IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2022i1p3-d1008560.html
   My bibliography  Save this article

Embedding-Graph-Neural-Network for Transient NOx Emissions Prediction

Author

Listed:
  • Yun Chen

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Chengwei Liang

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Dengcheng Liu

    (Nanchang Automotive Institute of Intelligence & New Energy, Nanchang 330001, China)

  • Qingren Niu

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Xinke Miao

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Guangyu Dong

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Liguang Li

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Shanbin Liao

    (Jiangling Motors Corporation, Nanchang 330001, China)

  • Xiaoci Ni

    (School of Automotive Studies, Tongji University, Shanghai 201804, China)

  • Xiaobo Huang

    (Jiangling Motors Corporation, Nanchang 330001, China)

Abstract

Recently, Acritical Intelligent (AI) methodologies such as Long and Short-term Memory (LSTM) have been widely considered promising tools for engine performance calibration, especially for engine emission performance prediction and optimization, and Transformer is also gradually applied to sequence prediction. To carry out high-precision engine control and calibration, predicting long time step emission sequences is required. However, LSTM has the problem of gradient disappearance on too long input and output sequences, and Transformer cannot reflect the dynamic features of historic emission information which derives from cycle-by-cycle engine combustion events, which leads to low accuracy and weak algorithm adaptability due to the inherent limitations of the encoder-decoder structure. In this paper, considering the highly nonlinear relation between the multi-dimensional engine operating parameters the engine emission data outputs, an Embedding-Graph-Neural-Network (EGNN) model was developed combined with self-attention mechanism for the adaptive graph generation part of the GNN to capture the relationship between the sequences, improve the ability of predicting long time step sequences, and reduce the number of parameters to simplify network structure. Then, a sensor embedding method was adopted to make the model adapt to the data characteristics of different sensors, so as to reduce the impact of experimental hardware on prediction accuracy. The experimental results show that under the condition of long-time step forecasting, the prediction error of our model decreased by 31.04% on average compared with five other baseline models, which demonstrates the EGNN model can potentially be used in future engine calibration procedures.

Suggested Citation

  • Yun Chen & Chengwei Liang & Dengcheng Liu & Qingren Niu & Xinke Miao & Guangyu Dong & Liguang Li & Shanbin Liao & Xiaoci Ni & Xiaobo Huang, 2022. "Embedding-Graph-Neural-Network for Transient NOx Emissions Prediction," Energies, MDPI, vol. 16(1), pages 1-20, December.
  • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:3-:d:1008560
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/1/3/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/1/3/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cao, Jian & Li, Zhi & Li, Jian, 2019. "Financial time series forecasting model based on CEEMDAN and LSTM," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 127-139.
    2. Deepak Gupta & Mahardhika Pratama & Zhenyuan Ma & Jun Li & Mukesh Prasad, 2019. "Financial time series forecasting using twin support vector regression," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-27, March.
    3. Bishop, Justin D.K. & Stettler, Marc E.J. & Molden, N. & Boies, Adam M., 2016. "Engine maps of fuel use and emissions from transient driving cycles," Applied Energy, Elsevier, vol. 183(C), pages 202-217.
    4. Wang, Huaiyu & Ji, Changwei & Shi, Cheng & Ge, Yunshan & Meng, Hao & Yang, Jinxin & Chang, Ke & Wang, Shuofeng, 2022. "Comparison and evaluation of advanced machine learning methods for performance and emissions prediction of a gasoline Wankel rotary engine," Energy, Elsevier, vol. 248(C).
    5. Liukkonen, M. & Heikkinen, M. & Hiltunen, T. & Hälikkä, E. & Kuivalainen, R. & Hiltunen, Y., 2011. "Artificial neural networks for analysis of process states in fluidized bed combustion," Energy, Elsevier, vol. 36(1), pages 339-347.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sumit Saroha & Marta Zurek-Mortka & Jerzy Ryszard Szymanski & Vineet Shekher & Pardeep Singla, 2021. "Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals," Energies, MDPI, vol. 14(19), pages 1-21, September.
    2. Xuliang Tang & Heng Wan & Weiwen Wang & Mengxu Gu & Linfeng Wang & Linfeng Gan, 2023. "Lithium-Ion Battery Remaining Useful Life Prediction Based on Hybrid Model," Sustainability, MDPI, vol. 15(7), pages 1-18, April.
    3. Lu, Hongfang & Ma, Xin & Huang, Kun & Azimi, Mohammadamin, 2020. "Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    4. Sanghyuk Yoo & Sangyong Jeon & Seunghwan Jeong & Heesoo Lee & Hosun Ryou & Taehyun Park & Yeonji Choi & Kyongjoo Oh, 2021. "Prediction of the Change Points in Stock Markets Using DAE-LSTM," Sustainability, MDPI, vol. 13(21), pages 1-15, October.
    5. Xiangzhou Chen & Zhi Long, 2023. "E-Commerce Enterprises Financial Risk Prediction Based on FA-PSO-LSTM Neural Network Deep Learning Model," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    6. Lin, Yu & Lu, Qin & Tan, Bin & Yu, Yuanyuan, 2022. "Forecasting energy prices using a novel hybrid model with variational mode decomposition," Energy, Elsevier, vol. 246(C).
    7. Dongsu Kim & Yongjun Lee & Kyungil Chin & Pedro J. Mago & Heejin Cho & Jian Zhang, 2023. "Implementation of a Long Short-Term Memory Transfer Learning (LSTM-TL)-Based Data-Driven Model for Building Energy Demand Forecasting," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
    8. Seyed Mehrzad Asaad Sajadi & Pouya Khodaee & Ehsan Hajizadeh & Sabri Farhadi & Sohaib Dastgoshade & Bo Du, 2022. "Deep Learning-Based Methods for Forecasting Brent Crude Oil Return Considering COVID-19 Pandemic Effect," Energies, MDPI, vol. 15(21), pages 1-23, October.
    9. Xie, Yiwei & Hu, Pingfang & Zhu, Na & Lei, Fei & Xing, Lu & Xu, Linghong & Sun, Qiming, 2020. "A hybrid short-term load forecasting model and its application in ground source heat pump with cooling storage system," Renewable Energy, Elsevier, vol. 161(C), pages 1244-1259.
    10. Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
    11. Li, Jingmiao & Wang, Jun, 2020. "Forcasting of energy futures market and synchronization based on stochastic gated recurrent unit model," Energy, Elsevier, vol. 213(C).
    12. Kaijian He & Qian Yang & Lei Ji & Jingcheng Pan & Yingchao Zou, 2023. "Financial Time Series Forecasting with the Deep Learning Ensemble Model," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
    13. Seungho Baek & Kwan Yong Lee & Merih Uctum & Seok Hee Oh, 2020. "Robo-Advisors: Machine Learning in Trend-Following ETF Investments," Sustainability, MDPI, vol. 12(16), pages 1-15, August.
    14. Xiaodong Zhang & Suhui Liu & Xin Zheng, 2021. "Stock Price Movement Prediction Based on a Deep Factorization Machine and the Attention Mechanism," Mathematics, MDPI, vol. 9(8), pages 1-21, April.
    15. Wen-Jie Liu & Yu-Ting Bai & Xue-Bo Jin & Ting-Li Su & Jian-Lei Kong, 2022. "Adaptive Broad Echo State Network for Nonstationary Time Series Forecasting," Mathematics, MDPI, vol. 10(17), pages 1-21, September.
    16. Yoonjae Noh & Jong-Min Kim & Soongoo Hong & Sangjin Kim, 2023. "Deep Learning Model for Multivariate High-Frequency Time-Series Data: Financial Market Index Prediction," Mathematics, MDPI, vol. 11(16), pages 1-18, August.
    17. Cheng Zhao & Ping Hu & Xiaohui Liu & Xuefeng Lan & Haiming Zhang, 2023. "Stock Market Analysis Using Time Series Relational Models for Stock Price Prediction," Mathematics, MDPI, vol. 11(5), pages 1-13, February.
    18. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
    19. Eckert, Jony Javorski & Silva, Fabrício L. & da Silva, Samuel Filgueira & Bueno, André Valente & de Oliveira, Mona Lisa Moura & Silva, Ludmila C.A., 2022. "Optimal design and power management control of hybrid biofuel–electric powertrain," Applied Energy, Elsevier, vol. 325(C).
    20. Zhou, Zhongbao & Gao, Meng & Liu, Qing & Xiao, Helu, 2020. "Forecasting stock price movements with multiple data sources: Evidence from stock market in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:3-:d:1008560. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.