Equalization of Lithium-Ion Battery Pack Based on Fuzzy Logic Control in Electric Vehicle

Ma, Yan; Duan, Ping; Sun, Yanshuai; Chen, Hong

doi:10.1109/tie.2018.2795578

Cited by 163 publications

(86 citation statements)

References 28 publications

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“…In the traditional Buck‐Boost circuit, the adjacent batteries form an equalization module, which uses inductance to transmit energy, as shown in the red box in Figure . The structure is simple and the equalization efficiency is high.…”

Section: Equalization Circuit Configurationmentioning

confidence: 99%

“…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. However, when the nonadjacent batteries in the circuit need to be equalized, they need to transfer energy through the middle batteries, which increases the equalization time and energy loss.…”

Section: Introductionmentioning

confidence: 99%

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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: Equalization Circuit Configurationmentioning

confidence: 99%

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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“…This approach has significant advantages in energy consumption and application prospects. Therefore, the active cell balancing has received considerable attention and is also a method that is currently studied …”

Section: Introductionmentioning

confidence: 99%

“…In addition, although the capacitor‐based balancing circuit is simple, the control strategy is complicated. Second, the inductor‐based balancing circuit uses the inductor to store electrical energy to perform energy balancing . The traditional inductor‐based balancing circuit only enables energy to be transferred between adjacent battery cells, and the energy of each cell in the entire battery pack tends to be uniform after multiple cycles.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the active cell balancing has received considerable attention and is also a method that is currently studied. [11][12][13][14][15][16][17][18][19][20][21] According to different energy transfer components and the balancing strategy, the active cell balancing circuit can be divided into three types. First, the capacitor-based balancing circuit achieves equalization by charging and discharging the capacitor that refers to the voltage difference between the battery cells.…”

Section: Introductionmentioning

confidence: 99%

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Active cell balancing of lithium‐ion battery pack based on average state of charge

Zhang

et al. 2020

Int J Energy Res

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Summary Differences in the environment and parameters of lithium‐ion battery (LiB) cells may lead the residual capacity between the battery cells to be inconsistent, and the battery cells may be damaged due to overcharging or overdischarging. In this study, an active balancing method for charging and discharging of LiB pack based on average state of charge (SOC) is proposed. Two different active balancing strategies are developed according to the different charging and discharging states of LiB pack. When the LiB pack is charging, charging balance strategy is performed, wherein the battery cells whose SOC is higher than the average SOC of the LiB pack are balanced to increase the charging capacity of the entire LiB pack. When the LiB pack is discharging or static standing, discharging balance strategy is performed, wherein the batter cells whose SOC is lower than the average SOC of the LiB pack are balanced to increase the discharging capacity of the entire LiB pack. The experimental results show that the proposed active balancing method can reduce the inconsistency of residual energy between the battery cells and improve the charging and discharging capacity of the LiB pack.

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Mitigating unbalance and improving voltage considering higher penetration of EVs and DG using hybrid optimization technique

Saravanan

Kannayeram

Muniraj³

2021

Int Trans Electr Energ Syst

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Summary In this manuscript, an efficient hybrid method is proposed aimed at mitigating imbalance, improving the voltage in the higher penetration of electric vehicles (EVs) and distributed generation (DG). The proposed hybrid method is the combination of the mayfly algorithm (MA) and the radial basis function neural network (RBFNN), called the mayfly algorithm‐radial basis function neural network (MA‐RBFNN) method. These major contributions of the proposed work mitigate network unbalancing and voltage profile with the loss of network energy, utilize obtainable network capacity and enlarge the comfort level for EV consumers. The MA is utilized for solving the multi‐objective optimization issue of coordinating EV together with DG in the distribution grid. The RBFNN agrees with the distribution system operators to mutually enhance the phase sequence with the optimum dispatch of DGs for improving the performance of the network. The proposed system allows distribution system operators to mutually enhance the phase sequence with an optimum DG dispatch for recovering the performance of the network and solve the multi‐objective optimization issue of coordinating EV and DG on the distribution network. This proposed technique is implemented on the MATLAB/Simulink platform as well as evaluated for efficiency, likened to the existing methods such as genetic algorithm (GA), particle swarm optimization (PSO) and MA technique. The performance of the proposed method was analysed in terms of quality of solution comparison, integral of the time‐weighted absolute error (ITAE), integral squared error (ISE) and loss reduction (%). These comparative outcomes display the eminence of the proposed method more than any other existing technique.

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Equalization of Lithium-Ion Battery Pack Based on Fuzzy Logic Control in Electric Vehicle

Cited by 163 publications

References 28 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

Active cell balancing of lithium‐ion battery pack based on average state of charge

Mitigating unbalance and improving voltage considering higher penetration of EVs and DG using hybrid optimization technique

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