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dc.contributor.authorDONATI ALBERTOen_GB
dc.contributor.authorKRAUSE JETTEen_GB
dc.contributor.authorTHIEL CHRISTIANen_GB
dc.contributor.authorWHITE BENen_GB
dc.contributor.authorHILL NIKOLASen_GB
dc.date.accessioned2020-06-09T00:05:49Z-
dc.date.available2020-06-08en_GB
dc.date.available2020-06-09T00:05:49Z-
dc.date.created2020-06-03en_GB
dc.date.issued2020en_GB
dc.date.submitted2016-11-25en_GB
dc.identifier.citationENERGIES vol. 13 no. 11 p. 2850en_GB
dc.identifier.issn1996-1073 (online)en_GB
dc.identifier.urihttps://publications.jrc.ec.europa.eu/repository/handle/JRC104229-
dc.description.abstractThe number and interdependency of vehicle CO2 reduction technologies, which can be employed to reduce greenhouse emissions for regulatory compliance in the European Union and other countries, has increasingly grown in the recent years. This paper proposes a method to optimally combine these technologies on cars or other road vehicles to improve their energy efficiency. The methodological difficulty is in the fact that these technologies have incompatibilities between them. Moreover, two conflicting objective functions are considered and have to be optimized to obtain Pareto optimal solutions: the CO2 reduction versus costs. For this NP‐complete combinatorial problem, a method based on a metaheuristic with Ant Colony Optimization (ACO) combined with a Local Search (LS) algorithm is proposed and generalized as the Technology Packaging Problem (TPP). It consists in finding, from a given set of technologies (each with a specific cost and CO2 reduction potential), among all their possible combinations, the Pareto front composed by those configurations having the minimal total costs and maximum total CO2 reduction. We compare the performance of the proposed method with a Genetic Algorithm (GA) showing the improvements achieved. Thanks to the increased computational efficiency, this technique has been deployed to solve thousands of optimization instances generated by the availability of these technologies by year, type of powertrain, segment, drive cycle, cost type and scenario (i.e., more or less optimistic technology cost for projected data) and inclusion of off‐cycle technologies. The total combinations of all these parameters give rise to thousands of distinct instances to be solved and optimized. Computational tests are also presented to show the effectiveness of this new approach. The outputs have been used as basis to assess the costs of complying with different levels of new vehicle CO2 standards, from the perspective of different manufacturer types as well as vehicle users in Europe.en_GB
dc.description.sponsorshipJRC.C.4-Sustainable Transporten_GB
dc.format.mediumOnlineen_GB
dc.languageENGen_GB
dc.publisherMDPIen_GB
dc.relation.ispartofseriesJRC104229en_GB
dc.titleAn Ant Colony Algorithm for Improving Energy Efficiency of Road Vehiclesen_GB
dc.typeArticles in periodicals and booksen_GB
dc.identifier.doi10.3390/en13112850 (online)en_GB
JRC Directorate:Energy, Transport and Climate

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