• Energy Research
• engineering and technology close
• QA close
• BH close
• Transport Research close

AbstractWith the evolution of the smart grid concept, the production of electric vehicles (EVs) is predicted to rise because of environmental concerns, technological advancements, and improvements in EV management. Vehicle‐to‐grid (V2G) is an enabling, realistic, and affordable technology to cope with a large number of EVs, increase energy sustainability, provide economical solutions, satisfy user‐side consumers, and facilitate power flow to the grid. Power electronics (PE) converters, particularly bidirectional power converters, are promising interfaces for V2G infrastructure because they determine the characteristics and functionalities of V2G. Therefore, this study provides an extensive review of the characteristics, technological aspects, and visions of V2G infrastructure. This review helps to identify the current state, most recent developments, and problems related to bidirectional interface topologies and control strategies in V2G infrastructure. It further examines the classification of chargers or dischargers based on numerous factors, including limitations and impacts. Furthermore, the benefits, challenges with possible mitigation solutions, and future outlooks in the implementation of V2G technology are discussed. This review is planned to serve as a reference for existing work in V2G frameworks, PE interfacing topologies, and control strategies, and to also facilitate a guideline for future work that can be implemented to flourish V2G technology.

Air pollution, which causes over seven million deaths per year, is the most significant and specifically related to health impacts. Nearly 90% of the urban population worldwide is exposed to pollution not meeting the World Health Organization guidelines for air quality. Many atmospheric carbon oxides, nitrogen oxides, and particulate matter emitting sources, such as inefficient energy and polluting transportation, directly impact health. Natural gas maritime transport from various parts of the world (carbon supplied to consuming areas) has become more critical. Natural gas liquefaction offers a cleaner and more efficient transportation option and also increases its storage capacity. It is expected that natural gas will reduce the human health impact compared with other traditional fuels consumed. This research establishes a life cycle assessment model of air emission and social human health impact related to LNG maritime transport to investigate the impact of each type of fuel used for the numerous maritime carriers. In order to build a model for air emissions and social human health impact assessments based on hypotheses on various unknown criteria, a calculation model is used. The results revealed Conventional-2 fuel type has the lowest human health impact for annual mode calculations, followed by Conventional-1, Q-Max, and finally Q-Flex. The analysis method for the per year demonstrated discrepancies in the relative human health impact due to the variation of the annual LNG demand by each destination and not only per the trip needs. The results show the importance of using a relatively cleaner fuel type such as Conventional-2 in reducing the health impact of LNG maritime transportation. Moreover, it shows differences in the air emissions as well as the human health impact based on the destination’s location and annual LNG demand.

Management of sustainable transportation currently is one of the most important aspects of a country's or a region's development from an economic and social point of view considering the net zero requirements The use of electric vehicles (EVs) is recognized as an essential means of achieving global net-zero emission goals. To promote their widespread adoption, the challenges they face must be addressed. These challenges are divided into several categories: infrastructure, adoption, costs, energy transition, awareness, and market-related challenges. Robust regulatory frameworks and incentive policies must be implemented to overcome most of these challenges. For EV adoption to increase rapidly and steadily, such frameworks must include fiscal and non-fiscal incentives that will encourage the masses to convert to EVs. The key findings of the work include identification of several barrier not widely discussed in the literature, emphasizing the need for non-fiscal incentives for EV adoption, and presenting a comprehensive analysis of various incentive policies alongside a detailed implementation framework. The implementation framework provides research directions for academics, engineers, policymakers, and industry stakeholders regarding further refinement and enhancement of policy incentives to facilitate the widespread adoption of EVs.

Environmental fluctuations, solar irradiance, and ambient temperature significantly affect photovoltaic (PV) system output. PV systems should be efficient at the Maximum Power Point in various weather climates to maximize their potential power output. The Maximum Power Point Tracking (MPPT) technique is employed to plan a specific location that yields the maximum amount of power. Operating dispersed alternative energy sources connected to the grid in this situation makes energy control an unavoidable task. This research article suggests designing a power electronics converter topology that links sustainable resources and electric vehicles to the power grid. There are four modes of operation for this proposed converter topology: grid-to-vehicle, vehicle-to-grid, renewable-to-vehicle, and renewable-to-grid discussed. The three power electronic converters and their uses are discussed, and their controllers are also designed to maintain the energy balance and stability in all cases. The battery characteristics indicate the operating mode. The work primarily focuses on the converter’s Triple Port Integrated Topology (TPIT) power flow and voltage control. Here, three power converters integrate the TPIT with three systems-the electric grid, renewable energy, and electric vehicles-into one system. The source battery and solar photovoltaic (PV) array cells are integrated using unidirectional and bidirectional DC-DC converters. The future scope of the work is to investigate the potential of adding additional ports for integrating other energy resources, such as hydrogen fuel cells or additional renewable sources, to create a more versatile and robust energy management system for EV charging stations.

Electric mobility is emerging all around the world to minimize environmental impacts, reduce dependency on petroleum, and diversify energy sources for transportation. Any emerging technology comes with uncertainties in terms of its environmental, economic, and social impacts on the global society, and history has shown that some technological changes have led also to great societal transformation thus shaping our future as humanity. Understanding, perceiving, and anticipating the potential changes are essential to managing as well as internalizing maximum benefits out of these technological advancements for a sustainable global community. In the literature, life cycle assessment approaches are mainly used to assess the potential environmental impacts of electric vehicles. Considering the potential impacts of emerging transportation technologies, traditional life cycle assessment is not sufficient to analyze economic and social impacts, ripple, side, or rebound effects, macro-economic impacts, and global-supply chain related impacts. In response to these knowledge gaps, traditional environmental life cycle assessment approaches are evolving into new more integrated, and broader approaches (e.g., life cycle sustainability assessment). This research aims to reveal research gaps in the sustainability assessment of electric vehicles and provide an outlook of the current state of knowledge, perspectives on research gaps, and potential ways for the adoption of integrated life-cycle modeling approaches. We conducted a comprehensive literature review focusing on sustainability assessment studies for emerging electric vehicle technologies for the period between 2009 and 2020 using the Scopus database. A total of 138 life cycle assessment studies focusing on electric and autonomous (electric) vehicles are analyzed. The reviewed studies are classified and analyzed based on sustainability indicators, life cycle approaches, life cycle phases, data sources and regions, and vehicle technology and class. We also compared the global warming potential of battery electric vehicles of different class sizes. According to the literature review, five major knowledge gaps are identified; 1) lack of socio-economic assessment, 2) lack of integrated modeling approaches and macro-level assessment; 3) limited consideration of end-of-life management and circular economy applications, 4) underrepresented developing world; 5) underrepresented emerging technologies. The findings of this review can help researchers worldwide to overview the state-of-art and state-of-practice in the field of sustainability assessment of emerging technologies and electric vehicles. This paper is an output of a project supported within the scope of the Qatar National Research Fund (QNRF), grant number NPRP13S-0203-200235. The authors acknowledge and appreciate QNRF for the generous continuous support for electric vehicle research at Qatar University.

To introduce the opportunities brought by plug-in hybrid electric vehicles (PHEVs) to the energy Internet, we propose a local vehicle-to-vehicle (V2V) energy trading architecture based on fog computing in social hotspots and model the social welfare maximization (SWM) problem to balance the interests of both charging and discharging PHEVs. Considering transaction security and privacy protection issues, we employ a consortium blockchain in our designed energy trading architecture, which is different from the traditional centralized power systems, to reduce the reliance on trusted third parties. Moreover, we improve the practical Byzantine fault tolerance (PBFT) algorithm and introduce it into a consensus algorithm, called the delegated proof of stake (DPOS) algorithm, to design a more efficient and promising consensus algorithm, called DPOSP, which greatly reduces resource consumption and enhances consensus efficiency. To encourage PHEVs to participate in V2V energy transactions, we design an energy iterative bidirectional auction (EIDA) mechanism to resolve the SWM problem and obtain optimal charging and discharging decisions and energy pricing. Finally, we conduct extensive simulations to verify the proposed DPOSP algorithm and provide numerical results for a comparison with the performance of the genetic algorithm and the Lagrange algorithm in achieving EIDA.

Electric vehicles (EVs) are commonly recognized as environmentally friendly modes of transportation. They function by converting electrical energy into mechanical energy using different types of motors, which aligns with the sustainable principles embraced by smart cities. The motors of EVs store and consume electrical power from renewable energy (RE) sources through interfacing connections using power electronics technology to provide mechanical power through rotation. The reliable operation of an EV mainly relies on the condition of interfacing connections in the EV, particularly the connection between the 3- $\phi $ inverter output and the brushless DC (BLDC) motor. In this paper, machine learning (ML) tools are deployed for detecting and classifying the faults in the connecting lines from 3- $\phi $ inverter output to the BLDC motor during operational mode in the EV platform, considering double-line and three-phase faults. Several machine learning-based fault identification and classification tools, namely the Decision Tree, Logistic Regression, Stochastic Gradient Descent, AdaBoost, XGBoost, K-Nearest Neighbour, and Voting Classifier, were tuned for identifying and categorizing faults to ensure robustness and reliability. The ML classifications were developed based on the datasets of healthy and faulty conditions considering the combination of six critical parameters that have significance in reliable EV operation, namely the current supplied to the BLDC motor from the inverter, the modulated DC voltage, output speed, and measured speed, as well as the output of the Hall-effect sensor. In addition, the superiority of the proposed fault detection and classification approaches using ML tools was assessed by comparing the detection and classification efficiency through some statistical performance parameter comparisons among the classifiers.

Diesel engine parameters, such as fuel and its additives, play an essential role in minimising the effects of engine vibration. This study aimed to use artificial neural networks (ANN) to model and analyse diesel engine vibration characteristics at different engine speeds using NH3 as an additive in hazelnut (HD), peanut (PD), and waste-cooking oil (WD) biodiesels. The results showed good correlations between the ANN models and experimental results using regression analysis methods. The ANN models for diesel engines showed high accuracy. The ANN models indicated that a 5 % NH3 additive decreased engine vibration for HD and PD.In comparison, 10 % and 15 % NH3 additive ratios increased engine vibration for HD, PD, and WD due to low combustion quality. The lowest vibration levels occurred with P100, P95A5, P90A10, and P85A15 at 1200 rpm. H100 and H95A5 produced the highest diesel engine resultant vibration (DERV) values. All ANN models generated the lowest and highest DERV values at 1200 rpm and 2100 rpm, respectively. The RMS method showed that H95A5, P85A15, and W85A15 contributed the most to diesel engine vibration. Using a low amount of NH3 additive positively affected DERV for HD and PD but not for WD.