• Energy Research
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• 11. Sustainability close
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Electric vehicles are an important part of governments’ environmental policies, and therefore understanding the factors affecting their market share is very important. So, this research is designed to investigate the factors affecting electric vehicle adoption, considering the effects of the COVID-19 pandemic and sustainable development level. Effective factors have been investigated in three categories. One is the characteristics of electric vehicles; the other is the impact of the COVID-19 pandemic on demand for these vehicles; and finally, the impact of the level of sustainable development of countries on adopting electric vehicles. Our analysis method is based on grey econometric and grey regression methods. The results show that vehicle dimensions, battery warranty conditions, battery life, and charging facilities are effective factors in the field of vehicle characteristics that can increase the adoption of electric vehicles. Also, the analysis shows that the COVID-19 pandemic has reduced the adoption of electric vehicles. Finally, we have shown that the market share of electric vehicles is higher in countries with a higher sustainable development level because of better economic, social, and cultural infrastructures.

Smart integration of the upcoming proliferation of electric forms of transport in our energy system is the key in order to make it more robust and ready for a greener future. As such, technologies as Vehicle to Home are key in order to optimize the house consumption on an individual level. This paper develops the key blocks of a modified Vehicle to Home system, where in addition to the EV, a stationary battery pack is added. The paper presents models for household consumption, bidirectional AC/DC converter, its control system, dc/dc controllers, and their controllers. The goal is to design a system capable of consuming a desired power over time from the AC grid. Any energy difference is used to charge or discharge the available energy sources. As such, optimal control strategies are developed for the control of both the charging/discharging process. The presented optimized models allow for system level simulation for several weeks on a typical computer. Using this model some allows bot sizing the dc energy storage needed, along with comparison of different control algorithms for the power converters.

Electrification of public utility vehicles plays a vital role in the transition towards a more sustainable transport system. However, the adoption of electric vehicles (EVs) encounters varying challenges ranging from financing issues, government policies, and public acceptance. Using the Philippines as a case, this research applies political, economic, social, technological, legal, and environmental (PESTLE) analysis to determine how different drivers affect the adoption of EVs in the public transport system from various transport stakeholders’ vantage points. Survey results identified economic and technological factors as the main barriers to the adoption of electric public transport. This includes high investment and operational costs, lack of charging infrastructure, issues in driving range and use in different terrains, and the availability of EV parts and repair stations. On the other hand, the main enabler is the significant public support for the modernization of the public transport system through EVs, backed up by policy and legal drivers. For a zero-emission public transport system, this study recommends that the government should invest in sustainable sources of energy, develop more public infrastructure, diversify the transport sector, fund the development of locally made EVs, and initiate a massive information campaign in educating the public of its advantages.

This study investigates the spatiotemporal distribution of electric vehicle (EV) charging demands and operating efficiency of the charging system. The travel behavior of EV drivers is analyzed by considering the heterogeneity of range anxiety and bound rationality. Building on existing charging choice models, our more holistic perspective of charging demand distribution is obtained through multiagent system (MAS) modeling. In our study, the charging demand distribution in different areas is compared by considering two charging price schemes. The performance of the charging system is then evaluated based on three indicators, the charging request rejection rate, charging pile utilization rate, and charging load deviation, thereby verifying the effectiveness of our charging demand prediction model. This is done using multiagent-based simulations applied to the National Household Travel Survey (NHTS) 2017 dataset. The results are analyzed according to different key indicators, mainly indicating (a) the limitations of the time-of-use (TOU) pricing strategy in reducing the peak-valley difference, (b) the transferability of charging demand among different functional areas, and (c) that there is less demand for charging piles in the workplace, as users mainly rely on home charging. These results facilitate further analyses to help the design and operation of the EV charging infrastructure.

In the context of data eruption, the data often show a short-term pattern and change rapidly which makes it difficult to use a single real value to express. For this kind of small-sample and interval data, how to analyze and predict multi-factor sequences efficiently becomes a problem. By this means, grey system theory (GST) is developed in which the interval grey numbers, as a typical object of GST, characterize the range of data and the grey relational and prediction models analyze the relations of multiple grey numbers and forecast the future. However, traditional grey relative relational model has some limitations: the results obtained always show low resolution, and there are no extractions for the interval feature information from the interval grey number sequence. In this paper, the grey relational analysis model (GRA) based on effective information transformation of interval grey numbers is established, which contains comprehensive information of area differences and slope variances and optimizes the resolution of traditional grey degree. Then, according to the relational results, the multivariable GM model (GM (1, N)) is proposed to forecast the interval grey number sequence. To verify the effectiveness of this novel model, it is established to analyze the relationship between the degree of traffic congestion and its relevant factors in the Yangtze River Delta of China and predict the development of urban traffic congestion degrees in this area over the next 5 years. In addition, some traditional statistical methods (principal component analysis, multiple linear regression models and curve regression models) are established for comparisons. The results show high performances of the novel GRA model and GM (1, N) model, which means the models proposed in this paper are suitable for interval grey numbers from regional data. The strengths which recommend the use of this novel method lie in its high recognition mechanism and multi-angle information transformation for interval grey numbers as well as its characteristic of timeliness in information processing.

Traditional fuel-powered vehicle emissions have long been recognized as a major barrier to a sustainable environment, and their minimization could ensure both economic support for the sustainable societal fundament and pollution prevention. Electrifying light-duty vehicle fleets, such as taxis, could provide a financial return as well as bring significant economic and environmental improvements. This paper offers a ranked selection of electric vehicles that are presently available on the market, as reviewed by taxi service representatives, as well as their own evaluation of the criteria that influence this selection. This paper provides stability and support when making decisions by deploying stepwise weight assessment ratio analysis and a modified standard deviation method for calculating the subjective and objective weights of the criteria, as well as performing sensitivity analysis to determine how a particular criterion affects the multi-attributive border approximation area. A comparison ranking of the alternatives discovered how a change in the weight value of one of the criteria affected the ranking of the electric vehicle alternatives. According to the research, led by the battery capacity criterion and its values, the Volkswagen ID.3 Pro has the best results and is the taxi of choice in the Brčko District of Bosnia and Herzegovina. Furthermore, the research has demonstrated that the development of electric vehicles for taxi service purposes should strive to extend the range of these vehicles while reducing the battery charging time.

The number of road vehicles is growing every year. At the same time, the negative impact of cars on the environment is increasing. For many countries, the environmental problem is already coming to the fore and opens up various ways to solve it. One of these ways could be the transition to electric vehicles running on environmentally friendly energy. In many countries, the transition to electric vehicles as an environmentally friendly mode of transport is adopted at the state level, through additional incentive systems and benefits. The spread of electric vehicles in Russia is not very active. One of the reasons is the lack of the necessary infrastructure – charging stations. The purpose of this research is to substantiate the economic efficiency of the construction and operation of an electric filling station (charging station). When performing the calculations, the methods of statistical analysis, the method of calculating the net present value of the project (NPV), the graphical method of presenting the final results of the study were used. Calculations showed that the net present value of the project for the construction and operation of the electric filling station will be 181 952 571 rubles during 9 years of operation with an annual energy consumption of 2 073 600 kWh. This research is of the greatest interest to private and public investors, as it is based on both environmental component and economic efficiency.

Natural gas is cheaper than fuel on an energy basis, making it an alternative ship fuel which leads to a reduced operating cost and clean gas environmental conditions. The current study analyses the retrofit of an ageing multi-purpose ship to use liquefied natural gas as a primary ship fuel in the context of a short-ship sea operation. The objective is to transform an existing commercial ageing ship propulsion system into a green energy propulsion one and to analyse the economic feasibility considering the high volatility and increased LNG price. Four scenarios were analysed based on the net present value representing Denying, Disinterested, Good and Acceptable financial cash outflow. It was concluded that in the present economic instability and price of LNG fuel and CO2 taxes, the ship owner needs to rely on the long-term contract of buying LNG fuel to implement measures to reduce greenhouse gas emissions and keep good benefits in shipping.