An Optimization Framework for Comparative Analysis of Multiple Vehicle Powertrains

Ganesh Mohan, Francis Assadian and Stefano Longo
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Ganesh Mohan: Department of Automotive Engineering, Cranfield University, Bedfordshire MK430AL, UK
Francis Assadian: Department of Automotive Engineering, Cranfield University, Bedfordshire MK430AL, UK
Stefano Longo: Department of Automotive Engineering, Cranfield University, Bedfordshire MK430AL, UK

Energies, 2013, vol. 6, issue 10, 1-31

Abstract: With a myriad of alternative vehicle powertrain architectures emerging in the industry, such as electric vehicles and hybrid electric vehicles, it is beneficial that the most appropriate system is chosen for the desired vehicle class and duty cycle, and to minimize a given cost function. This paper investigates this issue, by proposing a novel framework that evaluates different types of powertrain architectures under a unified modular powertrain structure. This framework provides a systematic and objective approach to comparing different types of powertrain architectures simultaneously, and will highlight the benefits that can be achieved from each architecture, thus making it possible to develop the reasoning for manufacturers to implement such systems, and potentially accelerate customer take-up of alternative powertrain technology. The results from this investigation have indicated that such analysis is indeed possible, by way of identifying the “cross-over point” between powertrain architectures, where one powertrain architecture transitions into a different architecture with increments in the required travel range.

Keywords: hybrid electric vehicle; modular powertrain structure; genetic algorithm; powertrain optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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