Enhancing battery efficiency for pervasive health-monitoring systems based on electronic textiles.
Electronic textiles are regarded as one of the most important computation platforms for future computer-assisted health-monitoring applications. In these novel systems, multiple batteries are used in order to prolong their operational lifetime, which is a significant metric for system usability. However, due to the nonlinear features of batteries, computing systems with multiple batteries cannot achieve the same battery efficiency as those powered by a monolithic battery of equal capacity. In this paper, we propose an algorithm aiming to maximize battery efficiency globally for the computer-assisted health-care systems with multiple batteries. Based on an accurate analytical battery model, the concept of weighted battery fatigue degree is introduced and the novel battery-scheduling algorithm called predicted weighted fatigue degree least first (PWFDLF) is developed. Besides, we also discuss our attempts during search PWFDLF: a weighted round-robin (WRR) and a greedy algorithm achieving highest local battery efficiency, which reduces to the sequential discharging policy. Evaluation results show that a considerable improvement in battery efficiency can be obtained by PWFDLF under various battery configurations and current profiles compared to conventional sequential and WRR discharging policies.
Zheng
Nenggan
N
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China. zng@cs.zju.edu.cn
Wu
Zhaohui
Z
Lin
Man
M
Yang
Laurence Tianruo
LT
D000465:Algorithms; D003020:Clothing; D011211:Electric Power Supplies; D004582:Electronics, Medical; D006801:Humans; D008283:Maintenance / Q000379:methods*; D008991:Monitoring, Physiologic / Q000295:instrumentation*; D013784:Textiles