4,796 research outputs found

    Advanced methodology for the optimal sizing of the energy storage system in a hybrid electric refuse collector vehicle using real routes

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    This paper presents a new methodology for optimal sizing of the energy storage system ( ESS ), with the aim of being used in the design process of a hybrid electric (HE) refuse collector vehicle ( RCV ). This methodology has, as the main element, to model a multi-objective optimisation problem that considers the specific energy of a basic cell of lithium polymer ( Li – Po ) battery and the cost of manufacture. Furthermore, optimal space solutions are determined from a multi-objective genetic algorithm that considers linear inequalities and limits in the decision variables. Subsequently, it is proposed to employ optimal space solutions for sizing the energy storage system, based on the energy required by the drive cycle of a conventional refuse collector vehicle. In addition, it is proposed to discard elements of optimal space solutions for sizing the energy storage system so as to achieve the highest fuel economy in the hybrid electric refuse collector vehicle design phase.Postprint (published version

    Multi-objective optimisation for battery electric vehicle powertrain topologies

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    Electric vehicles are becoming more popular in the market. To be competitive, manufacturers need to produce vehicles with a low energy consumption, a good range and an acceptable driving performance. These are dependent on the choice of components and the topology in which they are used. In a conventional gasoline vehicle, the powertrain topology is constrained to a few well-understood layouts; these typically consist of a single engine driving one axle or both axles through a multi-ratio gearbox. With electric vehicles, there is more flexibility, and the design space is relatively unexplored. In this paper, we evaluate several different topologies as follows: a traditional topology using a single electric motor driving a single axle with a fixed gear ratio; a topology using separate motors for the front axle and the rear axle, each with its own fixed gear ratio; a topology using in-wheel motors on a single axle; a four-wheel-drive topology using in-wheel motors on both axes. Multi-objective optimisation techniques are used to find the optimal component sizing for a given requirement set and to investigate the trade-offs between the energy consumption, the powertrain cost and the acceleration performance. The paper concludes with a discussion of the relative merits of the different topologies and their applicability to real-world passenger cars

    Battery sizing for a stand alone passive wind system using statistical techniques

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    In this paper, an original optimization method to jointly determine a reduced study term and an optimum battery sizing is investigated. This storage device is used to connect a passive wind turbine system with a stand alone network. A Weibull probability density function is used to generate different wind speed data. The passive wind system is composed of a wind turbine, a permanent magnet synchronous generator feeding a diode rectifier associated with a very low voltage DC battery bus. This study is essentially based on a similitude model applied on an 8 kW wind turbine system. Our reference model is taken from a 1.7 kW optimized system. The wind system generated power and the load demand are coupled through a battery sized using a statistical approach

    Optimized energy management strategies and sizing of hybrid storage systems for transport applications

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    205 p. El contenido del capĂ­tulo 4, secciĂłn 4.3 estĂĄ sujeto a confidencialidad.Esta tesis doctoral aborda la temĂĄtica acerca del Ăłptimo dimensionamiento y operaciĂłn de sistemashĂ­bridos de almacenamiento de energĂ­a (HESS), combinando baterĂ­as y supercapacitores, con el objetivode ser integrados en vehĂ­culos para movilidad pĂșblica en entornos urbanos. Por una parte, se propone unainnovadora estrategia energĂ©tica, basada en lĂłgica difusa, para gestionar la divisiĂłn de la demanda depotencia entre las fuentes de energĂ­a disponibles a bordo del vehĂ­culo. La estrategia adaptativa que sepropone evalĂșa la informaciĂłn energĂ©tica actual y futura (estimada) para adaptar, de una formaoptimizada y eficiente, la operaciĂłn del sistema con el objetivo de mejorar el aprovechamiento de laenergĂ­a almacenada en los recursos a bordo del vehĂ­culo.Por otro lado, se ha propuesto una metodologĂ­a para la co-optimizaciĂłn de la estrategia de gestiĂłn ydimensionamiento del HESS. Esta metodologĂ­a de optimizaciĂłn evalĂșa tanto tĂ©cnica comoeconĂłmicamente las posibles soluciones mediante un problema multi-objetivo basado en algoritmosgenĂ©ticos. Para determinar el costo de reemplazo del HESS han sido aplicados modelo de envejecimientoy estimaciĂłn de vida y se ha considerado la vida Ăștil del vehĂ­culo.Con el objetivo de validar la propuesta de esta tesis doctoral, dos casos de estudio relevantes en latransportaciĂłn pĂșblica han sido seleccionados: TranvĂ­a ElĂ©ctrico HĂ­brido y AutobĂșs ElĂ©ctrico HĂ­brido

    Clustering analysis of railway driving missions with niching

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    A wide number of applications requires classifying or grouping data into a set of categories or clusters. Most popular clustering techniques to achieve this objective are K-means clustering and hierarchical clustering. However, both of these methods necessitate the a priori setting of the cluster number. In this paper, a clustering method based on the use of a niching genetic algorithm is presented, with the aim of finding the best compromise between the inter-cluster distance maximization and the intra-cluster distance minimization. This method is applied to three clustering benchmarks and to the classification of driving missions for railway applications

    A toolbox for multi-objective optimisation of low carbon powertrain topologies

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    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

    Cost of ownership-efficient hybrid electric vehicle powertrain sizing for multi-scenario driving cycles

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    During the last decade, hybrid electric vehicles have gained a presence in the automotive market. On the streets, in motorsports and in society, hybrid electric vehicles are increasingly common. Many manufacturers have become involved in hybrid electric vehicles, while others have hybrid electric vehicle projects in development. Thus, there is already a great variety of hybrid electric vehicles in production, from small microhybrid vehicles to range extenders. Although there are some hybrid electric vehicles designed for urban driving or luxury segments of the market, most of the market share is aimed to the same kind of use and driving, resulting in potentially subsized or oversized hybrid systems that could lead to inefficient use of the vehicle's fuel-saving capabilities in many situations. The present work studies the influence of the sizes of the powertrain components (i.e. the engine, the motor and the battery) on the fuel economy under different assumptions: city driving, highway driving and mixed driving. The utilized framework permits the calculation of the theoretically optimum powertrain sizes assuming a particular target. Different drivers and different traffic conditions are also evaluated. Finally, a long-term cost evaluation is carried out to estimate the optimal sizes of the hybrid electric vehicle powertrain as functions of the type of use of the vehicle throughout its life cycle.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been supported by Conselleria de Educacio Cultura i Esports de la Generalitat Valenciana through Project GV/2013/044 AECOSPH.LujĂĄn, JM.; Guardiola, C.; Pla Moreno, B.; Reig, A. (2016). Cost of ownership-efficient hybrid electric vehicle powertrain sizing for multi-scenario driving cycles. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 230(3):382-394. doi:10.1177/0954407015586333S382394230
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