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A Real-Time Optimal Car-Following Power Management Strategy for Hybrid Electric Vehicles with ACC Systems

Author

Listed:
  • Xiaobo Sun

    (School of Computer Science and Engineering, Central South University, Changsha 410083, China
    School of Automation, Central South University, Changsha 410083, China)

  • Weirong Liu

    (School of Computer Science and Engineering, Central South University, Changsha 410083, China)

  • Mengfei Wen

    (Changsha College for Preschool Education, Changsha 410007, China)

  • Yue Wu

    (School of Automation, Central South University, Changsha 410083, China)

  • Heng Li

    (School of Computer Science and Engineering, Central South University, Changsha 410083, China)

  • Jiahao Huang

    (School of Automation, Central South University, Changsha 410083, China)

  • Chao Hu

    (Big Date Institute, Central South University, Changsha 410087, China)

  • Zhiwu Huang

    (School of Automation, Central South University, Changsha 410083, China)

Abstract

This paper develops a model predictive multi-objective control framework based on an adaptive cruise control (ACC) system to solve the energy allocation and battery state of charge (SOC) maintenance problems of hybrid electric vehicles in the car-following scenario. The proposed control framework is composed of a car-following layer and an energy allocation layer. In the car-following layer, a multi-objective problem is solved to maintain safety and comfort, and the generated speed sequence in the prediction time domain is put forward to the energy allocation layer. In the energy allocation layer, an adaptive equivalent-factor-based consumption minimization strategy with the predicted velocity sequences is adopted to improve the engine efficiency and fuel economy. The equivalent factor reflects the extent of SOC variation, which is used to maintain the battery SOC level when optimizing the energy. The proposed controller is evaluated in the New York City Cycle (NYCC) driving cycle and the Urban Dynamometer Driving Schedule (UDDS) driving cycle, and the comparison results demonstrate the effectiveness of the proposed controller.

Suggested Citation

  • Xiaobo Sun & Weirong Liu & Mengfei Wen & Yue Wu & Heng Li & Jiahao Huang & Chao Hu & Zhiwu Huang, 2021. "A Real-Time Optimal Car-Following Power Management Strategy for Hybrid Electric Vehicles with ACC Systems," Energies, MDPI, vol. 14(12), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3438-:d:572693
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    References listed on IDEAS

    as
    1. Hu, Xiaosong & Zhang, Xiaoqian & Tang, Xiaolin & Lin, Xianke, 2020. "Model predictive control of hybrid electric vehicles for fuel economy, emission reductions, and inter-vehicle safety in car-following scenarios," Energy, Elsevier, vol. 196(C).
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    3. Ahmed M. Ali & Dirk Söffker, 2018. "Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions," Energies, MDPI, vol. 11(3), pages 1-24, February.
    4. Shabbir, Wassif & Evangelou, Simos A., 2014. "Real-time control strategy to maximize hybrid electric vehicle powertrain efficiency," Applied Energy, Elsevier, vol. 135(C), pages 512-522.
    5. Xie, Shaobo & Hu, Xiaosong & Qi, Shanwei & Lang, Kun, 2018. "An artificial neural network-enhanced energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 163(C), pages 837-848.
    6. Xie, Shaobo & Hu, Xiaosong & Liu, Teng & Qi, Shanwei & Lang, Kun & Li, Huiling, 2019. "Predictive vehicle-following power management for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 166(C), pages 701-714.
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