History-based, Online, Battery Lifetime Prediction for Embedded and Mobile Devices
Ye Wen, Rich Wolski, and Chandra Krintz
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.