Impacts of road conditions on the energy consumption of electric vehicular flow

2017 ◽  
Vol 31 (11) ◽  
pp. 1750121 ◽  
Author(s):  
Hong Xiao ◽  
Hai-Jun Huang ◽  
Tie-Qiao Tang
Keyword(s):  
Energy Consumption ◽  
Traffic Flow ◽  
Rarefaction Wave ◽  
Small Perturbation ◽  
Electricity Consumption ◽  
Numerical Results ◽  
Consumption Model ◽  
Positive Impacts

In this paper, we use the electricity consumption model for electric vehicular flow [H. Xiao, H. J. Huang and T. Q. Tang, Mod. Phys. Lett. B 30 (2016) 1650325] to study the effects of road conditions on the electricity consumption of electric vehicular flow during the evolutions of shock, rarefaction wave and small perturbation. The numerical results indicate that road conditions have negative influences on the electricity consumption during the evolutions of shock and rarefaction wave (i.e. the electricity consumption increases when road conditions become better) and positive impacts on the electricity consumption during the evolution of small perturbation when the traffic flow is unstable (i.e. the electricity consumption produces oscillation, but its amplitude decreases when road conditions become better).

An electricity consumption model for electric vehicular flow

2016 ◽  
Vol 30 (26) ◽  
pp. 1650325 ◽  
Author(s):  
Hong Xiao ◽  
Hai-Jun Huang ◽  
Tie-Qiao Tang
Keyword(s):  
Traffic Flow ◽  
Rarefaction Wave ◽  
Small Perturbation ◽  
Electricity Consumption ◽  
Uniform Flow ◽  
Numerical Results ◽  
Total Power ◽  
Proposed Model ◽  
Consumption Model

In this paper, we apply the relationships between the macro and micro variables of traffic flow to develop an electricity consumption model for electric vehicular flow. We use the proposed model to study the quantitative relationships between the electricity consumption/total power and speed/density under uniform flow, and the electricity consumptions during the evolution processes of shock, rarefaction wave and small perturbation. The numerical results indicate that the proposed model can perfectly describe the electricity consumption for electric vehicular flow, which shows that the proposed model is reasonable.

Analysis of energy consumption and emission of the heterogeneous traffic flow consisting of traditional vehicles and electric vehicles

2017 ◽  
Vol 31 (34) ◽  
pp. 1750324 ◽  
Author(s):  
Hong Xiao ◽  
Hai-Jun Huang ◽  
Tie-Qiao Tang
Keyword(s):  
Energy Consumption ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Traffic Flow ◽  
Small Perturbation ◽  
Electric Vehicles ◽  
Electricity Consumption ◽  
Heterogeneous Traffic ◽  
Rarefaction Waves ◽  
Fuel Consumption And Emissions

Electric vehicle (EV) has become a potential traffic tool, which has attracted researchers to explore various traffic phenomena caused by EV (e.g. congestion, electricity consumption, etc.). In this paper, we study the energy consumption (including the fuel consumption and the electricity consumption) and emissions of heterogeneous traffic flow (that consists of the traditional vehicle (TV) and EV) under three traffic situations (i.e. uniform flow, shock and rarefaction waves, and a small perturbation) from the perspective of macro traffic flow. The numerical results show that the proportion of electric vehicular flow has great effects on the TV’s fuel consumption and emissions and the EV’s electricity consumption, i.e. the fuel consumption and emissions decrease while the electricity consumption increases with the increase of the proportion of electric vehicular flow. The results can help us better understand the energy consumption and emissions of the heterogeneous traffic flow consisting of TV and EV.

scholarly journals Effects of Exchanging Battery on the Electric Vehicle’s Electricity Consumption in a Single-Lane Traffic System

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Shi-Chun Yang ◽  
Wen-Zhuang Gou ◽  
Tie-Qiao Tang ◽  
Hua-Yan Shang
Keyword(s):  
Electric Vehicle ◽  
Traffic Flow ◽  
Electric Vehicles ◽  
Electricity Consumption ◽  
Numerical Results ◽  
Traffic System ◽  
Car Following ◽  
Car Following Model ◽  
The Stability ◽  
Charging Stations

We propose a car-following model to explore the influences of exchanging battery on each vehicle’s electricity consumption under three traffic situations from the numerical perspective. The numerical results show that exchanging battery will destroy the stability of traffic flow, but the effects are related to each vehicle’s initial headway, the time that each electric vehicle exchanges the battery, the proportion of the electric vehicles that should exchange the battery, the number of charging stations, and the distance between two adjacent charging stations.

CLOUD BASED INTERNET OF THINGS FOR SMART CONNECTED OBJECTS

2019 ◽  
Vol 01 (02) ◽  
pp. 31-39 ◽  
Author(s):  
Duraipandian M. ◽  
Vinothkanna R.
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Smart Home ◽  
Electricity Consumption ◽  
Considerable Improvement ◽  
The Internet ◽  
The Internet Of Things ◽  
At Home

The paper proposing the cloud based internet of things for the smart connected objects, concentrates on developing a smart home utilizing the internet of things, by providing the embedded labeling for all the tangible things at home and enabling them to be connected through the internet. The smart home proposed in the paper concentrates on the steps in reducing the electricity consumption of the appliances at the home by converting them into the smart connected objects using the cloud based internet of things and also concentrates on protecting the house from the theft and the robbery. The proposed smart home by turning the ordinary tangible objects into the smart connected objects shows considerable improvement in the energy consumption and the security provision.

scholarly journals Machine Learning-Based Energy System Model for Tissue Paper Machines

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 655
Author(s):  
Huanhuan Zhang ◽  
Jigeng Li ◽  
Mengna Hong
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Gradient Boosting ◽  
Paper Machine ◽  
Steam Consumption ◽  
Tissue Paper ◽  
Paper Machines ◽  
Energy Consumption Model ◽  
Extreme Gradient Boosting ◽  
Consumption Model

With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines.

scholarly journals Temperature Effects on Electricity and Gas Consumption: Empirical Evidence from Mexico and Projections under Future Climate Conditions

2020 ◽  
Vol 13 (1) ◽  
pp. 305
Author(s):  
W.J. Wouter Botzen ◽  
Tim Nees ◽  
Francisco Estrada
Keyword(s):  
Climate Change ◽  
Energy Consumption ◽  
Fixed Effects ◽  
Electricity Consumption ◽  
Energy Transition ◽  
Net Energy ◽  
Economic Costs ◽  
Climate Conditions ◽  
Linear Relationships ◽  
Gas Consumption

Fixed effects panel models are used to estimate how the electricity and gas consumption of various sectors and residents relate to temperature in Mexico, while controlling for the effects of income, manufacturing output per capita, electricity and gas prices and household size. We find non-linear relationships between energy consumption and temperature, which are heterogeneous per state. Electricity consumption increases with temperature, and this effect is stronger in warm states. Liquified petroleum gas consumption declines with temperature, and this effect is slightly stronger in cold states. Extrapolations of electricity and gas consumption under a high warming scenario reveal that electricity consumption by the end of the century for Mexico increases by 12%, while gas consumption declines with 10%, resulting in substantial net economic costs of 43 billion pesos per year. The increase in net energy consumption implies greater efforts to comply with the mitigation commitments of Mexico and requires a much faster energy transition and substantial improvements in energy efficiency. The results suggest that challenges posed by climate change also provide important opportunities for advancing social sustainability goals and the 2030 Agenda for Sustainable Development. This study is part of Mexico’s Sixth National Communication to the United Nations Framework Convention on Climate Change.

scholarly journals Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4046 ◽  
Author(s):  
Sooyoun Cho ◽  
Jeehang Lee ◽  
Jumi Baek ◽  
Gi-Seok Kim ◽  
Seung-Bok Leigh
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Simulation Analysis ◽  
Residential Buildings ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Building Energy ◽  
Major Effect ◽  
Measuring Instruments ◽  
Building Energy Consumption

Although the latest energy-efficient buildings use a large number of sensors and measuring instruments to predict consumption more accurately, it is generally not possible to identify which data are the most valuable or key for analysis among the tens of thousands of data points. This study selected the electric energy as a subset of total building energy consumption because it accounts for more than 65% of the total building energy consumption, and identified the variables that contribute to electric energy use. However, this study aimed to confirm data from a building using clustering in machine learning, instead of a calculation method from engineering simulation, to examine the variables that were identified and determine whether these variables had a strong correlation with energy consumption. Three different methods confirmed that the major variables related to electric energy consumption were significant. This research has significance because it was able to identify the factors in electric energy, accounting for more than half of the total building energy consumption, that had a major effect on energy consumption and revealed that these key variables alone, not the default values of many different items in simulation analysis, can ensure the reliable prediction of energy consumption.

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