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This paper implements a simultaneous solar and thermal energy harvesting system, as a hybrid energy harvesting (HEH) system, to convert ambient light into electrical energy through photovoltaic (PV) cells and heat absorbed in the body of PV cells. Indeed, a solar panel equipped with serially connected thermoelectric generators not only converts the incoming light into electricity but also takes advantage of heat emanating from the light. In a conventional HEH system, the diode block is used to provide the path for the input source with the highest value. In this scheme, at each time, only one source can be handled to generate its output, while other sources are blocked. To handle this challenge of combining resources in HEH systems, this paper proposes a method for collecting all incoming energies and conveying its summation to the load via the current mirror cells in an approach similar to the maximum power point tracking. This technique is implemented using off‐the‐shelf components. The measurement results show that the proposed method is a realistic approach for supplying electrical energy to wireless sensor nodes and low‐power electronics. [ABSTRACT FROM AUTHOR]

Summary: Recurrent neural networks (RNN) emerged as powerful tools to model and analyze the nonlinear behavior of electronic circuits accurately and quickly. Efforts to improve the accuracy of RNN will lead to the design of better‐quality products, which is essential in various fields such as the design of energy harvesting (EH) systems. EH techniques can provide the electrical energy needed for low‐power electronics without the need for a battery or with minimal dependency. Due to the importance of the active voltage balancing circuit in EH systems, we have proposed a new macromodeling method called dropout local‐feedback deep recurrent neural network (Dropout‐LFDRNN) to model and analyze this circuit along with two other nonlinear circuits as examples. This technique is an advance over the LFDRNN macromodeling method, and based on the obtained results from the measurements, we were able to build a fast macromodel for the active balancing circuit, which outperforms conventional deep recurrent neural network modeling method in terms of accuracy without sacrificing speed. It is worth mentioning that this proposed technique in this paper can be considered a viable approach for modeling and analysis of nonlinear electronic components and circuits. In addition to the advantage of generating a more accurate model, the model based on the Dropout‐LFDRNN approach is much faster than the existing transistor‐level models. [ABSTRACT FROM AUTHOR]

Balancing the voltage of series connected supercapacitors is a necessity. Various passive and active balancing techniques are reported for alleviating the problems of leakage and overvoltage. In this paper, a novel active balancing approach based on boost converter is presented leading to the implementation of a piezoelectric energy harvesting (EH) system. Besides, this nonlinear boost converter is designed, implemented, and modeled using a new macromodeling approach. In this regard, data measured by the implemented boost converter passed through local feedback deep recurrent neural networks (LFDRNNs), in order to model the nonlinear behavior of this converter, and this model can be used to design the EH system. LFDRNN can be trained directly using the input–output waveform samples of the main circuit without knowing its internal details, and the obtained model has similar accuracy compared to the original circuit. The main focus of this paper is the new LFDRNN macromodeling method which is associated with the boost converter‐based active balancing technique. Our experimental results show that LFDRNN extends the ability of conventional neural network‐based models to express the dynamic behavior of nonlinear circuits while increasing the accuracy. Additionally, LFDRNN‐based models are much faster than existing models in simulation tools. [ABSTRACT FROM AUTHOR]