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Comparative Analysis of Multiple Powertrain Architectures based on a Novel Optimization Framework
Technical Paper
2014-01-1105
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Identifying the most appropriate powertrain technology for a given vehicle class and duty cycle can be beneficial to further drive down on carbon emissions. However, with a myriad of powertrain architectures that are emerging in the industry, such as those in Electric Vehicles and Hybrid Vehicles, it becomes more challenging to carry out comprehensive comparative analyses across different permutations of powertrain topologies.
This has motivated the authors to research on improving the method used to compare different types of powertrain architectures, and develop a tool that can be used by practitioners for this purpose. Literature survey has indicated that whilst there have been many comparisons made between different types of powertrains, such analyses were often carried out by comparing only limited types of architectures at a time. Additionally, many commercially available tools lack the combination of an optimization algorithm with the ability to simulate different combinations of powertrain architectures concurrently to provide a meaningful comparison.
In this paper, a new approach is proposed that simultaneously optimizes powertrain architecture selection and component sizing. Results from this investigation have indicated that such analysis is indeed possible, and this was demonstrated by way of identifying the “transition point” between powertrain architectures. The investigation included the use of a multi-objective optimization with a novel powertrain framework. The capabilities of this framework potentially opens a way for vehicle manufacturers to quantify the benefits that can be achieved from each type of powertrain architecture, and help accelerate the implementation of alternative powertrain technology.
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Citation
Mohan, G., Assadian, F., and Longo, S., "Comparative Analysis of Multiple Powertrain Architectures based on a Novel Optimization Framework," SAE Technical Paper 2014-01-1105, 2014, https://doi.org/10.4271/2014-01-1105.Also In
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