Design considerations for battery-powered electronics
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
A multi-level strategy for software power estimation
ISSS '00 Proceedings of the 13th international symposium on System synthesis
Discrete-time battery models for system-level low-power design
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
An analytical high-level battery model for use in energy management of portable electronic systems
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Battery Life Estimation of Mobile Embedded Systems
VLSID '01 Proceedings of the The 14th International Conference on VLSI Design (VLSID '01)
Battery-Driven System Design: A New Frontier in Low Power Design
ASP-DAC '02 Proceedings of the 2002 Asia and South Pacific Design Automation Conference
Balancing batteries, power, and performance: system issues in cpu speed-setting for mobile computing
Balancing batteries, power, and performance: system issues in cpu speed-setting for mobile computing
Proceedings of the conference on Design, automation and test in Europe - Volume 1
Utility Functions in Autonomic Systems
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Battery-powered digital CMOS design
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Efficient power profiling for battery-driven embedded system design
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Fundamental design issues for the future Internet
IEEE Journal on Selected Areas in Communications
Battery voltage modeling for portable systems
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Intelligent power management: promoting power-consciousness in teams of mobile robots
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Dependable, efficient, scalable architecture for management of large-scale batteries
Proceedings of the 1st ACM/IEEE International Conference on Cyber-Physical Systems
Large-scale battery system modeling and analysis for emerging electric-drive vehicles
Proceedings of the 16th ACM/IEEE international symposium on Low power electronics and design
A Semantic Enhanced Service Proxy Framework for Internet of Things
GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
Charge migration efficiency optimization in hybrid electrical energy storage (HEES) systems
Proceedings of the 17th IEEE/ACM international symposium on Low-power electronics and design
Optimal control of mobile monitoring agents in immune-inspired wireless monitoring networks
Journal of Network and Computer Applications
Automatic Battery Replacement System for UAVs: Analysis and Design
Journal of Intelligent and Robotic Systems
Battery management for grid-connected PV systems with a battery
Proceedings of the 2012 ACM/IEEE international symposium on Low power electronics and design
Proceedings of the International Conference on Computer-Aided Design
State of health aware charge management in hybrid electrical energy storage systems
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
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Predicting the residual energy of the battery source that powers a portable electronic device is imperative in designing and applying an effective dynamic power management policy for the device. This paper starts up by showing that a 30% error in predicting the battery capacity of a lithium-ion battery can result in up to 20% performance degradation for a dynamic voltage and frequency scaling algorithm. Next, this paper presents a closed form analytical expression for predicting the remaining capacity of a lithium-ion battery. The proposed high-level model, which relies on online current and voltage measurements, correctly accounts for the temperature and cycle aging effects. The accuracy of the highlevel model is validated by comparing it with DUALFOIL simulation results, demonstrating a maximum of 5% error between simulated and predicted data.