Lithium ion battery performance and charge control

Carter, B.; Matsumoto, Junichi; Prater, A.; Smith, D. L.

doi:10.1109/iecec.1996.552905

Cited by 28 publications

(11 citation statements)

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“…Apart from automobile, laptops, cell phone or mobiles, toys, and many other consumer products use lithium-ion batteries as the main or secondary power source [9][10][11]. The precautions during charging and discharging must be taken for these batteries.…”

Section: Introductionmentioning

confidence: 99%

Experimental temperature distributions in a prismatic lithium-ion battery at varying conditions

Panchal

Dinçer

Agelin‐Chaab

et al. 2016

International Communications in Heat and Mass Transfer

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Section: Introductionmentioning

confidence: 99%

Experimental temperature distributions in a prismatic lithium-ion battery at varying conditions

Panchal

Dinçer

Agelin‐Chaab

et al. 2016

International Communications in Heat and Mass Transfer

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“…Thus, no energy prediction scheme is needed in our design, avoiding complexity and inaccuracy introduced by non-ideal prediction mechanisms. Moreover, the stable energy source makes it possible to charge the battery with a steady constant current for more effective charging [6];  The supercapacitors can support embedded processors directly, taking advantage of a much lower charging/discharging overhead compared to an electrochemical battery;  The battery offers high capacity to preserve energy especially in scenarios with excessive harvested energy.…”

Section: Battery-supercapacitor Hybrid Energy Storage: Inspired Bymentioning

confidence: 99%

“…All of these prior efforts on harvesting aware task scheduling assume an ideal battery as the energy storage medium limited merely by it capacity, ignoring other factors such as rate capacity, recovery effect, and lifetime in terms of recharge cycles [6]. When applied to real-world platforms, overlooking these factors can result in suboptimal or even false scheduling that diminishes system efficiency, stability and lifespan.…”

Section: Introductionmentioning

confidence: 99%

Harvesting-aware energy management for multicore platforms with hybrid energy storage

Xiang

Pasricha

2013

Proceedings of the 23rd ACM International Conference on Great Lakes Symposium on VLSI

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In this paper, we propose a novel framework for energy and workload management in multi-core embedded systems with solar energy harvesting and a periodic hard real-time task set as the workload. Compared to prior work, our energy management framework possesses several advantages, including (i) a batterysupercapacitor hybrid energy storage module for more efficient system energy management, (ii) a semi-dynamic scheduling heuristic that continuously adapts to run-time harvested power variations without losing the consistency of the periodic task set, and (iii) a coarse-grained core shutdown heuristic for additional energy savings. Experimental studies show that our framework results in a reduction in task miss rate by up to 61% and task miss penalty by up to 65% compared to the best known prior work.

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“…Most prior efforts on harvesting-aware task scheduling assume a near-ideal battery as the energy storage medium that is limited merely by its capacity, ignoring other factors such as nonlinear efficiency, slow charge rate, and limited lifetime in terms of recharge cycles [19]. When applied to real-world platforms, overlooking these factors can result in suboptimal or even unrealistic design and scheduling techniques that diminish system efficiency, stability, and lifespan.…”

Section: B Motivation For Hybrid Energy Storagementioning

confidence: 99%

Run-Time Management for Multicore Embedded Systems With Energy Harvesting

Xiang

Pasricha

2015

IEEE Trans. VLSI Syst.

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In this paper, we propose a novel framework for runtime energy and workload management in multicore embedded systems with solar energy harvesting and a periodic hard real-time task set as the workload. Compared with prior work, our framework makes several novel contributions and possesses several advantages, including the following: 1) a semidynamic scheduling heuristic that dynamically adapts to runtime harvested power variations without losing the consistency of periodic tasks; 2) a battery-supercapacitor hybrid energy storage module for more efficient system energy management; 3) a coarse-grained core shutdown heuristic for additional energy saving; 4) energy budget planning and task allocation heuristics with process variation tolerance; 5) a novel dual-speed method specifically designed for periodic tasks to address discrete frequency levels and dynamic voltage/frequency scaling switching overhead at the core level; and 6) an extension to prepare the system for thermal issues arising at runtime during extreme environmental conditions. The experimental studies show that our framework results in a reduction in task miss rate by up to 70% and task miss penalty by up to 65% compared with the best known prior work.Index Terms-Dynamic voltage and frequency scaling, energy harvesting, multicore processing, scheduling algorithm.

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Lithium ion battery performance and charge control

Cited by 28 publications

References 0 publications

Experimental temperature distributions in a prismatic lithium-ion battery at varying conditions

Experimental temperature distributions in a prismatic lithium-ion battery at varying conditions

Harvesting-aware energy management for multicore platforms with hybrid energy storage

Run-Time Management for Multicore Embedded Systems With Energy Harvesting

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