An Automatic Equalizer Based on Forward–Flyback Converter for Series-Connected Battery Strings

Shang, Yunlong; Xia, Bing; Cui, Naxin; Yang, Jufeng; Mi, Chris

doi:10.1109/tie.2017.2674617

Cited by 163 publications

(77 citation statements)

References 39 publications

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“…The dissipation equalization method uses parallel resistors in each single battery for discharge equalization, but has the disadvantages of large energy consumption, slow equalization speed, low efficiency, and large heat dissipation . The nondissipation equalization method uses a suitable energy storage component or an energy transfer circuit to transfer energy from one single battery to another, with low energy consumption, fast equalization, and high efficiency . The traditional Buck‐Boost circuit belongs to nondissipative equalization circuit, which uses inductance to equalize adjacent batteries.…”

Section: Introductionmentioning

confidence: 99%

Equalization of series connected lithium‐ion batteries based on back propagation neural network and fuzzy logic control

Wang

Qin²,

Zhao

et al. 2020

Int J Energy Res

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Summary In this article, a nondissipative equalization scheme is proposed to reduce the inconsistency of series connected lithium‐ion batteries. An improved Buck‐Boost equalization circuit is designed, in which the series connected batteries can form a circular energy loop, equalization speed is improved, and modularization is facilitated. This article use voltage and state of charge (SOC) together as equalization variables according to the characteristics of open‐circuit voltage (OCV)‐SOC curve of lithium‐ion battery. The second‐order RC equivalent circuit model and back propagation neural network are used to estimate the SOC of lithium‐ion battery. Fuzzy logic control (FLC) is used to adjust the equalization current dynamically to reduce equalization time and improve efficiency. Simulation results show that the traditional Buck‐Boost equalization circuit and the improved Buck‐Boost equalization circuit are compared, and the equalization time of the latter is reduced by 34%. Compared with mean‐difference algorithm, the equalization time of FLC is decreased by 49% and the energy efficiency is improved by 4.88% under static, charging and discharging conditions. In addition, the proposed equalization scheme reduces the maximum SOC deviation to 0.39%, effectively reducing the inconsistency of batteries.

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Section: Introductionmentioning

confidence: 99%

Equalization of series connected lithium‐ion batteries based on back propagation neural network and fuzzy logic control

Wang

Qin²,

Zhao

et al. 2020

Int J Energy Res

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show abstract

“…The battery equalizing technology plays a vital role in the performance improvement for lithium-ion batteries. Nowadays, there are two main battery-balancing methods: passive equalizing and active equalizing [7][8][9][10]. The passive equalizing method is that the energy of higher-power batteries is dissipated by one simple resistive circuit, and the energy is wasted in passive equalizing [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

An Active and Passive Hybrid Battery Equalization Strategy Used in Group and between Groups

Gao

Liang

et al. 2020

Electronics

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Active battery equalization and passive battery equalization are two important methods which can solve the inconsistency of battery cells in lithium battery groups. In this paper, a new hybrid battery equalization strategy combinfigureing the active equalizing method with a passive equalizing method is proposed. Among them, the implementation of the active equalizing method uses the bidirectional Flyback converter and Forward converter. This hybrid equalizing strategy adopts the concept of hierarchical equilibrium: it can be divided into two layers, the top layer is the equalization between groups, and the bottom layer is the equalization of group. There are three active equilibrium strategies and one passive equilibrium strategy. For verification purposes, a series of experiments were conducted in MATLAB 2018b/Simulink platform. The simulation and experiment results show that this hybrid battery equalizing method is efficient and feasible.

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“…In order to equalize the cells, a large number of battery equalization circuits have been proposed [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. These circuits can be divided into two types: dissipative circuits and non-dissipative circuits [4].…”

Section: Introductionmentioning

confidence: 99%

“…The advantages of this circuit are small volume, simple control and low cost. As shown in Figure 2, the number of the energy storage components are about half of the number of the batteries in the second type [4,[7][8][9][10]. This type of circuit has several cells working at the same time and the equalization current is higher than the first type, so its equalization speed is faster than the first type.…”

Section: Introductionmentioning

confidence: 99%

A Novel Lithium Battery Equalization Circuit with Any Number of Inductors

Kang

Luo

et al. 2019

Energies

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Even with the same voltage level, different types of battery packs have different requirements for the volume of the battery equalization circuit. However, most equalization circuits have the same problem: the volume of the equalization circuit is fixed once the voltage level of the battery pack is determined. In order to solve this problem, this paper proposes a novel lithium battery equalization circuit with any number of inductors (ECANI). It can select any number of inductors less than half the number of batteries, even when the voltage level of the battery pack is determined. Simulation and experiments are used to verify the performance of the equalization circuit. The current error and the average final voltage error in the experiment are 1.69% and 0.33% lower than those in the simulation, respectively. So the circuit can achieve equalization with good accuracy.

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An Automatic Equalizer Based on Forward–Flyback Converter for Series-Connected Battery Strings

Cited by 163 publications

References 39 publications

Equalization of series connected lithium‐ion batteries based on back propagation neural network and fuzzy logic control

Equalization of series connected lithium‐ion batteries based on back propagation neural network and fuzzy logic control

An Active and Passive Hybrid Battery Equalization Strategy Used in Group and between Groups

A Novel Lithium Battery Equalization Circuit with Any Number of Inductors

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