Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook

Lipu, Molla Shahadat Hossain; Hannan, M. A.; Karim, Tahia Fahrin; Hussain, Aini; Saad, Mohamad Hanif Md; Ayob, Afida; Miah, Md. Sazal; Mahlia, T.M.I.

doi:10.1016/j.jclepro.2021.126044

Cited by 221 publications

(70 citation statements)

References 175 publications

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“…Data abundance and data variety are the main challenges in executing intelligent algorithms in battery models as the accuracy of these approaches depends on these factors. However, it takes a lot of time to collect a large amount of diverse data which also increases the computational complexity and leads to over-fitting issues due to the training time extension [50]. Similarly, data integrity is another issue as the existing data base has a permanent charge/discharge pattern and temperature conditions used in a laboratory environment.…”

Section: Data Abundance Variety and Integrity Issuesmentioning

confidence: 99%

“…Moreover, battery test benches used in laboratories suffer from equipment precision, noise impact, and electromagnetic interference issues, etc. Therefore, BMS evaluation under various changing conditions is needed in real world environments [46,50].…”

Section: Data Abundance Variety and Integrity Issuesmentioning

confidence: 99%

“…Designing a suitable structure and appropriate selection of hyper parameters for intelligent algorithms is a difficult task which may also cause data under fitting and over fitting issues. The existing trial and error approaches for the selection of hyper parameters suffers loss of human energy and takes a lot of time [50].…”

Section: Parameter Selection Issues For Intelligent Algorithmsmentioning

confidence: 99%

“…The integration of intelligent methods and different control schemes needs optimization, but it has many challenges as optimization algorithms have different convergence speed, execution time, and complexity. Therefore, the selection of suitable optimization technique is needed for intelligent algorithms [50].…”

Section: Optimization Issues For Intelligent Algorithmsmentioning

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: Data Abundance Variety and Integrity Issuesmentioning

confidence: 99%

Section: Data Abundance Variety and Integrity Issuesmentioning

confidence: 99%

Section: Parameter Selection Issues For Intelligent Algorithmsmentioning

confidence: 99%

Section: Optimization Issues For Intelligent Algorithmsmentioning

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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“…The performance of conventional recurrent neural networks (RNNs) has shortcomings of the vanishing or exploding gradient during backpropagation training. To handle this problem, LSTM can capture the long-term conditions through the usage of memory units rather than conventional hidden layers [88]. The structure of the LSTM memory unit includes a series of gates including forget gate, input gate, output gate and memory units connected through nodes.…”

Section: ) Long Short-term Memory-based Hybrid Approachmentioning

confidence: 99%

Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

et al. 2021

Self Cite

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Globally, wind energy is growing rapidly and has received huge consideration to fulfill global energy requirements. An accurate wind power forecasting is crucial to achieve a stable and reliable operation of the power grid. However, the unpredictability and stochastic characteristics of wind power affect the grid planning and operation adversely. To address these concerns, a substantial amount of research has been carried out to introduce an efficient wind power forecasting approach. Artificial Intelligence (AI) approaches have demonstrated high precision, better generalization performance and improved learning capability, thus can be ideal to handle unstable, inflexible and intermittent wind power. Recently, AI-based hybrid approaches have become popular due to their high precision, strong adaptability and improved performance. Thus, the goal of this review paper is to present the recent progress of AI-enabled hybrid approaches for wind power forecasting emphasizing classification, structure, strength, weakness and performance analysis. Moreover, this review explores the various influential factors toward the implementations of AI-based hybrid wind power forecasting including data preprocessing, feature selection, hyperparameters adjustment, training algorithm, activation functions and evaluation process. Besides, various key issues, challenges and difficulties are discussed to identify the existing limitations and research gaps. Finally, the review delivers a few selective future proposals that would be valuable to the industrialists and researchers to develop an advanced AI-based hybrid approach for accurate wind power forecasting toward sustainable grid operation.INDEX TERMS Wind power forecasting, artificial intelligence, machine learning, deep learning, optimization, hybrid approaches.

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Bi₂O₃ Nanosheets for Early Warning Thermal Runaway of Lithium Battery

Pan,

Chu,

Zhang

et al. 2024

Adv Funct Materials

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H2 and CO are mostly regarded as the signature products before the thermal runaway of lithium batteries. In fact, most small‐molecule gases result from the electrolyte decomposition inside the lithium battery under high temperature. The main component of electrolyte, dimethyl carbonate (DMC) can spill out of the case much earlier than H2 and CO. Herein, it is studied that the gas production of a lithium battery before its thermal runaway, and verified that gaseous DMC is a much earlier marker to warn thermal runaway. To solve the lack of highly DMC‐sensitive sensors, a novel semiconductor gas sensor is fabricated by Bi2O3 nanosheets. It performs high sensitivity and selectivity toward DMC with ultra‐low limit of detection (50 ppb) and high selectivity (>24 times). Notably, in comparison with temperature measurement and other commercial gas sensors, the as‐prepared Bi2O3 sensor detecting gaseous DMC can provide an early warning over 15 min before a thermal runaway happened. So, this Bi2O3 sensor has a great potential for practical application on warning thermal runaway. The in situ infrared spectroscopy and the in situ Raman spectroscopy are employed to investigate the nature behind the outstanding performances, which is ascribed to the direct interaction between electrolyte molecules and Bi2O3nanosheets.

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Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook

Cited by 221 publications

References 175 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

Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

Bi₂O₃ Nanosheets for Early Warning Thermal Runaway of Lithium Battery

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Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook

Cited by 221 publications

References 175 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

Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

Bi2O3 Nanosheets for Early Warning Thermal Runaway of Lithium Battery

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Product

Resources

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Bi₂O₃ Nanosheets for Early Warning Thermal Runaway of Lithium Battery