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An Optimization Framework for Comparative Analysis of Multiple Vehicle Powertrains
Department of Automotive Engineering, Cranfield University, Bedfordshire MK430AL, UK
* Author to whom correspondence should be addressed.
Received: 30 July 2013; in revised form: 3 October 2013 / Accepted: 7 October 2013 / Published: 22 October 2013
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
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MDPI and ACS Style
Mohan, G.; Assadian, F.; Longo, S. An Optimization Framework for Comparative Analysis of Multiple Vehicle Powertrains. Energies 2013, 6, 5507-5537.
Mohan G, Assadian F, Longo S. An Optimization Framework for Comparative Analysis of Multiple Vehicle Powertrains. Energies. 2013; 6(10):5507-5537.
Mohan, Ganesh; Assadian, Francis; Longo, Stefano. 2013. "An Optimization Framework for Comparative Analysis of Multiple Vehicle Powertrains." Energies 6, no. 10: 5507-5537.