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

机译:荷电状态估算可改善节能效果并延长无线传感器网络节点的电池寿命

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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.
机译:无线传感器网络是无处不在的系统,它通过分支到各种应用程序来不断证明其增长。充电状态是一种指示器,可传达电池中可用的电量,该信息通过创建智能跨层设计有助于更好的决策和节能协议。 WSN的研究趋势通过将能源效率排在吞吐量,信道利用率,等待时间等其他可同等重要的方面来优先考虑能源效率系统的重要性。这证明了改进节能技术和延长传感器节点电池寿命的影响。通过使用贝叶斯推断,更具体地说是粒子滤波,表明可以在电压-SOC曲线的线性区域内准确估计电荷状态。使用状态空间表示模型将电池放电实验与电压-SOC演化行为的仿真进行了比较,结果表明结果之间具有良好的一致性。通过粒子滤波器获得的SOC估计会得出可以并且应该并入MAC协议的基本信息。

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