Active cell voltage balancing of Electric vehicle batteries by using an optimized switched capacitor strategy

Singirikonda, Srinivas; Obulesu, Y. P.

doi:10.1016/j.est.2021.102521

Cited by 53 publications

(13 citation statements)

References 9 publications

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“…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 In this paper, we have introduced a novel active balancing method based on a Zener diode.…”

Section: Voltage Balancing Techniquesmentioning

confidence: 99%

“…The passive method employs identical resistors, or Zener diodes that are placed across the cells, and accordingly, it is associated with considerable losses. 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%

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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%

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

View full text Add to dashboard Cite

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“…From the hardware perspective, the energy from the cells with the highest SOCs or voltages is stored in auxiliary energy storage components such as inductors [21,22], capacitors [23,24], and transformers [25,26]. Then, this energy is injected as "balancing current" to the cells with the lower SOCs or voltages by using controllable power electronic circuits.…”

Section: Introductionmentioning

confidence: 99%

Robust control of a forward‐converter active battery cell balancing

Abareshi

Hamzeh

Farhangi

et al. 2023

IET Power Electronics

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Control of balancing current is important for the safety of battery cells and active cell balancing (ACB) power electronics. This paper presents a method, based on quantitative feedback theory (QFT), for robust control of balancing current despite uncertainties, which exist in the battery cells' and power electronics' dynamical models. A remarkable feature of QFT is its interactive graphical design environment, which gives useful insights for the selection of desired robust stability and performance specifications, controller structure, and parameters tuning. Without loss of generality, this paper describes the QFT‐based balancing current robust control system design for a forward‐converter‐based ACB. This paper also presents an average model of the forward‐converter‐based ACB circuit, operating in more than two modes; a case that has not been addressed in literatures. The effectiveness of the proposed QFT‐based balancing current robust control system is evaluated experimentally.

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“…Second, considering the circuit topology, balancing circuits can be classified into six types: cell bypass, cell-to-cell, cell-to pack, pack-to-cell, cell-to-pack-to-cell and proactive designs [25]- [27]. Several sophisticated active balancing methods, based on capacitive [19], [28]- [31] or inductive [32]- [35] charge transfer mechanisms can be found. Furthermore, in [35], [36] and [37] different transformer or converter based balancing topologies are shown, respectively.…”

Section: Introductionmentioning

confidence: 99%

Efficient Battery Cell Balancing Methods for Low-Voltage Applications: A Review

Abdul-jabbar

Kersten

Mashayekh

et al. 2022

2022 IEEE International Conference in Power Engineering Application (ICPEA)

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Battery balancing technologies are a crucial mechanism for the safe operation of electrochemical energy storage systems, such as lithium-ion batteries. Moreover, balancing between battery cells is essential for battery systems' life. Without any balancing circuitry, individual cell voltages can reach their maximum/minimum battery voltage limit faster than others, posing safety hazards. Furthermore, battery capacity reduction can occur when overcharging/over-discharging individual cells. So far, many balancing methodologies have been proposed and discussed in available literature. This paper presents a review of different state-of-the-art cell balancing methods suitable for low voltage applications. The required control complexity, switch stress, balancing speed, cost and circuit size are considered as key aspects. Typically, cell bypass techniques, such as passive balancing, have the lowest cost and require no complex control strategies. In contrast, cell-to-cell balancing techniques can significantly increase the energy efficiency compared to cell bypass balancers, but these come with higher system costs and control complexity.

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Active cell voltage balancing of Electric vehicle batteries by using an optimized switched capacitor strategy

Cited by 53 publications

References 9 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

Robust control of a forward‐converter active battery cell balancing

Efficient Battery Cell Balancing Methods for Low-Voltage Applications: A Review

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