Optimizing Power Consumption of the Electric Vehicle Traction

The study refers to optimizing energy consumption from electric trains, underground trains and electric locomotives. The actual requirements of the dynamic market economy are forcing the railway system to transform into a reliable alternative to the road and air traffic. From this perspective, the railways have to fulfill two key elements: Economical efficiency and reliability and to offer what the potential customer needs. One of the main elements is the respect of the timetables or (if possible) the decrease of the running times. The running time is the main referential, especially when it’s related to the power consumption. The optimization of the running times and the power consumption is the most important target for railway operators and depends on the correct choice of the drive regimes. Article deals with energy optimization from the perspective of traction. Analysis can be applied also to diesel railcars and diesel locomotives.

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Intellectualized testing & evaluation application based on unmanned test platform

E3S Web of Conferences ◽

10.1051/e3sconf/202126801036 ◽

2021 ◽

Vol 268 ◽

pp. 01036

Author(s):

Rongliang Liang ◽

Chang Yang

Keyword(s):

Energy Consumption ◽

Electric Vehicle ◽

Electric Vehicles ◽

High Speed ◽

Test Results ◽

Road Test ◽

The Road ◽

Speed Test ◽

Test Platform ◽

Long Time

Taking three pure electric vehicles as the research object, the energy consumption and acceleration performance of the electric vehicle are tested and evaluated through the use of the intelligent unmanned test platform of the whole vehicle, which ensures that the accurate and high-speed test of the road test can be realized on the basis of no driver in the vehicle. For the electric vehicle energy consumption test, the intelligent unmanned test platform is used for road test, which not only effectively avoids the driver driving the test vehicle for a long time, but also ensures the accuracy and reliability of the test data. According to the test results, the acceleration response and energy consumption test results of three pure electric vehicles are analyzed and evaluated.

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Utilization of Electrical Vehicle Power to Different Load

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35646 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 4751-4754

Author(s):

Jayash M. Dakhole

Keyword(s):

Energy Consumption ◽

Power Consumption ◽

Electric Vehicle ◽

Power Grid ◽

Energy System ◽

Load Profile ◽

System Load ◽

Electric Vehicle Charging ◽

Electrical Vehicle ◽

Flexible Load

When the power is in control of its own fleet of vehicles, the power grid will experience an increase in the amount of fluctuating energy consumption depending on the nature of the load presentation. Depending on the drawing density, electric batteries can be integrated to create a new volume of the overall load profile can increase the voltage of the tips. Fees and charges the pattern is not random, as they can affect the driver's travel habits and charging capabilities, which means that ANY integration as well as a significant impact will have cargo. An increasing number of loads and peaks in load may lead to the need to upgrade the network infrastructure in order to reduce the risk of loss, abandoned services and or damage to any components. But with well-designed incentives for users, the HOME variable that is in the electric vehicle charging (EVC) - based power consumption can be flexible load, which can help in the energy system load and reduce charging at the tips.

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An Attention-Based Model for Travel Energy Consumption of Electric Vehicle with Traffic Information

Advances in Civil Engineering ◽

10.1155/2021/5571271 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Shen Li ◽

Hailong Zhang ◽

Huachun Tan ◽

Zhiyu Zhong ◽

Zhuxi Jiang

Keyword(s):

Neural Network ◽

Energy Consumption ◽

Electric Vehicle ◽

Electric Vehicles ◽

Research Area ◽

Traffic Information ◽

Driving Experience ◽

The Road ◽

Proposed Model ◽

Battery Capacity

Mileage anxiety is one of the most important factors that affect the driving experience due to the limitation of battery capacity. Robust and accurate prediction of the energy consumption of the journey of the electric vehicle can guide the driver to allocate the power rationally and relieve the anxiety of the mileage. Since vehicle sharing is the biggest application scenario of electric vehicles, it is a critical challenge in share mobility research area. In this paper, a travel energy consumption prediction model of electric vehicles is proposed in order to improve the mobility of shared cars and reduce the anxiety of drivers because they are worried about insufficient power. A recurrent neural network with attention mechanism and deep neural network is used to build the model. To validate the proposed model, a simulation is demonstrated based on both traffic and vehicle information. After the simulation, experimental results show that the proposed model has high prediction accuracy, and we also show through visualization how the model finds high relevant road segments of the road network while dealing with corresponding traffic state input.

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Parameters Influencing on Electric Vehicle Range

Energies ◽

10.3390/en14164821 ◽

2021 ◽

Vol 14 (16) ◽

pp. 4821

Author(s):

Emilia M. Szumska ◽

Rafał S. Jurecki

Keyword(s):

Energy Consumption ◽

Electric Vehicle ◽

Limited Range ◽

Operating Conditions ◽

Consumption Pattern ◽

Initial State ◽

The Road ◽

Depth Of Discharge ◽

Vehicle Operation ◽

On The Road

There is a range of anxiety-related phenomena among users and potential buyers of electric vehicles. Chief among them is the fear of the vehicle stopping and its users getting “stuck” before reaching their designated destination. The limited range of an electric vehicle makes EV users worry that the battery will drain while driving and the vehicle will stall on the road. It is therefore important to know the factors that could further reduce the range during daily vehicle operation. The purpose of this study was to determine the effect of selected parameters on a battery’s depth of discharge (DOD). In a simulation study of an electric vehicle, the effects of the driving cycle, ambient temperature, load, and initial state of charge of the accumulator on the energy consumption pattern and a battery’s depth of discharge (DOD) were analyzed. The simulation results confirmed that the route taken has the highest impact on energy consumption. The presented results show how significantly the operating conditions of an electric vehicle affect the energy life. This translates into an electric vehicle’s range.

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Analysis of Energy Consumption in an Electric Vehicle through Virtual Reality Set-Up

2020 AEIT International Annual Conference (AEIT) ◽

10.23919/aeit50178.2020.9241187 ◽

2020 ◽

Author(s):

Marika Lamanuzzi ◽

Jacopo Andrea Di Antonio ◽

Federica Foiadelli ◽

Michela Longo ◽

Andrea Labombarda ◽

...

Keyword(s):

Virtual Reality ◽

Energy Consumption ◽

Electric Vehicle ◽

Set Up

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Power and Performance Evaluation of Memory-Intensive Applications

Energies ◽

10.3390/en14144089 ◽

2021 ◽

Vol 14 (14) ◽

pp. 4089

Author(s):

Kaiqiang Zhang ◽

Dongyang Ou ◽

Congfeng Jiang ◽

Yeliang Qiu ◽

Longchuan Yan

Keyword(s):

Energy Efficiency ◽

Energy Consumption ◽

Power Consumption ◽

Job Scheduling ◽

Memory System ◽

Processor Core ◽

Memory Efficiency ◽

And Performance ◽

Reasonable Use ◽

Server System

In terms of power and energy consumption, DRAMs play a key role in a modern server system as well as processors. Although power-aware scheduling is based on the proportion of energy between DRAM and other components, when running memory-intensive applications, the energy consumption of the whole server system will be significantly affected by the non-energy proportion of DRAM. Furthermore, modern servers usually use NUMA architecture to replace the original SMP architecture to increase its memory bandwidth. It is of great significance to study the energy efficiency of these two different memory architectures. Therefore, in order to explore the power consumption characteristics of servers under memory-intensive workload, this paper evaluates the power consumption and performance of memory-intensive applications in different generations of real rack servers. Through analysis, we find that: (1) Workload intensity and concurrent execution threads affects server power consumption, but a fully utilized memory system may not necessarily bring good energy efficiency indicators. (2) Even if the memory system is not fully utilized, the memory capacity of each processor core has a significant impact on application performance and server power consumption. (3) When running memory-intensive applications, memory utilization is not always a good indicator of server power consumption. (4) The reasonable use of the NUMA architecture will improve the memory energy efficiency significantly. The experimental results show that reasonable use of NUMA architecture can improve memory efficiency by 16% compared with SMP architecture, while unreasonable use of NUMA architecture reduces memory efficiency by 13%. The findings we present in this paper provide useful insights and guidance for system designers and data center operators to help them in energy-efficiency-aware job scheduling and energy conservation.

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Practical Model for Energy Consumption Analysis of Omnidirectional Mobile Robot

Sensors ◽

10.3390/s21051800 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1800

Author(s):

Linfei Hou ◽

Fengyu Zhou ◽

Kiwan Kim ◽

Liang Zhang

Keyword(s):

Energy Consumption ◽

Power Consumption ◽

Mobile Robot ◽

Load Capacity ◽

Working Environment ◽

Energy Consumption Analysis ◽

Mecanum Wheel ◽

Save Energy ◽

Energy Consumption Modeling ◽

Robot Platform

The four-wheeled Mecanum robot is widely used in various industries due to its maneuverability and strong load capacity, which is suitable for performing precise transportation tasks in a narrow environment. While the Mecanum wheel robot has mobility, it also consumes more energy than ordinary robots. The power consumed by the Mecanum wheel mobile robot varies enormously depending on their operating regimes and environments. Therefore, only knowing the working environment of the robot and the accurate power consumption model can we accurately predict the power consumption of the robot. In order to increase the applicable scenarios of energy consumption modeling for Mecanum wheel robots and improve the accuracy of energy consumption modeling, this paper focuses on various factors that affect the energy consumption of the Mecanum wheel robot, such as motor temperature, terrain, the center of gravity position, etc. The model is derived from the kinematic and kinetic model combined with electrical engineering and energy flow principles. The model has been simulated in MATLAB and experimentally validated with the four-wheeled Mecanum robot platform in our lab. Experimental results show that the accuracy of the model reached 95%. The results of energy consumption modeling can help robots save energy by helping them to perform rational path planning and task planning.

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Designing a Parallel-Through-the-Road Plug-in Hybrid Electric Vehicle

10.4271/2012-01-1763 ◽

2012 ◽

Cited By ~ 3

Author(s):

Haley M. Moore ◽

Bryan Whitney Belt ◽

Christopher Rhoades ◽

Ashish Vora ◽

Haotian Wu ◽

...

Keyword(s):

Electric Vehicle ◽

Hybrid Electric Vehicle ◽

The Road ◽

Hybrid Electric

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A Novel Method for Detecting Abnormal Energy Data in Building Energy Monitoring System

Journal of Energy ◽

10.1155/2014/231571 ◽

2014 ◽

Vol 2014 ◽

pp. 1-5 ◽

Cited By ~ 3

Author(s):

Liang Zhao

Keyword(s):

Energy Consumption ◽

Power Consumption ◽

Real Time ◽

Difference Method ◽

Building Energy ◽

Energy Monitoring ◽

Order Difference ◽

Energy Data ◽

First Order ◽

Novel Method

This paper presents a novel abnormal data detecting algorithm based on the first order difference method, which could be used to find out outlier in building energy consumption platform real time. The principle and criterion of methodology are discussed in detail. The results show that outlier in cumulative power consumption could be detected by our method.

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