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Optimized Fuel Economy Control of Power-Split Hybrid Electric Vehicle with Particle Swarm Optimization

Author

Listed:
  • Hsiu-Ying Hwang

    (Department of Vehicle Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Jia-Shiun Chen

    (Department of Vehicle Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

Abstract

This research focused on real-time optimization control to improve the fuel consumption of power-split hybrid electric vehicles. Particle swarm optimization (PSO) was implemented to reduce fuel consumption for real-time optimization control. The engine torque was design-variable to manage the energy distribution of dual energy sources. The AHS II power-split hybrid electric system was used as the powertrain system. The hybrid electric vehicle model was built using Matlab/Simulink. The simulation was performed according to US FTP-75 regulations. The PSO design objective was to minimize the equivalent fuel rate with the driving system still meeting the dynamic performance requirements. Through dynamic vehicle simulation and PSO, the required torque value for the whole drivetrain system and corresponding high-efficiency engine operating point can be found. With that, the two motor/generators (M/Gs) supplemented the rest required torques. The composite fuel economy of the PSO algorithm was 46.8 mpg, which is a 9.4% improvement over the base control model. The PSO control strategy could quickly converge and that feature makes PSO a good fit to be used in real-time control applications.

Suggested Citation

  • Hsiu-Ying Hwang & Jia-Shiun Chen, 2020. "Optimized Fuel Economy Control of Power-Split Hybrid Electric Vehicle with Particle Swarm Optimization," Energies, MDPI, vol. 13(9), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2278-:d:354132
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    References listed on IDEAS

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    Cited by:

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    2. Xingxing Wang & Shengren Liu & Peilin Ye & Yu Zhu & Yinnan Yuan & Linfei Chen, 2023. "Study of a Hybrid Vehicle Powertrain Parameter Matching Design Based on the Combination of Orthogonal Test and Cruise Software," Sustainability, MDPI, vol. 15(14), pages 1-28, July.
    3. Dapai Shi & Junjie Guo & Kangjie Liu & Qingling Cai & Zhenghong Wang & Xudong Qu, 2023. "Research on an Improved Rule-Based Energy Management Strategy Enlightened by the DP Optimization Results," Sustainability, MDPI, vol. 15(13), pages 1-13, July.
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    5. Nikolaos Aletras & Stylianos Doulgeris & Zissis Samaras & Leonidas Ntziachristos, 2023. "Comparative Assessment of Supervisory Control Algorithms for a Plug-In Hybrid Electric Vehicle," Energies, MDPI, vol. 16(3), pages 1-17, February.

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