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Design of an Adaptive Power Management Strategy for Range Extended Electric Vehicles

Author

Listed:
  • Jen-Chiun Guan

    (Department of Vehicle Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Bo-Chiuan Chen

    (Department of Vehicle Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Yuh-Yih Wu

    (Department of Vehicle Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

Abstract

The cruising distance of the range extended electric vehicle (REEV) can be further extended using a range extender, which consists of an engine and a generator, i.e., a genset. An adaptive power management strategy (PMS) based on the equivalent fuel consumption minimization strategy (ECMS) is proposed for the REEV in this paper. The desired trajectory of the state of charge (SOC) is designed based on the energy-to-distance ratio, which is defined as the difference between the initial SOC and the minimum allowable SOC divided by the remaining travel distance, for discharging the battery. A self-organizing fuzzy controller (SOFC) with SOC feedback is utilized to modify the equivalence factor, which is defined as the fuel consumption rate per unit of electric power, for tracking the desired SOC trajectory. An instantaneous cost function, that consists of the fuel consumption rate of the genset and the equivalent fuel consumption rate of the battery, is minimized to find the optimum power distribution for the genset and the battery. Dynamic programming, which is a global minimization method, is employed to obtain the performance upper bound for the target REEV. Simulation results show that the proposed algorithm is adaptive for different driving cycles and can effectively increase the fuel economy of the thermostat control strategy (TCS) by 11.1% to 16%. The proposed algorithm can also reduce average charging/discharging powers and low SOC operations for possibly extending the battery life and increasing the battery efficiency, respectively. An experiment of the prototype REEV on a chassis dynamometer is set up with the proposed algorithm implemented on a real-time controller. Experiment results show that the proposed algorithm can increase the fuel economy of the TCS by 7.8% for the tested driving cycle. In addition, the proposed algorithm can reduce the average charge/discharge powers of TCS by 7.9% and 11.7%, respectively.

Suggested Citation

  • Jen-Chiun Guan & Bo-Chiuan Chen & Yuh-Yih Wu, 2019. "Design of an Adaptive Power Management Strategy for Range Extended Electric Vehicles," Energies, MDPI, vol. 12(9), pages 1-24, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1610-:d:226540
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    References listed on IDEAS

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    Cited by:

    1. García, Antonio & Monsalve-Serrano, Javier & Martínez-Boggio, Santiago & Rückert Roso, Vinícius & Duarte Souza Alvarenga Santos, Nathália, 2020. "Potential of bio-ethanol in different advanced combustion modes for hybrid passenger vehicles," Renewable Energy, Elsevier, vol. 150(C), pages 58-77.
    2. Xiao, B. & Ruan, J. & Yang, W. & Walker, P.D. & Zhang, N., 2021. "A review of pivotal energy management strategies for extended range electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    3. Wu, Xiao-long & Xu, Yuan-wu & Zhao, Dong-qi & Zhong, Xiao-bo & Li, Dong & Jiang, Jianhua & Deng, Zhonghua & Fu, Xiaowei & Li, Xi, 2020. "Extended-range electric vehicle-oriented thermoelectric surge control of a solid oxide fuel cell system," Applied Energy, Elsevier, vol. 263(C).
    4. Xiao, Boyi & Yang, Weiwei & Wu, Jiamin & Walker, Paul D. & Zhang, Nong, 2022. "Energy management strategy via maximum entropy reinforcement learning for an extended range logistics vehicle," Energy, Elsevier, vol. 253(C).
    5. Da Huo & Peter Meckl, 2022. "Power Management of a Plug-in Hybrid Electric Vehicle Using Neural Networks with Comparison to Other Approaches," Energies, MDPI, vol. 15(15), pages 1-19, August.

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