Comparison of CNN and LSTM for Modeling Virtual Sensors in an Engine

The automotive industry makes extensive use of virtual models to increase efficiency during the development stage. The complexity of such virtual models increases with the complexity of the process that they describe, and thus new methods for their development are constantly evaluated. Among many others, data-driven techniques and machine learning offer promising results, creating deep neural networks that map complex input-output relations. This work aims at comparing the performance of two different neural network architectures for the estimation of the engine state and emissions (flow fuel, NOx and soot). More specifically, Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) will be evaluated in terms of performance, using different techniques to increase the model generalization. During the learning stage data from different engine cycles are fed to the neural networks. In order to evaluate the generalization of the model, the networks are tested over new, previously unseen, engine cycles. Results show that our models over-perform other state-of-the-art models, the best performance was found for the LSTM model with 2.40%, 2.80% and 18.19% error for flow fuel, NOx and soot sensor respectively.

• Record URL:
  https://doi.org/10.4271/2020-01-0735
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
• Supplemental Notes:
  • Abstract reprinted with permission of SAE International.
• Authors:
  • Bellone, Mauro
  • Faghani, Ethan
  • Karayiannidis, Yiannis
• Conference:
  • WCX SAE World Congress Experience
  • Location: Detroit Michigan, United States
  • Date: 2020-4-21 to 2020-4-23
• Publication Date: 2020-4-14

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 2632-2639
• Serial:
  • SAE Technical Paper
  • Volume: 2
  • Issue Number: 5
  • Publisher: Society of Automotive Engineers (SAE)
  • ISSN: 0148-7191
  • EISSN: 2688-3627
  • Serial URL: http://papers.sae.org/

Subject/Index Terms

• TRT Terms: Actuators; Engines; Machine learning; Neural networks; Product development; Sensors; Simulation
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01740225
• Record Type: Publication
• Source Agency: SAE International
• Report/Paper Numbers: 2020-01-0735
• Files: TRIS, SAE
• Created Date: May 26 2020 10:20AM