**2. Proposed method**

*A Guide to Small-Scale Energy Harvesting Techniques*

frequencies, the measured power is about 0 and −20 dB.

from these sources is in the order of 1 to 10\_\_\_\_

*W cm*<sup>2</sup>

**1.1 Preprocessing in energy harvesting system**

and is in the order of 50\_\_\_\_

exploited in this chapter:

Examples of these transceivers in today's world are Frequency Modulation (FM)

By saying RF energy harvesting, we mean that we capture the energy from the RF signal existing in the ambient and transform this power into DC power and using it for supplying battery. Passive ambient RF energy harvesting is exactly defined as this procedure [7]. In this case, sources can be FM radio, Wi-Fi, DTV or military communication transmitters [8] and the amount of energy harvested

> *W cm*<sup>2</sup>

[9]. One example for this category is RFID chips [10].

energy harvesting, i.e. RF energy harvesting from a dedicated source. In this scenario, the amount of harvested energy is higher comparing to passive ambient case

Spectrum is a scarce source. In wireless communication systems, efforts have been made to use frequency spectrum with policies and priorities in order to

maximize the spectrum efficiency. The main idea here is to allocate empty spectrum holes over time, frequency and space to secondary users while the interference with primary user is minimum. Several approaches are proposed for spectrum sensing, such as energy detection [11–13], matched filter [11, 12, 14], cyclostationary detection [15, 16], spectrum sensing based on covariance matrix [17, 18] and wavelet based spectrum sensing [19]. By studying energy detection, it can be understood that this approach is based on detecting the signal power such a way that secondary users detect the signal power received from primary users. Then they compare it to some predefined threshold level and then decide whether they can use the primary frequency band or not. Well, here is the novel preprocessing idea which we are

"In RF energy harvesting, we use RF signal power and convert it to DC power for charging batteries. On the other hand, energy detection algorithms give us the

*DTV signal spectrum measured in Tokyo City (left side graph) and Cellular signal spectrum measured in* 

[9]. Also there is another type of RF

radio, Analog TV (ATV), Digital TV (DTV), mobile and cellular networks and Wi-Fi. To have a more clear understanding of the issue and seeing RF signals as a energy source, in **Figure 1**, DTV and cellular signal spectrums for Tokyo City and Yokohama City are indicated [6]. As it can be seen in this figure, for some certain

**96**

**Figure 1.**

*Yokohama City (right side graph) [6].*

Our proposed system is indicated in **Figure 2**. As it is indicated in this figure, by preprocessing stage, the frequency containing the high amount of energy is selected. After that, this signal is selected as the input of matching circuit and rectified. Then a DC-DC converter circuit is used to level up the DC signal and finally it is fed to the battery for charging.

## **2.1 Battery model**

For simulation stage and performance evaluation of our proposed system, we must be able to model the battery that we intend to charge. There are various battery models with different structures and complexities. Electrochemical models [20–22] are usually used for battery physical design, performance and power generation optimization. Mathematical models [23–26], are much more effective. Random events for predicting battery systematic behaviors like battery life time and efficiency are discussed using mathematical equations. Electrical models [27–33] are placed somewhere between mathematical and chemical models in terms of accuracy and utilize the combination of voltage sources, resistors and capacitors.

**Figure 2.** *Schematic of proposed system.*

**Figure 3.** *Battery model [34].*

For the goal of this chapter, an accurate and effective battery model is proposed based on battery model proposed in [34]. This model provides an easy extraction procedure, gives run time, static and transient responses and also contains all of the electrodynamic characteristics of the batteries. **Figure 3** shows this proposed model.
