The lifetime of wireless sensor networks deployments depends strongly on the nodes battery state of health (SoH). It is important to detect promptly those motes whose batteries are affected and degraded by ageing, environmental conditions, failures, etc. There are several parameters that can provide significant information of the battery SoH, such as the number of charge/discharge cycles, the internal resistance, voltage, drained current, temperature, etc. The combination of these parameters can be used to generate analytical models capable of predicting the battery SoH. The generation of these models needs a previous process to collect dense data traces with sampled values of the battery parameters during a large number of discharge cycles under different operating conditions. The collected data allow the development of mathematical models that can predict the battery SoH. These models are required to be simple because they must be executed in motes with low computational capabilities. The paper shows the complete process of acquiring the training data, the models generation and its experimental validation using rechargeable batteries connected to Telosb motes. The obtained results provide significant insight of the battery SoH at different temperatures and charge/discharge cycles.

中文翻译：

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预测无线传感器网络应用中的电池健康状态

无线传感器网络部署的寿命在很大程度上取决于节点电池健康状态 (SoH)。及时检测电池因老化、环境条件、故障等而受到影响和退化的微粒非常重要。有几个参数可以提供电池 SoH 的重要信息，例如充电/放电循环次数、内部电阻、电压、消耗电流、温度等。这些参数的组合可用于生成能够预测电池 SoH 的分析模型。这些模型的生成需要一个先前的过程，以在不同操作条件下的大量放电循环期间收集具有电池参数采样值的密集数据轨迹。收集到的数据允许开发可以预测电池 SoH 的数学模型。这些模型要求简单，因为它们必须在计算能力低的节点中执行。该论文展示了使用连接到 Telosb 节点的可充电电池获取训练数据、模型生成及其实验验证的完整过程。获得的结果提供了对不同温度和充电/放电循环下电池 SoH 的重要洞察。