Photovoltaic (PV) panels are increasingly used to convert sunlight into electricity, as a source of sustainable energy. It can be used in a wide variety of applications ranging from the well-known power generation to the emerging energy harvesting in Internet of Things (IoT). Hence, an accurate model is required to evaluate and predict the performance of the PV panel. However, the non-linear characteristics of PV panels make the modeling of their electrical response a challenging task. In the literature, most of the previous PV models have been developed for large wattage PVs under high irradiance, or for small wattage PVs under lower irradiance. Those that can model both usually require more information, including the I-V curves data at different irradiances, which is not always provided by the manufacturers. Therefore, this paper presents a simple PV modeling that can be applied for different wattage panels at different operating levels of irradiance, using only the commonly provided datasheet values at standard test condition (STC). The model uses the characteristic points translation technique to translate the short circuit current, open circuit voltage and maximum power voltage points, at STC to other operating conditions. These translated values are then used by the parameter extraction technique to extract the model's parameters. The proposed model's techniques can model the losses across the resistors at low irradiance, which reduces the error. The accuracy of the proposed model is validated using two representative commercial PV panels. Results are generated for the proposed model and other comparative works. The results show that the proposed model can improve the accuracy over the other compared works, with a consistent percentage difference of below 5% across all levels of irradiances for both panels.

Modeling the behavior of the battery is non-trivial. Nevertheless, an accurate battery model is required in the design and testing of systems such wireless sensor network (WSN) and internet of things (IoT). This paper presents the one resistive-capacitance (1RC) battery model with simple parameterization technique for nickel metal hydride (NiMH). This model offers a good trade-off between accuracy and parameterization effort. The model’s parameters are extracted through the pulse measurement technique and implemented in a physical and dynamic simulator. Finally, the performance of the model is validated with the real-life NiMH battery by applying current pulses and real wireless sensor node current profiles. The results of the voltage response obtained from both the model and experiments showed excellent accuracy, with difference of less than 2%.

The use of photovoltaic (PV) panels in interior spaces is expected to increase due to the proliferation of low-power sensor devices in the IoT domain. PV models are critical for estimating the I–V curves that define their performance at various light intensities. These models and the extraction of their parameters have been extensively studied under outdoor conditions, but their indoor illumination performance is less studied. With respect to the latter, several studies have used the parameter-scaling technique. However, the model’s accuracy degrades when the light level decreases. In this study, we propose a simple PV modeling technique that can be applied at various illuminance levels by only using characteristic points (short-circuit current, open-circuit voltage, and maximum-power voltage points) at a reference illumination level. The model uses the characteristic point translation technique to translate the reference characteristic points to other operating conditions. Then, parameter extraction technique is used to extract the model’s parameters. The proposed model’s accuracy is verified using two commercial PV panels and different indoor lighting technologies. The results indicate that the proposed model outperforms the other examined works in terms of accuracy, with an average improvement of 15.75%.

Currently, there is lack of implementation of practical power-saving schemes in most of the batteries powered by IoT smart city applications available in the market that can extend the battery lifetime, even though numerous researches have been carried to reduce the average power consumption. This is because electronics consume similar amounts of power during the idling state as compared to the active state, resulting in low power efficiency of the application. Thus, power consumption is affected by the modes of the electronic operations. Different electronics also have their own types of settings that can be configured to reduce the power consumption, and this will be further investigated in this study. This chapter will address the issue of how to create a power-saving IoT application by applying power-saving schemes and creating an accurate model to predict the battery lifetime of the IoT application.