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A Rule-Based Energy Management Strategy for a Plug-in Hybrid School Bus Based on a Controller Area Network Bus

Author

Listed:
  • Jiankun Peng

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Hao Fan

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Hongwen He

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Collaborative Innovation Center of Electric Vehicles in Beijing, Beijing Institute of Technology, Beijing 100081, China)

  • Deng Pan

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

Abstract

This paper presents a rule-based energy management strategy for a plug-in hybrid school bus (PHSB). In order to verify the effectiveness and rationality of the proposed energy management strategy, the powertrain and control models were built with MATLAB/Simulink. The PHSB powertrain model includes an engine model, ISG (integrated started and generator) model, drive motor model, power battery packs model, driver model, and vehicle longitudinal dynamics model. To evaluate the controller area network (CAN) bus performance features such as the bus load, signal hysteresis, and to verify the reliability and real-time performance of the CAN bus multi-node control method, a co-simulation platform was built with CANoe and MATLAB/Simulink. The co-simulation results show that the control strategy can meet the requirements of the PHSB’s dynamic performance. Meanwhile, the charge-depleting mode (CD) and charge-sustaining mode (CS) can switch between each other and maintain a state-of-charge (SoC) of around 30%, indicating that the energy management strategy effectively extends the working period of the CD mode and improves the fuel economy further. The energy consumption per 100 km includes 13.7 L diesel and 10.5 kW·h electricity with an initial SoC of 75%. The CANoe simulation results show that the bus communication performs well without error frames.

Suggested Citation

  • Jiankun Peng & Hao Fan & Hongwen He & Deng Pan, 2015. "A Rule-Based Energy Management Strategy for a Plug-in Hybrid School Bus Based on a Controller Area Network Bus," Energies, MDPI, vol. 8(6), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:6:p:5122-5142:d:50496
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    References listed on IDEAS

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    1. Rulu Pan & Xiangxi Tang & Yanyan Tan & Qiaoqiao Zhu, 2014. "The Chinese Stock Dividend Puzzle," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(3), pages 178-195, May.
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    Cited by:

    1. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    2. Kong, Xiangdong & Zheng, Yuejiu & Ouyang, Minggao & Li, Xiangjun & Lu, Languang & Li, Jianqiu & Zhang, Zhendong, 2017. "Signal synchronization for massive data storage in modular battery management system with controller area network," Applied Energy, Elsevier, vol. 197(C), pages 52-62.
    3. Yang, Dongpo & Liu, Tong & Song, Dafeng & Zhang, Xuanming & Zeng, Xiaohua, 2023. "A real time multi-objective optimization Guided-MPC strategy for power-split hybrid electric bus based on velocity prediction," Energy, Elsevier, vol. 276(C).
    4. Jingxian Hao & Zhuoping Yu & Zhiguo Zhao & Peihong Shen & Xiaowen Zhan, 2016. "Optimization of Key Parameters of Energy Management Strategy for Hybrid Electric Vehicle Using DIRECT Algorithm," Energies, MDPI, vol. 9(12), pages 1-24, November.
    5. Jiankun Peng & Jiwan Jiang & Fan Ding & Huachun Tan, 2020. "Development of Driving Cycle Construction for Hybrid Electric Bus: A Case Study in Zhengzhou, China," Sustainability, MDPI, vol. 12(17), pages 1-19, September.
    6. Yongpeng Shen & Zhendong He & Dongqi Liu & Binjie Xu, 2016. "Optimization of Fuel Consumption and Emissions for Auxiliary Power Unit Based on Multi-Objective Optimization Model," Energies, MDPI, vol. 9(2), pages 1-18, February.
    7. Ye Yang & Youtong Zhang & Jingyi Tian & Si Zhang, 2018. "Research on a Plug-In Hybrid Electric Bus Energy Management Strategy Considering Drivability," Energies, MDPI, vol. 11(8), pages 1-22, August.
    8. Pengxiang Song & Yulong Lei & Yao Fu, 2020. "Multi-Objective Optimization and Matching of Power Source for PHEV Based on Genetic Algorithm," Energies, MDPI, vol. 13(5), pages 1-20, March.
    9. He, Hongwen & Zhou, Nana & Guo, Jinquan & Zhang, Zheng & Lu, Bing & Sun, Chao, 2018. "Tolerance analysis of electrified vehicles on the motor demagnetization fault: From an energy perspective," Applied Energy, Elsevier, vol. 227(C), pages 239-248.
    10. 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.
    11. Wu, Jingda & He, Hongwen & Peng, Jiankun & Li, Yuecheng & Li, Zhanjiang, 2018. "Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus," Applied Energy, Elsevier, vol. 222(C), pages 799-811.
    12. Stefano Rinaldi & Marco Pasetti & Emiliano Sisinni & Federico Bonafini & Paolo Ferrari & Mattia Rizzi & Alessandra Flammini, 2018. "On the Mobile Communication Requirements for the Demand-Side Management of Electric Vehicles," Energies, MDPI, vol. 11(5), pages 1-27, May.
    13. Qiwei Xu & Yunqi Mao & Meng Zhao & Shumei Cui, 2018. "A Hybrid Electric Vehicle Dynamic Optimization Energy Management Strategy Based on a Compound-Structured Permanent-Magnet Motor," Energies, MDPI, vol. 11(9), pages 1-17, August.
    14. Shen, Peihong & Zhao, Zhiguo & Zhan, Xiaowen & Li, Jingwei, 2017. "Particle swarm optimization of driving torque demand decision based on fuel economy for plug-in hybrid electric vehicle," Energy, Elsevier, vol. 123(C), pages 89-107.

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