Fuel cell (FC) is a viable alternative power source for portable applications; it has higher energy density than traditional Li-ion battery and thus can achieve longer lifetime for the same weight or volume. However, because of its limited power density, it can hardly track fast fluctuations in the load current of digital systems. A hybrid power source, which consists of a FC and a Li-ion battery, has the advantages of long lifetime and good load following capabilities. In this paper, we consider the problem of extending the lifetime of a fuel-cell-based hybrid source that is used to provide power to an embedded system which supports dynamic voltage scaling (DVS). We propose an energy-based optimization framework that considers the characteristics of both the energy consumer (the embedded system) and the energy provider (the hybrid power source). We use this framework to develop algorithms that determine the output power level of the FC and the scaling factor of the DVS processor during task scheduling. Simulations on task traces based on a real-application (Path Finder) and a randomized version demonstrate significant superiority of our algorithms with respect to a conventional DVS algorithm which only considers energy minimization of the embedded system.

Dynamic voltage scaling (DVS) is a well-known low-power design technique that reduces the processor energy by slowing down the DVS processor and stretching the task execution time. However, in a DVS system consisting of a DVS processor and multiple devices, slowing down the processor increases the device energy consumption and thereby the system-level energy consumption. In this paper, we first use system-level energy consideration to derive the “optimal ” scaling factor by which a task should be scaled if there are no deadline constraints. Next, we develop dynamic task-scheduling algorithms that make use of dynamic processor utilization and optimal scaling factor to determine the speed setting of a task. We present algorithm duEDF , which reduces the CPU energy consumption and algorithm duSYS and its reduced preemption version, duSYS_PC , which reduce the system-level energy. Experimental results on the video-phone task set show that when the CPU power is dominant, algorithm duEDF results in up to 45% energy savings compared to the non-DVS case. When the CPU power and device power are comparable, algorithms duSYS and duSYS_PC achieve up to 25% energy saving compared to CPU energy-efficient algorithm duEDF , and up to 12% energy saving over the non-DVS scheduling algorithm. However, if the device power is large compared to the CPU power, then we show that a DVS scheme does not result in lowest energy. Finally, a comparison of the performance of algorithms duSYS and duSYS_PC show that preemption control has minimal effect on system-level energy reduction.

This article presents our work on the development of a fuel cell (FC) and battery hybrid (FC-Bh) system for use in portable microelectronic systems. We describe the design and control of the hybrid system, as well as a dynamic power management (DPM)-based energy management policy that extends its operational lifetime. The FC is of the proton exchange membrane (PEM) type, operates at room temperature, and has an energy density which is 4--6 times that of a Li-ion battery. The FC cannot respond to sudden changes in the load, and so a system powered solely by the FC is not economical. An FC-Bh power source, on the other hand, can provide the high energy density of the FC and the high power density of a battery. In this work we first describe the prototype FC-Bh system that we have built. Such a prototype helps to characterize the performance of a hybrid power source, and also helps explore new energy management strategies for embedded systems powered by hybrid sources. Next we describe a Matlab/Simulink-based FC-Bh system simulator which serves as an alternate experimental platform and that enables quick evaluation of system-level control policies. Finally, we present an optimization framework that explicitly considers the characteristics of the FC-Bh system and is aimed at minimizing the fuel consumption. This optimization framework is applied on top of a prediction-based DPM policy and is used to derive a new fuel-efficient DPM scheme. The proposed scheme demonstrates up to 32% system lifetime extension compared to a competing scheme when run on a real trace-based MPEG encoding example.

DPM (Dynamic Power Management) is an effective technique for reducing the energy consumption of embedded systems that is based on migrating to a low power state when possible. While conventional DPM minimizes the energy consumption of the embedded system, it does not utilize the properties of the power source. Alternative power sources such as fuel cells (PCs) have substantially different power and efficiency characteristics that have to be taken into account while developing policies that maximize their operational lifetime. In this paper, we present a new DPM policy for embedded systems powered by FC based hybrid source. We develop an optimization framework that explicitly considers the FC system efficiency and is aimed at minimizing the fuel consumption. Next we apply this optimization framework on top of a prediction based DPM policy to develop a new fuel-efficient DPM scheme. The proposed algorithm was applied to a real trace based MPEG encoding example and demonstrated up to 32% more system lifetime extension compared to a competing scheme.

Dynamic voltage scaling (DVS) and dynamic power management (DPM) are the two main techniques for reducing the energy consumption of embedded systems. The effectiveness of both DVS and DPMneeds to be considered in the development of an energy management policy for a system that consists of both DVS-enabled and DPM-enabled components. The characteristics of the power source also have to be explicitly taken into account. In this paper, we propose a policy to maximize the operational lifetime of a DVS-DPM enabled embedded system powered by a fuel cell-battery (FC-B) hybrid source. We show that the lifetime of the system is determined by the fuel consumption of the fuel cell (FC), and that the fuel consumption can be minimized by a combination of a load energy minimization policy and an optimal fuel flow control policy. The proposed method, when applied to a randomized task trace, demonstrated superior performance compared to competing policies based on DVS and/or DPM.

Fuel cells are clean power sources that have much higher energy densities and lifetimes compared to batteries. However, fuel cells have limited load following capabilities and cannot be efficiently utilized if used in isolation. In this work, we consider a hybrid system where a fuel cell based hybrid power source is used to provide power to a DVFS processor. The hybrid power source consists of a room temperature fuel cell operating as the primary power source and a Li-ion battery (that has good load following capability) operating as the secondary source. Our goal is to develop polices to extend the lifetime of the fuel cell based hybrid system. First, we develop a charge based optimization framework which minimizes the charge loss of the hybrid system (and not the energy consumption of the DVFS processor). Next, we propose a new algorithm to minimize the charge loss by judiciously scaling the load current. We compare the performance of this algorithm with one that has been optimized for energy, and demonstrate its superiority. Finally, we evaluate the performance of the hybrid system under different system configurations and show how to determine the best combination of fuel cell size and battery capacity for a given embedded application.

Fuel cells are a viable alternative power source for portable applications. They have higher energy density than traditional Li-ion batteries and can achieve longer lifetime for the same weight or volume. However, because of their limited power density, they can not track fluctuations in the load current fast. A hybrid power source, that consists of a fuel cell and a Li-ion battery, has the advantages of long lifetime and good load following capabilities. In this work, we consider the problem of extending the lifetime of a fuel-cell based hybrid source that is used to provide power to a DVFS processor. We propose a new algorithm that is built on top of an energy based optimization framework. The algorithm simultaneously adjusts the fuel flow rate (at the producer end), and judiciously scales the load current (at the consumer end) to minimize the energy loss of the hybrid system. Simulations on randomly generated task sets demonstrate the superiority of this algorithm with respect to an algorithm that does not allow adjustment of the fuel flow rate.

Battery lifetime enhancement is a critical design parameter for mobile computing devices. Maximizing battery life-time is a particularly difficult problem due to the non-linearity of the battery behavior and its dependence on the characteristics of the discharge profile. In this paper we address the problem of dynamic task scheduling with voltage scaling in a battery-powered DVS system. The objective is to maximize the battery performance measured in terms of charge consumption during execution of the tasks. We present a new battery-aware dynamic task scheduling algorithm, darEDF, based on an efficient slack utilization scheme that employs dynamic speed setting of tasks in run queue. We compare darEDF with three state of the art energy-efficient algorithms, lpfpsEDF, lppsEDF, lpSEH, with respect to battery performance and energy consumption. We show that darEDF has better performance than lpSEH (which has close to optimal energy value), and has lower run-time complexity.

Dynamic voltage scaling (DVS) is a well-known low power design technique that reduces the processor energy by slowing down the DVS processor and stretching the task execution time. But in a DVS system consisting of a DVS processor and multiple devices, slowing down the processor increases the device energy consumption and thereby the system-level energy consumption. In this paper, we present dynamic task scheduling algorithms for periodic tasks that minimize the system-level energy (CPU energy + device standby energy). The algorithms use a combination of (i) optimal speed setting, which is the speed that minimizes the system energy for a specific task, and (ii) limited preemption which reduces the numbers of possible preemptions. For the case when the CPU power and device power are comparable, these algorithms achieve up to 43 % energy savings compared to [1], but only up to 12 % over the non-DVS scheduling. If the device power is large compared to the CPU power, we show that DVS should not be employed.