Analysis and Optimization of Star-Structured Switched-Capacitor Equalizers for Series-Connected Battery Strings

Shang, Yunlong; Cui, Naxin; Duan, Bin

doi:10.1109/tpel.2017.2787909

Cited by 103 publications

(52 citation statements)

References 36 publications

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“…There are three commonly used equalization indicators: battery voltage, battery capacity, and state of charge (SOC) . Since the battery voltage can be easily measured, several studies use it as the equalization variable. However, it is easily affected by the internal parameters of the battery and long‐term battery usage will lead to unstable equalization control performance.…”

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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“…Lithium-ion batteries have been widely deployed in electric vehicles (EVs) and energy storage systems of power grids due to their high energy/power density, no memory effect and long lifespan [1]. However, with the cyclic charging and discharging operations, battery's capacity degradation and electrical performance deterioration can influence vehicle operation performance and safety.…”

Section: Introductionmentioning

confidence: 99%

Capacity Prediction and Validation of Lithium-Ion Batteries Based on Long Short-Term Memory Recurrent Neural Network

Chen

Xue

et al. 2020

IEEE Access

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Capacity prediction of lithium-ion batteries represents an important function of battery management systems. Conventional machine learning-based methods for capacity prediction are inefficient to learn long-term dependencies during capacity degradations. This paper investigates the deep learning method for lithium-ion battery's capacity prediction based on long short-term memory recurrent neural network, which is employed to capture the latent long-term dependence of degraded capacity. The neural network is adaptively optimized by the Adam optimization algorithm, and the dropout technique is exploited to prevent overfitting. Based on the offline cycling aging data of batteries, the capacity prediction performance is validated and evaluated. The experimental results demonstrate that the proposed algorithm can accurately track the nonlinear degradation trend of capacity within the whole lifespan with a maximum error of only 2.84%. INDEX TERMS Lithium-ion batteries, capacity prediction, aging factors, long short-term memory (LSTM).

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Section: Introductionmentioning

confidence: 99%

“…Depending on the storage and energy transmission components, equalization on series battery pack is mainly divided into resistor equalizer [3], capacitor equalizer [4], LC oscillating circuit equalizer [5], transformer equalizer [6], and inductor equalizer [7][8][9]. Among them, the resistor equalizer [3] consumes energy and dissipates heat, which cannot meet the requirements of energy saving and environmental protection; the capacitor equalizer relies on the voltage difference between the cells, and when the voltage difference between the cells is small, it cannot be effectively balanced [4]; in [5], the proposed topology will increase the capital cost. What is more the transformer itself has drawbacks such as large volume and low energy transfer efficiency; LC oscillator circuit equalizer was proposed in reference [6], the capacitor voltage is increased by LC oscillation, the energy is transferred in the form of voltage, and the controllability is poor; the inductor equalizer is used in [7][8][9], the energy is transferred in the form of current with high controllability.…”

Section: Introductionmentioning

confidence: 99%

Switch Matrix Algorithm for Series Lithium Battery Pack Equilibrium Based on Derived Acceleration Information Gauss‐Seidel

Wei

Dai

Wang

et al. 2019

Mathematical Problems in Engineering

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With the rapid development of energy internet and new energy-related industries, lithium-ion batteries are widely used in various fields due to their superior energy storage characteristics. To reduce the influence of the inconsistency of the individual cells in the battery pack, a switch array DC equalization circuit together with an acceleration information Gauss-Seidel (DAIGS) method is proposed. The aging factor is added in the system state matrix considering the battery time effect. In this method, the energy transfer path can be optimized by updating the switch control matrix in each iteration. Hence, it can avoid the repeated charging and discharging of the battery and also reduce energy loss in rapid equalization. A four-cell battery string equalization model simulation was built in the PSIM and the experiment was also carried out to prove the feasibility of the proposed method. It was verified that the DAIGS had better performance under the same precision or number of iterations.

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Analysis and Optimization of Star-Structured Switched-Capacitor Equalizers for Series-Connected Battery Strings

Cited by 103 publications

References 36 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

Capacity Prediction and Validation of Lithium-Ion Batteries Based on Long Short-Term Memory Recurrent Neural Network

Switch Matrix Algorithm for Series Lithium Battery Pack Equilibrium Based on Derived Acceleration Information Gauss‐Seidel

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