A GA optimization for lithium–ion battery equalization based on SOC estimation by NN and FLC

Zhang, Shumei; Yang, Lin; Zhao, Xiaowei; Qiang, Jiaxi

doi:10.1016/j.ijepes.2015.05.018

Cited by 70 publications

(29 citation statements)

References 18 publications

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“…The approach presented in [120] uses a fuzzy rule-based system to compute the nonlinear gain of a KF that estimates SoC, and a Genetic Algorithm (GA) is used to evolve the definition of the rule base. In [121] a Thevenin ECM in a weighted combination with the CC method is adopted by the fuzzy logic control scheme to obtain a static SoC estimation. Then the dynamic battery SoC is precisely estimated on the basis of static SoC using an ANN.…”

Section: Adaptive Artificial-intelligence-based Techniques Estimationmentioning

confidence: 99%

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

2017

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Abstract:Energy storage emerged as a top concern for the modern cities, and the choice of the lithium-ion chemistry battery technology as an effective solution for storage applications proved to be a highly efficient option. State of charge (SoC) represents the available battery capacity and is one of the most important states that need to be monitored to optimize the performance and extend the lifetime of batteries. This review summarizes the methods for SoC estimation for lithium-ion batteries (LiBs). The SoC estimation methods are presented focusing on the description of the techniques and the elaboration of their weaknesses for the use in on-line battery management systems (BMS) applications. SoC estimation is a challenging task hindered by considerable changes in battery characteristics over its lifetime due to aging and to the distinct nonlinear behavior. This has led scholars to propose different methods that clearly raised the challenge of establishing a relationship between the accuracy and robustness of the methods, and their low complexity to be implemented. This paper publishes an exhaustive review of the works presented during the last five years, where the tendency of the estimation techniques has been oriented toward a mixture of probabilistic techniques and some artificial intelligence.

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Section: Adaptive Artificial-intelligence-based Techniques Estimationmentioning

confidence: 99%

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

2017

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“…FLC is a kind of language controller, which is suitable for the controlled object which is not easy to obtain accurate mathematical model, but has operation experience or knowledge. FLC simulates the process of manual control based on heuristic knowledge and language decision rules, which has the advantages of good robustness, good real‐time performance, and simple control parameters . It can dynamically adjust the equalization current according to the average and difference of voltage and SOC, which can reduce battery inconsistency and improve equalization efficiency.…”

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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“…Isolated bidirectional direct‐current to direct‐current (DC/DC) converters were applied to balance between the modules in the study. Zhang et al proposed genetic algorithm optimization to enhance the energy and time efficiency of battery balancing systems. Shang et al introduced a cell‐to‐cell battery equalizer, consisting of a boost DC/DC converter and a quasi‐resonant inductance‐capacitor converter.…”

Section: Introductionmentioning

confidence: 99%

“…Inconsistency among cells is a serious problem in battery energy storage systems, in which the lithium batteries have to be connected in series to meet the voltage and capacity requirements of the application [1][2][3][4][5][6][7][8][9]. Because of the surrounding environments, self-discharge rates, and fabrication process of batteries, the charge level tends to vary from cell to cell [4,[9][10][11]. Battery inconsistency is usually expressed as differences in internal resistance, voltage, and charge imbalance [3,10,12,13].…”

Section: Introductionmentioning

confidence: 99%

“…Many approaches for equalization have been investigated [4,5,[18][19][20] and can be divided into active and passive balancing [5,14]. Passive balancing removes excess charge in the form of heat through a parallel shunt resistor, which wastes energy and generates a significant amount of heat [4,14]. Active balancing is more efficient and diverts charge from higher energy batteries or external auxiliary sources to lower energy batteries [4,[21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

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Influence of equalization on LiFePO₄battery inconsistency

Song

Lin

et al. 2017

Int. J. Energy Res.

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SUMMARY Capacity inconsistency among cells is a serious problem in battery energy storage systems. The inconsistency reduces battery pack lifetimes and increases their usage costs. Active balancing is an effective technique for performance equalization between cells. Here, a quantitative analysis method based on an active balancing circuit is developed to explain how battery capacity inconsistency is affected by balancing parameters. Four balancing parameters, namely, charge/discharge current rate, initial capacity difference before balancing, balancing current, and balancing time, have been considered to explain the effects on battery capacity consistency. The results indicate that (1) the inconsistency of batteries can be reduced more effectively for discharging with active balancing at small current rates; (2) for an improved balancing effect, it is necessary to maintain a high ratio (β) of balancing current to charging/discharging current during the balancing process; thus, a larger balancing current is significant; (3) the value of capacity difference reduction ratio (α) grows more dramatically in discharging at large initial battery capacity differences; and (4) when the balancing current only marginally changes, following the increase of current rate, the ratio (γ) of balancing time to charging duration decreases and the balancing effect becomes inconspicuous in charging, while γ increases and the balancing effect reduces in discharging. These influence regulations can help us make and optimize balancing control strategies for LiFePO4 batteries. Copyright © 2017 John Wiley & Sons, Ltd.

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A GA optimization for lithium–ion battery equalization based on SOC estimation by NN and FLC

Cited by 70 publications

References 18 publications

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

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

Influence of equalization on LiFePO₄battery inconsistency

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A GA optimization for lithium–ion battery equalization based on SOC estimation by NN and FLC

Cited by 70 publications

References 18 publications

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

SoC Estimation for Lithium-ion Batteries: Review and Future Challenges

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

Influence of equalization on LiFePO4battery inconsistency

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Influence of equalization on LiFePO₄battery inconsistency