Fast two‐stage charge equaliser based on state‐of‐charge (SOC) balancing for series‐connected supercapacitors

Luo, Cheng; Zhao, Jinbo; Ma, Siyuan; Zha, Ming; Xiong, Qiaopo; Luo, Zhiqing; He, Jie; Yang, Miao; Long, Gen; Liu, Jian-Feng; Bao, Jiayi

doi:10.1049/joe.2018.8565

Cited by 3 publications

(1 citation statement)

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“…A self‐voltage balancing methodology in the structure of a switched‐capacitor multilevel inverter is reported in Kumari et al 38 which is focused on reducing the number of voltage sources while supplying different types of loads. An equalizing circuit composed of single‐input multiple‐output synchronous rectification is reported in Luo et al 39 Indeed, this method is the mixture of active voltage balancing circuit together with the state‐of‐charge (SOC) balancing strategy of the module which offers fast balancing speed and provides a uniform state of all the cell voltages 39 . In addition, an energy nondissipative or active voltage balancing circuit is proposed in Ahasan Habib et al, 40 which is based on single switched‐capacitor and series LC resonant converter.…”

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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