Browsing by Author "Mohan, Ganesh"

Now showing 1 - 4 of 4
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    Comparative analysis of forward-facing models vs backward-facing models in powertrain component sizing
    (Institution of Engineering and Technology, 2013-11-11) Mohan, Ganesh; Assadian, Francis; Longo, Stefano
    Powertrain size optimisation based on vehicle class and usage profile is advantageous for reducing emissions. Backward-facing powertrain models, which incorporate scalable powertrain components, have often been used for this purpose. However, due to their quasi-static nature, backward-facing models give very limited information about the limits of the system and drivability of the vehicle. This makes it difficult for control system development and implementation in hardware-in-the-loop (HIL) test systems. This paper investigates the viability of using forward-facing models in the context of powertrain component sizing optimisation. The vehicle model used in this investigation features a conventional powertrain with an internal combustion engine, clutch, manual transmission, and final drive. Simulations that were carried out have indicated that there is minimal effect on the optimal cost with regards to variations in the driver model sensitivity. This opens up the possibility of using forward-facing models for the purpose of powertrain component sizing.
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    Impact of battery ageing on an electric vehicle powertrain optimisation
    (International Centre for Sustainable Development of Energy, Water and Environment Systems, 2014-12-01) Auger, Daniel J.; Groff, Maxime F.; Mohan, Ganesh; Longo, Stefano; Assadian, Francis
    An electric vehicle’s battery is its most expensive component, and it cannot be charged and discharged indefinitely. This affects a consumer vehicle’s end-user value. Ageing is tolerated as an unwanted operational side-effect; manufacturers have little control over it. Recent publications have considered trade-offs between efficiency and ageing in plug-in hybrids (PHEVs) but there is no equivalent literature for pure EVs. For PHEVs, battery ageing has been modelled by translating current demands into chemical degradation. Given such models it is possible to produce similar trade-offs for EVs. We consider the effects of varying battery size and introducing a parallel supercapacitor pack. (Supercapacitors can smooth current demands, but their weight and electronics reduce economy.) We extend existing EV optimisation techniques to include battery ageing, illustrated with vehicle case studies. We comment on the applicability to similar EV problems and identify where additional research is needed to improve on our assumptions.
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    An Optimization Framework for Comparative Analysis of Multiple Vehicle Powertrains
    (MDPI , 2013-10-22T00:00:00Z) Mohan, Ganesh; Assadian, Francis; Longo, Stefano
    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.
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    A toolbox for multi-objective optimisation of low carbon powertrain topologies
    (Cranfield University, 2016-05) Mohan, Ganesh; Assadian, Francis; Longo, Stefano
    Stricter regulations and evolving environmental concerns have been exerting ever-increasing pressure on the automotive industry to produce low carbon vehicles that reduce emissions. As a result, increasing numbers of alternative powertrain architectures have been released into the marketplace to address this need. However, with a myriad of possible alternative powertrain configurations, which is the most appropriate type for a given vehicle class and duty cycle? To that end, comparative analyses of powertrain configurations have been widely carried out in literature; though such analyses only considered limited types of powertrain architectures at a time. Collating the results from these literature often produced findings that were discontinuous, which made it difficult for drawing conclusions when comparing multiple types of powertrains. The aim of this research is to propose a novel methodology that can be used by practitioners to improve the methods for comparative analyses of different types of powertrain architectures. Contrary to what has been done so far, the proposed methodology combines an optimisation algorithm with a Modular Powertrain Structure that facilitates the simultaneous approach to optimising multiple types of powertrain architectures. The contribution to science is two-folds; presenting a methodology to simultaneously select a powertrain architecture and optimise its component sizes for a given cost function, and demonstrating the use of multi-objective optimisation for identifying trade-offs between cost functions by powertrain architecture selection. Based on the results, the sizing of the powertrain components were influenced by the power and energy requirements of the drivecycle, whereas the powertrain architecture selection was mainly driven by the autonomy range requirements, vehicle mass constraints, CO2 emissions, and powertrain costs. For multi-objective optimisation, the creation of a 3-dimentional Pareto front showed multiple solution points for the different powertrain architectures, which was inherent from the ability of the methodology to concurrently evaluate those architectures. A diverging trend was observed on this front with the increase in the autonomy range, driven primarily by variation in powertrain cost per kilometre. Additionally, there appeared to be a trade-off in terms of electric powertrain sizing between CO2 emissions and lowest mass. This was more evident at lower autonomy ranges, where the battery efficiency was a deciding factor for CO2 emissions. The results have demonstrated the contribution of the proposed methodology in the area of multi-objective powertrain architecture optimisation, thus addressing the aims of this research.