A model for battery lifetime analysis for organizing applications on a pocket computer

Rakhmatov, Daler; Vrudhula, Sarma; Wallach, Deborah A.

doi:10.1109/tvlsi.2003.819320

Cited by 187 publications

(153 citation statements)

References 11 publications

Supporting

Mentioning

150

Contrasting

Unclassified

Order By: Relevance

“…Analytical models are computationally efficient, but limited in the discharge effects they model. One of these models is Sarma and Rakhmatov model which is an abstraction of a real battery [15] that we used in this work. Rakhmatov model is chosen for estimating the real residual battery capacity at a specific time, because it is the simplest accurate analytical model.…”

Section: A Battery Modelsmentioning

confidence: 99%

“…Rakhmatov model is chosen for estimating the real residual battery capacity at a specific time, because it is the simplest accurate analytical model. Other models require solving complex Partial Differential Equations (PDEs) which are difficult to optimize [7] [15]. For the model to adequately mimic real behaviour of the batteries, one can utilize this formula:…”

Section: A Battery Modelsmentioning

confidence: 99%

“…The value of α is a battery related parameter and its value is decided by manufacture of battery designer [15]. Formula (7) indicates that the total capacity of the battery is the sum of two terms, the linear ideal behaviour plus the nonlinear behaviour.…”

Section: A Battery Modelsmentioning

confidence: 99%

“…The value of β is estimated from the data sheet of the battery. For example, the data sheet of a battery might model rated capacity (in Ahr) vs. discharge current (in hour) [15]. Thus, before one can use the proposed model, the parameters need to be estimated from experimental data for the modeled battery.…”

Section: A Battery Modelsmentioning

confidence: 99%

“…Li et al [8] proposed an analytical model that computes the life time of a low duty cycled star sensor network. In their model they considered nonlinearities of lithium-ion battery following Rakhmatov model [15] and they aimed to minimize the total energy consumption of the lithium-ion battery by finding the optimal idle and sleep period while guaranteeing energy efficiency, reliability and reasonable latency. In their proposal, they considered the trade-off between energy that is dissipated in sending frequent preambles and the period thereby sensors stay idle waiting for the preamble.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Battery Aware Beacon Enabled IEEE802.15.4: An Adaptive and Cross-Layer Approach

Salayma¹,

Al‐Dubai²,

Romdhani³

et al. 2015

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

Section: A Battery Modelsmentioning

confidence: 99%

Section: A Battery Modelsmentioning

confidence: 99%

Section: A Battery Modelsmentioning

confidence: 99%

Section: A Battery Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Battery Aware Beacon Enabled IEEE802.15.4: An Adaptive and Cross-Layer Approach

Salayma¹,

Al‐Dubai²,

Romdhani³

et al. 2015

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

Revolutionizing the Afterlife of EV Batteries: A Comprehensive Guide to Echelon Utilization Technologies

Wang,

Hu,

Wang

et al. 2023

ChemElectroChem

View full text Add to dashboard Cite

In the burgeoning new energy automobile industry, repurposing retired power batteries stands out as a sustainable solution to environmental and energy challenges. This paper comprehensively examines crucial technologies involved in optimizing the reuse of batteries, spanning from disassembly techniques to safety management systems. The review assesses the viability of retired batteries, comparing their performance with that of new units, and evaluates scenarios for echelon utilization. Early safety warning systems based on temperature, voltage, and gas emissions are analyzed to enhance the understanding of safety management. In addition, the current state and enhancement opportunities for the second life of electric vehicle batteries are presented. The research highlights the integral role of retired power batteries in applications such as energy storage, communication bases, and streetlights. It is indicated that ensuring safety through robust early warning systems is of paramount importance. Ultimately, this paper envisions a sustainable future, driven by innovative strategies that facilitate the effective utilization of retired batteries in the new energy automobile industry.

show abstract

Critical review and analysis of available capacity estimation of LiFePO ₄ battery using Peukert's law: A case study

Zhang

Cui

Zhou

et al. 2022

Intl J of Energy Research

View full text Add to dashboard Cite

Summary Internal state estimation of lithium‐ion batteries (LIBs) is the core function of the battery management system (BMS) of electric vehicles (EVs). State of charge, state of health, and state of energy are all related to the capacity of LIBs. The original Peukert's law was proposed for the available capacity estimation of lead‐acid batteries at a constant current, in which the effects of temperature and variable current are not considered. Thus, it only works in ideal environments, and far from the working environment of electric vehicle power batteries. In this paper, the applied research and future challenges of Peukert's law are reviewed and summarized, and then available capacity modeling of LIBs is achieved based on improved Peukert's law considering temperature and variable current, respectively. As a case study of lithium‐ion battery for vehicle, a module with 20 battery cells in parallel was tested and analyzed under different conditions in the MKLtech battery test system (BTS). The experimental results show that the maximum error of the proposed method is less than 2.81% under different conditions. More importantly, the simple parameter identification and high computational efficiency of the proposed model are integral to the application in the BMSs of EVs.

show abstract

A model for battery lifetime analysis for organizing applications on a pocket computer

Cited by 187 publications

References 11 publications

Battery Aware Beacon Enabled IEEE802.15.4: An Adaptive and Cross-Layer Approach

Battery Aware Beacon Enabled IEEE802.15.4: An Adaptive and Cross-Layer Approach

Revolutionizing the Afterlife of EV Batteries: A Comprehensive Guide to Echelon Utilization Technologies

Critical review and analysis of available capacity estimation of LiFePO ₄ battery using Peukert's law: A case study

Contact Info

Product

Resources

About