IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i17p4362-d403274.html
   My bibliography  Save this article

Optimal Eco-Driving Cycles for Conventional Vehicles Using a Genetic Algorithm

Author

Listed:
  • Subramaniam Saravana Sankar

    (Automotive Safety and Assessment Engineering Research Centre, The Sirindhorn International Thai–German Graduate School of Engineering (TGGS), King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
    Institut für Kraftfahrzeuge (ika), RWTH Aachen University, Aachen, 52074 North Rhine-Westphalia, Germany)

  • Yiqun Xia

    (Institut für Kraftfahrzeuge (ika), RWTH Aachen University, Aachen, 52074 North Rhine-Westphalia, Germany)

  • Julaluk Carmai

    (Automotive Safety and Assessment Engineering Research Centre, The Sirindhorn International Thai–German Graduate School of Engineering (TGGS), King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand)

  • Saiprasit Koetniyom

    (Automotive Safety and Assessment Engineering Research Centre, The Sirindhorn International Thai–German Graduate School of Engineering (TGGS), King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand)

Abstract

The goal of this work is to compute the eco-driving cycles for vehicles equipped with internal combustion engines by using a genetic algorithm (GA) with a focus on reducing energy consumption. The proposed GA-based optimization method uses an optimal control problem (OCP), which is framed considering both fuel consumption and driver comfort in the cost function formulation with the support of a tunable weight factor to enhance the overall performance of the algorithm. The results and functioning of the optimization algorithm are analyzed with several widely used standard driving cycles and a simulated real-world driving cycle. For the selected optimal weight factor, the simulation results show that an average reduction of eight percent in fuel consumption is achieved. The results of parallelization in computing the cost function indicates that the computational time required by the optimization algorithm is reduced based on the hardware used.

Suggested Citation

  • Subramaniam Saravana Sankar & Yiqun Xia & Julaluk Carmai & Saiprasit Koetniyom, 2020. "Optimal Eco-Driving Cycles for Conventional Vehicles Using a Genetic Algorithm," Energies, MDPI, vol. 13(17), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4362-:d:403274
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/17/4362/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/17/4362/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. P. Spellucci, 1998. "A new technique for inconsistent QP problems in the SQP method," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 47(3), pages 355-400, October.
    2. Thomas Levermore & M. Necip Sahinkaya & Yahya Zweiri & Ben Neaves, 2016. "Real-Time Velocity Optimization to Minimize Energy Use in Passenger Vehicles," Energies, MDPI, vol. 10(1), pages 1-18, December.
    3. Yang Yang & Zhen Zhong & Fei Wang & Chunyun Fu & Junzhang Liao, 2020. "Real-time Energy Management Strategy for Oil-Electric-Liquid Hybrid System based on Lowest Instantaneous Energy Consumption Cost," Energies, MDPI, vol. 13(4), pages 1-23, February.
    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. Triluck Kusalaphirom & Thaned Satiennam & Wichuda Satiennam & Atthapol Seedam, 2022. "Development of a Real-World Eco-Driving Cycle for Motorcycles," Sustainability, MDPI, vol. 14(10), pages 1-14, May.

    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. Abdullah Mohiuddin & Tarek Taha & Yahya Zweiri & Dongming Gan, 2019. "UAV Payload Transportation via RTDP Based Optimized Velocity Profiles," Energies, MDPI, vol. 12(16), pages 1-25, August.
    2. Flisberg, Patrik & Nilsson, Stefan & Rönnqvist, Mikael, 2007. "Optimized on-line process control of bleaching operations with OptCab," Discussion Papers 2007/9, Norwegian School of Economics, Department of Business and Management Science.
    3. Emms, Paul, 2007. "Pricing general insurance with constraints," Insurance: Mathematics and Economics, Elsevier, vol. 40(2), pages 335-355, March.
    4. Meade, N. & Beasley, J.E. & Adcock, C.J., 2021. "Quantitative portfolio selection: Using density forecasting to find consistent portfolios," European Journal of Operational Research, Elsevier, vol. 288(3), pages 1053-1067.
    5. Jian Yang & Tiezhu Zhang & Hongxin Zhang & Jichao Hong & Zewen Meng, 2020. "Research on the Starting Acceleration Characteristics of a New Mechanical–Electric–Hydraulic Power Coupling Electric Vehicle," Energies, MDPI, vol. 13(23), pages 1-20, November.
    6. Pia Domschke & Bjorn Geißler & Oliver Kolb & Jens Lang & Alexander Martin & Antonio Morsi, 2011. "Combination of Nonlinear and Linear Optimization of Transient Gas Networks," INFORMS Journal on Computing, INFORMS, vol. 23(4), pages 605-617, November.
    7. Domschke, Pia & Kolb, Oliver & Lang, Jens, 2015. "Adjoint-based error control for the simulation and optimization of gas and water supply networks," Applied Mathematics and Computation, Elsevier, vol. 259(C), pages 1003-1018.
    8. Patrik Flisberg & Mikael Rönnqvist & Stefan Nilsson, 2009. "Billerud Optimizes Its Bleaching Process Using Online Optimization," Interfaces, INFORMS, vol. 39(2), pages 119-132, April.
    9. Xiong, Jie & Zhao, Haibo & Zhang, Chao & Zheng, Chuguang & Luh, Peter B., 2012. "Thermoeconomic operation optimization of a coal-fired power plant," Energy, Elsevier, vol. 42(1), pages 486-496.
    10. Kolb, Oliver & Göttlich, Simone, 2015. "A continuous buffer allocation model using stochastic processes," European Journal of Operational Research, Elsevier, vol. 242(3), pages 865-874.

    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:13:y:2020:i:17:p:4362-:d:403274. 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.