Battery electric vehicles, hydrogen fuel cells and biofuels. Which will be the winner?

In the current study, sulfonated graphene oxide-silica (S-GO-SiO 2 ) nanohybrid particles were obtained first. The FTIR, TGA, XRD, Raman, AFM, FE-SEM and EDX characterizations were employed to confirm the successful decoration of SiO 2 onto GO surface and the attachment of sulfonic acid groups onto the GO-SiO 2 surface. Then, S-GO-SiO 2 /Nafion proton exchange membranes (PEMs) were prepared via solution casting. S-GO-SiO 2 had a good dispersibility inside the membrane matrix. The increased water uptake and the incorporated -SO 3 H groups bestowed a large increase in proton conductivity upon these composite PEMs. Meanwhile, the barrier effect of two-dimensional S-GO-SiO 2 contributed to the obvious reduction in methanol permeability of the composite PEMs, as a result of the increased tortuosity of the transport channels. Therefore, novel S-GO-SiO 2 /Nafion PEMs with enhanced selectivity (the ratio of proton conductivity to methanol permeability) were obtained. Even under harsh conditions, such as high methanol concentration and/or increased temperature, the membrane selectivity of S-GO-SiO 2 /Nafion composite membranes was still nearly two-orders-of-magnitude higher than that of the recast Nafion membrane. It renders this type of composite PEMs a very promising candidate for the application in DMFC. All the conclusions were demonstrated by various characterizations, such as (FE-) SEM, TEM, AFM, FTIR, TGA, water uptake, etc.

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“…The thickness of a PEM n A constant n 1 The refractive indexes of Naon n 2 The refractive indexes of the ATR crystal t…”

Section: A Drymentioning

confidence: 99%

Sulfonated graphene oxide–silica for highly selective Nafion-based proton exchange membranes

2014

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“…Lack of proper and adequate charging infrastructure is another issue regarding PHEV compared to typical fuel station. As a result, uncertainty in energy management for the driver named as 'Range anxiety' [24] develops which restricts the conventional vehicle owner to switch their vehicle to PHEV. Selecting appropriate place for charging station can decrease this range anxiety.…”

Section: Problem Statementmentioning

confidence: 99%

Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

et al. 2016

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Green technologies gain popularity to reduce the pollution and give higher penetration of renewable energy source in the transportation. This research induce that the extensive involvement of plug-in hybrid electric vehicles (PHEVs) requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. It is also noticed that daytime charging station are necessary for daily usage of PHEVs due to the limited all-electric-range. Most of the research in the past have been stated that only proper charging control and infrastructure management can assure the larger participation of PHEVs.Therefore, researchers are trying to develop efficient control mechanism for charging infrastructure in order to facilitate upcoming PHEVs penetration in highway. Nevertheless, most of the past researcher already aware with the issue related to intelligent energy management. Yet, these studies could not fill the gap of the problem associated with intelligent energy management and require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. The outcome of this research study provides four optimization techniques that includes Hybrid method within swarm intelligence group for the State-of-Charge (SoC) optimization of PHEVs. The finding of this research simulation results obtained for maximizing the highly nonlinear objective function evaluate the comparative performance of all four techniques in terms of best fitness, convergence speed and computation time. Finally, the hybridization method (PSOGSA) presented in this dissertation uses the advantages of both PSO and GSA optimization and thus produce higher best fitness values. This study evaluates the performance of standard PSO, then Accelerated version of PSO (APSO), GSA algorithm and then Hybrid of PSO and GSA. The hybridization method (PSOGSA) uses the advantages of both PSO and GSA optimization and thus produce higher best fitness values. However, PSOGSA method takes much longer computational time than single methods because of incorporating two single methods in one algorithm. This research study suggests that PSOGSA method is a great promise for SoC optimization but it takes much longer computational time.

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“…In this paper, a baseline ICE model that is naturally aspirated and spark ignited was selected, as it is widely available on the market today [15,16]. Other configurations, such as forced induction and compression-ignited ICEs are possible; however, they were not investigated further due to the lack of experimental data at this time.…”

Section: Icementioning

confidence: 99%

An Optimization Framework for Comparative Analysis of Multiple Vehicle Powertrains

2013

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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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Battery electric vehicles, hydrogen fuel cells and biofuels. Which will be the winner?

Cited by 183 publications

References 24 publications

Sulfonated graphene oxide–silica for highly selective Nafion-based proton exchange membranes

Sulfonated graphene oxide–silica for highly selective Nafion-based proton exchange membranes

Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

An Optimization Framework for Comparative Analysis of Multiple Vehicle Powertrains

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