Battery Life Estimation of Mobile Embedded Systems

Battery Life Estimation of Mobile Embedded Systems Debashis Panigrahi y, Carla Chiasserini z, Sujit Dey y, Ramesh Rao y, Anand Raghunathan x and Kanishka Lahiri y yDept. of Electrical and Computer Engg., University of California, San Diego, CA. zDipartimento di Electtronica, Politecnico di Torino, Italy. xC & C Research Labs, NEC USA, Princeton, NJ. Abstract Since battery life directly impacts the extent and duration of mobility, one of the key considerations in the design of a mobile embedded system should be to maximize the energy delivered by the battery, and hence the battery lifetime. To facilitate exploration of alternative implementations for a mobile embedded system, in this paper we address the issue of developing a fast and accurate battery model, and providing a framework for battery life estimation of Hardware/Software (HW/SW) embedded systems. We introduce a stochastic model of a battery, which can simultaneously model two key phenomena affecting the battery life and the amount of energy that can be delivered by the battery: the Rate Capacity effect and the Recovery effect. We model the battery behavior mathematically in terms of parameters that can be related to physical characteristics of the electro-chemical cell. We show how this model can be used for battery life estimation of a HW/SW embedded system, by calculating battery discharge demand waveforms using a power co-estimation technique. Based on the discharge demand, the battery model estimates the battery lifetime as well as the delivered energy. Application of the battery life estimation methodology to three system implementations of an example TCP/IP network interface subsystem demonstrate that different system architectures can have significantly different delivered energy and battery lifetimes.