Battery-aware power management based on Markovian decision processes

This paper is concerned with 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, battery-powered electronic system is presented. The model, which is...

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Bibliographic Details
Published in:Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design pp. 707 - 713
Main Authors: Rong, Peng, Pedram, Massoud
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 10.11.2002
IEEE
Series:ACM Conferences
Subjects:
Applied sciences
Design. Technologies. Operation analysis. Testing
Electronics
Exact sciences and technology
Hardware > Integrated circuits > Semiconductor memory
Integrated circuits
Mathematics of computing > Probability and statistics > Probabilistic representations > Markov networks
Mathematics of computing > Probability and statistics > Stochastic processes
Mathematics of computing > Probability and statistics > Stochastic processes > Markov processes
Other multijunction devices. Power transistors. Thyristors
Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices
Theory of computation > Theory and algorithms for application domains > Machine learning theory > Markov decision processes
Performance evaluation
Stochastic model
Circuit design
Linear programming
Continuous time
Secondary cell
Power electronics
Mean value
Stochastic process
Optimization
Relaxation
Power consumption
Heuristic method
Portable equipment
Computer aided design
ISBN:0780376072, 9780780376076
ISSN:1092-3152
Online Access:Get full text
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Summary:This paper is concerned with 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, battery-powered 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 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 heuristic methods for battery management by as much as 17% in terms of the average energy delivered per unit weight of battery cells.
Bibliography:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:0780376072
9780780376076
ISSN:1092-3152
DOI:10.1145/774572.774676