Battery life estimation of mobile embedded systems

Panigrahi, D.; Chiasserini, Carla Fabiana; Dey, Sujit; Rao, Ravishankar; Raghunathan, Anand; Lahiri, Kanishka

doi:10.1109/icvd.2001.902640

Cited by 171 publications

(94 citation statements)

References 13 publications

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“…This model is mainly focused on the recovery effect. In a later work [12], the authors extended this model to incorporate the rate-capacity effect. Both models are based on discrete time Markov chain construction and are merely developed to predict the battery lifetime.…”

Section: Related Workmentioning

confidence: 99%

Battery-aware power management based on Markovian decision processes

Peng

Pedram

2006

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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-This paper addresses the problem of maximizing capacity utilization of the battery power source in a portable electronic system under latency and loss rate constraints. First, a detailed stochastic model of a power-managed, batterypowered electronic system is presented. The model, which is based on the theories of continuous-time Markovian decision processes and stochastic networks, captures two important characteristics of today's rechargeable battery cells, i.e., the current rate-capacity characteristic and the relaxation-induced capacity recovery. Next, the battery-aware dynamic power management problem is formulated as a policy optimization problem and solved exactly by using a linear programming approach. Experimental results show that the proposed method outperforms existing methods by more than 20% in terms of battery service lifetime.

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Section: Related Workmentioning

confidence: 99%

Battery-aware power management based on Markovian decision processes

Peng

Pedram

2006

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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“…Once the neighbors list for all nodes are created, to these nodes, the average relative distance, the average relative speed and the average link stability can be obtained in list 2, 3 and 5, respectively. We choose some appropriate values for the residual energy of each node based on the stochastic model of a battery [31], [32]. A single battery cell is characterized by the open-circuit potential (V OC ), i.e., the initial potential of a fully charged cell under no-load conditions, and the cut-off potential (V cut ) at which the cell is considered discharged.…”

Section: Factorial Designmentioning

confidence: 99%

Cluster Based Location-Aided Routing Protocol for Large Scale Mobile Ad Hoc Networks

Wang

Dong

Liang

et al. 2009

IEICE Trans. Inf. & Syst.

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SUMMARYRouting algorithms with low overhead, stable link and independence of the total number of nodes in the network are essential for the design and operation of the large-scale wireless mobile ad hoc networks (MANET). In this paper, we develop and analyze the Cluster Based Location-Aided Routing Protocol for MANET (C-LAR), a scalable and effective routing algorithm for MANET. C-LAR runs on top of an adaptive cluster cover of the MANET, which can be created and maintained using, for instance, the weight-based distributed algorithm. This algorithm takes into consideration the node degree, mobility, relative distance, battery power and link stability of mobile nodes. The hierarchical structure stabilizes the end-to-end communication paths and improves the networks' scalability such that the routing overhead does not become tremendous in large scale MANET. The clusterheads form a connected virtual backbone in the network, determine the network's topology and stability, and provide an efficient approach to minimizing the flooding traffic during route discovery and speeding up this process as well. Furthermore, it is fascinating and important to investigate how to control the total number of nodes participating in a routing establishment process so as to improve the network layer performance of MANET. C-LAR is to use geographical location information provided by Global Position System to assist routing. The location information of destination node is used to predict a smaller rectangle, isosceles triangle, or circle request zone, which is selected according to the relative location of the source and the destination, that covers the estimated region in which the destination may be located. Thus, instead of searching the route in the entire network blindly, C-LAR confines the route searching space into a much smaller estimated range. Simulation results have shown that C-LAR outperforms other protocols significantly in route set up time, routing overhead, mean delay and packet collision, and simultaneously maintains low average end-to-end delay, high success delivery ratio, low control overhead, as well as low route discovery frequency.

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“…The battery lifetime is here understood as the time during which the battery can supply energy to the device before a new recharge [17]. There are different classes of mathematical models [4,6,10,15,17,18] capable of battery lifetime prediction, and among those classes, there is the electrical models class [3,4,22]. A considerably versatile electrical model is the so called Battery model [9,21,22], and a crucial aspect of its application is the parametrization process.…”

Section: Introductionmentioning

confidence: 99%

Battery Model Parameters Estimation Using Simulated Annealing

Brondani

Sausen

et al. 2017

Tend. Mat. Apl. Comput.

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ABSTRACT. In this paper, a Simulated Annealing (SA) algorithm is proposed for the Battery model parametrization, which is used for the mathematical modeling of the Lithium Ion Polymer (LiPo) batteries lifetime. Experimental data obtained by a testbed were used for model parametrization and validation. The proposed SA algorithm is compared to the traditional parametrization methodology that consists in the visual analysis of discharge curves, and from the results obtained, it is possible to see the model efficacy in batteries lifetime prediction, and the proposed SA algorithm efficiency in the parameters estimation.

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Battery life estimation of mobile embedded systems

Cited by 171 publications

References 13 publications

Battery-aware power management based on Markovian decision processes

Battery-aware power management based on Markovian decision processes

Cluster Based Location-Aided Routing Protocol for Large Scale Mobile Ad Hoc Networks

Battery Model Parameters Estimation Using Simulated Annealing

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