IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i23p8121-d694647.html
   My bibliography  Save this article

Novel Mathematical Method to Obtain the Optimum Speed and Fuel Reduction in Heavy Diesel Trucks

Author

Listed:
  • Maria Torres-Falcon

    (Industrial Technologies Division, Universidad Politécnica de Querétaro, Querétaro 76240, Mexico
    Red de Investigación OAC Optimización, Automatización y Control, Querétaro 76240, Mexico)

  • Omar Rodríguez-Abreo

    (Industrial Technologies Division, Universidad Politécnica de Querétaro, Querétaro 76240, Mexico
    Red de Investigación OAC Optimización, Automatización y Control, Querétaro 76240, Mexico)

  • Francisco Antonio Castillo-Velásquez

    (Red de Investigación OAC Optimización, Automatización y Control, Querétaro 76240, Mexico
    Information Technology Division, Universidad Politécnica de Querétaro, Querétaro 76240, Mexico)

  • Alejandro Flores-Rangel

    (Industrial Technologies Division, Universidad Politécnica de Querétaro, Querétaro 76240, Mexico
    Red de Investigación OAC Optimización, Automatización y Control, Querétaro 76240, Mexico)

  • Juvenal Rodríguez-Reséndiz

    (Red de Investigación OAC Optimización, Automatización y Control, Querétaro 76240, Mexico
    Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico)

  • José Manuel Álvarez-Alvarado

    (Facultad de Ingeniería, Universidad Autónoma de Querétaro, Querétaro 76010, Mexico)

Abstract

In Mexico and many parts of the world, land cargo transport units (UTTC) operate at high speeds, causing accidents, increased fuel costs, and high levels of polluting emissions in the atmosphere. The speed in road driving, by the carriers, has been a factor little studied; however, it causes serious damage. This problem is reflected in accidents, road damage, low efficiency in the life of the engine and tires, low fuel efficiency, and high polluting emissions, among others. The official Mexican standard NOM-012-SCT-2-2017 on the weight and maximum dimensions with which motor transport vehicles can circulate, which travel through the general communication routes of the federal jurisdiction, establishes the speed limit at the one to be driven by an operator. Because of the new reality, the uses and customs of truck operators have been affected, mainly in their operating expenses. In this work, a mathematical model is presented with which the optimum driving speed of a UTTC is obtained. The speed is obtained employing the equality between the forces required to move the motor unit and the force that the tractor has available. The required forces considered are the force on the slope, the aerodynamic force, and the friction force, and the force available was considered the engine torque. This mathematical method was tested in seven routes in Mexico, obtaining significant savings of fuel above 10%. However, the best performance route possesses 65% flat terrain and 35% hillocks without mountainous terrain, regular type of highway, and a load of 20,000 kg, where the savings increase up to 16.44%.

Suggested Citation

  • Maria Torres-Falcon & Omar Rodríguez-Abreo & Francisco Antonio Castillo-Velásquez & Alejandro Flores-Rangel & Juvenal Rodríguez-Reséndiz & José Manuel Álvarez-Alvarado, 2021. "Novel Mathematical Method to Obtain the Optimum Speed and Fuel Reduction in Heavy Diesel Trucks," Energies, MDPI, vol. 14(23), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8121-:d:694647
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/23/8121/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/23/8121/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. T. M. Yunus Khan & Manzoore Elahi M. Soudagar & S. V. Khandal & Syed Javed & Imran Mokashi & Maughal Ahmed Ali Baig & Khadiga Ahmed Ismail & Ashraf Elfasakhany, 2021. "Performance of Common Rail Direct Injection (CRDi) Engine Using Ceiba Pentandra Biodiesel and Hydrogen Fuel Combination," Energies, MDPI, vol. 14(21), pages 1-16, November.
    2. Adhirath Mandal & Haengmuk Cho & Bhupendra Singh Chauhan, 2021. "ANN Prediction of Performance and Emissions of CI Engine Using Biogas Flow Variation," Energies, MDPI, vol. 14(10), pages 1-18, May.
    3. Melinda Timea Fülöp & Miklós Gubán & György Kovács & Mihály Avornicului, 2021. "Economic Development Based on a Mathematical Model: An Optimal Solution Method for the Fuel Supply of International Road Transport Activity," Energies, MDPI, vol. 14(10), pages 1-22, May.
    4. Karol Tucki, 2021. "A Computer Tool for Modelling CO 2 Emissions in Driving Tests for Vehicles with Diesel Engines," Energies, MDPI, vol. 14(2), pages 1-30, January.
    5. Huifu Jiang & Wei Zhou & Chang Liu & Guosheng Zhang & Meng Hu, 2020. "Safe and Ecological Speed Control for Heavy-Duty Vehicles on Long–Steep Downhill and Sharp-Curved Roads," Sustainability, MDPI, vol. 12(17), pages 1-35, August.
    6. Huang, Yuhan & Ng, Elvin C.Y. & Zhou, John L. & Surawski, Nic C. & Chan, Edward F.C. & Hong, Guang, 2018. "Eco-driving technology for sustainable road transport: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 596-609.
    7. Karol Tucki, 2021. "A Computer Tool for Modelling CO 2 Emissions in Driving Cycles for Spark Ignition Engines Powered by Biofuels," Energies, MDPI, vol. 14(5), pages 1-33, March.
    8. Vedat Kiray & Mehmet Orhan & John Nwankwo Chijioke, 2021. "Significant Increase in Fuel Efficiency of Diesel Generators with Lithium-Ion Batteries Documented by Economic Analysis," Energies, MDPI, vol. 14(21), pages 1-21, October.
    Full references (including those not matched with items on IDEAS)

    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. Gintaras Valeika & Jonas Matijošius & Olga Orynycz & Alfredas Rimkus & Antoni Świć & Karol Tucki, 2023. "Smoke Formation during Combustion of Biofuel Blends in the Internal Combustion Compression Ignition Engine," Energies, MDPI, vol. 16(9), pages 1-16, April.
    2. Saeed Vasebi & Yeganeh M. Hayeri, 2021. "Collective Driving to Mitigate Climate Change: Collective-Adaptive Cruise Control," Sustainability, MDPI, vol. 13(16), pages 1-30, August.
    3. Santos, Alberto & Maia, Pedro & Jacob, Rodrigo & Wei, Huang & Callegari, Camila & Oliveira Fiorini, Ana Carolina & Schaeffer, Roberto & Szklo, Alexandre, 2024. "Road conditions and driving patterns on fuel usage: Lessons from an emerging economy," Energy, Elsevier, vol. 295(C).
    4. Li, Menglin & Yin, Long & Yan, Mei & Wu, Jingda & He, Hongwe & Jia, Chunchun, 2024. "Hierarchical intelligent energy-saving control strategy for fuel cell hybrid electric buses based on traffic flow predictions," Energy, Elsevier, vol. 304(C).
    5. Jaller, Miguel & Pahwa, Anmol & Zhang, Michael, 2021. "Cargo Routing and Disadvantaged Communities," Institute of Transportation Studies, Working Paper Series qt9qg2318x, Institute of Transportation Studies, UC Davis.
    6. Adhirath Mandal & HaengMuk Cho & Bhupendra Singh Chauhan, 2022. "Experimental Investigation of Multiple Fry Waste Soya Bean Oil in an Agricultural CI Engine," Energies, MDPI, vol. 15(9), pages 1-14, April.
    7. Bi, Huibo & Shang, Wen-Long & Chen, Yanyan & Wang, Kezhi & Yu, Qing & Sui, Yi, 2021. "GIS aided sustainable urban road management with a unifying queueing and neural network model," Applied Energy, Elsevier, vol. 291(C).
    8. Yang Wang & Alessandra Boggio-Marzet, 2018. "Evaluation of Eco-Driving Training for Fuel Efficiency and Emissions Reduction According to Road Type," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
    9. Robaina, Margarita & Neves, Ana, 2021. "Complete decomposition analysis of CO2 emissions intensity in the transport sector in Europe," Research in Transportation Economics, Elsevier, vol. 90(C).
    10. Rocio Camarena-Martinez & Rocio A. Lizarraga-Morales & Roberto Baeza-Serrato, 2021. "Classification of Geomembranes as Raw Material for Defects Reduction in the Manufacture of Biodigesters Using an Artificial Neuronal Network," Energies, MDPI, vol. 14(21), pages 1-13, November.
    11. Wojciech Adamski & Krzysztof Brzozowski & Jacek Nowakowski & Tomasz Praszkiewicz & Tomasz Knefel, 2021. "Excess Fuel Consumption Due to Selection of a Lower Than Optimal Gear—Case Study Based on Data Obtained in Real Traffic Conditions," Energies, MDPI, vol. 14(23), pages 1-15, November.
    12. Panagiotis Fafoutellis & Eleni G. Mantouka & Eleni I. Vlahogianni, 2020. "Eco-Driving and Its Impacts on Fuel Efficiency: An Overview of Technologies and Data-Driven Methods," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    13. Juan Francisco Coloma & Marta García & Gonzalo Fernández & Andrés Monzón, 2021. "Environmental Effects of Eco-Driving on Courier Delivery," Sustainability, MDPI, vol. 13(3), pages 1-21, January.
    14. Huang, Yuhan & Surawski, Nic C. & Zhuang, Yuan & Zhou, John L. & Hong, Guang, 2021. "Dual injection: An effective and efficient technology to use renewable fuels in spark ignition engines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    15. Zhang, Hanyu & Du, Lili, 2023. "Platoon-centered control for eco-driving at signalized intersection built upon hybrid MPC system, online learning and distributed optimization part I: Modeling and solution algorithm design," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 174-198.
    16. Chengcheng Xu & Shuyue Wu, 2019. "Evaluating the Effects of Household Characteristics on Household Daily Traffic Emissions Based on Household Travel Survey Data," Sustainability, MDPI, vol. 11(6), pages 1-12, March.
    17. Alexander García-Mariaca & Eva Llera-Sastresa, 2021. "Review on Carbon Capture in ICE Driven Transport," Energies, MDPI, vol. 14(21), pages 1-30, October.
    18. Federico Orsini & Mariaelena Tagliabue & Giulia De Cet & Massimiliano Gastaldi & Riccardo Rossi, 2021. "Highway Deceleration Lane Safety: Effects of Real-Time Coaching Programs on Driving Behavior," Sustainability, MDPI, vol. 13(16), pages 1-16, August.
    19. Liu, Yonggang & Chen, Qianyou & Li, Jie & Zhang, Yuanjian & Chen, Zheng & Lei, Zhenzhen, 2023. "Collaborated eco-routing optimization for continuous traffic flow based on energy consumption difference of multiple vehicles," Energy, Elsevier, vol. 274(C).
    20. Zhang, Yu & Huang, Ronghua & Huang, Yuhan & Huang, Sheng & Zhou, Pei & Chen, Xi & Qin, Tian, 2018. "Experimental study on combustion characteristics of an n-butanol-biodiesel droplet," Energy, Elsevier, vol. 160(C), pages 490-499.

    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:14:y:2021:i:23:p:8121-:d:694647. 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.