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Development of Near Optimal Rule-Based Control for Plug-In Hybrid Electric Vehicles Taking into Account Drivetrain Component Losses

Author

Listed:
  • Hanho Son

    (School of Mechanical Engineering, Sungkyunkwan University, Seobu-ro, Suwon-si 2066, Korea)

  • Hyunsoo Kim

    (School of Mechanical Engineering, Sungkyunkwan University, Seobu-ro, Suwon-si 2066, Korea)

Abstract

A near-optimal rule-based mode control (RBC) strategy was proposed for a target plug-in hybrid electric vehicle (PHEV) taking into account the drivetrain losses. Individual loss models were developed for drivetrain components including the gears, planetary gear (PG), bearings, and oil pump, based on experimental data and mathematical governing equations. Also, a loss model for the power electronic system was constructed, including loss from the motor-generator while rotating in the unloaded state. To evaluate the effect of the drivetrain losses on the operating mode control strategy, backward simulations were performed using dynamic programming (DP). DP selects the operating mode, which provides the highest efficiency for given driving conditions. It was found that the operating mode selection changes when drivetrain losses are included, depending on driving conditions. An operating mode schedule was developed with respect to the wheel power and vehicle speed, and based on the operating mode schedule, a RBC was obtained, which can be implemented in an on-line application. To evaluate the performance of the RBC, a forward simulator was constructed for the target PHEV. The simulation results show near-optimal performance of the RBC compared with dynamic-programming-based mode control in terms of the mode operation time and fuel economy. The RBC developed with drivetrain losses taken into account showed a 4%–5% improvement of the fuel economy over a similar RBC, which neglected the drivetrain losses.

Suggested Citation

  • Hanho Son & Hyunsoo Kim, 2016. "Development of Near Optimal Rule-Based Control for Plug-In Hybrid Electric Vehicles Taking into Account Drivetrain Component Losses," Energies, MDPI, vol. 9(6), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:6:p:420-:d:71102
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    References listed on IDEAS

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    1. Zou Yuan & Liu Teng & Sun Fengchun & Huei Peng, 2013. "Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle," Energies, MDPI, vol. 6(4), pages 1-14, April.
    2. Mohammad Ali Karbaschian & Dirk Söffker, 2014. "Review and Comparison of Power Management Approaches for Hybrid Vehicles with Focus on Hydraulic Drives," Energies, MDPI, vol. 7(6), pages 1-25, May.
    3. Ximing Wang & Hongwen He & Fengchun Sun & Jieli Zhang, 2015. "Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles," Energies, MDPI, vol. 8(4), pages 1-20, April.
    4. Onori, Simona & Tribioli, Laura, 2015. "Adaptive Pontryagin’s Minimum Principle supervisory controller design for the plug-in hybrid GM Chevrolet Volt," Applied Energy, Elsevier, vol. 147(C), pages 224-234.
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    Citations

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    Cited by:

    1. Hyunhwa Kim & Junbeom Wi & Jiho Yoo & Hanho Son & Chiman Park & Hyunsoo Kim, 2018. "A Study on the Fuel Economy Potential of Parallel and Power Split Type Hybrid Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-19, August.
    2. Jing Lian & Shuang Liu & Linhui Li & Xuanzuo Liu & Yafu Zhou & Fan Yang & Lushan Yuan, 2017. "A Mixed Logical Dynamical-Model Predictive Control (MLD-MPC) Energy Management Control Strategy for Plug-in Hybrid Electric Vehicles (PHEVs)," Energies, MDPI, vol. 10(1), pages 1-18, January.
    3. Hanho Son & Kyusik Park & Sungho Hwang & Hyunsoo Kim, 2017. "Design Methodology of a Power Split Type Plug-In Hybrid Electric Vehicle Considering Drivetrain Losses," Energies, MDPI, vol. 10(4), pages 1-18, March.
    4. Hanho Son & Hyunhwa Kim & Sungho Hwang & Hyunsoo Kim, 2018. "Development of an Advanced Rule-Based Control Strategy for a PHEV Using Machine Learning," Energies, MDPI, vol. 11(1), pages 1-15, January.
    5. Yuying Wang & Xiaohong Jiao & Zitao Sun & Ping Li, 2017. "Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 10(11), pages 1-21, November.
    6. Hongwen He & Jinquan Guo & Nana Zhou & Chao Sun & Jiankun Peng, 2017. "Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles," Energies, MDPI, vol. 10(11), pages 1-19, November.

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