- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | ZHAO Jianhui, LAN Zhongze, LU Xiangdong, YANG Jintao. Injection Quantity Prediction of High-Pressure Common Rail Systems under Multiple Injections Based on Gaussian Process Regression[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20240101
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In high-pressure common rail systems under multiple injections, the pressure waves induced by the pilot injection cause fluctuations in the main injection quantity, thereby reducing in-cylinder combustion efficiency and increasing pollutant emissions. A data-driven prediction model for the main injection quantity based on Gaussian process regression (GPR) was proposed to achieve the accurate control of injection quantity under multiple injections. First, D-optimal design and a second-order response surface method were employed to build a response surface model for the main injection quantity by utilizing rail pressure, pilot-injection pulse width, pilot-main injection interval, and main-injection pulse width as factors. Analysis of variance indicates that the four operating parameters all have extremely significant effects on the main injection quantity. Then, based on a self-developed multi-physics coupled digital simulation platform, a dataset containing 528 operating conditions was constructed, and the model was trained. On this basis, several combinations of mean functions (zero, constant, linear, and quadratic polynomials) and different kernel functions (Seiso, Rqard, and Matérn) were systematically compared, and the linear mean function combined with the rational quadratic kernel function was identified as the optimal configuration. Results show that in test conditions, the mean absolute percentage error (MAPE) of main injection quantity predicted by the GPR-based model is 0.347% and the coefficient of determination
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