A novel intelligent transport system charging scheduling for electric vehicles using Grey Wolf Optimizer and Sail Fish Optimization algorithms

Rajamoorthy, Rajasekaran; Arunachalam, Gokulalakshmi; Kasinathan, Padmanathan; Devendiran, Ramkumar; Ahmadi, Pouria; Pandiyan, Santhiya; Muthusamy, Suresh; Panchal, Hitesh; Kazem, Hussein A.; Sharma, Prabhakar

doi:10.1080/15567036.2022.2067268

Cited by 53 publications

(23 citation statements)

References 27 publications

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“…In nanofluid-based heat transfer applications, the machine learning methods discussed in the previous subsections were mainly reported in the literature. To enhance prediction accuracy and computing efficiency, such machine learning approaches can be coupled with genetic algorithms, particle swarm optimization, and imperialist competitive algorithm . The important historical progress in machine learning application in the domain of nanofluids is shown in Figure .…”

Section: Most Relevant Machine Learning Techniquesmentioning

confidence: 99%

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Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Sharma¹,

Said

Kumar³

et al. 2022

Energy Fuels

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208

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Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required.

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Section: Most Relevant Machine Learning Techniquesmentioning

confidence: 99%

“…To enhance prediction accuracy and computing efficiency, such machine learning approaches can be coupled with genetic algorithms, particle swarm optimization, and imperialist competitive algorithm. 106 The important historical progress in machine learning application in the domain of nanofluids is shown in Figure 7.…”

mentioning

confidence: 99%

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Sharma¹,

Said

Kumar³

et al. 2022

Energy Fuels

Self Cite

208

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“…Calculating the approach decision factor yields the route taken by EV to go to charging station. along with the charging technologies [32][33]. The different charging and discharging issues in respect to the performance of the system which include overloading, deteriorating power quality and power loss are discussed [34].…”

Section: Introductionmentioning

confidence: 99%

Electric Vehicle in Smart Grid Integrated Photovoltaic Environment with Storage System

MANISSEGARANE¹,

RAJAN²

2023

Preprint

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This research introduces a novel hybrid sliding mode controller integrated with the artificial neural network (SMC-ANN) used for purpose of real power exchange using Intelligence Power Management System (IPMS). This paper's purpose is to explore the transient and steady state voltage, transient current, transient power of EV, along with the EV torque and EV speed. In giving a nonlinear signal that simulates the network to "glide" across a cross-section of its normal behavior, sliding mode control is a nonlinear control strategy that affects a dynamic of a nonlinear system. The setup consists of utility grid, integrated Photovoltaic source (PV) energy with Battery Storage System (BSS). A high gain DC/DC boost converter is implemented to interface BSS to the DC-bus. The Power Conditioning System receives the DC output voltage from the PV panel (PCS). These converters' bidirectional nature offers the benefit of power transfer between storage systems, loads, and PV systems. The results obtained using newly developed algorithm is compared with the conventional sliding mode controller (SMC) and it is seen that the a newly developed algorithm produced better outcomes than the conventional one. Simulation is performed using MATLAB software.

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“…The advancement of this field of study has been facilitated by the improved spatial-temporal resolution of transport data made possible by the incorporation of additional data sources [57] and the utilization of Big Data technology, which has resulted in the development of novel techniques and extraordinary findings that much beyond those produced by conventional theories and instruments. In this problem, the variations in traffic capacity planning, predicting traffic conditions, and managing traffic demand are mutated clearly by Big Data technology [58], [59]. In general, Big Data has concentrated on some specific fields so far, but it cannot be denied that the potentiality of Big Data in the traffic domain that helped the transportation industry has become more intelligent and more developed [60]- [62].…”

Section: Introductionmentioning

confidence: 99%

Applications of Big Data Analytics in Traffic Management in Intelligent Transportation Systems

Nguyen

Bui³

2022

JOIV : Int. J. Inform. Visualization

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Big Data technology is emerging as a mass technology that can be applied to many industries in life. Decisions in a wide range of fields may benefit greatly from the information provided by Big Data and Analytics research. One of the areas that have benefited the most from this technology is transportation, which is known as an important field in the development of each nation and possesses a huge treasure of data that traditional technologies cannot handle. Indeed, many countries have applied Big Data-based intelligent transportation systems because it is a traffic system that interacts with vehicles and people on the road, thereby reducing traffic congestion and traffic accidents year by year in many countries. The article presents the applications of Big Data technology in smart traffic systems, thereby providing the perspective of a smart city with a smart traffic system as a critical factor. This paper's analysis indicated that smart cities could be born and further developed through the linkage of Big Data technology and smart traffic systems with smart traffic systems as the core. In addition, the results also showed that the obstacle that needs to be studied at this time is the policy and legal framework for Big Data technology. Therefore, a system managed by the state or shared between the state and the private sector should be studied in the future, aiming to harmonize interests and develop the system extensively.

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A novel intelligent transport system charging scheduling for electric vehicles using Grey Wolf Optimizer and Sail Fish Optimization algorithms

Cited by 53 publications

References 27 publications

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System

Electric Vehicle in Smart Grid Integrated Photovoltaic Environment with Storage System

Applications of Big Data Analytics in Traffic Management in Intelligent Transportation Systems

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