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Analytical/Numerical Methodology - Design & Development Aspects of Electric Vehicle Powertrain
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
In recent years, customers who are looking to buy or lease a car have placed more interest in exploring electrified vehicles including Battery Electric Vehicles (BEV), Hybrid Electric Vehicles (HEV), Plug-in Hybrid Electric Vehicles (PHEV) and Fuel Cell Electric Vehicles (FCEV) as new options in the automotive market. As the recent trends suggest, this interest is likely to be solidified, and people will start buying EVs more and more. These market trends show that the internal combustion engine (ICE) drivetrain soon will be progressively replaced by the electric drive unit for passenger car applications. The electric vehicles provide positive impacts such as instant torque delivery, overall vehicle comfort, noise and vibration. They also provide local environmental benefits by reducing greenhouse gas emissions. However, there are some major complexities for EVs to overcome before completely replacing ICE vehicles. One of these obstacle is the development time and the overall system optimization efforts which can be reduced by utilizing analytical/numerical tools to study the voice of the customers and translate them to specific design and vehicle architecture.
This paper is focused on battery characterization methods, battery selection, battery and motor sizing, vehicle architecture choice, and thermal management and range assessment for typical EVs. The main objective of the paper is to provide an overall picture of the EV design and engineering development process and tools as well as ways of development to assist in future research in the automotive and transportation industry.
CitationSiavoshani, S. and Nicolas, R., "Analytical/Numerical Methodology - Design & Development Aspects of Electric Vehicle Powertrain," SAE Technical Paper 2020-01-1439, 2020, https://doi.org/10.4271/2020-01-1439.
Data Sets - Support Documents
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