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Adaptive optimal control based on driving style recognition for plug-in hybrid electric vehicle

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  • Guo, Qiuyi
  • Zhao, Zhiguo
  • Shen, Peihong
  • Zhan, Xiaowen
  • Li, Jingwei

Abstract

Vehicle energy economy is affected by different driving styles of individual drivers. To improve energy economy of plug-in hybrid electric vehicles (PHEVs), it is of great importance to develop the driving style adaptive optimal control strategy. In fact, driving styles are often influenced and restricted by different driving cycles. Therefore, to recognize driving style more accurately, this paper decouples driving styles from driving cycles. Based on classification and identification of driving cycles, the accelerator pedal opening and its change rate in different driving cycles are analyzed and the fuzzy-logic recognizer is built to identify driving styles. Afterwards, the driving style adaptive optimal control strategy is realized by combining the recognized driving style with the equivalent consumption minimization strategy (ECMS) and adopting a hybrid particle swarm optimization-genetic algorithm (PSO-GA) to optimize the relationship between the driving style and the equivalence factor (EF). The effectiveness of proposed driving style adaptive control strategy is validated by real vehicle test, which indicates that, compared with the original ECMS, the proposed driving style recognition based adaptive optimal control strategy improves the energy economy by 3.69% in the New European Driving Cycle (NEDC). This adaptive optimal strategy provides guidance for incorporating driving style into PHEV energy management strategy to improve fuel economy.

Suggested Citation

  • Guo, Qiuyi & Zhao, Zhiguo & Shen, Peihong & Zhan, Xiaowen & Li, Jingwei, 2019. "Adaptive optimal control based on driving style recognition for plug-in hybrid electric vehicle," Energy, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:energy:v:186:y:2019:i:c:s0360544219314963
    DOI: 10.1016/j.energy.2019.07.154
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    Citations

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    Cited by:

    1. Yang, Yang & He, Qiang & Fu, Chunyun & Liao, Shuiping & Tan, Peng, 2020. "Efficiency improvement of permanent magnet synchronous motor for electric vehicles," Energy, Elsevier, vol. 213(C).
    2. Yongliang Zheng & Feng He & Xinze Shen & Xuesheng Jiang, 2020. "Energy Control Strategy of Fuel Cell Hybrid Electric Vehicle Based on Working Conditions Identification by Least Square Support Vector Machine," Energies, MDPI, vol. 13(2), pages 1-18, January.
    3. Kun He & Dongchen Qin & Jiangyi Chen & Tingting Wang & Hongxia Wu & Peizhuo Wang, 2023. "Adaptive Equivalent Consumption Minimization Strategy for Fuel Cell Buses Based on Driving Style Recognition," Sustainability, MDPI, vol. 15(10), pages 1-17, May.
    4. Hu, Lin & Tian, Qingtao & Zou, Changfu & Huang, Jing & Ye, Yao & Wu, Xianhui, 2022. "A study on energy distribution strategy of electric vehicle hybrid energy storage system considering driving style based on real urban driving data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    5. Alcázar-García, Désirée & Romeral Martínez, José Luis, 2022. "Model-based design validation and optimization of drive systems in electric, hybrid, plug-in hybrid and fuel cell vehicles," Energy, Elsevier, vol. 254(PA).
    6. 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).
    7. Liu, Huanlong & Chen, Guanpeng & Li, Dafa & Wang, Jiawei & Zhou, Jianyi, 2021. "Energy active adjustment and bidirectional transfer management strategy of the electro-hydrostatic hydraulic hybrid powertrain for battery bus," Energy, Elsevier, vol. 230(C).
    8. Wen, Lei & Song, Qianqian, 2023. "ELCC-based capacity value estimation of combined wind - storage system using IPSO algorithm," Energy, Elsevier, vol. 263(PB).
    9. Wang, Yue & Li, Keqiang & Zeng, Xiaohua & Gao, Bolin & Hong, Jichao, 2022. "Energy consumption characteristics based driving conditions construction and prediction for hybrid electric buses energy management," Energy, Elsevier, vol. 245(C).
    10. Baodi Zhang & Sheng Guo & Xin Zhang & Qicheng Xue & Lan Teng, 2020. "Adaptive Smoothing Power Following Control Strategy Based on an Optimal Efficiency Map for a Hybrid Electric Tracked Vehicle," Energies, MDPI, vol. 13(8), pages 1-25, April.
    11. Liu, Huanlong & Chen, Guanpeng & Xie, Chixin & Li, Dafa & Wang, Jiawei & Li, Shun, 2020. "Research on energy-saving characteristics of battery-powered electric-hydrostatic hydraulic hybrid rail vehicles," Energy, Elsevier, vol. 205(C).
    12. Cui, Wei & Cui, Naxin & Li, Tao & Cui, Zhongrui & Du, Yi & Zhang, Chenghui, 2022. "An efficient multi-objective hierarchical energy management strategy for plug-in hybrid electric vehicle in connected scenario," Energy, Elsevier, vol. 257(C).
    13. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2021. "Real-Time Approximate Equivalent Consumption Minimization Strategy Based on the Single-Shaft Parallel Hybrid Powertrain," Energies, MDPI, vol. 14(23), pages 1-22, November.
    14. Shangguan, Jinyong & Guo, Hongqiang & Yue, Ming, 2020. "Robust energy management of plug-in hybrid electric bus considering the uncertainties of driving cycles and vehicle mass," Energy, Elsevier, vol. 203(C).
    15. Chen, Z. & Liu, Y. & Ye, M. & Zhang, Y. & Chen, Z. & Li, G., 2021. "A survey on key techniques and development perspectives of equivalent consumption minimisation strategy for hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).

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