Active cell balancing control strategy for parallel connected LiFePO4 batteries

Qays, Ohirul; Buswig, Yonis M.; Hossain, Liton; Rahman, Md. Momtazur; Abu-Siada, A.

doi:10.17775/cseejpes.2020.00740

Cited by 11 publications

(8 citation statements)

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“…Non-dissipative balancing utilizes capacitors [59], [60], inductors [61], [62], transformers [63]- [65], and various common power electronic converter topologies [66]- [70] to transfer the energy among the cells within the pack. Energy is transferred from more charged to less charged cells, preventing the waste of energy present for dissipative methods.…”

Section: B Non-dissipative Balancing Methodsmentioning

confidence: 99%

“…Common dc-dc converter topologies can also be used for balancing, such as bidirectional buck-boost [66], bidirectional Cuk [67], bidirectional flyback [68], full-bridge [69], and quasi-resonant [70] converters. Typically, one converter per cell is utilized, and the converters transfer power between adjacent cells.…”

Section: ) Common Converter Topology-based Balancingmentioning

confidence: 99%

“…Rather than simply allowing the voltage of cells to be matched like many of the prior methods discussed, the converters can control the flow of power in any way the BMS commands allowing more flexibility for managing SOC of the cells. The bidirectional buck-boost converter is utilized in [66] to transfer energy between two adjacent battery cells. Another bidirectional converter, the Cûk converter, has the same principle of operation but utilizes capacitors as the energy transfer elements instead of inductors [67].…”

Section: ) Common Converter Topology-based Balancingmentioning

confidence: 99%

See 2 more Smart Citations

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

2021

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Lithium-Ion battery packs are an essential component for electric vehicles (EVs). These packs are configured from hundreds of series and parallel connected cells to provide the necessary power and energy for the vehicle. An accurate, adaptable battery management system (BMS) is essential to monitor and control such a large number of cells. Series and parallel connected cells also experience different production and operational conditions, which makes it challenging for the BMS to ensure the safe operation of each individual cell. The main functions of the BMS include battery state estimation, cell balancing, thermal management, and fault diagnosis. Robust estimation of the state of charge (SOC) is crucial for providing the driver with an accurate indication of the remaining range. This paper presents the state of art of battery pack SOC estimation methods along with the impact of cell inconsistency on pack performance and SOC estimation. Cell balancing methods, which are necessary due to cell inconsistencies, are discussed as well. Four categories of pack SOC estimation methods are presented, including individual cell, lumped cell, reference cell, and mean cell and difference estimation methods. The SOC estimation methods are compared in terms of algorithm type, computational load, and engineering effort to help practitioners decide which method best fits their application.INDEX TERMS Lithium-ion battery packs, battery management systems, electric vehicles, cell inconsistency, state of charge, cell balancing.

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Section: B Non-dissipative Balancing Methodsmentioning

confidence: 99%

Section: ) Common Converter Topology-based Balancingmentioning

confidence: 99%

Section: ) Common Converter Topology-based Balancingmentioning

confidence: 99%

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

2021

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“…The proposed method can also be used in renewable energy dependent devices as well as energy conversion systems [42][43][44][45][46][47][48][49][50]. It can also be adapted to monitor the condition of critical high voltage assets, power converters, and various flexible ac transmission system devices [51][52][53][54].…”

Section: Ishdb Tested With Iot Implementationmentioning

confidence: 99%

Cost-Effective Design of IoT-Based Smart Household Distribution System

Ahmed

Qays

Abu-Siada

et al. 2021

Designs

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The Internet of Things (IoT) plays an indispensable role in present-day household electricity management. Nevertheless, practical development of cost-effective intelligent condition monitoring, protection, and control techniques for household distribution systems is still a challenging task. This paper is taking one step forward into a practical implementation of such techniques by developing an IoT Smart Household Distribution Board (ISHDB) to monitor and control various household smart appliances. The main function of the developed ISHDB is collecting and storing voltage, current, and power data and presenting them in a user-friendly way. The performance of the developed system is investigated under various residential electrical loads of different energy consumption profiles. In this regard, an Arduino-based working prototype is employed to gather the collected data into the ThingSpeak cloud through a Wi-Fi medium. Blynk mobile application is also implemented to facilitate real-time monitoring by individual consumers. Microprocessor technology is adopted to automate the process, and reduce hardware size and cost. Experimental results show that the developed system can be used effectively for real-time home energy management. It can also be used to detect any abnormal performance of the electrical appliances in real-time through monitoring their individual current and voltage waveforms. A comparison of the developed system and other existing techniques reveals the superiority of the proposed method in terms of the implementation cost and execution time.

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“…In [22] active cell monitoring is done using a transformer switching. A machine learning based battery management system with a DC converter is found to produce good efficiency in balancing batteries in parallel with an error of 1.15% [23].…”

Section: Introductionmentioning

confidence: 99%

Investigation of active cell balancing performance for series connected lithium‐ion cells in electric vehicle applications

Krishnamoorthy,

Satheesh Kumar,

Barua

et al. 2023

IET Power Electronics

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Lithium‐ion batteries have a very wide application range. They can power up small electronic devices such as smart watches to larger electric vehicles. Due to its varied range of applications, they come in different packaging and in such battery packs, even when individual cell voltage exceeds by a few milli‐volts above 4.2 V, it may result in thermal runaway and explode the cell. During discharge cycle, cell imbalances hinder the use of battery to its full capacity. This in turn decreases the battery lifetime. The individual battery cells should be equalized on a regular basis to keep the imbalances to a minimum and to have a good battery life. The process of balancing the individual cell charges by measuring the cell state of charge (SoC) and its voltage in a battery pack is known as cell balancing. This paper details an active cell balancing technique that uses a buck converter for balancing a series connected battery pack of lithium‐ion cells. A buck converter along with a pair of MOSFET switches for each cell, one turned on for charging the cell and the other one turned on while discharging the cell is used in this experiment. An algorithmic model suitable for reconfigurable battery systems that measures the individual cell voltages and is developed for balancing a pack of series connected Li‐ion battery cells. The developed model is simulated using MATLAB for verifying its performance. A state of charge of 25% is maintained across the cells and when SoC value drops below this even a difference of 0.02% is sensed by the algorithm to initiate balancing function. This balancing is found to take 275 ms to balance three 3.7 V batteries and thus the model is found to respond faster. The results show that this method can self‐adaptively attain satisfactory performance within a limited equalizing period.

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Active cell balancing control strategy for parallel connected LiFePO4 batteries

Cited by 11 publications

References 0 publications

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

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

Cost-Effective Design of IoT-Based Smart Household Distribution System

Investigation of active cell balancing performance for series connected lithium‐ion cells in electric vehicle applications

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