Systematic Comparison of Active Balancing: A Model-Based Quantitative Analysis

Caspar, Maurice; Eiler, Torsten; Hohmann, Sören

doi:10.1109/tvt.2016.2633499

Cited by 71 publications

(20 citation statements)

References 38 publications

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“…Many different methods and circuits for active charge balancing were discussed in the literature in recent years. Reviews and a general topology overview are available in [4], [5] and [6]. The superiority of active balancing over passive balancing in terms of energy efficiency and balancing speed has been proven in theory ( [6], [7], [8]) and by measurement ( [9], [10], [11], [12]).…”

Section: State Of Artmentioning

confidence: 99%

Analysis of an Active Charge Balancing Method Based on a Single Nonisolated DC/DC Converter

Raeber

Heinzelmann

Abdeslam

2021

IEEE Trans. Ind. Electron.

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Section: State Of Artmentioning

confidence: 99%

Analysis of an Active Charge Balancing Method Based on a Single Nonisolated DC/DC Converter

Raeber

Heinzelmann

Abdeslam

2021

IEEE Trans. Ind. Electron.

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“…The proposed model estimates SOC for every single cell individually as indicated in Fig.5. Three parameters: voltage, current and temperature are collected from each cell in addition to estimate SOC bestowing the equations from (4) to (7). Weights are attuned according to the Root Mean Square Error (RMSE) of the SOC estimation so as to construe the assessed-outcomes more accurately.…”

Section: B Buck-boost Convertermentioning

confidence: 99%

“…On the other hand, active cell balancing method agree to collect or allocate energy from higher ones to lower ones. Consequently, active charge balancing method can decline the power losses and battery cooling necessities [7], [8]. Typically, Open circuit voltage (OCV), terminal voltage and SOC are chosen as a parameter of charge equalization technique.…”

Section: Introductionmentioning

confidence: 99%

Active Cell Balancing Control Method for Series-Connected Lithium-Ion Battery

Qays¹,

Buswig²,

Anyi³

2019

IJITEE

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Power conveyance potentiality for series and parallel allied battery-packages are constrained by the wickedest cell of the string. Every cell contains marginally dissimilar capability and terminal voltage because of industrialized acceptances and functional situations. During charging or discharging progression, the charge status of the cell strings become imbalanced and incline to loss equalization. Therefore, the enthusiasm of this paper is to design an active charge balancing system for Lithium-ion battery pack with the help of online state of charge (SOC) estimation technique. A Battery Management System (BMS) is modeled by means of controlling the SOC of the cells to upsurge the efficacy of rechargeable batteries. The capacity of each cell is calculated by dint of SOC function estimated as a result of Backpropagation Neural Network (BPNN) algorithm through four switched DC/DC Buck-Boost converter. The simulation results confirm that the designed BMS can synchronize the cell equalization via curtailing the SOC estimation error (RMSE 1.20%) productively.

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“…Figure 1 shows that a group of series connected batteries may encompass dissimilar levels of SOC. Thus, a minimum-labeled SOC battery is deep-discharged during the discharging period, while a maximum-labeled SOC battery will be over-charged in the charging period [10,11]. The least squares variable projection algorithm (LSVPA) for lithium-ion batteries (LIB) was employed in [12] in order to improve the noise immunity and unbiased model parameter identification.…”

Section: Introductionmentioning

confidence: 99%

Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System

et al. 2020

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Charging a group of series-connected batteries of a PV-battery hybrid system exhibits an imbalance issue. Such imbalance has severe consequences on the battery activation function and the maintenance cost of the entire system. Therefore, this paper proposes an active battery balancing technique for a PV-battery integrated system to improve its performance and lifespan. Battery state of charge (SOC) estimation based on the backpropagation neural network (BPNN) technique is utilized to check the charge condition of the storage system. The developed battery management system (BMS) receives the SOC estimation of the individual batteries and issues control signal to the DC/DC Buck-boost converter to balance the charge status of the connected group of batteries. Simulation and experimental results using MATLAB-ATMega2560 interfacing system reveal the effectiveness of the proposed approach.

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Systematic Comparison of Active Balancing: A Model-Based Quantitative Analysis

Cited by 71 publications

References 38 publications

Analysis of an Active Charge Balancing Method Based on a Single Nonisolated DC/DC Converter

Analysis of an Active Charge Balancing Method Based on a Single Nonisolated DC/DC Converter

Active Cell Balancing Control Method for Series-Connected Lithium-Ion Battery

Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System

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