An Enhanced Kalman Filter-Based Hybrid Battery Management System for Energy Optimization in IoT-Enabled Smart Agriculture
TL;DR
A new hybrid BMS for SOC estimation based on a transaction-oriented adaptive Kalman Filter that merges traditional voltage-based SOC estimation has been developed in this study and demonstrates that it serves as an efficient and effective alternative for achieving long-term energy optimization in IoT-enabled smart agriculture applications.
Abstract
Battery-powered sensor nodes operating in a remote environment are used in IoT-based smart agriculture deployments, where the maintenance of these devices is impractical. Thus, precise SOC (state-of-charge) estimation is critical to enhance both energy efficiency and system stability. A new hybrid BMS for SOC estimation based on a transaction-oriented adaptive Kalman Filter that merges traditional voltage-based SOC estimation has been developed in this study. Results show that our proposed method removes noise sensitivity and reduces SOC drift subjected to dynamic IoT workloads by integrating model prediction with measurement correction. Application experiments based on 30 days' real sensor workload data indicate that the cumulative battery consumption index of the proposed algorithm decreased from 78% to 56%, and provided a performance improvement of approximately 22.7% compared to the baseline method. Moreover, the proposed method shows better stability in terms of daily consumption fluctuation and drift. The findings demonstrate that the improved Kalman Filter–based hybrid BMS serves as an efficient and effective alternative for achieving long-term energy optimization in IoT-enabled smart agriculture applications.