Energy harvesting, along with effective storage of the energy, is a very common approach to attain sustainable computing in today’s embedded systems. Employing a hybrid energy storage system (HESS), which constitutes of two or more types of energy storage systems (ESSs), helps to compensate for the weaknesses of one ESS type using the strengths of another type. The capacity of an ESS, and thus that of a HESS, can be modeled by dividing it into Instantly Available Charge (IAC) and Instantly Unavailable Charge (IUC) parts; the existing charge in an ESS always flows from the part with higher voltage to the other one. The main idea of this study is to intelligently control the flows in the HESS to maximizing either the IAC or the IUC charge. We propose the HLPF real-time task scheduling algorithm to do so through deciding to execute the tasks in the ascending or descending order of their power requirements. Extensive simulations show impressive lifetime improvements of up to 20 % in comparison to the classical real-time task scheduling algorithms.

能量收集以及能量的有效存储，是在当今嵌入式系统中实现可持续计算的一种非常普遍的方法。使用由两种或更多种类型的能量存储系统（ESS）构成的混合能量存储系统（HESS），有助于利用另一种类型的优势来补偿一种ESS类型的弱点。可以通过将ESS的容量分为即时可用费用（IAC）和即时不可用费用（IUC）部分来建模。ESS中的现有电荷始终从电压较高的部分流向另一部分。这项研究的主要思想是智能地控制HESS中的流量，以最大化IAC或IUC费用。为此，我们提出了HLPF实时任务调度算法，该算法通过决定以其功率需求的升序或降序执行任务来实现。与经典的实时任务调度算法相比，广泛的仿真显示出令人印象深刻的生命周期改进，最多可提高20％。