Greener plug-in hybrid electric vehicles incorporating renewable energy and rapid system optimization

Energy ◽  
2016 ◽  
Vol 111 ◽  
pp. 971-980 ◽  
Author(s):  
Xiaosong Hu ◽  
Yuan Zou ◽  
Yalian Yang
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
System Optimization ◽  
Hybrid Electric

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.

scholarly journals ANN Based Current Controller for Hybrid Electric Vehicles

2021 ◽  
Vol 309 ◽  
pp. 01065
Author(s):  
Kavati Nagendar ◽  
V. Vijaya Rama Raju
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
System Optimization ◽  
Power Delivery ◽  
Control Technique ◽  
Vehicle To Grid ◽  
Charging System ◽  
Current Controller ◽  
Hybrid Electric ◽  
Artificial Neural Network Ann

The use of Hybrid Electric Vehicles (HEVs) across the world is growing enormously every day. The single-phase bi-directional convertors are presented in this study for HEVs on-board charging(OBC). In HEVs, we use power electronics converters for the converting and inverting operations. Artificial Neural Network(ANN) is presented in this study for simple operation and high optimization approaches. ANN control technique regulates the system's THD and enhances charging system optimization, enables two-way power delivery that is from the grid to vehicle and the vehicle to grid. An ANN based current controller model that achieves fast-dynamic reaction and that improves grid current harmonic characteristics is proposed in this study. The system's THD is reduced by the ANN controller being suggested. The results prove the validity and feasibility of design and control technique of the proposed integrated charging system.

scholarly journals Performance of Two-Area Interconnected Power System with High Wind Power Penetration in Presence of Plug-in Hybrid Electric Vehicles

2018 ◽  
Vol 14 (30) ◽  
pp. 311
Author(s):  
Mahmoud Essam Harby ◽  
Said Elsayed Elmasry ◽  
Adel Elsamahy ◽  
Luis Marroyo ◽  
Javier Marcos
Keyword(s):  
Renewable Energy ◽  
Power System ◽  
Wind Energy ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Power Grids ◽  
Energy Resources ◽  
Renewable Energy Resources ◽  
Interconnected Power System ◽  
Hybrid Electric

Most of existing power grids are designed neither by latest technologies nor to comply with quickly climate changes, the new intelligent power grids are urgently needed and will soon be applied to the power markets. In the smart grid, the large-scale renewable energy contribution tends to expand vastly. This paper is focusing on the wind energy. Wind’s inherent intermittency and unpredictability make its increased penetration into the power system grids an area requiring significant analysis and research. Notwithstanding, because of the changeable nature of the wind energy, this may lead to a high oscillation on the power system frequency. From another aspect, A lot of scientific research is searching for smart solutions and tools to support and enhance the integration of the renewable energy resources into the electrical grids without additional costs for the power system, so the world scientific research is directed to exploit the plugin hybrid electric vehicles (PHEVs) which are considered as the sustainable and environmental friendly transportation system in the next period around the world. PHEVs are considered as the scattered batteries, which will enhance the integrating of the renewable energy resources into the electrical power system. In this paper, the performance of two-area interconnected power system with high wind energy penetration is analyzed in the presence of the plug-in hybrid electric vehicles when using Ziegler and Nicholas method (Nguyen & Mitra, 2018).

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