Diesel Engine Performance Prediction Method Based on
Generative Adversarial Network and Machine Learning

doi:10.3969/j.issn.1001-2222.2025.05.009

Vehicle Engine ›› 2025, Vol. 0 ›› Issue (5) : 61. DOI: 10.3969/j.issn.1001-2222.2025.05.009

Diesel Engine Performance Prediction Method Based on Generative Adversarial Network and Machine Learning

LI Xiang^1,2,DENG Zhonghang¹,GUO Zhikun³,SHI Qingjue³,ZHANG Fan¹,LU Lili²

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Abstract

A joint prediction framework of data augmentation and machine learning was proposed to address the challenges of high-dimensional feature selection and imbalanced small-sample data in diesel engine performance prediction. The BorutaShap algorithm was first used to select key parameters from 48 features, which reduced the generalization limitations caused by subjective feature selection. Then a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) was applied to address the small-sample problems in brake-specific fuel consumption (BSFC) and peak cylinder pressure (PCP). The distribution rationality of samples was enhanced by dynamic gradient punishment. The predictive performances of data generated by WGAN-GP, SMOTE, and VQ-VAE models were finally compared across 4 models of XGBoost, SVR, RF, and GBDT to validate the effectiveness of the method. The results showed that WGAN-GP improved the determination coefficient R² of XGBoost to 0.985 (MAE=1.659 g/(kW·h)) for BSFC and 0.943 (MAE=0.006 MPa) for PCP. Accuracy improvements of 5%-8% were achieved over SMOTE and VQ-VAE. In summary, the integration of feature selection and data augmentation effectively addressed performance prediction challenges of diesel engine under diverse feature characteristics and limited data samples.

Key words

diesel engine / performance prediction / generative adversarial network / machine learning

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LI Xiang, DENG Zhonghang, GUO Zhikun, SHI Qingjue, ZHANG Fan, LU Lili. Diesel Engine Performance Prediction Method Based on Generative Adversarial Network and Machine Learning[J]. Vehicle Engine. 2025, 0(5): 61 https://doi.org/10.3969/j.issn.1001-2222.2025.05.009