scholarly journals Energy Efficiency of Electric Vehicles

10.5772/55237 ◽  
2012 ◽  
Author(s):  
Zoran Stevic ◽  
Ilija Radovanovic
Keyword(s):  
Energy Efficiency ◽  
Electric Vehicles

scholarly journals Towards DC Energy Efficient Homes

2021 ◽  
Vol 11 (13) ◽  
pp. 6005
Author(s):  
Daniel Villanueva ◽  
Moisés Cordeiro-Costas ◽  
Andrés E. Feijóo-Lorenzo ◽  
Antonio Fernández-Otero ◽  
Edelmiro Miguez-García
Keyword(s):  
Energy Efficiency ◽  
Electric Vehicles ◽  
Energy Efficient ◽  
Electricity Consumption ◽  
Real Data ◽  
Mean Values ◽  
Led Lighting ◽  
Shed Light

The aim of this paper is to shed light on the question regarding whether the integration of an electric battery as a part of a domestic installation may increase its energy efficiency in comparison with a conventional case. When a battery is included in such an installation, two types of electrical conversion must be considered, i.e., AC/DC and DC/AC, and hence the corresponding losses due to these converters must not be forgotten when performing the analysis. The efficiency of the whole system can be increased if one of the mentioned converters is avoided or simply when its dimensioning is reduced. Possible ways to achieve this goal can be: to use electric vehicles as DC suppliers, the use of as many DC home devices as possible, and LED lighting or charging devices based on renewables. With all this in mind, several scenarios are proposed here in order to have a look at all possibilities concerning AC and DC powering. With the aim of checking these scenarios using real data, a case study is analyzed by operating with electricity consumption mean values.

scholarly journals Energy efficiency trade-offs in small to large electric vehicles

2020 ◽  
Vol 32 (1) ◽  
Author(s):  
Martin Weiss ◽  
Kira Christina Cloos ◽  
Eckard Helmers
Keyword(s):  
Energy Efficiency ◽  
Electric Vehicles ◽  
Trade Offs

scholarly journals A Multitier Approach to Estimating the Energy Efficiency of Urban Passenger Mobility

2020 ◽  
Vol 12 (24) ◽  
pp. 10263
Author(s):  
Daniel Neves Schmitz Gonçalves ◽  
Renata Albergaria de Mello Bandeira ◽  
Mariane Gonzalez da Costa ◽  
George Vasconcelos Goes ◽  
Tássia Faria de Assis ◽  
...  
Keyword(s):  
Decision Making ◽  
Energy Efficiency ◽  
Electric Vehicles ◽  
Complex Nature ◽  
Decision Making Process ◽  
Data Sets ◽  
Urban Mobility ◽  
Energy Data ◽  
Wide Range ◽  
Motorized Transport

As society has experiences new modes of mobility in recent years, cities have planned to increase their energy efficiency as a way of reducing environmental impacts and promoting economic development. However, governments face difficulties in establishing mechanisms to determine the best actions in the management of urban mobility regarding energy efficiency and to elaborate a ranking of cities based on energy efficiency in order to better allocate resources. This is due to the complex nature of obtaining a wide range of activity and energy data from a single municipality, especially in data-scarce regions. This paper develops and applies a model for estimating the energy efficiency of urban mobility that is applicable to different contexts and backgrounds. The main contribution of the article is the use of a multitier approach to compare and adjust outputs, considering different transport configurations and data sets. The results indicate that variations in vehicle occupancy and individual motorized transport rates have a significant impact on energy efficiency, which reached 0.70 passenger-kilometers/MJ in Sorocaba, Brazil. However, as the use of electric vehicles increases in this city, this scenario is expected to change. Additionally, the method has been proven to be an important mechanism for benchmarking purposes and for the decision-making process for transport investments.

Forecasting the Ecology Effects of Electric Cars Deployment in Krasnodar Region: Learning Curves Approach

2018 ◽  
Vol 9 (1) ◽  
pp. 82 ◽  
Author(s):  
Svetlana RATNER ◽  
Marina ZARETSKAYA
Keyword(s):  
Energy Efficiency ◽  
Electric Vehicles ◽  
Environmental Effects ◽  
Combustion Engine ◽  
Environmental Indicators ◽  
Learning Curves ◽  
Transport Systems ◽  
Clear Understanding ◽  
Electric Cars ◽  
Krasnodar Region

One of the most urgent problems of modern urban agglomerations is the optimization of the structure and technological maintenance of transport systems. As one of the options to solve this problem, the development of electric vehicles (EV) is usually suggested. But the scientific community has still not developed a clear understanding of whether electric vehicles are a better alternative to traditional cars, considering all environmental indicators. The aim of this work is to develop a method of forecasting the environmental effects of diffusion of EV technologies and test it on the example of the Krasnodar region of Russia as a region with the highest motorization ratios in the country, a complicated ecologic situation in large cities, a high population density and a modern structure for energy generation.  The technical progress in energy efficiency of each technology is taken into consideration. We use learning theory as a methodological framework, which is common for solution of problems of forecasting technological development. According to the calculations, the total emissions from private motor vehicles, with an increase in energy efficiency of vehicles with internal combustion engine and increase penetration of electric vehicles should decrease in 2025 by 15% comparing business-as-usual scenario, despite a significant increase in the level of motorization (almost 65%). Thus, a wide spread of EV technologies is preferable from an environmental point of view. The proposed approach to predict the environmental effects of diffusion of EV technologies allows us to estimate the reduction in emissions from road transport in any region while maintaining the direction and speed of the following key trends: the growth of energy efficiency and environmental performance of traditional cars with combustion engines, the growth of the level of motorization of the population in Russia, and reduction of EVs costs. Additional effects of stimulating (or de-stimulating) policies are not considered in this model.

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