Design and Analysis of Micro-Solar Power Systems for Wireless Sensor Networks Jaein Jeong with Xiaofan Jiang and David Culler Computer Science, UC Berkeley INSS08, June 19 th, 2008

2 Typical Wireless Sensornet Application Typical sensornet application runs on battery. GDI Sampling Rate8.33 x 10-4 Hz TX Rate0.03 B/s Power Consumption1.6 mW Battery Capacity860mAh Lifetime63 days Great Duck Island [SMP+04]Golden Gate Bridge [Kim07] GGB Sampling Rate1 KHz TX Rate441 B/s Power Consumption mW – mW Battery Capacity4 x 18000mAh at 6V Lifetime35 days Limited Lifetime with Battery-Powered Node!

3 Previous Works on Micro-Solar Power Systems Solar-energy harvesting can be used as alternative to battery. Several systems exist with a unique set of requirements. But, they represent only particular points in the design space. Little analysis on performance in entire range of situations. [Everlast, 2006] [Ambimax, 2006] MPP Tracking [Prometheus, 2005] [Trio, 2006] Multi-Level Storage [Heliomote, 2005] [Fleck, 2006] Simple Design

4 Contributions Present a model for micro-solar power systems and Develop a taxonomy of micro-solar design space. Empirical analysis of two well-studied designs. A design guideline for micro-solar systems. Heliomote [Raghunathan et al 05] Trio [Dutta et al 06]

5 Organization System Architecture for Micro-Solar System Design Considerations for Four Components. –External Environment –Solar Collector –Energy Storage –Load Concrete Examples: Trio and Heliomote Conclusion

6 System Architecture External Environment Solar Collector Energy Storage Load Mote Solar Panel Regulating Circuit Level-1 storage Charging Controller and Switch Software Charging Control (optional) Storage Monitoring (optional) Sun E solar_in E storage_in E cons Level-n storage E sol = E L1 E Ln

7 Architecture – External Environment Astronomical Model –Estimate solar radiation using angle Θ. –Solar panel output is given as P sol = cos Θ * Eff panel * A Statistical Model –Refines the astronomical model by using weather variation statistics. N VsVs Solar Panel Θ Effect of Obstructions

8 Architecture – Solar Collector Converts solar energy to electricity. Solar panel I-V curve describes possible operating point. I-V curve moves depending on solar radiation. Operating point dictated by output impedance.

9 Architecture – Energy Storage Buffers energy and delivers in a predictable fashion. Considerations: –System Requirements: Lifetime, capacity, current draw, size and weight. –Trade-offs between efficient energy transfer and charging logic. Storage Elements –NiMH, Li+ for high energy density and supercap for long lifetime. Configurations of energy storage : –Single element or multiple-level of storage elements

10 Architecture – Load Mote is end consumer of energy in micro-solar system. We abstract its behavior as load. –Radio, sensing and computation are main causes. –Duty-cycling is used to save energy consumption. –When the duty-cycle rate is R, average load is given as : I estimate = R * I active + (1 – R) * I sleep

11 Trio Block Diagram Load Telos rev.B Mote RU6730 Solar Cell Zener ( SMAZ5V6 ) and Schottky ( LLSD103A ) Diodes Supercap (L1) DC/DC Software Charging Control (Charging Switch, Thresholds) Storage Monitoring using uC ADC (CapV, BattV, Status) Sun E solar_in E storage_in E cons Li+ (L2) Solar Collector Switch Energy Storage E sol = E cap E bat Heliomote Block Diagram Load Mica2 Mote SolarWorld Solar Cell Diode 2x AA NiMH HW Charge Controller and Switch Sun E solar_in E storage_in E cons Solar Collector Energy Storage DC/DC HW Battery Monitor E sol = E bat Comparative Study - Trio and Heliomote

12 Comparative Study (1) Solar-Collector Operation Evaluate solar-collector matching by comparing E op with E mpp –E op : daily solar radiation from the solar collector. –E mpp : daily solar radiation that can be achieved with MPP. Experiment (a) measures operating point (I op, V op ) Experiment (b) measures I-V curve at that moment.

13 Comparative Study (1) Solar-Collector Operation Difference between E op and E maxP : –Trio: 4.8% of MPP, Heliomote: 22.0% of MPP –For Trio, SW charging allows setting V op close to MPP after the measurement. –For Heliomote, V op is set by battery voltage and protection circuit. This makes it hard to change V op once the system is designed. TrioHeliomote

14 Comparative Study (1) Solar-Collector Operation Useful range of the solar panel in a particular system is very narrow. Power tracking circuits or algorithms are only meaningful within this small range.

15 Comparative Study (2) Energy Flow and Energy Efficiency System efficiency for daily operation –Eff sys = (E bat + E cap + E cons ) / E sol Daily cycle of a system: –Charge, Discharge, Saturation Efficiency at different daily phase –Eff bat−dis = E cons / E bat−dis –Eff cap−dis = E cons / E cap−dis –Eff chg = (E bat−chg + E cap−chg + E cons ) / E sol Discharge (battery) Charge Discharge (supercap) Discharge (battery)

16 Comparative Study (2) Energy Flow and Energy Efficiency System Energy Efficiency –Trio node : 19.5% to 33.4% –Heliomote : 6.9% to 14.6% What makes this difference?

17 Comparative Study (2) Energy Flow and Energy Efficiency Charging-discharging efficiency of Heliomote is as good as that of Trio, but its system efficiency is much smaller. Much of solar energy is wasted during saturation phase. Efficiency of Heliomote would be 31.9% to 41.9% without saturation.

18 Comparative Study (2) Energy Flow and Energy Efficiency With Trio, supercap discharge period exists. –System runs on the supercap not on battery. –Effective battery lifetime increases by T cap-dis / (T bat-dis + T cap-dis )

19 Conclusion Presented a system model for micro-solar power system. Analyzed two well-studied platforms, Trio and Heliomote. Insights from the analysis: –Solar-collector: Useful range of solar-panel voltage is narrow. Can closely match operating point to MPP by setting operating point to this range without using MPPT. –Energy storage: Multi-level storage improves system energy efficiency and lifetime.