scholarly journals Exploring the Energy Efficiency of Electric Vehicles with Driving Behavioral Data from a Field Test and Questionnaire

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Kezhen Hu ◽  
Jianping Wu ◽  
Mingyu Liu
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Field Test ◽  
Electric Vehicles ◽  
Field Tests ◽  
Driving Behavior ◽  
Future Research ◽  
Driving Style ◽  
Traffic Conditions ◽  
Energy Consumption Prediction

With increasing concerns about urban air quality and carbon emissions, electric vehicles (EVs) have gained popularity in megacities, especially in Europe and Asia. The energy consumption of EVs has subsequently caught researchers’ attention. However, the exploration of energy consumption of EVs has largely focused on people’s revealed driving behavior and rarely touched on their self-perception of driving styles. In this paper, we developed a more human-centric approach, aiming to investigate how the energy efficiency of EVs is shaped by the driving behavior and driving style in the urban scenario from field test data and driving style questionnaires (DSQs). Field tests were carried out on a designated route for a total of 13 drivers in the city of Beijing, where vehicle operation parameters were recorded under both congested and smooth traffic conditions. DSQs were collected from a larger pool of drivers including the field test drivers to be applied to driving style factor analysis. The results of a correlation analysis demonstrate the dynamic interaction between drivers’ revealed behavior and stated driving style under different traffic conditions. We also proposed an energy consumption prediction model with the fusion of collected driving parameters and DSQ data and the result is promising. We hope that this study would draw inspiration for future research on people’s transitioning driving behavior in an electric-mobility era.

scholarly journals Differences in Energy Consumption in Electric Vehicles: An Exploratory Real-World Study in Beijing

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Kezhen Hu ◽  
Jianping Wu ◽  
Tim Schwanen
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Electric Vehicles ◽  
Real World ◽  
Time Series Data ◽  
Urban Traffic ◽  
Environmental Benefits ◽  
Series Data ◽  
Traffic Conditions ◽  
Infrastructure Design

Electric vehicles (EVs) are widely regarded as a promising solution to reduce air pollution in cities and key to a low carbon mobility future. However, their environmental benefits depend on the temporal and spatial context of actual usage (journey energy efficiency) and the rolling out of EVs is complicated by issues such as limited range. This paper explores how the energy efficiency of EVs is affected and shaped by driving behavior, personal driving styles, traffic conditions, and infrastructure design in the real world. Tests have been conducted with a Nissan LEAF under a typical driving cycle on the Beijing road network in order to improve understanding of variations in energy efficiency among drivers under different urban traffic conditions. Energy consumption and operation parameters were recorded in both peak and off-peak hours for a total of 13 drivers. The analysis reported in this paper shows that there are clear patterns in energy consumption along a route that are in part related to differences in infrastructure design, traffic conditions, and personal driving styles. The proposed method for analyzing time series data about energy consumption along routes can be used for research with larger fleets of EVs in the future.

scholarly journals Evaluation of the influence of terrain and traffic road conditions on the driver’s driving performances by applying machine learning

2021 ◽  
pp. 355-355
Author(s):  
Davor Vujanovic ◽  
Sladjana Jankovic ◽  
Marko Stokic ◽  
Stefan Zdravkovic
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Co2 Emission ◽  
Aggressive Driving ◽  
Driving Style ◽  
The Road ◽  
Traffic Conditions ◽  
Road Section ◽  
Vehicle Fleet ◽  
Cargo Transportation

In this paper, research is done in the influence of different terrain and traffic conditions on road sections on the driver?s driving performances, i.e. on the car energy efficiency and CO2 emission. A methodology aimed at determining to which extent unfavorable traffic and/or terrain conditions on a road section contribute to the driver?s worse driving performances, and also to determine when the driver?s aggressive driving style is responsible for greater fuel consumption and greater CO2 emission is proposed. In order to apply the proposed methodology, a research study was carried out in a cargo transportation company and 12 drives who drove the same vehicle on five different road sections were selected. As many as 284 014 of the instances of the data about the defined parameters of the road section and the driver?s driving style were collected, based on which and with the help of machine learning a prediction of the scores for the road section and the scores for the driver?s driving style was performed. The obtained results have shown that the proposed methodology is a useful tool for managers enabling them to simply and quickly determine potential room for increasing the energy efficiency of the vehicle fleet and decreasing CO2 emission.

scholarly journals Photovoltaic systems – types of installations, materials, monitoring and modeling - review

2020 ◽  
pp. 40-49 ◽  
Author(s):  
Angelika Anduła ◽  
Dariusz Heim
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Residential Buildings ◽  
Future Research ◽  
Photovoltaic Systems ◽  
Zero Energy ◽  
Thermal Systems ◽  
Photovoltaic Panels ◽  
Common Solution

Photovoltaic systems have become a common solution for, both small residential buildings as well as large service buildings. When buildings are being designed, it is important to focus on the aspect of the object’s energy efficiency as lowering the energy consumption of a given facility is crucial. The article discusses the use of photovoltaic panels such as so-called BAPV (Building Applied Photovoltaics) and BIPV (Building Installed Photovoltaics) installations as well as photovoltaic thermal systems (PV/T), which generate both electricity and heat. The role of PV installation in so-called zero energy buildings and proposals for future research and solutions are also discussed.

scholarly journals Software energy consumption prediction using software code metrics

2021 ◽  
Author(s):  
Sedef Akinli Koçak
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Prediction Models ◽  
Code Size ◽  
Research Attention ◽  
Software Products ◽  
Code Metrics ◽  
Software Code ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

In recent years, a significant amount of energy consumption of ICT products has resulted in environmental concerns. Growing demand for mobile devices, personal computers, and the widespread adaptation of cloud computing and data centers are the main drivers for the energy consumption of the ICT systems. Finding solutions for improving the energy efficiency of the systems has become an important objective for both industry and academia. In order to address the increase in ICT energy consumption, hardware technology, such as production of energy efficient processors, has been substantially improved. However, demand for energy is growing faster than improvements are being made on these energy-aware technologies. Therefore, in addition to hardware, software technologies must also be a focus of research attention. Although software does not consume energy by itself, its characteristics determine which hardware resources are made available and how much electrical energy is used. Current literature on the energy efficiency of software, highlights, in particular, a lack of measurements and models. In this dissertation, first, the relationship between software code properties and energy consumption is explored. Second, using static code metrics regression based energy consumption prediction models are investigated. Finally, the models performance are assessed using within product and cross-product energy consumption prediction approaches. For this purpose, a quantitative based retrospective cohort study was employed. As research methods, observational data collection, mining software repositories, and regression analysis were utilized. This research results show inconsistent relationships between energy consumption and code size and complexity attributes considering different types of software products. Such results provide a foundation of knowledge that static code attributes may give some insights but would not be the sole predictors of energy consumption of software products.

scholarly journals Exhaust Emissions and Energy Consumption Analysis of Conventional, Hybrid, and Electric Vehicles in Real Driving Cycles

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6423
Author(s):  
Jacek Pielecha ◽  
Kinga Skobiej ◽  
Karolina Kurtyka
Keyword(s):  
Energy Consumption ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Carbon Dioxide Emissions ◽  
Gasoline Engine ◽  
Exhaust Emissions ◽  
Hybrid Vehicles ◽  
The European Union ◽  
Passenger Cars ◽  
Traffic Conditions

One of the environmental aims of the European Union is to achieve climate neutrality by 2050. According to European Parliament data, transport emissions accounted for about 25% of global carbon dioxide emissions in 2016, in which road transport had the largest share (approximately 72%). This phenomenon is particularly visible in urban agglomerations. The solution examples are the popularization of hybrid vehicles and the development of electromobility. The aim of this paper is an assessment of the energy consumption and exhaust emissions from passenger cars fitted with different powertrains in actual operation. For the tests, passenger cars with conventional engines of various emission classes were used as well as the latest hybrid vehicles and an electric car. It enabled a comparative assessment of the energy consumption under different traffic conditions, with particular emphasis on the urban phase and the entire RDE (Real Driving Emissions) test. The results were analyzed to identify changes in these environmental factors that have occurred with the technical advancement of vehicles. The lowest total energy consumption in real traffic conditions is characteristic of an electric vehicle; the plug-in hybrid vehicle with a gasoline engine is about 10% bigger, and the largest one is a combustion vehicle (30% bigger than an electric vehicle). These data may contribute to the classification of vehicles and identification of advantages of the latest developments in conventional, hybrid, and electric vehicles.

Energy efficiency of machining operations: A review

2016 ◽  
Vol 231 (11) ◽  
pp. 1871-1889 ◽  
Author(s):  
Mariyeh Moradnazhad ◽  
Hakki Ozgur Unver
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Metal Cutting ◽  
Cutting Parameters ◽  
Future Research ◽  
Manufacturing Processes ◽  
Comprehensive Overview ◽  
Cutting Processes ◽  
Machining Operations ◽  
Optimization Of Cutting Parameters

Manufacturing processes are among the most energy intensive on earth. As negative ecological and economic impacts increase, reducing energy consumption is becoming critically important. In this article, a comprehensive overview of energy-saving strategies and opportunities for increasing energy efficiency in manufacturing operations is presented, with a focus on metal cutting processes. The issues and approaches involved in energy efficiency of machine tools and machining operations are reported in the literature and a structured research methodology is proposed for this purpose including prediction and modelling of machine energy consumption, determining the relationship between process energy consumption and process variables for material removal processes and optimization of cutting parameters in order to reduce energy consumption. Numerous techniques for increasing energy efficiency in manufacturing processes are identified and summarized, strengths and weaknesses of previous studies are discussed and potential avenues for future research are suggested.

scholarly journals Optimal Energy Management of Plug-In Hybrid Electric Vehicles Concerning the Entire Lifespan of Lithium-Ion Batteries

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2543 ◽  
Author(s):  
Zeyu Chen ◽  
Jiahuan Lu ◽  
Bo Liu ◽  
Nan Zhou ◽  
Shijie Li
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Lithium Ion Batteries ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Lithium Ion ◽  
Energy Management Strategy ◽  
Optimal Energy Management ◽  
Hybrid Electric

The performance of lithium-ion batteries will inevitably degrade during the high frequently charging/discharging load applied in electric vehicles. For hybrid electric vehicles, battery aging not only declines the performance and reliability of the battery itself, but it also affects the whole energy efficiency of the vehicle since the engine has to participate more. Therefore, the energy management strategy is required to be adjusted during the entire lifespan of lithium-ion batteries to maintain the optimality of energy economy. In this study, tests of the battery performances under thirteen different aging stages are involved and a parameters-varying battery model that represents the battery degradation is established. The influences of battery aging on energy consumption of a given plug-in hybrid electric vehicle (PHEV) are analyzed quantitatively. The results indicate that the variations of capacity and internal resistance are the main factors while the polarization and open circuit voltage (OCV) have a minor effect on the energy consumption. Based on the above efforts, the optimal energy management strategy is proposed for optimizing the energy efficiency concerning both the fresh and aging batteries in PHEV. The presented strategy is evaluated by a simulation study with different driving cycles, illustrating that it can balance out some of the harmful effects that battery aging can have on energy efficiency. The energy consumption is reduced by up to 2.24% compared with that under the optimal strategy without considering the battery aging.

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