Machine Learning Application to Predict Turbocharger Performance under Steady-State and Transient Conditions

Kanto Kobayashi*, Arravind Jeyamoorthy, Iku Tanabe, Rio Asakawa, Kyohei Yamaguchi, Jin Kusaka

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

Performance predictions of advanced turbocharged engines are becoming difficult because conventional engine models are built using performance map data of turbochargers with a proportional integral derivative (PID) controller. Improving prediction capabilities under transient test cycles or real driving conditions is a challenging task. This study applies a machine learning technique to predict turbocharger performances with high accuracy under steady-state and transient conditions. The manipulated signals of engine speed and torque created based on Compressed High-Intensity Radiated Pulse (Chirp signal) and Amplitude-modulated Pseudo-Random Binary Signal (APRBS) are used as inputs to the engine testbed. Data from the engine experiments are used as training data for the AI-based turbocharger model. High prediction accuracy of the AI turbocharger model is achieved with the co-efficient of determination in the model, and cross-validation results are higher than 0.8. Further, an integrated engine model coupled with the AI turbocharger model is developed and simulated to compare its performance result against the conventional turbocharged engine model (performance map + PID controller). The results show that the diesel engine system's performance predictions can be achieved with reasonable accuracy using the integrated engine-AI turbocharger model. The results also confirm the suitability of the proposed method to develop the advanced turbocharger model.

Original languageEnglish
JournalSAE Technical Papers
Issue number2021
DOIs
Publication statusPublished - 2021 Sep 5
EventSAE 15th International Conference on Engines and Vehicles, ICE 2021 - Capri, Italy
Duration: 2021 Sep 122021 Sep 16

ASJC Scopus subject areas

  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Pollution
  • Industrial and Manufacturing Engineering

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