IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v5y2012i11p4624-4642d21477.html
   My bibliography  Save this article

Novel Speed Bumps Design and Optimization for Vehicles' Energy Recovery in Smart Cities

Author

Listed:
  • Andrea Pirisi

    (Underground Power, Via Garibaldi 144, 20834 Nova Milanese, Milano, Italy)

  • Francesco Grimaccia

    (Department of Energy, Politecnico di Milano, Via La Masa 34, I-20156 Milano, Italy)

  • Marco Mussetta

    (Department of Energy, Politecnico di Milano, Via La Masa 34, I-20156 Milano, Italy)

  • Riccardo E. Zich

    (Department of Energy, Politecnico di Milano, Via La Masa 34, I-20156 Milano, Italy)

Abstract

Recently the technology development and increasing amounts of investment in renewables has led to a growing interest towards design and optimization of green energy systems. In this context, advanced Computational Intelligence (CI) techniques can be applied by engineers to several technical problems in order to find out the best structure and to improve efficiency in energy recovery. This research promises to give new impulse to using innovative unconventional renewable sources and to develop the so-called Energy Harvesting Devices (EHDs). In this paper, the optimization of a Tubular Permanent Magnet-Linear Generator for energy harvesting from vehicles to grid is presented. The optimization process is developed by means of hybrid evolutionary algorithms to reach the best overall system efficiency and the impact on the environment and transportation systems. Finally, an experimental validation of the designed EHD prototype is presented.

Suggested Citation

  • Andrea Pirisi & Francesco Grimaccia & Marco Mussetta & Riccardo E. Zich, 2012. "Novel Speed Bumps Design and Optimization for Vehicles' Energy Recovery in Smart Cities," Energies, MDPI, vol. 5(11), pages 1-19, November.
  • Handle: RePEc:gam:jeners:v:5:y:2012:i:11:p:4624-4642:d:21477
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/5/11/4624/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/5/11/4624/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. João Soares & Bruno Canizes & Cristina Lobo & Zita Vale & Hugo Morais, 2012. "Electric Vehicle Scenario Simulator Tool for Smart Grid Operators," Energies, MDPI, vol. 5(6), pages 1-19, June.
    2. Min Ye & Shengjie Jiao & Binggang Cao, 2010. "Energy Recovery for the Main and Auxiliary Sources of Electric Vehicles," Energies, MDPI, vol. 3(10), pages 1-18, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pan, Hongye & Qi, Lingfei & Zhang, Zutao & Yan, Jinyue, 2021. "Kinetic energy harvesting technologies for applications in land transportation: A comprehensive review," Applied Energy, Elsevier, vol. 286(C).
    2. Zhang, Zutao & Zhang, Xingtian & Rasim, Yagubov & Wang, Chunbai & Du, Bing & Yuan, Yanping, 2016. "Design, modelling and practical tests on a high-voltage kinetic energy harvesting (EH) system for a renewable road tunnel based on linear alternators," Applied Energy, Elsevier, vol. 164(C), pages 152-161.
    3. Tatiana Tucunduva Philippi Cortese & Jairo Filho Sousa de Almeida & Giseli Quirino Batista & José Eduardo Storopoli & Aaron Liu & Tan Yigitcanlar, 2022. "Understanding Sustainable Energy in the Context of Smart Cities: A PRISMA Review," Energies, MDPI, vol. 15(7), pages 1-38, March.
    4. Maksymilian Mądziel, 2023. "Future Cities Carbon Emission Models: Hybrid Vehicle Emission Modelling for Low-Emission Zones," Energies, MDPI, vol. 16(19), pages 1-16, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Da Xie & Haoxiang Chu & Yupu Lu & Chenghong Gu & Furong Li & Yu Zhang, 2015. "The Concept of EV’s Intelligent Integrated Station and Its Energy Flow," Energies, MDPI, vol. 8(5), pages 1-28, May.
    2. Bruno Canizes & João Soares & Angelo Costa & Tiago Pinto & Fernando Lezama & Paulo Novais & Zita Vale, 2019. "Electric Vehicles’ User Charging Behaviour Simulator for a Smart City," Energies, MDPI, vol. 12(8), pages 1-20, April.
    3. Sousa, Tiago & Morais, Hugo & Vale, Zita & Castro, Rui, 2015. "A multi-objective optimization of the active and reactive resource scheduling at a distribution level in a smart grid context," Energy, Elsevier, vol. 85(C), pages 236-250.
    4. Pol Olivella-Rosell & Roberto Villafafila-Robles & Andreas Sumper & Joan Bergas-Jané, 2015. "Probabilistic Agent-Based Model of Electric Vehicle Charging Demand to Analyse the Impact on Distribution Networks," Energies, MDPI, vol. 8(5), pages 1-28, May.
    5. Almeida, José & Soares, Joao & Lezama, Fernando & Vale, Zita & Francois, Bruno, 2024. "Comparison of evolutionary algorithms for solving risk-based energy resource management considering conditional value-at-risk analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 224(PB), pages 87-110.
    6. Andrii Shekhovtsov & Volodymyr Kozlov & Viktor Nosov & Wojciech Sałabun, 2020. "Efficiency of Methods for Determining the Relevance of Criteria in Sustainable Transport Problems: A Comparative Case Study," Sustainability, MDPI, vol. 12(19), pages 1-23, September.
    7. Vaclav Kaczmarczyk & Zdenek Bradac & Petr Fiedler, 2017. "A Heuristic Algorithm to Compute Multimodal Criterial Function Weights for Demand Management in Residential Areas," Energies, MDPI, vol. 10(7), pages 1-28, July.
    8. Tobias Rösch & Peter Treffinger, 2019. "Cluster Analysis of Distribution Grids in Baden-Württemberg," Energies, MDPI, vol. 12(20), pages 1-25, October.
    9. Seog-Chan Oh & Alfred J. Hildreth, 2013. "Decisions on Energy Demand Response Option Contracts in Smart Grids Based on Activity-Based Costing and Stochastic Programming," Energies, MDPI, vol. 6(1), pages 1-19, January.
    10. Yang, Zhile & Li, Kang & Foley, Aoife, 2015. "Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 396-416.
    11. Soares, João & Fotouhi Ghazvini, Mohammad Ali & Vale, Zita & de Moura Oliveira, P.B., 2016. "A multi-objective model for the day-ahead energy resource scheduling of a smart grid with high penetration of sensitive loads," Applied Energy, Elsevier, vol. 162(C), pages 1074-1088.
    12. Junjie Hu & Hugo Morais & Tiago Sousa & Shi You & Reinhilde D’hulst, 2017. "Integration of Electric Vehicles into the Power Distribution Network with a Modified Capacity Allocation Mechanism," Energies, MDPI, vol. 10(2), pages 1-20, February.
    13. João Soares & Nuno Borges & Zita Vale & P.B. De Moura Oliveira, 2016. "Enhanced Multi-Objective Energy Optimization by a Signaling Method," Energies, MDPI, vol. 9(10), pages 1-23, October.
    14. Sousa, Tiago & Vale, Zita & Carvalho, Joao Paulo & Pinto, Tiago & Morais, Hugo, 2014. "A hybrid simulated annealing approach to handle energy resource management considering an intensive use of electric vehicles," Energy, Elsevier, vol. 67(C), pages 81-96.
    15. Wojciech Sałabun & Krzysztof Palczewski & Jarosław Wątróbski, 2019. "Multicriteria Approach to Sustainable Transport Evaluation under Incomplete Knowledge: Electric Bikes Case Study," Sustainability, MDPI, vol. 11(12), pages 1-19, June.
    16. Monica Alonso & Hortensia Amaris & Jean Gardy Germain & Juan Manuel Galan, 2014. "Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms," Energies, MDPI, vol. 7(4), pages 1-27, April.
    17. Thomas J.T. Van der Wardt & Amro M. Farid, 2017. "A Hybrid Dynamic System Assessment Methodology for Multi-Modal Transportation-Electrification," Energies, MDPI, vol. 10(5), pages 1-25, May.
    18. Julian Garcia-Guarin & David Alvarez & Arturo Bretas & Sergio Rivera, 2020. "Schedule Optimization in a Smart Microgrid Considering Demand Response Constraints," Energies, MDPI, vol. 13(17), pages 1-19, September.
    19. Soares, João & Ghazvini, Mohammad Ali Fotouhi & Borges, Nuno & Vale, Zita, 2017. "Dynamic electricity pricing for electric vehicles using stochastic programming," Energy, Elsevier, vol. 122(C), pages 111-127.
    20. Wang, Yunqi & Wang, Hao & Razzaghi, Reza & Jalili, Mahdi & Liebman, Ariel, 2024. "Multi-objective coordinated EV charging strategy in distribution networks using an improved augmented epsilon-constrained method," Applied Energy, Elsevier, vol. 369(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:5:y:2012:i:11:p:4624-4642:d:21477. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.