State-of-charge estimation to improve energy conservation and extend battery life of wireless sensor network nodes

Abstract

Wireless sensor networks are pervasive systems that continuously demonstrate increase in growth by branching into diverse applications. The state of charge is an indicator that conveys the amount of energy available in the battery, information that contributes to better decision-making and energy-efficient protocols by creating smart cross-layer designs. WSN research trends portray the importance of energy-efficient systems by prioritizing energy efficiency over other arguably equally important aspects as throughput, channel utilization, latency, etc. This demonstrates the impact of improving the energy conservation techniques and extending the battery life of the sensor nodes. By using Bayesian inference, more specifically particle filtering, it is shown that the state of charge can be accurately estimated within the linear region of the voltage-SOC curve. Battery discharge experiments are compared to simulations of the voltage-SOC evolution behavior using a state-space representation model, which showed good agreement between the results. The SOC estimation obtained by the particle filter yields essential information that can, and should, be incorporated into MAC protocols.