An analytical high-level battery model for use in energy management of portable electronic systems

Rakhmatov, Daler; Vrudhula, Sarma

doi:10.1109/iccad.2001.968687

Cited by 168 publications

(190 citation statements)

References 8 publications

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“…A complex real battery is represented by a simpler equivalent battery, and a high-level analytical expression is derived on the basis of lowlevel analysis. One previous modeling attempt in this direction was reported in [13]. In this paper, we provide a new model which is substantially improved in terms of computational complexity, accuracy and usefulness.…”

Section: Introductionmentioning

confidence: 97%

“…In comparison to [13], the battery model described by the equation 4 is better in terms of accuracy and computational complexity. Unlike the model in [13], it does not involve square roots and does not require an approximation of the complementary error function (see [13] for details).…”

Section: Model Descriptionmentioning

confidence: 99%

“…Unlike the model in [13], it does not involve square roots and does not require an approximation of the complementary error function (see [13] for details). However, note that the both models produce the same results numerically.…”

Section: Model Descriptionmentioning

confidence: 99%

See 2 more Smart Citations

Battery lifetime prediction for energy-aware computing

Rakhmatov

Vrudhula

Wallach

2002

Proceedings of the 2002 International Symposium on Low Power Electronics and Design - ISLPED '02

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Predicting the time of full discharge of a finite-capacity energy source, such as a battery, is important for the design of portable electronic systems and applications. In this paper we present a novel analytical model of a battery that not only can be used to predict battery lifetime, but also can serve as a cost function for optimization of the energy usage in battery-powered systems. The model is physically justified, and involves only two parameters, which are easily estimated. The paper includes the results of extensive experimental evaluation of the model with respect to numerical simulations of the electrochemical cell, as well as measurements taken on a real battery. The model was tested using constant, interrupted, periodic and non-periodic discharge profiles, which were derived from standard applications run on a pocket computer.

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Section: Introductionmentioning

confidence: 97%

Section: Model Descriptionmentioning

confidence: 99%

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Battery lifetime prediction for energy-aware computing

Rakhmatov

Vrudhula

Wallach

2002

Proceedings of the 2002 International Symposium on Low Power Electronics and Design - ISLPED '02

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“…The design objective of battery usage is introduced with battery characteristics. The processor aging and its influence on Rakhmatov and Vrudhula [2001] the control system is analyzed. Then the optimization framework and flow are shown.…”

Section: Organizationmentioning

confidence: 99%

Resource-aware Automotive Control Systems Design: A Cyber-Physical Systems Approach

Chang

Chakraborty²

2016

FNT in Electronic Design Automation

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“…Each node N i computes its own state of charge (SOC) which is given by Q/Q max where Q is the remaining battery capacity and Q max is the initial battery capacity. The remaining battery capacity can be computed by the Rakhmatov battery model [19]. Initially N i initializes its SOC min and SOC max to SOC.…”

Section: A Location Service and Setup Phasementioning

confidence: 99%

TPR: Dead end aware table less position based routing scheme for low power data-centric wireless sensor networks

Madani

Weber

Mahlknecht

2008

2008 International Symposium on Industrial Embedded Systems

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Abstract-In this paper we present a Table less Position based Routing (TPR) scheme for low power data centric wireless sensor networks. TPR is localized, uses greedy forwarding approach, and does not rely on neighborhood information. These characteristics reduce the communication overhead (no neighborhood information exchange), make the protocol highly scalable (no routing tables are maintained and beacons are not exchanged when a node leaves or enters the networks), and it performs better in mobile environments (as the next hop is non-deterministic and is computed at real time). It also deals with dead end problems by a recovery strategy in a distributed and localized way. TPR is implemented in the OMNET++ based discrete event simulation environment PAWiS (simulation framework for low power wireless sensor networks). The results show that TPR provides guaranteed delivery, extended network life time, and a mechanism to route on the basis of end to end delay and/or energy consumption.

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An analytical high-level battery model for use in energy management of portable electronic systems

Cited by 168 publications

References 8 publications

Battery lifetime prediction for energy-aware computing

Battery lifetime prediction for energy-aware computing

Resource-aware Automotive Control Systems Design: A Cyber-Physical Systems Approach

TPR: Dead end aware table less position based routing scheme for low power data-centric wireless sensor networks

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