A Modular Battery Voltage-Balancing System Using a Series-Connected Topology

Tavakoli, Atrin; Khajehoddin, S. Ali; Salmon, John

doi:10.1109/tpel.2019.2950409

Cited by 28 publications

(11 citation statements)

References 28 publications

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“…The charging current can be reduced so errors due to internal resistance are minimized. Meanwhile, it gives least complexity in implementation and is a commonly used method in the electric vehicle domain [15]. In BEV, balancing is frequently done using voltage based methods at the final stage of the charging process [16].…”

Section: A Passive Cell Balancing Approachesmentioning

confidence: 99%

Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System

Duraisamy¹,

Deepa

2021

IEEE Access

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Cell balancing is a vital function of battery management system (BMS), which is implemented to extend the battery run time and service life. Various cell balancing techniques are being focused due to the growing requirements of larger and superior performance battery packs. The passive balancing approach is the most popular because of its low cost and easy implementation. As the balancing energy is dissipated as heat by the balancing resistors, an appropriate thermal scheme of the balancing system is necessary, to keep the BMS board temperature under a tolerable limit. In this paper, optimum selection of balancing resistor with respect to degree of cell imbalance, balancing time, C-rate, and temperature rise using machine learning (ML) based balancing control algorithm is proposed to improve the balancing time and optimal power loss management. Variable resistors are utilised in the passive balancing system, in order to optimize the power loss and to obtain optimal thermal characterization. The performance of the proposed system is evaluated using back propagation neural network (BPNN), radial basis neural network (RBNN), and long short term memory (LSTM). Error analysis of the balancing system is done to optimize balancing parameters and the proposed algorithms are compared using performance indices such as mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) to validate the balancing model performance. The possible optimization scope for implementing passive balancing using machine learning algorithms are experimented in the Matlab-Simscape environment.

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Section: A Passive Cell Balancing Approachesmentioning

confidence: 99%

Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System

Duraisamy¹,

Deepa

2021

IEEE Access

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“…The terminal voltage can be measured directly with a satisfying accuracy, therefore, VBESs become the most classical and feasible methods. In Atrin's design, 124 only one voltage sensor could be used per two batteries. Yang et al 125 presented a rigorous analysis and design of VBESs.…”

Section: Equalization Strategiesmentioning

confidence: 99%

A comprehensive review on inconsistency and equalization technology of lithium‐ion battery for electric vehicles

et al. 2020

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The rapid growth of transportation demand has been enlarged strongly which has promoted electric vehicles powered by lithium-ion batteries. However, the inconsistencies within the battery pack will deteriorate over the lifecycle and affect the performance of electric vehicles. Therefore, various thermal management systems and equalization systems have been applied in battery management system to deal with the inconsistencies, extend battery service life, and improve safety performance. This review summarizes the origination of inconsistency within lithium-ion batteries from production to usage process, and then introduces the classification methods and application scenarios of the balance management system in detail. Based on the circuit topology, equalization systems can be classified into passive and active topologies. Active topologies

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“…Active cell balancing methods remove charge from higher energy cells and deliver it to lower energy cells through active cell equalizing circuits. They have different topologies according to the circuit and active element used to store energy, such as capacitors and/or inductive components (Daowd et al, 2011;Cao et al, 2008;Yu et al, 2019;Tang et al, 2019;Zhang et al, 2019;Tavakoli et al, 2019). There are three subcategories of active balancing methods sorted by circuit topology, namely, charge shunting (Hsieh et al, 2002;Shibata et al, 2001), charge shuttling (Cao et al, 2008;Baughman and Ferdowsi, 2008;Ye and Cheng, 2015;Shousha et al, 2017), and energy converting method-based balancing techniques, among which the energy converter-based balancing method is the most widely used technique.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Balancing Control of Cell Voltage in the Charging/Discharging Mode for Battery Energy Storage Systems

et al. 2022

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To improve the balancing time of battery energy storage systems with “cells decoupled and converters serial-connected,” a new cell voltage adaptive balancing control method in both charging and discharging modes is proposed in this study. The overall system architecture and basic operating principle of the active balancing system with “cells decoupled and converters serial-connected” are presented first. Then, by dynamically regulating the balancing acceleration coefficient of each cell according to the cell voltage deviation, the adaptive balancing control of cell voltage in charging and discharging modes is analyzed. An experimental prototype consisting of six smart cells is developed, and experiments in charging and discharging modes were carried out. The experiment results demonstrate that compared with the balancing process with a static acceleration coefficient, the proposed adaptive balancing control of cell voltage shows significant improvement in balancing speed and smaller cell voltage discrepancy.

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A Modular Battery Voltage-Balancing System Using a Series-Connected Topology

Cited by 28 publications

References 28 publications

Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System

Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System

A comprehensive review on inconsistency and equalization technology of lithium‐ion battery for electric vehicles

Adaptive Balancing Control of Cell Voltage in the Charging/Discharging Mode for Battery Energy Storage Systems

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