Lithium-Ion Battery Pack Robust State of Charge Estimation, Cell Inconsistency, and Balancing: Review

Naguib, Mina; Kollmeyer, Phillip J.; Emadi, Ali

doi:10.1109/access.2021.3068776

Cited by 117 publications

(43 citation statements)

References 74 publications

(141 reference statements)

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“…In general, SOC [ 3 , 17 , and 18 ] is the ratio of the remaining electric quantity to the rated electric quantity. Lithium-ion batteries have typical nonlinear characteristics, and it is difficult to measure the total power released by existing means or methods.…”

Section: Methodsmentioning

confidence: 99%

SOC Estimation of Lithium-Ion Battery for Electric Vehicle Based on Deep Multilayer Perceptron

Li¹,

Jiang²,

Guo

et al. 2022

Computational Intelligence and Neuroscience

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The state of charge (SOC) is one of the main indexes of the lithium-ion battery, which affects the practice range of new energy vehicles and the safety of the battery. Nevertheless, the value of SOC cannot be measured directly. At present, the algorithm for estimating the state of charge is not very satisfactory. The multilayer perceptron algorithm designed during this paper encompasses a sensible impact on state estimation. During this paper, the multilayer network is designed to estimate the charged state of lithium batteries from the three-layer artificial neural network to the eleven-layer artificial neural network. After preprocessing the dataset and comparing several activation functions, the ten-layer fully connected neural network is the most efficient to estimate the SOC. In order to prevent over-fitting of the multilayer perceptron algorithm, the two techniques of the BatchNormalization layer and Dropout layer work together to inhibit over-fitting. At the same time, the accuracy of extended Kalman filter, long and short memory network, and recurrent neural network are compared. The multilayer perceptron network designed during this paper has the highest accuracy. Finally, in the open dataset, both the training and test errors achieve good results. The algorithm developed in this paper has made some progress in SOC estimation.

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Section: Methodsmentioning

confidence: 99%

SOC Estimation of Lithium-Ion Battery for Electric Vehicle Based on Deep Multilayer Perceptron

Li¹,

Jiang²,

Guo

et al. 2022

Computational Intelligence and Neuroscience

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“…Similarly, cell balancing is the most crucial task of a BMS.A BMS has to try to maintain an equal state of charge in each cell. Figure 4 shows the impact of SOC imbalance during charging and discharging scenarios, [32,33].…”

Section: Cell Monitoring and Cell Balancingmentioning

confidence: 99%

“…However, to implement efficient bypassing technique is expensive in a BMS. Similarly, over discharging may also reduce cell capacity due to different chemical reactions and may cause a detonation [6,17,33].…”

Section: Early Discharge Termination and Over-discharging Issuesmentioning

confidence: 99%

“…In case of active cell balancing, energy will be shared among the cells by transferring the energy from higher cell X to lower cell Z, and thus the cells will acquire an equal SOC of 75% [16]. Figure 8 shows the comparison of these topologies [33]. The next section presents the comparison between active and passive cell balancing by doing modeling and simulation.…”

Section: Comparison Between Passive and Active Cell Balancingmentioning

confidence: 99%

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Characteristics of Battery Management Systems of Electric Vehicles with Consideration of the Active and Passive Cell Balancing Process

Uzair

Abbas

Hosain

2021

WEVJ

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Energy shortage and environmental pollution issues can be reduced considerably with the development and usage of electric vehicles (EVs). However, electric vehicle performance and battery lifespan depend on a suitable battery arrangement to meet the various battery performance demands. The safety, reliability, and efficiency of EVs largely depends on the constant monitoring of the batteries and management of battery packs. This work comprehensively reviews different aspects of battery management systems (BMS), i.e., architecture, functions, requirements, topologies, fundamentals of battery modeling, different battery models, issues/challenges, recommendations, and active and passive cell balancing approaches, etc., as compared to the existing works which normally discuss one or two aspects only. The work describes BMS functions, battery models and their comparisons in detail for an efficient operation of the battery pack. Similarly, the work presents a comprehensive overview of issues and challenges faced by BMS and also provides recommendations to address these challenges. Cell balancing is very important for the battery performance and in this work various cell balancing methodologies and their comparisons are also presented in detail. Modeling of a cell balancer is presented and a comparative study is also carried out for active and passive cell balance technique in MATLAB/Simulink with an eight cell battery packcell balancing approach. The result shows that the active cell balancing technique is more advantageous than passive balancing for electrical vehicles using lithium-ion batteries.

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“…Considering the widespread hysteresis in lead-acid batteries, the SOC estimation technique based on differential evolution was adopted to minimize the hysteresis effect and realize the accurate estimation of SOC [73]. As a general view, many scholars have made a detailed review on SOC estimation [74][75][76][77].…”

Section: Other Relevant Researchmentioning

confidence: 99%

Construction Planning and Operation of Battery Swapping Stations for Electric Vehicles: A Literature Review

Feng

2021

Energies

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The popularity of electric vehicles has been limited by factors such as range, long charging times and fast power failure in winter. In order to overcome these challenges, battery swapping stations (BSS) have been constructed and greatly promoted in recent years. In this paper, the related literature on electric vehicle service is reviewed and the co-occurrence of keywords is analyzed using CiteSpace. The literature is classified according to clustering results and recurring themes, such as the location of BSS, inventory decisions, charging strategies and BSS assignment. In each topic, typical optimization models and algorithms proposed in previous studies are summarized. Then, this paper gives a case about the business model and revenue capacity calculation of BSS. Finally, it points out the future research direction of battery swapping stations for electric vehicles.

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Lithium-Ion Battery Pack Robust State of Charge Estimation, Cell Inconsistency, and Balancing: Review

Cited by 117 publications

References 74 publications

SOC Estimation of Lithium-Ion Battery for Electric Vehicle Based on Deep Multilayer Perceptron

SOC Estimation of Lithium-Ion Battery for Electric Vehicle Based on Deep Multilayer Perceptron

Characteristics of Battery Management Systems of Electric Vehicles with Consideration of the Active and Passive Cell Balancing Process

Construction Planning and Operation of Battery Swapping Stations for Electric Vehicles: A Literature Review

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