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Study on precise fuel injection under multiple injections of high pressure common rail system based on deep learning

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  • Lu, Xiangdong
  • Zhao, Jianhui
  • Markov, Vladimir
  • Wu, Tianyu

Abstract

The fluctuation of fuel injection quantity under multiple injections of high pressure common rail system leads to a decline in engine economy and emission performance, the precise injection control is the key problem in current research. In this study, a deep learning data-driven model of injection quantity under multiple injections was established based on generalized regression neural network (GRNN), and the particle swarm optimization (PSO) was introduced to optimize the smoothing factor in the model, thereby improving its prediction accuracy. An injection quantity correction method based on the data-driven model was proposed and applied to actual common rail fuel injection system for experimental verification. The results demonstrate that the proposed data-driven model has excellent prediction performance and generalization ability for the predicted main injection quantity with mean absolute percentage error of 1.10 % and determination coefficient of 0.997. Through the experimental verification of the correction method under 144 groups of operating conditions, it was observed that the maximum deviation of main injection quantity decreased from −25.83～17.91 mm3 to −9.81～7.64 mm3, and the maximum absolute deviation reduced by 62.02 %. The precise of injection control under multiple injections is improved.

Suggested Citation

  • Lu, Xiangdong & Zhao, Jianhui & Markov, Vladimir & Wu, Tianyu, 2024. "Study on precise fuel injection under multiple injections of high pressure common rail system based on deep learning," Energy, Elsevier, vol. 307(C).
    • Handle: RePEc:eee:energy:v:307:y:2024:i:c:s0360544224025581
    DOI: 10.1016/j.energy.2024.132784
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    Cited by:

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    8. Zareen A. Khan & Waqar Afzal & Mujahid Abbas & Kwara Nantomah, 2025. "Some Novel Inequalities for Godunova–Levin Preinvex Functions via Interval Set Inclusion (⊆) Relation," Journal of Mathematics, John Wiley & Sons, vol. 2025(1).

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