Online Prediction of Battery Lifetime for Embedded and Mobile Devices [chapter]

Ye Wen, Rich Wolski, Chandra Krintz
2005 Lecture Notes in Computer Science  
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
more » ... ile 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.
doi:10.1007/978-3-540-28641-7_5 fatcat:53kudzpktnb3dp4lfcvdhlnxmq