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

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WCX SAE World Congress Experience
Authors Abstract
Content
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.
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DOI
https://doi.org/10.4271/2020-01-0735
Pages
8
Citation
Bellone, M., Faghani, E., and Karayiannidis, Y., "Comparison of CNN and LSTM for Modeling Virtual Sensors in an Engine," SAE Int. J. Adv. & Curr. Prac. in Mobility 2(5):2632-2639, 2020, https://doi.org/10.4271/2020-01-0735.
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Publisher
Published
Apr 14, 2020
Product Code
2020-01-0735
Content Type
Journal Article
Language
English