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Optimization of intelligent charge compression ignition engine in hybrid electric powertrain by adaptive equivalent consumption management strategy

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  • Zhang, Yaoyuan
  • Wu, Haoqing
  • Mi, Shijie
  • He, Zhuoyao
  • Qian, Yong
  • Lu, Xingcai

Abstract

Dual fuel intelligent charge compression ignition (ICCI), taking advantage of two direct injection fuel supply systems, substituted low-carbon fuels for conventional fuels, improving fuel economy and emissions. This study focused on the application of the ICCI engine on the power-split hybrid powertrain and carried out the influence of the real-time energy management strategy on ICCI engine performance. Taking the ideal solution calculated by dynamic programming (DP) as the benchmark, equivalent fuel consumption minimum strategy (ECMS) and adaptive equivalent fuel consumption minimum strategy (AECMS) were compared in aspects of ICCI engine performance and state of charge (SOC) control robustness. The results showed that AECMS had better performance on SOC control, where the maximum deviation from the ideal control by DP was lower than 0.5 % during the charge sustaining stage, and the maximum deviation during the charging and discharging stages was no more than 1.5 %. In terms of engine performance control, the accumulative fuel consumption increased by no more than 20 %, and the average E85 (85 % ethanol in ethanol-gasoline blend) substitution ratio reduced by less than 8 %. Despite consuming a high proportion of E85, the accumulative fuel mass consumption of the hybrid powertrain was still 4 % lower than the conventional diesel vehicle.

Suggested Citation

  • Zhang, Yaoyuan & Wu, Haoqing & Mi, Shijie & He, Zhuoyao & Qian, Yong & Lu, Xingcai, 2024. "Optimization of intelligent charge compression ignition engine in hybrid electric powertrain by adaptive equivalent consumption management strategy," Energy, Elsevier, vol. 307(C).
    • Handle: RePEc:eee:energy:v:307:y:2024:i:c:s0360544224023594
    DOI: 10.1016/j.energy.2024.132585
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    References listed on IDEAS

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