Applications of Clustering Methods for Different Aspects of Electric Vehicles

Nazari, Masooma; Hussain, Akhtar; Musilek, Petr

doi:10.3390/electronics12040790

Cited by 12 publications

(3 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several features of electric vehicles can be mentioned here, such as the driving cycle, the batteries that are utilized, and the charging stations. These features were examined in the work of Nazari, Hussain, and Musilek [41], utilizing unsupervised learning techniques. Using clusteringbased algorithms, they were able to replicate the behavior of EV users, the cycle of EV driving, the classification of EV batteries, and EV charging stations.…”

Section: B Unsupervised Learningmentioning

confidence: 99%

A Comprehensive Literature Review on Artificial Dataset Generation for Repositioning Challenges in Shared Electric Automated and Connected Mobility

Kayisu,

Kambale,

Benarbia

et al. 2024

Symmetry

View full text Add to dashboard Cite

In the near future, the incorporation of shared electric automated and connected mobility (SEACM) technologies will significantly transform the landscape of transportation into a sustainable and efficient mobility ecosystem. However, these technological advances raise complex scientific challenges. Problems related to safety, energy efficiency, and route optimization in dynamic urban environments are major issues to be resolved. In addition, the unavailability of realistic and various data of such systems makes their deployment, design, and performance evaluation very challenging. As a result, to avoid the constraints of real data collection, using generated artificial datasets is crucial for simulation to test and validate algorithms and models under various scenarios. These artificial datasets are used for the training of ML (Machine Learning) models, allowing researchers and operators to evaluate performance and predict system behavior under various conditions. To generate artificial datasets, numerous elements such as user behavior, vehicle dynamics, charging infrastructure, and environmental conditions must be considered. In all these elements, symmetry is a core concern; in some cases, asymmetry is more realistic; however, in others, reaching/maintaining as much symmetry as possible is a core requirement. This review paper provides a comprehensive literature survey of the most relevant techniques generating synthetic datasets in the literature, with a particular focus on the shared electric automated and connected mobility context. Furthermore, this paper also investigates central issues of these complex and dynamic systems regarding how artificial datasets could be used in the training of ML models to address the repositioning problem. Hereby, symmetry is undoubtedly a crucial consideration for ML models. In the case of datasets, it is imperative that they accurately emulate the symmetry or asymmetry observed in real-world scenarios to be effectively represented by the generated datasets. Then, this paper investigates the current challenges and limitations of synthetic datasets, such as the reliability of simulations to the real world, and the validation of generative models. Additionally, it explores how ML-based algorithms can be used to optimize vehicle routing, charging infrastructure usage, demand forecasting, and other important operational elements. In conclusion, this paper outlines a series of interesting new research avenues concerning the generation of artificial data for SEACM systems.

show abstract

Section: B Unsupervised Learningmentioning

confidence: 99%

A Comprehensive Literature Review on Artificial Dataset Generation for Repositioning Challenges in Shared Electric Automated and Connected Mobility

Kayisu,

Kambale,

Benarbia

et al. 2024

Symmetry

View full text Add to dashboard Cite

show abstract

“…Renewable energy sources and EV growth provide new challenges for grid stabilization, requiring smart grid techniques to reconfigure and compensate for load fluctuation and stabilize power losses and voltage fluctuation [24]. The growing penetration of electric vehicles can pose several challenges for power systems, especially distribution systems, due to the introduction of significant uncertain load, which can be solved by clustering methods [25]. According to the randomness of photovoltaic power generation and EV charging, the dynamic response capability, power support capability, effective convergence time, system stability, system failure rate, and other characteristics of regional loads are comprehensively analyzed, and the grid energy management model of EV charging network and distributed photovoltaic is proposed, while, according to certain statistical characteristics, the distributed photovoltaic will be concentrated, and EV charging will be prioritized to achieve nearby consumption [26].…”

Section: Related Workmentioning

confidence: 99%

Model-Driven Developed Terminal for Remote Control of Charging Station for Electric Vehicles Powered by Renewable Energy

2023

View full text Add to dashboard Cite

A terminal for remote control of charging stations for electric vehicles (EV) powered by renewable energy has been presented in this paper. This terminal enables remote control of EV chargers, smart batteries, smart electricity meters, fiscal cash registers (FCR), as well as remote control of renewable energy sources and other devices within the station. This terminal also makes charging stations more accessible to electric vehicles users, to electricity distribution companies, to electricity suppliers, to tax administrations, and finally to users and owners of charging stations. Therefore, communication and control with all these devices and systems is integrated in one device. Realization of hardware and software of such a terminal has also been described in this paper. The net result of development and commercialization of terminals would encourage an increase in the use of electric vehicles powered by energy from renewable sources, which would cause a decrease in the level of air pollution and all negative effects it causes in the future. Different categories of this device are considered. Moreover, although it is a device with embedded software, a very advanced method was used, that is, a model-driven development method, which enables fast and more efficient development and maintenance of the device. The results of the application of this method to the terminal for remote control of fiscal cash registers are provided. They were compared with the results of the development of the terminal for remote control of smart meters without applying this method. A simulation of the development of the terminal for remote control of the station is also provided. The presented method can be used in the future for faster and better-quality development of embedded software.

show abstract

“…In this paper, three different interpolation methods will be used to preprocess the collected data. The principal component analysis [14] and K-means clustering algorithm [15] will be used to reduce and classify the feature parameter matrix. The silhouette index [16] will be the standard for measuring clustering results.…”

Section: Introductionmentioning

confidence: 99%

Influence of Different Data Interpolation Methods for Sparse Data on the Construction Accuracy of Electric Bus Driving Cycle

Wang

Deng

et al. 2023

Electronics

View full text Add to dashboard Cite

Battery electric vehicles (BEVs) are one of the most promising new energy models for industrialization and marketization at this stage, which is an important way to solve the current urban haze air pollution, high fuel cost and sustainable development of the automobile industry. This paper takes pure electric buses as the research object and relies on the operation information management platform of new energy buses in Nantong city to propose an electric bus cycle construction method based on the mixed interpolation method to process sparse data. Three different interpolation methods, linear interpolation, step interpolation and mixed interpolation, were used to preprocess the collected data. The principal component analysis method and K-means clustering algorithm were used to reduce and classify the eigen parameter matrix. According to the clustering results, different categories of moving section and idle section libraries were established. According to the length of time and the correlation among various types, several moving sections and idle sections were selected to form a representative driving cycle of Nantong city buses. The results show that the mixed interpolation method, based on linear interpolation and cubic spline interpolation, has a good processing effect. The average relative error between the synthesized working conditions and the measured data are 15.71%, and the relative error of the seven characteristic parameters is less than 10%, which meets the development requirements. In addition, the comparison and analysis with the characteristic parameters of the world typical cycle conditions (NEDC, WLTC) show that the constructed cycle conditions of Nantong city are reasonable and reliable to represent the driving conditions of pure electric buses in Nantong city, which can provide a reference for the optimization of the bus energy control strategy.

show abstract

Applications of Clustering Methods for Different Aspects of Electric Vehicles

Cited by 12 publications

References 74 publications

A Comprehensive Literature Review on Artificial Dataset Generation for Repositioning Challenges in Shared Electric Automated and Connected Mobility

A Comprehensive Literature Review on Artificial Dataset Generation for Repositioning Challenges in Shared Electric Automated and Connected Mobility

Model-Driven Developed Terminal for Remote Control of Charging Station for Electric Vehicles Powered by Renewable Energy

Influence of Different Data Interpolation Methods for Sparse Data on the Construction Accuracy of Electric Bus Driving Cycle

Contact Info

Product

Resources

About