Review of Battery Management Systems (BMS) Development and Industrial Standards

Gabbar, Hossam A.; Othman, Ahmed M.; Abdussami, Muhammad R.

doi:10.3390/technologies9020028

Cited by 246 publications

(125 citation statements)

References 36 publications

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“…The cathode can be decomposed if the maximum voltage is exceeded producing high heat and increasing short circuit risks. Decomposition of the electrolyte can also happen if voltages are extremely high, which is very dangerous [54,55].…”

Section: Safety Issues and Handling Of Potential Risksmentioning

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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Section: Safety Issues and Handling Of Potential Risksmentioning

confidence: 99%

“…It is always recommended to remove whole battery bank instead of few batteries. A safety logbook should be maintained with regular safety checks of BMS to fulfill any requirements and modifications [54].…”

Section: Bms Installations Recommendationsmentioning

confidence: 99%

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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“…Owing to their high energy density, high power density, long service life, environmental friendliness and low self-discharge rate, lithium-ion batteries (LIBs) have become the prime energy storage system for many applications such as electric vehicles (EVs), gridlevel power storage and several other consumer electronics [1,2]. However, the safe and reliable operating area of the LIB is very narrow, which necessitates a battery management system (BMS) for effective operational control, protection and energy management [3][4][5][6][7]. In addition, due to the limitation of the cell voltage and storage capacity of a single LIB cell, high power applications of LIBs such as EVs and grid-tied energy storage systems require hundreds or even thousands of single battery cells [8].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

2021

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Fault detection/diagnosis has become a crucial function of the battery management system (BMS) due to the increasing application of lithium-ion batteries (LIBs) in highly sophisticated and high-power applications to ensure the safe and reliable operation of the system. The application of Machine Learning (ML) in the BMS of LIB has long been adopted for efficient, reliable, accurate prediction of several important states of LIB such as state of charge, state of health and remaining useful life. Inspired by some of the promising features of ML-based techniques over the conventional LIB fault detection/diagnosis methods such as model-based, knowledge-based and signal processing-based techniques, ML-based data-driven methods have been a prime research focus in the last few years. This paper provides a comprehensive review exclusively on the state-of-the-art ML-based data-driven fault detection/diagnosis techniques to provide a ready reference and direction to the research community aiming towards developing an accurate, reliable, adaptive and easy to implement fault diagnosis strategy for the LIB system. Current issues of existing strategies and future challenges of LIB fault diagnosis are also explained for better understanding and guidance.

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“…Despite the large number of BESS deployed [7,8], most of the published studies so far are focused on modeling [9][10][11][12][13][14][15][16][17] with sizing, load modeling, or life cost analysis, as well as battery management system development [18,19]. Within the modeling studies, little to no effort was devoted to degradation modeling using realistic battery models.…”

Section: Introductionmentioning

confidence: 99%

Battery Durability and Reliability under Electric Utility Grid Operations: Analysis of On-Site Reference Tests

et al. 2021

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Grid-tied energy storage will play a key role in the reduction of carbon emissions. Systems based on Li-ion batteries could be good candidates for the task, especially those using lithium titanate negative electrodes. In this work, we will present the study of seven years of usage of a lithium titanate-based battery energy storage system on an isolated island grid. We will show that, even after seven years, the modules’ capacity loss is below 10% and that overall the battery is still performing within specifications. From our results, we established a forecast based on the internal degradation mechanisms of the hottest and coldest modules to show that the battery full lifetime on the grid should easily exceed 15 years. We also identified some inaccuracies in the online capacity estimation methodology which complicates the monitoring of the system.

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Review of Battery Management Systems (BMS) Development and Industrial Standards

Cited by 246 publications

References 36 publications

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

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

Machine Learning-Based Data-Driven Fault Detection/Diagnosis of Lithium-Ion Battery: A Critical Review

Battery Durability and Reliability under Electric Utility Grid Operations: Analysis of On-Site Reference Tests

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