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Adaptive real-time ECMS with equivalent factor optimization for plug-in hybrid electric buses

Author

Listed:
  • Sun, Xiaodong
  • Chen, Zongzhe
  • Han, Shouyi
  • Tian, Xiang
  • Zhijia Jin,
  • Cao, Yunfei
  • Xue, Mingzhou

Abstract

—The plug-in hybrid electric bus (PHEB) is one of the vehicles that can address global environmental and energy problems. Due to the complex characteristics of the urban cycle condition, a fixed parameter energy management strategy may not be suitable for fuel economy. Thus, an adaptive real-time control strategy seems to be of great significance for PHEBs. The paper designs an advanced equivalent consumption minimization strategy (ECMS) with segments divided from the driving cycle and optimized equivalent factor (EF). The classification of different segments is based on the time that the vehicle stops. The EF is optimized under two different driving conditions by using the grey wolf optimization (GWO) algorithm. A new model, as the difference between the actual state of charge (SOC) and the reference SOC, is presented and minimized by optimizing the EF. Segmented EF optimization adds a correction factor, which can adjust EF according to the kinematic characteristics of each segment. The results show that the fuel consumption decreased by 18.72 % compared with the conventional ECMS.

Suggested Citation

  • Sun, Xiaodong & Chen, Zongzhe & Han, Shouyi & Tian, Xiang & Zhijia Jin, & Cao, Yunfei & Xue, Mingzhou, 2024. "Adaptive real-time ECMS with equivalent factor optimization for plug-in hybrid electric buses," Energy, Elsevier, vol. 304(C).
    • Handle: RePEc:eee:energy:v:304:y:2024:i:c:s0360544224017882
    DOI: 10.1016/j.energy.2024.132014
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