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International Journal of Distributed Sensor Networks

Volume 2013 (2013), Article ID 627963, 11 pages

http://dx.doi.org/10.1155/2013/627963

## Efficiency-Aware: Maximizing Energy Utilization for Sensor Nodes Using Photovoltaic-Supercapacitor Energy Systems

^{1}Computer Science and Technology Department, School of Electronics & Information Engineering, Xi’an Jiaotong University, Xi'an 710049, China^{2}Department of Computing, Imperial College London, London SW7 2AZ, UK

Received 4 December 2012; Revised 25 March 2013; Accepted 27 March 2013

Academic Editor: George P. Efthymoglou

Copyright © 2013 Zheng Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Recently, photovoltaic-supercapacitor-based energy
systems have become more and more popular in the design
of energy harvesting wireless sensor networks (EH-WSNs) as
an alternative to battery power. Existing research on this area
mainly focuses on hardware design and the improvement of the
charging efficiency. However, energy is wasted not only by the inefficient charging process, but also the inefficient discharging process and energy leakage. Therefore, to maximize node lifetime and
energy utilization, all the previous energy loss should be considered.
In this paper, we develop realistic hardware models of
the complete photovoltaic-supercapacitor energy systems and
propose the *efficiency-aware*, a systematic duty cycling framework
to maximize energy utilization. We formalize the maximization
problem as a nonlinear optimization problem and develop two
efficient algorithms for its optimal solutions. The performance of
our approaches is evaluated via extensive numeric simulations,
and the results show that our efficiency-aware framework can,
respectively, achieve 60% and 56% more active time (i.e. energy
utilization) than the fixed duty cycle scheme and *leakage-aware*,
a state-of-the-art scheme for photovoltaic-supercapacitor energy
systems.

#### 1. Introduction

Harvesting energy from the environment provides a promising solution to address the issues arising from energy scarcity in wireless sensor networks (WSNs) [1–4]. More and more researchers choose photovoltaic-supercapacitor hardware systems to design the sensor nodes that utilize energy-harvesting WSNs (EN-WSNs). The main reason is that solar energy is readily available and supercapacitors have larger charging-discharging efficiencies and much longer recharging cycles (thousands times or more) than batteries. More importantly, the residual energy of supercapacitors is easy to meter online with high accuracy, which can support power management algorithms and emergent energy-aware network protocols [5–9].

The key issue of sensor nodes with photovoltaic-supercapacitor energy systems is how to maximize the utility of the harvested energy. Due to the time-varying nature of solar energy and the highly nonlinear output versus voltage characteristics of supercapacitor, it is challenging to model the the whole energy system as well as to maximize its efficiency. There are several efforts [3, 10–14] aiming to improve the efficiency of energy systems. A quick-charge circuit to switch between parallel and series connection of photovoltaic (*PV*) cells is used in [3] to shorten the supercapacitor charging time. The well-known MPPT techniques [2, 3, 10–12] have obtained the maximum output power by adjusting the operating point of the solar panels dynamically. Reference [13] points out that the charging efficiency of MPPTs has a strong relationship with the mismatch between the maximum power point voltage of the solar panels () and the supercapacitor voltage () and achieves high charging efficiency by selecting supercapacitors with appropriate capacities. However, they assume that the supercapacitor does not discharge in day time and their optimal solution is for a fixed solar irradiance model only. Leakage-aware [4] is the first indepth work that focuses on reducing the leakage of supercapacitors by dynamically adjusting the duty cycle. However, it does not consider the charging and discharging efficiency (charging efficiency, discharging efficiency, and charging-discharging efficiency are the energy conversion efficiencies of input regulator, output regulator, and energy storages (batteries or supercapacitors)) caused by the state of the supercapacitor, and it is quite difficult to find a suitable adjustment step length when the daily solar irradiation changes frequently. Besides, it needs to wake up the node frequently and can only assign duty cycles for a small duration in each calculation (tens of seconds, compared with tens of minutes of our efficiency-aware framework).

The energy transfer model of a representative photovoltaic-supercapacitor energy system can be simply illustrated by Figure 1. The power (the power harvested by a solar panel is highly depending on solar irradiance and the states of the solar panel. Here, it represents the harvesting power on the maximum power point of a solar panel.) () harvested by the solar panel (*PV* Array) is transferred into the supercapacitor through the input regulator with the efficiency , and energy in the supercapacitor can be used by the node (e.g., Micaz) through the output regulator with the efficiency as well as being leaked away with the power . According to [4, 11–13], , , and highly depend on the voltage of the supercapacitor (). Through a number of experiments, we also find that these variables can be expressed by functions of through theoretical analysis or model fitting. As can be predicted by prediction algorithms such as EWMA [15] and WCMA-PDR [16], can be controlled to the states beneficial to energy harvesting by adjusting the power () consumed by the node, using duty cycle scheduling schemes.

In this context, this paper proposes efficiency-aware: a realistic and complete way to maximize the energy utilization efficiency of photovoltaic-supercapacitor energy systems. Efficiency-aware is a three-layer design as shown in Figure 2. All the models in hardware layer are based on actual measurements or empirical formulas, and the duty cycle controller uses energy control algorithms to suggest optimal duty cycle to the adaptation layer. The major contributions of our work are as follows.(i)Modeling all the power models of a commonly used photovoltaic-supercapacitor energy systems, describing the results, and using them to develop energy-harvesting-aware algorithms and systems.(ii)Proposing a framework that can be used in photovoltaic-supercapacitor energy systems with predictable solar energy to maximize energy utilization in such systems.(iii)Formulating the utility maximization problem as an optimization problem that produces duty cycle schedules and proposing algorithms to solve this optimization. We also make an extensive comparative performance analysis of leakage-aware, fixed duty cycle scheme, and our method.

The rest of this paper is organized as follows. An overview of the efficiency-aware design architecture is presented in Section 2. In Section 3, we present power models of individual hardware components of the photovoltaic-supercapacitor energy systems. The formalization of the optimization problem and algorithms to solve the problem are presented in Section 4. Numeric results are presented in Section 5. Finally, we conclude the paper in Section 6.

#### 2. Overview of Efficiency-Aware Framework

The efficiency-aware framework for the photovoltaic-supercapacitor energy systems is a three-layer architecture as shown in Figure 2.

The hardware layer provides offline hardware models and predicted solar energy to the control layer. The offline hardware models include a solar panel model, efficiency (i.e., charging and discharging) models, a residual energy model, and a leakage model.

Continuous time is divided into discrete slots in our design (as shown in Figure 3). At the beginning of every prediction interval, the control layer computes the optimal duty cycles for those slots based on the information provided by the hardware layer, predicted solar power, and QoS requirements.

The adaptation layer changes its schedules (e.g., sensing and communication) according to the duty cycle determined by the control layer.

#### 3. Power Models

In general, a photovoltaic-supercapacitor energy system includes a solar panel, an input regulator (except direct connection), a supercapacitor, and an output regulator. In order to provide a systematic understanding of the photovoltaic-supercapacitor energy system, it is necessary to know the characteristics of each individual component. To this end, we present the solar model, charging efficiency model for the input regulator, discharging efficiency model for the output regulator, the residual energy model, and the leakage model for the supercapacitor. In addition, a simple duty cycle model is given in this section.

##### 3.1. Solar Panel Model

Previous work [12] presents an accurate simulation model of solar panels. By neglecting the shunt resistance and considering that the series resistor is large enough, the *I-V* characteristic of solar panels can be given by the following equation [12, 17]:
where is the generated current, is the electron charge, is diode saturation current, is an ideality factor, is the Boltzmann’s constant, and is the solar panel temperature in degree Kelvin.

and depend on solar irradiance and temperature as [12, 17] where and are the diode saturation current and the generated current in Standard Test Condition (STC). is the reference temperature. is the energy gap. is the ideality constant. is the temperature coefficient. is the solar irradiance in STC, typically 1000 . is the energy conversion efficiency of the solar panel, and is the area of the solar panels. can be considered equal to the short-circuit current in STC, and can be presented as

is the open-circuit voltage in STC. Given the solar irradiance and the solar panel temperature , and are determined by (3) and (4). Then can also be determined by (2). Finally the relationship between and can be explicitly determined according to (1).

##### 3.2. Charging Efficiency Model

The charging efficiency is determined by , , the solar panel temperature , and :

Since the charging-discharging efficiency of supercapacitors is close to 100% in practice, we assume that it equals 100% in our model. In direct connection, equals and the charging current equals . So is where can be determined by combining (1), (2), (3), and (4) as , , and have been determined.

##### 3.3. Residual Energy Model

The residual energy of supercapacitors can be given by

##### 3.4. Discharging Efficiency Model

As the voltage range of supercapacitors is wide and the operating voltage of sensor nodes may be very different from (e.g., 0–2.7 V for supercapacitors, compared with 2.7–3.3 V for MicaZ), it is essential to adopt an output regulator between them. The DC-DC converter discharging efficiency () is determined by input voltage, output voltage (), and output current ():

We assume that the node operates in two states only, active (MCU active and radio on for MicaZ) and sleep states. In sleep state, the current is tens of and can be ignored. In the active state, the current as measured is about 22.3 , and is a fixed voltage (e.g., 3 V). So is

##### 3.5. Duty Cycle Model

Duty cycle () can be represented as where is the node active duration and is the node sleep duration. Therefore, the average power consumed by the node () is where and are the power consumptions of the active and sleep states, respectively. Since is small (about tens of compared with tens of of ), can be approximately represented as

##### 3.6. Leakage Model

The leakage power of a supercapacitor, , is mainly determined by , and it can be approximately represented as

#### 4. Duty Cycling Scheme Design

The duty cycle controller uses energy information (the models mentioned previously) and predicted solar energy to allocate duty cycles for several future slots, resulting in a maximum sum of duty cycles of those time slots.

##### 4.1. Problem Formulation

As shown in Figure 3, is the predicted interval consisting of successive time slots with the duration . We can choose a small (e.g., 30 minutes), during which the solar irradiance changes slightly. Hence, we can assume that the solar power keeps constant during a slot and use to represent the solar power in the th slot. Let be the duty cycle in slot . Then the residual energy of the supercapacitor in slot can be given by

Since it is difficult to know , and due to the changing , we divide into smaller time slots (s-slot) with a duration . must be small enough (e.g., 30 seconds), so the supercapacitor voltage can be considered almost constant over this duration. Let be the s-slot index, and let the tuple represent the beginning of the s-slot in slot . Then (14) can be replaced by (15).

According to (5), (7), (9), (12), (13), and (15), we have

Reference [1] presents a performance model in terms of system utility to the user. To maximize the system performance and save energy, we have where is the application that defined minimal duty cycle requirement and is the maximum duty cycle. Any assigned duty cycle that is larger than cannot improve the system performance further and will lead to more energy consumption.

In order to ensure the survival time of the node (work at until death), there should be some energy left in the supercapacitor at the end of every prediction interval. Meanwhile, the supercapacitor voltage should be higher than the startup voltage () of the DC-DC converter and smaller than the maximum operating voltage of the supercapacitor (). So we have

Then, we formalize *the cumulated duty cycle maximization problem* as follows:
where is the total available energy in the prediction interval . It equals the sum of the energy growth of the supercapacitor and energy consumption of the node. If and both equal , then the system achieves energy neutral operation (ENO) [18] and (21) can be rewritten as follows:

For a link, the previous optimization problems can be extended as data rate utility optimization problems. As shown in Figure 4, we assume that ABSink is a fixed routing in a data collection sensor network. Node A and node B collect and transmit their data to the sink. Then, *the data rate utility maximization problem* can be formalized as follows:
where and are the data rates of node A and node B in slot . and are the energetic costs to receive and to transmit one bit of data.

##### 4.2. Duty Cycle Assignment

According to (16) and (17), the determination of is an iterative process and cannot be expressed as linear combination of duty cycles under other slots. So the maximization problems previously mentioned can neither be solved by convex optimization nor standard linear programming. However, for quantized energy storage, the problem can be solved by a dynamic programming routine algorithm (see Algorithm 1).

In Algorithm 1, the supercapacitor voltage is quantized from to with the step . For every loop on line 2, the algorithm calculates the maximum cumulated duty cycle () for every quantized supercapacitor voltage in slot and stores the serial number as well as the duty cycle , corresponding to the maximum cumulated duty cycle, in two arrays and . Going “backwards”, Algorithm 1 determines a vector that maximizes the cumulated duty cycle, and the vector is the optimal energy allocation. The running time of Algorithm 1 is .

Since , , and (line 7 in Algorithm 1) vary with , it is hard to calculate these values if the slot time () is very large (e.g., tens of minutes). In fact, in most of the solar prediction algorithms is set to be tens of minutes. So we further divide every slot into small slots. Then, (in line 7) can be calculated as shown in Algorithm 2.

In Algorithm 2, is quantized from to with the step . The algorithm starts with and calculates for every loop on line 1. If there are some duty cycles which can make the supercapacitor voltages at the end of the slot () and equal, the algorithm will find the maximum duty cycle and assign it to . is a small threshold compared to (e.g., 0.01 V).

In order to solve the cumulated duty cycle maximization problem, we developed Algorithms 1 and 2. With the supercapacitor voltage being quantized from to , the constraint (20) is satisfied. The constraint (18) can be obtained in Algorithm 2. We denote that the cumulated duty cycle is maximized when inequality (19) is equality. (Suppose that is the optimum duty cycle vector of and is the optimum duty cycle vector of ; then can be a feasible vector of . It is quite obvious that is larger than . So, .) Then the cumulated duty cycle maximization problem can be solved by Algorithm 1 with its line 7 being replaced by Algorithm 2. Algorithm 2 runs in . Thus, to solve the cumulated duty cycle maximization problem, it runs time.

For quantized energy values and duty cycles, the data rate utility maximization problem can be solved by an extension of Algorithms 1 and 2. Over all that satisfy constraints of (24) and (25), the maximum utility can be determined as follows: Vectors and that maximize are the optimal, and it has the complexity to solve this problem. Solving this problem directly may be computationally expensive for nodes with limited capabilities. Instead, the nodes can determine their duty cycles (e.g., and ) independently using Algorithms 1 and 2, and then for each slot , under constraints (24) and (25), the nodes can adjust their rates to obtain a solution to . For example, if we use as the function of data rate utility, the subproblem solution is and .

#### 5. Numerical Results

This section provides numerical results that demonstrate the high performance of efficiency-aware. Models based on actual measurements described in Section 3 are used as inputs to the algorithms described in Section 4.

Figure 5 shows the - measurements and simulations of a solar panel under different temperatures and solar irradiance. Figure 6 shows - measurements and simulations of the same solar panel under the same conditions. As shown, the error between numerical simulation and measurement results is less than 6% when is smaller than the max power point voltage. Although the error will increase with the increase of temperature and solar irradiance when is larger than the max power point voltage, this can be avoided by choosing a suitable solar panel. In fact, to achieve higher charging efficiency, it is better to choose a solar panel with its max power point voltage under sufficient solar radiation (e.g., at noon in a sunny day) being close to the maximum operating voltage of the supercapacitor, and therefore can rarely be larger than the max power point voltage.

Previous works [12, 19] provide energy models of switching converters and MPPT circuits. For simplicity, we model the MPPT circuit in [12] using piecewise linear approximation based on real data measurements. However, it is worth noting that our efficiency-aware framework is not restricted to a specific model or a charging circle because our approach does not depend on the characteristics of the analysis model. The modeling results are shown in Figure 7. As shown, the charging efficiency of the MPPT circuit increases with the increasing of the supercapacitor voltage when the supercapacitor voltage is smaller than 1.8 V and decreases slowly as the voltage goes larger than 1.8 V. The variation trend of the efficiency is similar to the results of [11]. Moreover, our model is also quite accurate (the relative error is no more than 5% when the irradiance is low).

Figure 8 shows discharging efficiencies of two commonly used DC-DC converter chips (NCP1402 [22] and LTC3401 [21]), and Figure 9 shows the leakage of a 10 F and a 100 F supercapacitors. We also model the efficiencies of those two chips and the leakage of a 100 F supercapacitor based on the measurements. As shown, the discharging efficiencies and the leakage both increase with the increasing of the supercapacitor voltage, and the models are also of high accuracies (the relative error is no more than 2%).

Figure 10 shows the solar profile of three common winter days in Ashland [20]. The solar radiation is collected once every 5 minutes and has been converted into the power that can be harvested by the solar panel.

We evaluated two sets of data under four conditions. MPPT, NCP1402, and a 100 F supercapacitor were used in condition 1; direct connection, NCP1402, and a 100 F supercapacitor were used in condition 2; MPPT, LTC3401, and a 100 F supercapacitor were used in condition 3; and direct connection, LTC3401, and a 100 F supercapacitor were used in condition 4. In all the four conditions, V, , , and we ignored the influence of temperature and the prediction error of the solar radiation. , , and are 0.02 V, 1%, and 0.01 V. The prediction interval was set to be one day. and were 30 minutes and 30 seconds. Figure 11 shows cumulated active time of the fixed duty cycle scheme under these four conditions. As shown, the cumulated active time increases with increasing duty cycle and decreases after some duty cycle. The reason is that energy surplus is large when the duty cycle is low and high duty cycle leads to low working voltage and inefficiency. Figures 12, 13, 14, and 15 show the supercapacitor voltage and cumulated active time of efficiency-aware, leakage-aware, and the fixed duty cycle scheme over 72 hours under these four conditions. The fixed duty cycles were set to 0.76, 0.45, 0.8, and 0.51 where the fixed duty cycle scheme can achieve the max cumulated active time. Table 1 shows the numerical results. The results reveal that our algorithms can achieve 56% more cumulated time than leakage-aware and 60% more cumulated time than fixed duty cycle scheme. In Figures 12(a), 13(a), 14(a), and 15(a), the supercapacitor voltages of our algorithms at 24th, 48th, and 72nd hour are 1.5 V, compared with 1.25 V of leakage-aware and 1 V with fixed duty cycle scheme. It illustrates that our algorithms perform better in terms of sustainable operation (e.g., nodes never run out of energy in their supercapacitors). It worth noting that leakage-aware uses leakage as the only factor in determining nodes’ life, while a practical one should consider the minimum requirements of the application as well as leakage.

Figure 16 shows the results for the data rate determination problems under condition 1. The solar profile of the first day was used as the energy input for both nodes A and B, and was used as the utility function of data rate (). We assumed that both and are 50 nJ/bit.

#### 6. Conclusion

In this paper, we study how to optimize the energy efficiency of photovoltaic-supercapacitor systems for sustainable node operations in solar-powered wireless sensor networks. Based on realistic power model of every hardware component, we present energy-aware, the first duty cycling framework that maximizes the energy utilization efficiency of the whole photovoltaic-supercapacitor system. We consider all possible energy losses in the energy conversion process (charging, discharging, and leakage) and model all the power models of a commonly used photovoltaic-supercapacitor energy systems. We have proposed a general duty cycling optimization problem and associated efficient algorithms to solve the problem. Numerical results show that our approach performs better than fixed duty cycling schemes and leakage-aware [4], a state-of-art duty cycling scheme for photovoltaic-supercapacitor systems. As efficiency-aware is model-independent, it can be used in most of photovoltaic-supercapacitor energy systems with predictable harvesting energy for local power management or to meet other QoS requirements in wireless sensor networks.

#### References

- A. Kansal, J. Hsu, M. Srivastava, and V. Raqhunathan, “Harvesting aware power management for sensor networks,” in
*Proceedings of the 2006 43rd ACM/IEEE Design Automation Conference*, pp. 651–656, September 2006. - C. Park and P. H. Chou, “AmbiMax: autonomous energy harvesting platform for multi-supply wireless sensor nodes,” in
*Proceedings of the 3rd Annual IEEE Communications Society on Sensor and Ad hoc Communications and Networks (SECON '06)*, vol. 1, pp. 168–177, September 2006. View at Publisher · View at Google Scholar · View at Scopus - W. S. Wang, T. O'Donnell, N. Wang, M. Hayes, B. O'Flynn, and C. O'Mathuna, “Design considerations of sub-mw indoor light energy harvesting for wireless sensor systems,”
*ACM Journal on Emerging Technologies in Computing Systems*, vol. 66, no. 2, pp. 1–6, 2008. - T. Zhu, Z. Zhong, Y. Gu, T. He, and Z. L. Zhang, “Leakage-aware energy synchronization for wireless sensor networks,” in
*Proceedings of the 7th ACM International Conference on Mobile Systems, Applications, and Services (MobiSys '09)*, pp. 319–332, ACM, Kraków, Poland, June 2009. View at Publisher · View at Google Scholar · View at Scopus - G. W. Challen, J. Waterman, and M. Welsh, “Poster abstract: integrated distributed energy awareness for wireless sensor networks,” in
*Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems (SenSys '09)*, pp. 381–382, ACM, Berkeley, Calif, USA, November 2009. View at Publisher · View at Google Scholar · View at Scopus - K.-W. Fan, Z. Zheng, and P. Sinha, “Steady and fair rate allocation for rechargeable sensors in perpetual sensor networks,” in
*Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems (SenSys '08)*, pp. 239–252, ACM, Raleigh, NC, USA, November 2008. - M. Gorlatova, A. Wallwater, and G. Zussman, “Networking low-power energy harvesting devices: measurements and algorithms,” in
*Proceedings of the IEEE International Conference on Computer Communications (INFOCOM '11)*, pp. 1602–1610, April 2011. View at Publisher · View at Google Scholar · View at Scopus - L. Huang and M. J. Neely, “Utility optimal scheduling in energy harvesting networks,” in
*Proceedings of the 12th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc '11)*, p. 21, ACM, Paris, France, May 2011. - I. C. Paschalidis and R. Wu, “Robust maximum lifetime routing and energy allocation in wireless sensor networks,” vol. 2012, Article ID 523787, 14 pages, 2012. View at Publisher · View at Google Scholar
- C. Alippi and C. Galperti, “An adaptive system for opimal solar energy harvesting in wireless sensor network nodes,”
*IEEE Transactions on Circuits and Systems I*, vol. 55, no. 6, pp. 1742–1750, 2008. View at Publisher · View at Google Scholar · View at Scopus - D. Brunelli, L. Benini, C. Moser, and L. Thiele, “An efficient solar energy harvester for wireless sensor nodes,” in
*Proceedings of the Design, Automation and Test in Europe Conference (DATE '08)*, pp. 104–109, Munich, Germany, March 2008. View at Publisher · View at Google Scholar · View at Scopus - D. Dondi, A. Bertacchini, D. Brunelli, L. Larcher, and L. Benini, “Modeling and optimization of a solar energy harvester system for self-powered wireless sensor networks,”
*IEEE Transactions on Industrial Electronics*, vol. 55, no. 7, pp. 2759–2766, 2008. View at Publisher · View at Google Scholar · View at Scopus - Y. Kim, N. Chang, Y. Wang, and M. Pedram, “Maximum power transfer tracking for a photovoltaic-supercapacitor energy system,” in
*Proceedings of the 16th ACM/IEEE International Symposium on Low-Power Electronics and Design (ISLPED '10)*, pp. 307–312, August 2010. View at Publisher · View at Google Scholar · View at Scopus - V. Raghunathan, A. Kansal, J. Hsu, J. Friedman, and M. Srivastava, “Design considerations for solar energy harvesting wireless embedded systems,” in
*Proceedings of the 4th International Symposium on Information Processing in Sensor Networks (IPSN '05)*, pp. 457–462, April 2005. View at Publisher · View at Google Scholar · View at Scopus - D. R. Cox, “Prediction by exponentially weighted moving averages and related methods,”
*Journal of the Royal Statistical Society B*, vol. 23, no. 2, pp. 414–422, 1961. - C. Bergonzini, D. Brunelli, and L. Benini, “Comparison of energy intake prediction algorithms for systems powered by photovoltaic harvesters,”
*Microelectronics Journal*, vol. 41, no. 11, pp. 766–777, 2010. View at Publisher · View at Google Scholar · View at Scopus - K. S. P. Kiranmai and M. Veerachary, “Maximum power point tracking: a PSPICE circuit simulator approach,” in
*Proceedings of the 6th International Conference on Power Electronics and Drive Systems (PEDS '05)*, vol. 2, pp. 1072–1077, December 2005. View at Scopus - A. Kansal, J. Hsu, S. Zahedi, and M. B. Srivastava, “Power management in energy harvesting sensor networks,”
*ACM Transactions on Embedded Computing Systems*, vol. 6, no. 4, article 32, 2007. - Y. Choi, N. Chang, and T. Kim, “DC-DC converter-aware power management for low-power embedded systems,”
*IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems*, vol. 26, no. 8, pp. 1367–1381, 2007. View at Publisher · View at Google Scholar · View at Scopus - University of Oregon Solar Radiation Monitoring Laboratory, “Solar data,” http://solardat.uoregon.edu/SelectArchival.html.
- Linear Technology. LTC3401 1A, 3 MHz micropower synchronous boost converter, http://www.linear.com/product/LTC3401.
- NCP1402. 200 mA, PFM Step-Up Micropower Switching Regulator, http://www.onsemi.cn/publink/Collateral/NCP1402-D.PDF.