scholarly journals Combined Economic and Emission Dispatch Incorporating Renewable Energy Sources and Plug-In Hybrid Electric Vehicles

2014 ◽  
Vol 4 (2) ◽  
pp. 60 ◽  
Author(s):  
Amin Gholami ◽  
Mahdi Jamei ◽  
Javad Ansari ◽  
Arif I. Sarwat
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Hybrid Electric ◽  
Emission Dispatch

scholarly journals Electric Vehicle and its Types

2021 ◽  
Vol 9 (VII) ◽  
pp. 3553-3555
Author(s):  
P. M. Tripathi
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Fossil Fuels ◽  
Hybrid Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Additional Energy ◽  
Charging Infrastructure ◽  
Hybrid Electric ◽  
The Impact

Electric vehicles are an important option for reducing greenhouse gas emissions. Electric vehicles not only reduce dependence on fossil fuels, but also reduce the impact of ozone-depleting substances and promote widespread adoption of renewable energies. Despite extensive research into the properties and characteristics of electric vehicles as well as the nature of their charging infrastructure, electric vehicle construction and grid modeling continue to evolve and become limited. regime. This paper presents market penetration surveys for electric vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles and battery electric vehicles, and describes optimal engineering and modeling approaches. their differences. Research on critical barriers and inadequate charging equipment targets developing countries like India, which makes the study unique. The development of the new Vehicle to Grid concept has created additional energy sources when renewable energy sources are not available. We conclude that considering the specific characteristics of an electric vehicle is important in the mobility of the electric vehicle.

Machine learning roles in advancing the power network stability due to deployments of renewable energies and electric vehicles

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Mohamad Nassereddine
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Electric Vehicles ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Power Network ◽  
Network Stability ◽  
Fast Charging ◽  
Electrical Loads ◽  
Charging Stations

AbstractRenewable energy sources are widely installed across countries. In recent years, the capacity of the installed renewable network supports large percentage of the required electrical loads. The relying on renewable energy sources to support the required electrical loads could have a catastrophic impact on the network stability under sudden change in weather conditions. Also, the recent deployment of fast charging stations for electric vehicles adds additional load burden on the electrical work. The fast charging stations require large amount of power for short period. This major increase in power load with the presence of renewable energy generation, increases the risk of power failure/outage due to overload scenarios. To mitigate the issue, the paper introduces the machine learning roles to ensure network stability and reliability always maintained. The paper contains valuable information on the data collection devises within the power network, how these data can be used to ensure system stability. The paper introduces the architect for the machine learning algorithm to monitor and manage the installed renewable energy sources and fast charging stations for optimum power grid network stability. Case study is included.

scholarly journals A Novel Deep Learning-Based State-of-Charge Estimation for Renewable Energy Management System in Hybrid Electric Vehicles

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 260
Author(s):  
Mahendiran T. Vellingiri ◽  
Ibrahim M. Mehedi ◽  
Thangam Palaniswamy
Keyword(s):  
Renewable Energy ◽  
Deep Learning ◽  
Energy Management ◽  
Electric Vehicles ◽  
Management System ◽  
Hybrid Electric Vehicles ◽  
State Of Charge ◽  
Accurate Estimation ◽  
Soc Estimation ◽  
Hybrid Electric

In recent years, alternative engine technologies are necessary to resolve the problems related to conventional vehicles. Electric vehicles (EVs) and hybrid electric vehicles (HEVs) are effective solutions to decarbonize the transportation sector. It also becomes important to shift from traditional houses to smart houses and from classical vehicles to EVs or HEVs. It is needed to combine renewable energy sources (RESs) such as solar photovoltaics, wind energy systems, and various forms of bio-energies. Among various HEV technologies, an effective battery management system (BMS) still remains a crucial issue that is majorly used for indicating the battery state of charge (SOC). Since over-charging and over-discharging result in inevitable impairment to the batteries, accurate SOC estimation desires to be presented by the BMS. Although several SOC estimation techniques exist to regulate the SOC of the battery cell, it is needed to improvise the SOC estimation performance on HEVs. In this view, this paper focuses on the design of a novel deep learning (DL) with SOC estimation model for secure renewable energy management (DLSOC-REM) technique for HEVs. The presented model employs a hybrid convolution neural network and long short-term memory (HCNN-LSTM) model for the accurate estimation of SOC. In order to improve the SOC estimation outcomes of the HCNN-LSTM model, the barnacles mating optimizer (BMO) is applied for the hyperpower tuning process. The utilization of the HCNN-LSTM model makes the modeling process easier and offers a precise depiction of the input–output relationship of the battery model. The design of BMO based HCNN-LSTM model for SOC estimation shows the novelty of the work. An extensive experimental analysis highlighted the supremacy of the proposed model over other existing methods in terms of different aspects.

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