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Towards self-control of service rate for battery management in energy harvesting devices

机译:努力实现能量收集设备中电池管理服务费率的自我控制

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We consider the operation of an energy harvesting wireless device (sensor node) powered by a rechargeable battery, taking non-idealities into account. In particular, we consider sudden decrease and increase of the battery level (leakage and charge recovery consequently) due to the inner diffusion processes in the battery. These processes are affecting the stability of the device operation. In particular, leakage accelerates the depletion of the battery, which results in inactive periods of the device and, thus, potential data loss. In this paper, we propose a simplified self-control management of a battery expressed by restrictions, which could be used for an efficient operational strategy of the device. To achieve this, we rely on the double-queue model which includes the imperfections of the battery operation and bi-dimensional battery value. This includes both apparent, i.e., available at the electrodes and true energy levels of a battery. These levels can be significantly different because of deep discharge events and can equalize thanks to charge recovery effect. We performed some simulation and observed that we can diminish the models variable number to predict possible unwanted events such as apparent discharge events (when the areas near electrodes are depleted while other areas of the battery still contain some energy) and data losses. This observation helps to achieve sufficiently effective self-control management by knowing and managing just few parameters, and therefore offers valuable directions for the development of autonomic and self-sustainable operation.
机译:考虑到非理想性,我们考虑了由可充电电池供电的能量收集无线设备(传感器节点)的操作。特别是,由于电池内部的扩散过程,我们考虑电池电量的突然下降和增加(因此导致泄漏和电荷恢复)。这些过程会影响设备操作的稳定性。特别是,泄漏会加速电池的耗尽,从而导致设备处于非活动状态,并因此导致潜在的数据丢失。在本文中,我们提出了一种由限制表示的简化的电池自我控制管理方法,该方法可用于该设备的有效运行策略。为了实现这一点,我们依赖于双队列模型,该模型包括电池操作和二维电池值的缺陷。这包括表观的(即在电极上可用的)和电池的真实能量水平。由于深度放电事件,这些电平可能会显着不同,并且由于电荷恢复效果,这些电平可以相等。我们进行了一些仿真,发现可以减少模型的变量数量,以预测可能发生的不良事件,例如表观放电事件(当电极附近的区域耗尽时,而电池的其他区域仍包含一些能量)和数据丢失。通过观察和管理很少的参数,该观察结果有助于实现足够有效的自我控制管理,从而为开发自主和自我可持续的操作提供了宝贵的指导。

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