History-based Online Battery Lifetime Prediction for Embedded Systems and Mobile Devices

Online battery lifetime prediction is an important facility for power-aware computing on battery-powered embedded systems and mobile devices. It provides critical battery lifetime information for both users and operating system schedulers. This paper proposes a novel history-based statistical approach for online battery lifetimeprediction. This approach first takes a one-time, full cycle, voltage measurement of a constant load, and then 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.We use twenty-two constant and variable workloads to verify the efficacy of our approach. The results show that our approach is efficient, accurate, and able to adapt to different systems and batteries easily.