Technological Advancements for Reduced Charging Time of Electric Vehicle Batteries: A Review

2021 ◽  
pp. 99-112
Author(s):  
Abdullah Naim ◽  
Devendra Vashist
Keyword(s):  
Electric Vehicle ◽  
Charging Time

scholarly journals Service Time Analysis For Electric Vehicle Charging Infrastructure

2018 ◽  
Vol 8 (2) ◽  
pp. 818
Author(s):  
Junghoon Lee ◽  
Gyung-Leen Park
Keyword(s):  
Electric Vehicle ◽  
Service Time ◽  
Open Data ◽  
Energy Integration ◽  
Charging Infrastructure ◽  
Tourist Attractions ◽  
Electric Vehicle Charging ◽  
Charging Time ◽  
Main Observation ◽  
Vehicle Charging

This paper analyzes electric vehicle charging patterns in Jeju City, taking advantage of open software such as MySQL, Hadoop, and R, as well as open data obtained from the real-time charger monitoring system currently in operation. Main observation points lie in average service time, maximum service time, and the number of transactions, while we measure the effect of both temporal and spatial factors to them. According to the analysis result, the average service time is almost constant for all parameters. The charging time of 88.7 % transactions ranges from 10 to 40 minutes, while abnormally long transactions occupy just 3.4 % for fast chargers. The day-by-day difference in the number of charging transactions is 28.6 % at maximum, while Wednesday shows the largest number of transactions. Additionally, geographic information-based analysis tells that the charging demand is concentrated in those regions having many tourist attractions and administrative offices. With this analysis, it is possible to predict when a charger will be idle and allocate it to another service such as V2G or renewable energy integration.

LEARNING FROM HISTORICAL DATA TO PREDICT ELECTRIC VEHICLE TAXI CONSUMPTION AND CHARGING TIME

2018 ◽  
Author(s):  
Kim Rioux-Paradis ◽  
Jonathan Gaudreault ◽  
Chloe Redmond ◽  
Kento Otomo-Lauzon ◽  
Frederic Bernard ◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Historical Data ◽  
Charging Time

Load Forecasting of Charging and Swapping in Large-Scale Electric Vehicle

2014 ◽  
Vol 568-570 ◽  
pp. 1969-1977 ◽  
Author(s):  
Jian Cheng Ye ◽  
Yu Ling Li ◽  
Dong Liang Zhang ◽  
Xiang Jing Zhu ◽  
Jin Da Zhu
Keyword(s):  
Electric Vehicle ◽  
Large Scale ◽  
Access Network ◽  
Calculation Model ◽  
Simulation Algorithm ◽  
Charging Time ◽  
Load Calculation ◽  
Other Information ◽  
Charging Mode ◽  
Ev Charging

This article combs the charging mode of electric vehicle,and analyzes different charging ways for buses,taxis and sedans,thereby drawing their appropriate charging time and characteristics of the interaction with grid. The paper establishes the load calculation model for the charging and swapping in Evs respectively. The load calculation model divides one day into 1440 minutes, and use the Monte Carlo simulation algorithm to extract the initial SOC, the initial charging time and other information for load calculation and analyze the EV charging load. The results show that the charging load of electric vehicle has obvious difference between peak and vally,and provide reference for the management and policy oriented electric vhicle access network.

scholarly journals Optimization of power losses in electric vehicle battery by wireless charging method with consideration of the laser optic effect

2020 ◽  
Vol 53 (3-4) ◽  
pp. 441-453
Author(s):  
V Senthil Nayagam ◽  
L Premalatha
Keyword(s):  
Electric Vehicle ◽  
Power Transmission ◽  
Power Transfer ◽  
Wireless Power ◽  
Wireless Power Transmission ◽  
Transmission Method ◽  
Charging Time ◽  
Electric Vehicle Battery ◽  
Transfer Method ◽  
Laser Optic

This work mainly deals with replacing the wired power transmission method for charging electric vehicle with the help of an efficient wireless power transmission method. For identifying an efficient wireless power transmission method, the inductive power transfer method and the laser optic method are taken into consideration to charge the electric vehicle battery. These methods are compared by hardware implementation for various conditions. Wireless power transmission is an emerging technology utilized to charge the electric vehicle battery through an air gap. The use of this new charging technique is due to its easy access from annoying charging cables, better efficiency, and smaller charging time. Also, it contributes to the remarkable reduction of pollutants and carbon dioxide (CO2) emissions into the atmosphere by the conventional vehicles. However, the implementation of inductive charging for electric vehicle still presents challenges in terms of power transfer efficiency, transmission distance, utilization of heavy batteries with ripple-free and charging time, and stress on compensation network to maintain resonant condition for maximum power transfer. This system will be verified through the simulation in MATLAB/Simulink environment. The simulation results of the inductive power transfer method and the comparison of hardware setup results with laser optic hardware setup have to be verified.

scholarly journals Nonlinear Influence Model of Built Environment of Residential Area on Electric Vehicle Miles Traveled

2021 ◽  
Vol 12 (4) ◽  
pp. 247
Author(s):  
Xinghua Hu ◽  
Yanshi Cao ◽  
Tao Peng ◽  
Runze Gao ◽  
Gao Dai
Keyword(s):  
Built Environment ◽  
Electric Vehicle ◽  
Travel Behavior ◽  
Ordinary Least Squares ◽  
Heterogeneous Data ◽  
Gradient Boosting ◽  
Vehicle Miles Traveled ◽  
Road Density ◽  
Charging Time ◽  
Business District

In this study, gradient boosting decision tree (GBDT) and ordinary least squares (OLS) models were constructed to systematically ascertain the influencing factors and electric vehicle (EV) use action laws from the perspective of travelers. The use intensity of EVs was represented by electric vehicle miles traveled (eVMT); variables such as the charging time, travel preference, and annual income were used to describe the travel characteristics. Seven variables, including distance to the nearest business district, road density, public transport service level, and land use mix were extracted from different dimensions to describe the built environment, explore the influence of the travel behavior mode and built environment on EV use. From the eVMT survey data, points of interest (POI) data, urban road network data, and other heterogeneous data from Chongqing, an empirical analysis of EV usage intensity was conducted. The results indicated that the deviation of the GBDT model (9.62%) was 11.72% lower than that of the OLS model (21.34%). The charging time was the most significant factor influencing the service intensity of EVs (18.37%). The charging pile density (15.24%), EV preference (11.52%), and distance to the nearest business district (10.28%) also exerted a significant influence.

Charging Characteristics of Electric Vehicles and Charging Cost Analysis

2014 ◽  
Vol 953-954 ◽  
pp. 1363-1366
Author(s):  
Xiao Lu Wang
Keyword(s):  
Cost Analysis ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Electric Vehicle Charging ◽  
Charging Time ◽  
Charging Mode ◽  
Vehicle Charging ◽  
Generator Sets ◽  
Fuel Costs

This paper investigates the “Vehicle-charging mode” and the “Battery-changing mode” of the electric vehicles. Firstly, it analyzes the vehicle-charging time, the battery-changing time and the charging power under two modes. Secondly, this paper searches into different sorts of charging and sets up three scenarios based on the differences: scenario1, disorderly; scenario2, continuous; scenario 3, off-peak. Thirdly, this paper considers the combination of generator sets that reaches the requirements of electric vehicle charging based on the characteristics of different sorts of charging, concerns the increased fuel costs, O&M costs and start cost generated along, and draws conclusions with the comparison of the elements.

Micro Gas Turbine Small-Scale Effects in Range Extended Electric Vehicles

2021 ◽  
pp. 1-18
Author(s):  
Adeel Javed ◽  
Hassan Abdullah Khalid ◽  
Syed Umer bin Arif ◽  
Mohammad Imran ◽  
Ahmed Rezk ◽  
...  
Keyword(s):  
Gas Turbine ◽  
Electric Vehicle ◽  
Gas Turbines ◽  
Scale Effects ◽  
Small Scale ◽  
Micro Gas Turbine ◽  
Range Extender ◽  
Charging Time ◽  
Driving Range ◽  
Battery Bank

Abstract Application of a range extender in an electric vehicle can reduce the battery bank size and extend the driving range on need basis. A micro gas turbine offers high power density, fuel flexibility, a reliable thermal efficiency (with recuperation) and less raw exhaust gaseous emissions compared to an internal combustion engine. However, micro gas turbines also incur low component performances due to small-scale effects related to high viscous losses, heat transfer between hot and cold sections, and manufacturing and assembly constraints compared to their larger counterparts. In this paper, the micro gas turbine thermodynamic cycle has been designed in Gas Turbine Simulation Program (GSP) and evaluated in terms of the small-scale effects simultaneously with the battery bank energy and charging time analysis. The key objective is to demonstrate the effectiveness of a micro gas turbine in saving weight of a range-extended electric vehicle while understanding the impact of small-scale effects on the battery bank energy and charging time. Results indicate that a relatively smaller 22 kWh battery bank can be utilized with prospects of cost savings together with a 47 kW micro gas turbine range extender to achieve an average driving range of 100 km and a charging time of 30 min for the baseline electric vehicle. Furthermore, the compressor and turbine isentropic efficiencies are found to have a significant impact on the overall battery bank performance.

A DISTRIBUTION OF CHARGERS USED BY ELECTRIC VEHICLES ON AN EXPRESSWAY WITH VARIOUS TYPES OF CHARGING PLACES

2014 ◽  
Vol 21 (05) ◽  
pp. 1450025
Author(s):  
JUNJI KOYANAGI
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Charging Time

In Japan, electric vehicle (EV) is spreading, because EV has several advantages compared with gasoline car. However, EV has two main disadvantages, shorter cruising range and longer charging time. These disadvantages may cause a serious problem on an expressway, because the queue of EV waiting for charge may be long, if enough chargers are not placed at charging places. It is important to estimate the number of chargers in a charging place to avoid the long queue. This paper proposes a model to estimate the number of chargers used by EV. In this model, it is assumed that the driver knows the charge level of EV and decides to charge at charging places according to the type of charging place and charge level.

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