Reducing charge redistribution loss for supercapacitor-operated energy harvesting wireless sensor nodes

With increasing demand of long term applications, supercapacitors have been widely chosen as energy storage devices for energy harvesting aware wireless sensor networks( WSNs) due to their long charging-discharging life cycles. However, few studies have focused on charge redistribution effect of supercapacitors in WSNs. In this paper, we investigate charge redistribution of supercapacitor and explore how it affects long term energy neutral operation (ENO). The Variable Leakage Resistance (VLR) model is used to analyze the charge redistribution effect. Our results indicate that charge redistribution may cause considerable amount of extra energy loss in long term ENO. A practical algorithm to minimize charge redistribution loss during energy neutral operation is proposed. The algorithm is computationally lightweight and can be incorporated into the state-of-the-art duty cycling power management strategies in WSNs. The proposed algorithm is proved to be effective in keeping the main branch and the delayed branch balanced and thus lowering energy dissipation from charge redistribution.

[1]  Naehyuck Chang,et al.  Constant-current regulator-based battery-supercapacitor hybrid architecture for high-rate pulsed load applications☆☆☆ , 2012 .

[2]  Luca Benini,et al.  Improving the efficiency of air-flow energy harvesters combining active and passive rectifiers , 2013, ENSSys '13.

[3]  W. Dargie,et al.  Dynamic Power Management in Wireless Sensor Networks: State-of-the-Art , 2012, IEEE Sensors Journal.

[4]  Ying Zhang,et al.  Self-discharge analysis and characterization of supercapacitors for environmentally powered wireless sensor network applications , 2011 .

[5]  Upkar Varshney,et al.  Pervasive Healthcare and Wireless Health Monitoring , 2007, Mob. Networks Appl..

[6]  Pai H. Chou,et al.  Everlast: Long-life, Supercapacitor-operated Wireless Sensor Node , 2006, ISLPED'06 Proceedings of the 2006 International Symposium on Low Power Electronics and Design.

[7]  Luca Benini,et al.  Adaptive Power Management for Environmentally Powered Systems , 2010, IEEE Transactions on Computers.

[8]  Prashant J. Shenoy,et al.  The case for efficient renewable energy management in smart homes , 2011, BuildSys '11.

[9]  Ying Zhang,et al.  Analysis of Supercapacitor Energy Loss for Power Management in Environmentally Powered Wireless Sensor Nodes , 2013, IEEE Transactions on Power Electronics.

[10]  David E. Culler,et al.  TinyOS: An Operating System for Sensor Networks , 2005, Ambient Intelligence.

[11]  Andrew G. Barto,et al.  Adaptive Control of Duty Cycling in Energy-Harvesting Wireless Sensor Networks , 2007, 2007 4th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[12]  Ting Zhu,et al.  Leakage-aware energy synchronization for wireless sensor networks , 2009, MobiSys '09.

[13]  Ying Zhang,et al.  Modeling and characterization of supercapacitors for wireless sensor network applications , 2011 .

[14]  David E. Culler,et al.  Perpetual environmentally powered sensor networks , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[15]  John Anderson,et al.  An analysis of a large scale habitat monitoring application , 2004, SenSys '04.

[16]  Gil Zussman,et al.  Networking Low-Power Energy Harvesting Devices: Measurements and Algorithms , 2011, IEEE Transactions on Mobile Computing.

[17]  Anantha Chandrakasan,et al.  Dynamic Power Management in Wireless Sensor Networks , 2001, IEEE Des. Test Comput..

[18]  Mani B. Srivastava,et al.  Power management in energy harvesting sensor networks , 2007, TECS.