Extending lifetime of portable systems by battery scheduling (original) (raw)
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Scheduling battery usage in mobile systems
IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2003
The use of multibattery power supplies is becoming common practice in electronic appliances of the latest generations. Economical and manufacturing constraints are at the basis of this choice. Unfortunately, a partitioned battery subsystem is not able to deliver the same amount of charge as a monolithic battery with the same total capacity. In this paper, we define the concept of battery scheduling, we investigate several policies for solving the problem of optimal charge delivery, and we study the relationship of such policies with different configurations of the battery subsystem. Experimental results, obtained for different kinds of current workloads, demonstrate that the choice of the proper scheduling can make system lifetime as close as 1% of the theoretical upper bound, that is, a monolithic power supply of equal capacity.
Computing Optimal Schedules for battery Usage in Embedded Systems
IEEE Transactions on Industrial Informatics, 2010
The use of mobile devices is often limited by the battery lifetime. Some devices have the option to connect an extra battery, or to use smart battery-packs with multiple cells to extend the lifetime. In these cases, scheduling the batteries or battery cells over the load to exploit the recovery properties of the batteries helps to extend the overall systems lifetime. Straightforward scheduling schemes, like round robin or choosing the best battery available, already provide a big improvement compared to a sequential discharge of the batteries. In this paper we compare these scheduling schemes with the optimal scheduling scheme produced with two different modeling approaches: an approach based on a priced-timed automaton model (implemented and evaluated in Uppaal Cora), as well as an analytical approach (partly formulated as non-linear optimization problem) for a slightly adapted scheduling problem. We show that in some cases the results of the simple scheduling schemes (round robin, and best-first) are close to optimal. However, the optimal schedules, computed according to both methods, also clearly show that in a variety of scenarios, the simple schedules are far from optimal.
Computing optimal schedules of battery usage in embedded systems
2010
Abstract The use of mobile devices is often limited by the battery lifetime. Some devices have the option to connect an extra battery, or to use smart battery-packs with multiple cells to extend the lifetime. In these cases, scheduling the batteries or battery cells over the load to exploit the recovery properties of the batteries helps to extend the overall systems lifetime. Straightforward scheduling schemes, like round-robin or choosing the best battery available, already provide a big improvement compared to a sequential discharge of the batteries.
Maximizing system lifetime by battery scheduling
2009
Abstract The use of mobile devices is limited by the battery lifetime. Some devices have the option to connect an extra battery, or to use smart battery packs with multiple cells to extend the lifetime. In these cases, scheduling the batteries over the load to exploit recovery properties usually extends the system lifetime. Straightforward scheduling schemes, like round robin or choosing the best battery available, already provide a big improvement compared to a sequential discharge of the batteries.
System lifetime extension by battery management
Proceedings of the international conference on Compilers, architecture, and synthesis for embedded systems - CASES '02, 2002
Many portable devices, like laptops and PDAs can be powered by different combinations of two or more battery packs to give the user the possibility to choose an optimal compromise between lifetime and weight/size. The common discharge policy for multiple battery packs is sequential, i.e., the system switches to the second pack when the first one is empty. In this work we demonstrate that this policy is not optimal by proving the effectiveness of two other policies, namely switched and series, which significantly extend battery lifetime, but require some additional control circuitry. We present a complete implementation of the power supply circuitry and detailed measurements on battery discharge times. Significant lifetime extensions (20 to 30 %) have been achieved for dual-battery systems under high current load.
Many portable devices rely on batteries for their power supply. The capacity of the batteries is finite, and the duration with which one can use the device is limited by the battery lifetime. Accordingly, to increase the efficiency of these systems, energy consumption and also managing the use of the batteries are too important. Given the characteristics of the nonlinear behaviour of the battery, for maximizing battery life, which is related to the discharge pattern of batteries, is one of np-hard problems. This paper to extending the system lifetime and maximizing the efficiency of the battery, presents a greedy algorithm for dynamic voltage scaling according to battery and power consumption characteristics of the tasks. These tasks have deadline and should be done on the specific time. In order to test the proposed algorithm offered in this paper, we test it with three algorithms to compare the results. Simulation results show that the proposed method (gjtbs) in different conditions (with different workload of the system) maximized systems lifetime
Adaptive battery charge scheduling with bursty workloads
2012 IEEE Global Communications Conference (GLOBECOM), 2012
Battery-powered wireless sensor devices need to be charged to provide the desired functionality after deployment. Task or even device failures can occur if the voltage of the battery is low. It is very important to schedule the recharge of batteries in time. Existing battery scheduling algorithms usually charge a battery when its voltage drops below a fixed level. Such algorithms work well when the workloads are predictable. However, workloads of wireless sensors can be highly bursty, i.e., extensive sensing and communication tasks usually occur in a very short time period. If such a bursty workload occurs when the battery voltage is low, the battery energy can be depleted very quickly, resulting in system task failures before the device can be recharged. To deal with unpredictable bursty workloads, we investigate battery characteristics with different workloads via experiments. Based on the empirical results, we build an adaptive linear model and propose a feedback control based battery charge scheduling algorithm. This algorithm dynamically adjusts the battery charge threshold for recharge scheduling, adapting to bursty workloads. We have tested our algorithms in extensive simulations with traces obtained from real experiments. Evaluation results show that our algorithms can adapt to bursty workloads. Compared to existing algorithms, our algorithm achieves a 68.26% lower task failure ratio with a 3.45% sacrifice on system lifetime under bursty workloads.
An analytical high-level battery model for use in energy management of portable electronic systems
IEEE/ACM International Conference on Computer Aided Design. ICCAD 2001. IEEE/ACM Digest of Technical Papers (Cat. No.01CH37281), 2001
Once the battery becomes fully discharged, a battery-powered portable electronic system goes off-line. Therefore, it is important to take the battery behavior into account. A system designer needs an adequate high-level model in order to make battery-aware decisions that target maximization of the system's lifetime on-line. We propose such a model: it allows a designer to predict the battery time-to-failure for a given load and provides a cost metric for lifetime optimization algorithms. Our model also allows for a tradeoff between the accuracy and the amount of computation performed. The quality of the proposed model is evaluated using a detailed low-level simulation of a lithium-ion electrochemical cell.
Energy management for battery-powered embedded systems
ACM Transactions on Embedded Computing Systems, 2003
Portable embedded computing systems require energy autonomy. This is achieved by batteries serving as a dedicated energy source. The requirement of portability places severe restrictions on size and weight, which in turn limits the amount of energy that is continuously available to maintain system operability. For these reasons, efficient energy utilization has become one of the key challenges to the designer of battery-powered embedded computing systems. In this paper, we first present a novel analytical battery model, which can be used for the battery lifetime estimation. The high quality of the proposed model is demonstrated with measurements and simulations. Using this battery model, we introduce a new "battery-aware" cost function, which will be used for optimizing the lifetime of the battery. This cost function generalizes the traditional minimization metric, namely the energy consumption of the system. We formulate the problem of battery-aware task scheduling on a single processor with multiple voltages. Then, we prove several important mathematical properties of the cost function. Based on these properties, we propose several algorithms for task ordering and voltage assignment, including optimal idle period insertion to exercise charge recovery. This paper presents the first effort toward a formal treatment of battery-aware task scheduling and voltage scaling, based on an accurate analytical model of the battery behavior.
Energy efficient battery management
IEEE Journal on Selected Areas in Communications, 2001
A challenging aspect of mobile communications consists in exploring ways in which the available run time of terminals can be maximized. In this paper, we present a detailed electrochemical battery model and a simple stochastic model that captures the fundamental behavior of the battery. The stochastic model is then matched to the electrochemical model and used to investigate battery management techniques that may improve the energy efficiency of radio communication devices. We consider an array of electrochemical cells. Through simple scheduling algorithms, the discharge from each cell is properly shaped to optimize the charge recovery mechanism, without introducing any additional delay in supplying the required power. Then, a battery management scheme, which exploits knowledge of the cells' state of charge, is implemented to achieve a further improvement in the battery performance. In this case, the discharge demand may be delayed. Results indicate that the proposed battery management techniques improve system performance no matter which parameters values are chosen to characterize the cells' behavior.