  • 170 hours: DLB-GA algorithm-achievable operation time with 99.9% reliability and 0.9% standard deviation.

Sources:

  • NewsRx LLC. Reports Outline Sensor and Actuator Networks Research from Universitas Muhammadiyah Surakarta (Optimized Dual-Battery System with Intelligent Auto-Switching for Reliable Soil Nutrient Monitoring in Remote IoT Applications). Agriculture Week. July 10, 2025; p 499.
  • Optimized Dual-Battery System with Intelligent Auto-Switching for Reliable Soil Nutrient Monitoring in Remote IoT Applications. Journal of Sensor and Actuator Networks, 2025, 14(3):53. (Journal of Sensor and Actuator Networks - http://www.mdpi.com/journal/jsan).