Investigations on Active Balancing Circuits for Supercapacitor Banks

Ionescu, Ciprian; Drumea, Andrei; Vasile, Alexandru; Codreanu, Norocel

doi:10.1109/isse.2018.8443679

Cited by 16 publications

(4 citation statements)

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“…49 There are a variety of ways to implement an active method; applying switched-resistors, switched-capacitors or DC-DC converters across the SC cells are some of these approaches. 50 In this paper, we have introduced a novel active balancing method based on a Zener diode. However, instead of placing them in parallel with each of the SC cells, they are placed in series with controllable switches before getting into the parallel configuration, as it is depicted in Figure 8.…”

Section: Voltage Balancing Techniquesmentioning

confidence: 99%

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Modeling and implementation of a novel active voltage balancing circuit using deep recurrent neural network with dropout regularization

Noohi

Faraji

Sadrossadat

et al. 2022

Circuit Theory & Apps

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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 asexamples. 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.

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Section: Voltage Balancing Techniquesmentioning

confidence: 99%

“…To avoid such losses, active balancing methods are proposed 49 . There are a variety of ways to implement an active method; applying switched‐resistors, switched‐capacitors or DC‐DC converters across the SC cells are some of these approaches 50 …”

Section: Hybrid Energy Harvesting and Its Implementationmentioning

confidence: 99%

Modeling and implementation of a novel active voltage balancing circuit using deep recurrent neural network with dropout regularization

Noohi

Faraji

Sadrossadat

et al. 2022

Circuit Theory & Apps

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“…This balancing circuit directly transfers the energy from higher capacitive energy storage cells to lower energy storage cells in the string and overcomes the drawback of voltage difference between the cells 40 . Overall, implementing the balancing with the transformer is costly, 41 switched capacitor approach is complex and time consuming as the large number of switches are needed, 42 utilizing the DC – DC converters is an efficient trend though the issue is its complex and costly implementation 43,44 . As mentioned above, most of the reported methods in the literature are focused on balancing series connected cells.…”

Section: Piezoelectric Eh Systemmentioning

confidence: 99%

Modeling and implementation of nonlinear boost converter using local feedback deep recurrent neural network for voltage balancing in energy harvesting applications

Noohi

Mirvakili

Sadrossadat

2021

Circuit Theory & Apps

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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.

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“…The control system or the Battery Management System (BMS) [1] has the task to charge and discharge them and manufactured a battery pack without any damage due to over voltages or over currents. Nowadays all BMSs use active voltage balancing techniques based on different methods [2]- [7].…”

Section: Introductionmentioning

confidence: 99%

Multicriterial evaluation and optimization of an algorithm for charging energy storage elements

Kishkin¹,

Arnaudov²,

Todorov³

et al. 2021

Position and Communication Papers of the 16th Conference on Computer Science and Intelligence Systems

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Investigations on Active Balancing Circuits for Supercapacitor Banks

Cited by 16 publications

References 0 publications

Modeling and implementation of a novel active voltage balancing circuit using deep recurrent neural network with dropout regularization

Modeling and implementation of a novel active voltage balancing circuit using deep recurrent neural network with dropout regularization

Modeling and implementation of nonlinear boost converter using local feedback deep recurrent neural network for voltage balancing in energy harvesting applications

Multicriterial evaluation and optimization of an algorithm for charging energy storage elements

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