Abstract
This paper presents a novel, history-based, statistical technique for online battery lifetime prediction. The approach first takes a one-time, full cycle, voltage measurement of a constant load, and uses it to transform the partial voltage curve of the current workload into a form with robust predictability. Based on the transformed history curve, we apply a statistical method to make a lifetime prediction. We investigate the performance of the implementation of our approach on a widely used mobile device (HP iPAQ) running Linux, and compare it to two similar battery prediction technologies: ACPI and Smart Battery. We employ twenty-two constant and variable workloads to verify the efficacy of our approach. Our results show that this approach is efficient, accurate, and able to adapt to different systems and batteries easily.
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© 2005 Springer-Verlag Berlin Heidelberg
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Wen, Y., Wolski, R., Krintz, C. (2005). Online Prediction of Battery Lifetime for Embedded and Mobile Devices. In: Falsafi, B., VijayKumar, T.N. (eds) Power-Aware Computer Systems. PACS 2003. Lecture Notes in Computer Science, vol 3164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28641-7_5
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DOI: https://doi.org/10.1007/978-3-540-28641-7_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24031-0
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