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An Ant Colony Algorithm for Improving Energy Efficiency of Road Vehicles

Author

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  • Alberto V. Donati

    (Joint Research Centre of the European Commission, Via Fermi 2749, 21027 Ispra (VA), Italy)

  • Jette Krause

    (Joint Research Centre of the European Commission, Via Fermi 2749, 21027 Ispra (VA), Italy)

  • Christian Thiel

    (Joint Research Centre of the European Commission, Via Fermi 2749, 21027 Ispra (VA), Italy)

  • Ben White

    (Ricardo Energy and Environment, 30 Eastbourne Terrace, Paddington, London W2 6LA, UK)

  • Nikolas Hill

    (Ricardo Energy and Environment, Gemini Building, Fermi Avenue, Harwell, Oxon OX11 0QR, UK)

Abstract

The number and interdependency of vehicle CO 2 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 CO 2 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 CO 2 reduction potential), among all their possible combinations, the Pareto front composed by those configurations having the minimal total costs and maximum total CO 2 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 CO 2 standards, from the perspective of different manufacturer types as well as vehicle users in Europe.

Suggested Citation

  • Alberto V. Donati & Jette Krause & Christian Thiel & Ben White & Nikolas Hill, 2020. "An Ant Colony Algorithm for Improving Energy Efficiency of Road Vehicles," Energies, MDPI, vol. 13(11), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2850-:d:366905
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    References listed on IDEAS

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    1. R. Montemanni & L. M. Gambardella & A. E. Rizzoli & A. V. Donati, 2005. "Ant Colony System for a Dynamic Vehicle Routing Problem," Journal of Combinatorial Optimization, Springer, vol. 10(4), pages 327-343, December.
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    Cited by:

    1. Dirk Deschrijver, 2021. "Special Issue: “Improving Energy Efficiency through Data-Driven Modeling, Simulation and Optimization”," Energies, MDPI, vol. 14(6), pages 1-3, March.
    2. Martin Filip & Tomas Zoubek & Roman Bumbalek & Pavel Cerny & Carlos E. Batista & Pavel Olsan & Petr Bartos & Pavel Kriz & Maohua Xiao & Antonin Dolan & Pavol Findura, 2020. "Advanced Computational Methods for Agriculture Machinery Movement Optimization with Applications in Sugarcane Production," Agriculture, MDPI, vol. 10(10), pages 1-20, September.

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