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Adaptive Equivalent Fuel Consumption Minimization Based Energy Management Strategy for Extended-Range Electric Vehicle

Author

Listed:
  • Dongwei Yao

    (College of Energy Engineering, Zhejiang University, Hangzhou 310027, China
    Key Laboratory of Smart Thermal Management Science & Technology for Vehicles of Zhejiang Province, Taizhou 317200, China)

  • Xinwei Lu

    (College of Energy Engineering, Zhejiang University, Hangzhou 310027, China)

  • Xiangyun Chao

    (College of Energy Engineering, Zhejiang University, Hangzhou 310027, China)

  • Yongguang Zhang

    (Hangzhou DV Technology Co., Ltd., Hangzhou 310023, China)

  • Junhao Shen

    (College of Energy Engineering, Zhejiang University, Hangzhou 310027, China)

  • Fanlong Zeng

    (College of Energy Engineering, Zhejiang University, Hangzhou 310027, China)

  • Ziyan Zhang

    (College of Energy Engineering, Zhejiang University, Hangzhou 310027, China)

  • Feng Wu

    (College of Energy Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

Unlike battery electric vehicles, extended-range electric vehicles have one more energy source, so a reasonable energy management strategy (EMS) is crucial to the fuel economy of the vehicles. In this paper, an adaptive equivalent fuel consumption minimization strategy (A-ECMS)-based energy management strategy is proposed for the extended-range electric vehicle. The equivalent fuel consumption minimization strategy (ECMS), which utilizes Pontryagin’s minimum principle (PMP), is introduced to design the EMS. Compared with other ECMS strategies, an adaptive equivalent factor algorithm, based on state of charge (SOC) feedback and a proportional–integral (PI) controller is designed to update the equivalent factor under different working conditions. Additionally, a start–stop penalty is added to the objective function to take the dynamic start–stop process of the range extender into account. As a result, under the WLTC driving cycle, the proposed strategy can achieve 6.78 L/100 km comprehensive fuel consumption, saving 6.2% and 3.4% fuel consumption compared with the conventional rule-based thermostat strategy and the power following strategy. Moreover, the proposed EMS achieves the lowest ampere-hour flux among the three EMSs, indicating its ability to improve battery life.

Suggested Citation

  • Dongwei Yao & Xinwei Lu & Xiangyun Chao & Yongguang Zhang & Junhao Shen & Fanlong Zeng & Ziyan Zhang & Feng Wu, 2023. "Adaptive Equivalent Fuel Consumption Minimization Based Energy Management Strategy for Extended-Range Electric Vehicle," Sustainability, MDPI, vol. 15(5), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4607-:d:1087815
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    References listed on IDEAS

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    1. 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.
    2. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    3. Zeng, Tao & Zhang, Caizhi & Zhang, Yanyi & Deng, Chenghao & Hao, Dong & Zhu, Zhongwen & Ran, Hongxu & Cao, Dongpu, 2021. "Optimization-oriented adaptive equivalent consumption minimization strategy based on short-term demand power prediction for fuel cell hybrid vehicle," Energy, Elsevier, vol. 227(C).
    4. Wang, Yaxin & Lou, Diming & Xu, Ning & Fang, Liang & Tan, Piqiang, 2021. "Energy management and emission control for range extended electric vehicles," Energy, Elsevier, vol. 236(C).
    5. Lu Han & Xiaohong Jiao & Zhao Zhang, 2020. "Recurrent Neural Network-Based Adaptive Energy Management Control Strategy of Plug-In Hybrid Electric Vehicles Considering Battery Aging," Energies, MDPI, vol. 13(1), pages 1-22, January.
    6. Zhang, Yuanjian & Chu, Liang & Fu, Zicheng & Xu, Nan & Guo, Chong & Zhao, Di & Ou, Yang & Xu, Lei, 2020. "Energy management strategy for plug-in hybrid electric vehicle integrated with vehicle-environment cooperation control," Energy, Elsevier, vol. 197(C).
    7. Qicheng Xue & Xin Zhang & Teng Teng & Jibao Zhang & Zhiyuan Feng & Qinyang Lv, 2020. "A Comprehensive Review on Classification, Energy Management Strategy, and Control Algorithm for Hybrid Electric Vehicles," Energies, MDPI, vol. 13(20), pages 1-30, October.
    8. Zhang, Fengqi & Hu, Xiaosong & Langari, Reza & Wang, Lihua & Cui, Yahui & Pang, Hui, 2021. "Adaptive energy management in automated hybrid electric vehicles with flexible torque request," Energy, Elsevier, vol. 214(C).
    9. Adnan Khattak & Rasool Bukhsh & Sheraz Aslam & Ayman Yafoz & Omar Alghushairy & Raed Alsini, 2022. "A Hybrid Deep Learning-Based Model for Detection of Electricity Losses Using Big Data in Power Systems," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
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    1. 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.

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