The Impact of Charging Battery Electric Vehicles on the Load Profile in the Presence of Renewable Energy

Yu, Danilo; Adhikari, Min Prasad; Guiral, Aurelien; Fung, Alan S.; Mohammadi, Farahnaz; Raahemifar, Kaamran

doi:10.1109/ccece.2019.8861730

Cited by 19 publications

(6 citation statements)

References 18 publications

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“…During the acceleration phase, the electrical machine operates as a motor. It propels the vehicle, while during the deceleration, the electric machine operates like a generator and generates electric power that recharges the battery pack [2]. Hybrid electric vehicles can have either a parallel or series configuration of the engine and battery pack.…”

Section: Electric Vehicle Types and Their Architecturesmentioning

confidence: 99%

“…The declaration seeks to lower the global temperature caused by global warming by 2°C. The transportation industry alone contributes to about 23% of the global Green House Gases released [2]. Some Green House Gases whose concentrations increase include carbon dioxide, nitrous oxide, methane, hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), and ozone.…”

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confidence: 99%

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A review of electric vehicle technology: Architectures, battery technology and its management system, relevant standards, application of artificial intelligence, cyber security, and interoperability challenges

Macharia,

Garg,

Kumar

2023

IET Electrical Syst in Trans

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Currently, there is an increased global climate change awareness, and the world is tending towards net zero emissions. The transportation sector is leading in Green House Gas emissions (GHG). Furthermore, the diminishing reserves of fossil fuels, the need for cheaper maintenance vehicles, more efficient drive trains, and better performance vehicles presents a challenge in ICE. Electric Vehicles (EVs) offer the perfect mobility solution which can replace the conventional ICE in the near future. This article comprehensively reviews the components and advances in the various technologies employed in electric vehicles to achieve efficiency in motion and optimise energy management in electric vehicles. This article gives an analysis of the current EV scenario globally. It then details the different configurations of electric vehicle architectures available. The battery is discussed, and the various electrochemical technologies are analysed. Battery Management Systems (BMS) to efficiently manage energy are discussed. The charging methods, voltage levels, and relevant standards are outlined in detail. The traction motors and power conversion technologies are reported with advancements in electric vehicle applications. Furthermore, this article gives a comprehensive overview of various emerging technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI) & Machine Learning (ML), to improve EVs' performance through digitalisation. On the other hand, cybersecurity has become an ever‐challenging issue in EVs due to the increasing use of digital technologies. This article thoroughly overviews cybersecurity challenges in EV applications and explains possible proactive measures. Interoperability arising from various EV manufacturers' many different charging designs is discussed.

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Section: Electric Vehicle Types and Their Architecturesmentioning

confidence: 99%

mentioning

confidence: 99%

A review of electric vehicle technology: Architectures, battery technology and its management system, relevant standards, application of artificial intelligence, cyber security, and interoperability challenges

Macharia,

Garg,

Kumar

2023

IET Electrical Syst in Trans

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“…EVs consume a high amount of real power in a short period of time due to the nonlinear nature of their loads, and this can cause instability in the power networks [34]. Overloading of charging may also affect aspects of the grid and distribution network, depending on driving and charging behaviour, so EVs can have a significant impact on the power network's load curve [35]. The KSA generates an estimated 362 TWh of electricity, mainly from crude oil and natural gas, [36]; however, demand can exceed supply in the hot summer months, and the widespread adoption of EVs will place additional burdens on an overstretched grid.…”

Section: Effect On the Electricity Gridmentioning

confidence: 99%

Identification of Potential Barriers to Electric Vehicle Adoption in Oil-Producing Nations—The Case of Saudi Arabia

Alotaibi

Omer

2022

Electricity

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Electric vehicles (EVs) are important elements in the global strategy to tackle climate change; however, research often fails to sufficiently identify the range of barriers which affect their adoption. Taking Saudi Arabia as a case study, this paper analyses responses from 698 potential drivers in order to identify and rank the infrastructure, performance, financial, social, and policy barriers to EV adoption in a major oil-producing nation with a hot climate and a desert terrain. According to this study’s findings, the most important barriers in this context are the lack of charging infrastructure and the additional load placed on the national grid, while others include the safety and effectiveness of batteries at high temperatures, and the ability of EVs to perform in desert conditions. Common themes also include concerns that EVs may damage Saudi’s oil-based economy, cost of purchase and maintenance, low resale value, and the absence of awareness about EVs. The study concludes that EV manufacturers must demonstrate that their vehicles are suitable for the Saudi climate. Governments should also provide subsidies, or other incentives, to promote adoption of EVs as the study also found that variations in the cost of different EV models in Saudi Arabia, for example, the Tesla Model 3, is up to 40% more expensive to own than a Toyota Camry, mean that owning EVs can cost significantly more than small sized internal combustion engine-based vehicles (ICEVs). This paper identifies and ranks the barriers to EV ownership in a desert nation which is a leading petroleum producer and compares the relative costs of EVs and ICEVs in the country. As such, it has immediate relevance in countries with similar economic, geographic, and climatic conditions.

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“…Emissions from automobiles have been identified to constitute a considerable amount of total greenhouse emissions into the atmosphere. It accounts for almost a quarter with an exact value of 23% of global energy-related emissions [1]. In the United States (U.S.), greenhouse gas (GHG) emissions from automobiles account for about 29% of the total U.S. greenhouse gas emissions, making it the most significant contributor to U.S. GHG emissions [2].…”

Section: Introductionmentioning

confidence: 99%

Short-Term EV Charging Demand Forecast with Feedforward Artificial Neural Network

Effah,

Kwegyir,

Opoku

et al. 2023

JNTE

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The global increase in greenhouse gas emissions from automobiles has brought about the manufacture and usage of large quantities of electric vehicles (EVs). However, to ensure proper integration of EVs into the grid, there is a need to forecast the charging demand of EVs accurately. This paper presents a short-term electric vehicle charging demand forecast using a feedforward artificial neural network optimized with a modified local leader phase spider monkey optimization (MLLP-SMO) algorithm, a proposed variant of spider monkey optimization. A proportionate fitness selection is employed to improve the update process of the local leader phase of the spider monkey optimization. The proposed algorithm trains a feedforward neural network to forecast electric vehicle charging demand. The effectiveness of the proposed forecasting model was tested and validated with electric vehicle public charging data from the United Kingdom Power Networks Low Carbon London Project. The model's performance was compared to a feedforward neural network trained with particle swarm optimization, genetic algorithm, classical spider monkey optimization, and two conventional forecasting models, multi-linear regression and Monte Carlo simulation. The performance of the proposed forecasting model was assessed using the mean absolute percentage error of forecast and forecasting accuracy. The model produced a forecast accuracy and mean absolute percentage error of 99.88% and 3.384%, respectively. The results show that MLLP-SMO as a trainer predicted better than the other forecasting models and met industry standard forecast accuracy.

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The Impact of Charging Battery Electric Vehicles on the Load Profile in the Presence of Renewable Energy

Cited by 19 publications

References 18 publications

A review of electric vehicle technology: Architectures, battery technology and its management system, relevant standards, application of artificial intelligence, cyber security, and interoperability challenges

A review of electric vehicle technology: Architectures, battery technology and its management system, relevant standards, application of artificial intelligence, cyber security, and interoperability challenges

Identification of Potential Barriers to Electric Vehicle Adoption in Oil-Producing Nations—The Case of Saudi Arabia

Short-Term EV Charging Demand Forecast with Feedforward Artificial Neural Network

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