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Research on diesel vehicle NH3 emission prediction method based on CNN-Transformer fusion framework
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Xiao-xin BAI1, *, Xiang-yang GUO1, Chun-ling WU1, 2, Feng-bin WANG1, Xu LI1, Wei-lin LIU1
China Environmental Science | 2025, 45(3) : 1231 - 1240
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China Environmental Science | 2025, 45(3): 1231-1240
• Air Pollution Control •
Research on diesel vehicle NH3 emission prediction method based on CNN-Transformer fusion framework
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Xiao-xin BAI1, *, Xiang-yang GUO1, Chun-ling WU1, 2, Feng-bin WANG1, Xu LI1, Wei-lin LIU1
Affiliations
  • 1.CATARC Automotive Test Center (Tianjin) Co., Ltd., Tianjin 300300, China
  • 2.School of Mechanical Engineering, Tianjin University, Tianjin 300072, China
Published: 2025-03-20
Outline
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In this study, a diesel vehicle NH3 emission prediction model based on the fusion framework of Convolutional Neural Network(CNN)and Transformer is proposed. The model was developed by integrating the local feature extraction capability of CNN with the global dependency modeling capability of Transformer, enabling the highly accurate prediction of NH3 emissions from diesel vehicles under real road driving conditions. The study was conducted based on the actual on-road emissions test data of an N3-class diesel vehicle. Feature screening was performed using the Pearson correlation coefficient method, and the key hyperparameters of the model were optimized through the application of the Bayesian algorithm, which enhanced its performance. Additionally, the SHapley Additive exPlanations(SHAP)algorithm was utilized to identify the pivotal factors influencing NH3 emissions. The results indicated that the proposed model achieved highly accurate predictions of NH3 emissions from diesel vehicles in real road driving conditions when tested on an independent dataset. The R2, MAE, and MSE values of the predicted NH3 concentration compared to the actual measured values were 0.986, 0.663, and 2.285, respectively, which were significantly superior to those obtained by the traditional Random Forest(RF)model, the Long Short-Term Memory(LSTM)neural network model, and the Transformer model. This study provided an efficient and reliable method for monitoring NH3 emissions from in-use diesel vehicles and offered a novel perspective for elucidating the principal factors influencing NH3 emissions from diesel vehicles on the road.

diesel vehicles  /  emission  /  NH3  /  convolutional neural network  /  transformer
白晓鑫, 郭向阳, 吴春玲, 王凤滨, 李旭, 刘卫林. 基于CNN-Transformer融合框架的柴油车氨排放预测方法. 中国环境科学, 2025 , 45 (3) : 1231 -1240 .
Xiao-xin BAI, Xiang-yang GUO, Chun-ling WU, Feng-bin WANG, Xu LI, Wei-lin LIU. Research on diesel vehicle NH3 emission prediction method based on CNN-Transformer fusion framework[J]. China Environmental Science, 2025 , 45 (3) : 1231 -1240 .
Year 2025 volume 45 Issue 3
395
201
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Article Info
  • Receive Date:2024-08-20
  • Online Date:2026-03-18
  • Published:2025-03-20
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  • Received:2024-08-20
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Affiliations
    1.CATARC Automotive Test Center (Tianjin) Co., Ltd., Tianjin 300300, China
    2.School of Mechanical Engineering, Tianjin University, Tianjin 300072, China
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小菇属 Mycena 11 5.26
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红菇属 Russula 17 8.13
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