scholarly journals A Comparative Study on Emerging Electric Vehicle Technology Assessments

2011 ◽  
Author(s):  
Jonathan Ford ◽  
Gannate Khowailed ◽  
Julia Blackburn ◽  
Karen Sikes
Keyword(s):  
Comparative Study ◽  
Electric Vehicle ◽  
Vehicle Technology

Manufacturing the Electric Vehicle: A Window of Technological Opportunity for Southern California

1995 ◽  
Vol 27 (6) ◽  
pp. 835-862 ◽  
Author(s):  
C O Quandt
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
The United States ◽  
Vehicle Design ◽  
Development Programs ◽  
Technological Opportunity ◽  
Public And Private ◽  
Vehicle Technology ◽  
Automobile Manufacturer ◽  
Vehicle Technologies

The California Air Resources Board has mandated that by 1998 2% of new vehicles sold in California must be zero emission, effectively, electric vehicles. This requirement is largely responsible for the electric vehicle development programs run by almost every global automobile manufacturer that does business in the United States. At present, no single electric vehicle technology, from battery type, to propulsion system, to vehicle design, represents a standard for a protoelectric vehicle industry. In this paper competing electric vehicle technologies are reviewed, leading public and private electric vehicle research programs worldwide are summarized, and the barriers faced by competing technological systems in terms of manufacturing and infrastructural requirements are examined.

Comparative Study between Cell Balancing Methods in Electric Vehicle

2021 ◽  
Author(s):  
Surendra Kumar Koganti ◽  
G Purnima ◽  
Pechetti Bhavana ◽  
Y Veera Raghava ◽  
R Resmi
Keyword(s):  
Comparative Study ◽  
Electric Vehicle ◽  
Cell Balancing

scholarly journals Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2692 ◽  
Author(s):  
Juncheng Zhu ◽  
Zhile Yang ◽  
Monjur Mourshed ◽  
Yuanjun Guo ◽  
Yimin Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Load Forecasting ◽  
Learning Approaches ◽  
Short Term ◽  
Learning Methods ◽  
Electric Vehicle Charging ◽  
Vehicle Charging

Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model.

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