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 164 publications

(38 citation statements)

References 175 publications

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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

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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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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

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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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Experimental Study on the Thermal Management Performance of Lithium‐Ion Battery Using Phase‐Change Material at Various Ambient Temperatures

Liu,

Shi,

Mei

et al. 2023

Energy Tech

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The implementation of a battery thermal management system (BTMS) is crucial to ensuring the optimal functioning of lithium‐ion batteries (LIBs) within an appropriate temperature range. In this study, BTMS with a combination of paraffin wax (PA), expanded graphite, and copper foam (CF) is investigated. The impact of various optimization strategies on battery surface temperature and discharge capacity is investigated, and charging and discharging experiments are performed at different ambient temperature conditions. Moreover, the influence of copper foam arrangement on BTMS is studied. The experimental results demonstrate that PA/CF can lead to a significant battery surface temperature reduction of up to 11.7%. Compared with the side radial arrangement, the front tangential arrangement of closely attaching copper foam to the battery leads to improved results. However, the variation in arrangement has minimal impact on the surface temperature of the battery in extreme environmental conditions. Under low‐temperature conditions, incorporating phase‐change materials could produce insulating effects that supported maintaining battery capacity. In all, this study shows the potential of designing a high‐performance BTMS and contributes to addressing heat accumulation and capacity attenuation during the use of LIBs.

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

Cited by 164 publications

References 175 publications

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

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

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

Experimental Study on the Thermal Management Performance of Lithium‐Ion Battery Using Phase‐Change Material at Various Ambient Temperatures

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