Electric Vehicles: Benefits, Challenges, and Potential Solutions for Widespread Adaptation

Alanazi, Fayez

doi:10.3390/app13106016

Cited by 212 publications

(65 citation statements)

References 93 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Different kinds of EVs have been made for road use, and they have different features based on their technology. They can be grouped into these categories (Alanazi, 2023).…”

Section: Introductionmentioning

confidence: 99%

A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm

Saeed,

M. El-Kenawy,

Ibrahim

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

The rising popularity of electric vehicles (EVs) can be attributed to their positive impact on the environment and their ability to lower operational expenses. Nevertheless, the task of determining the most suitable EV types for a specific site continues to pose difficulties, mostly due to the wide range of consumer preferences and the inherent limits of EVs. This study introduces a new voting classifier model that incorporates the Al-Biruni earth radius optimization algorithm, which is derived from the stochastic fractal search. The model aims to predict the optimal EV type for a given location by considering factors such as user preferences, availability of charging infrastructure, and distance to the destination. The proposed classification methodology entails the utilization of ensemble learning, which can be subdivided into two distinct stages: pre-classification and classification. During the initial stage of classification, the process of data preprocessing involves converting unprocessed data into a refined, systematic, and well-arranged format that is appropriate for subsequent analysis or modeling. During the classification phase, a majority vote ensemble learning method is utilized to categorize unlabeled data properly and efficiently. This method consists of three independent classifiers. The efficacy and efficiency of the suggested method are showcased through simulation experiments. The results indicate that the collaborative classification method performs very well and consistently in classifying EV populations. In comparison to similar classification approaches, the suggested method demonstrates improved performance in terms of assessment metrics such as accuracy, sensitivity, specificity, and F-score. The improvements observed in these metrics are 91.22%, 94.34%, 89.5%, and 88.5%, respectively. These results highlight the overall effectiveness of the proposed method. Hence, the suggested approach is seen more favorable for implementing the voting classifier in the context of the EV population across different geographical areas.

show abstract

“…Different kinds of EVs have been made for road use, and they have different features based on their technology. They can be grouped into these categories (Alanazi, 2023).…”

Section: Introductionmentioning

confidence: 99%

A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm

Saeed,

M. El-Kenawy,

Ibrahim

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

“…This increased power draw can generate heat within the battery, potentially causing it to overheat if not managed effectively. Furthermore, when the battery is fully discharged, it may experience a buildup of heat and elevated temperatures due to the strain of providing power until it is completely drained [23]. This scenario can stress the battery and contribute to temperature-related issues.…”

Section: Electric Vehicle Batteriesmentioning

confidence: 99%

Investigations of phase change materials in battery thermal management systems for electric vehicles: a review

Dolla,

Fetene

2024

Mater. Res. Express

View full text Add to dashboard Cite

Taking advantage of electric vehicles' low pollution, the world is changing its face to electric vehicle (EV) production. As EVs rely heavily on specialized batteries, it's important to manage them safely and properly to prevent thermal runaway. High ambient temperatures and varied charging/discharging rates increases battery temperature. To address these challenges, Battery Thermal Management System (BTMS) come into play. This work focuses on passive cooling in BTMS, which is one of two categories of BTMS, with the other being active cooling using liquid-air systems. Passive BTMS has gained prominence in research due to its cost-effectiveness, reliability, and energy efficiency, as it avoids the need for additional components like pumps/fans. This article specifically discusses recent experimental studies regarding phase change material (PCM)-based thermal management techniques for battery packs. It explores methods for enhancing thermal conductivity in PCMs and identifies methodologies for BTMS experiments using PCMs. Also recommends the importance of optimization techniques like machine learning, temperature sensors, and state-of-charge management, to ensure accuracy and uniform temperature distribution across the pack. While paraffin wax has been a popular choice in experimental studies for its capacity to absorb and release heat during phase transitions, as a matter of its low thermal conductivity (0.2 to 0.3 Wk-1m-1) limits reaction in rapid charging/discharging of batteries. So integration with highly thermally conductive additives is recommended. Additives such as heat pipes offer superior thermal conductivity compared to expanded graphite (5 to 200 Wk-1m-1). As a result, the integration of heat pipes further reduces the temperature of battery by 28.9% in addition to the reduction of 33.6% by pure PCMs in time of high charge/discharge rates (5C to 8C). So high-conductivity additives correlate directly with improved thermal performance and are essential for maintaining optimal battery temperatures and overall reliability in EV battery packs.

show abstract

“…In this section, an overview and summary of the published articles are provided. In the first study, Chaudhary et al [1] synthesized cerium oxide (CeO 2 ) nanoparticles (NPs) using cerium nitrate as the precursor via a simple precipitation method. The authors used a calcination process to obtain the CeO 2 NPs.…”

Section: Summary Of the Contributed Articlesmentioning

confidence: 99%

“…Modern science and technology are central to the smooth running of daily life [1]. The modern world relies on gadgets and appliances such as mobile phones, computers, microwave ovens, air conditioners, televisions with remote controls, smoke detectors, LED lights, fans, and infrared (IR) thermometers, which provide support and enable us to interact with the physical environment [2].…”

Section: Introductionmentioning

confidence: 99%

Advanced Functional Materials for Electrochemical and Biosensors

Ahmad

2024

Biosensors

View full text Add to dashboard Cite

show abstract

Electric Vehicles: Benefits, Challenges, and Potential Solutions for Widespread Adaptation

Cited by 212 publications

References 93 publications

A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm

A novel voting classifier for electric vehicles population at different locations using Al-Biruni earth radius optimization algorithm

Investigations of phase change materials in battery thermal management systems for electric vehicles: a review

Advanced Functional Materials for Electrochemical and Biosensors

Contact Info

Product

Resources

About