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Powering Mode-Integrated Energy Management Strategy for a Plug-In Hybrid Electric Truck with an Automatic Mechanical Transmission Based on Pontryagin’s Minimum Principle

Author

Listed:
  • Shaobo Xie

    (School of Automotive Engineering, Chang’an University, Xi’an 710064, China
    The authors equally contributed to this research work.)

  • Xiaosong Hu

    (State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China
    The authors equally contributed to this research work.)

  • Kun Lang

    (School of Automotive Engineering, Chang’an University, Xi’an 710064, China)

  • Shanwei Qi

    (School of Automotive Engineering, Chang’an University, Xi’an 710064, China)

  • Tong Liu

    (School of Automotive Engineering, Chang’an University, Xi’an 710064, China)

Abstract

Pontryagin’s Minimum Principle (PMP) has a significant computational advantage over dynamic programming for energy management issues of hybrid electric vehicles. However, minimizing the total energy consumption for a plug-in hybrid electric vehicle based on PMP is not always a two-point boundary value problem (TPBVP), as the optimal solution of a powering mode will be either a pure-electric driving mode or a hybrid discharging mode, depending on the trip distance. In this paper, based on a plug-in hybrid electric truck (PHET) equipped with an automatic mechanical transmission (AMT), we propose an integrated control strategy to flexibly identify the optimal powering mode in accordance with different trip lengths, where an electric-only-mode decision module is incorporated into the TPBVP by judging the auxiliary power unit state and the final battery state-of-charge (SOC) level. For the hybrid mode, the PMP-based energy management problem is converted to a normal TPBVP and solved by using a shooting method. Moreover, the energy management for the plug-in hybrid electric truck with an AMT involves simultaneously optimizing the power distribution between the auxiliary power unit (APU) and the battery, as well as the gear-shifting choice. The simulation results with long- and short-distance scenarios indicate the flexibility of the PMP-based strategy. Furthermore, the proposed control strategy is compared with dynamic programming (DP) and a rule-based charge-depleting and charge-sustaining (CD-CS) strategy to evaluate its performance in terms of computational accuracy and time efficiency.

Suggested Citation

  • Shaobo Xie & Xiaosong Hu & Kun Lang & Shanwei Qi & Tong Liu, 2018. "Powering Mode-Integrated Energy Management Strategy for a Plug-In Hybrid Electric Truck with an Automatic Mechanical Transmission Based on Pontryagin’s Minimum Principle," Sustainability, MDPI, vol. 10(10), pages 1-23, October.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3758-:d:176562
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    References listed on IDEAS

    as
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    5. 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.
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    Cited by:

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    2. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    3. Giyeon Hwang & Kyungmin Lee & Jongmyung Kim & Kyu-Jin Lee & Sangyul Lee & Minjae Kim, 2020. "Energy Management Optimization of Series Hybrid Electric Bus Using an Ultra-Capacitor and Novel Efficiency Improvement Factors," Sustainability, MDPI, vol. 12(18), pages 1-19, September.
    4. Julian Marius Müller & Raphael Kunderer, 2019. "Ex-Ante Prediction of Disruptive Innovation: The Case of Battery Technologies," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
    5. Tiancheng Lin & Wei Fan & Canbo Xiao & Zhongzhi Yao & Zhujun Zhang & Ruolan Zhao & Yiwen Pan & Ying Chen, 2019. "Energy Management and Operational Planning of an Ecological Engineering for Carbon Sequestration in Coastal Mariculture Environments in China," Sustainability, MDPI, vol. 11(11), pages 1-20, June.

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