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

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  • 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
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    References listed on IDEAS

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    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.
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    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.

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