Energy management for battery-powered embedded systems

Abstract

Portable embedded computing systems require energy autonomy. This is achieved by batteries serving as a dedicated energy source. The requirement of portability places severe restrictions on size and weight, which in turn limits the amount of energy that is continuously available to maintain system operability. For these reasons, efficient energy utilization has become one of the key challenges to the designer of battery-powered embedded computing systems.In this paper, we first present a novel analytical battery model, which can be used for the battery lifetime estimation. The high quality of the proposed model is demonstrated with measurements and simulations. Using this battery model, we introduce a new "battery-aware" cost function, which will be used for optimizing the lifetime of the battery. This cost function generalizes the traditional minimization metric, namely the energy consumption of the system. We formulate the problem of battery-aware task scheduling on a single processor with multiple voltages. Then, we prove several important mathematical properties of the cost function. Based on these properties, we propose several algorithms for task ordering and voltage assignment, including optimal idle period insertion to exercise charge recovery.This paper presents the first effort toward a formal treatment of battery-aware task scheduling and voltage scaling, based on an accurate analytical model of the battery behavior.

Energy Efficient Monitoring for Intrusion Detection in Battery-Powered Wireless Mesh Networks

“…As shown in Figure 1(b) the router lifetime is prolonged by 5h when using the 60s on/off duty-cycling, and by 3h when using the 30s on/off interval. This experiment validated battery recovery effects [11], that have been mentioned briefly in the context of WMN [8]. We used the data collected during these experiments to enhance a simulator we have developed so that it accounts for the new source of energy efficiency, namely battery recovery.…”

“…Reducing the load on battery was proposed for giving battery recovery time [6] and [8]. An on/off controller was proposed theoretically for battery recovery [11].…”

Análise Comparativa do Tempo de Vida de Baterias em Dispositivos Móveis a partir da Utilização de Modelos Analíticos

“…A capacidade remanescente é calculada sempre que ocorrer mudança na taxa de descarga. Já o modelo analítico de Rakhmatov-Vrudhula [10] foi originalmente desenvolvido para o cálculo do tempo de vida de uma bateria de íon(s) de lítio alimentada por carga constante ou variável. Na equação (3.10), considerando uma carga variá-vel, é descrito o impacto do perfil de descarga no tempo de vida da bateria, onde I k−1 é a corrente de descarga durante o período k − 1.…”

“…Neste trabalho será realizada uma comparação entre o modelo analítico de difusão de Rakhmatov-Vrudhula [4,10] e o modelo Linear [11], na estimação do tempo de vida de uma bateria. O modelo de Rakhmatov-Vrudhula foi escolhido em virtude do mesmo conseguir capturar o efeito de taxa de capacidade e o efeito de recuperação, bem como ser de fácil implementação quando comparado aos demais modelos citados [11].…”

Maximizing Lifetime of Target Coverage in Wireless Sensor Networks Using Learning Automata

“…The motivation for this scheme is not just to turn off redundant sensors to save energy. Research shows that if batteries are given sufficient recovering period between two intensive consumption periods, the actual battery lifetime is extended [24]. Therefore appropriate scheduling will not only improve sensor network lifetime, but also individual battery's performance.…”