Modeling, diagnostics, optimization, and control of internal combustion engines via modern machine learning techniques: A review and future directions

A critical review of the existing Internal Combustion Engine (ICE) modeling, optimization, diagnosis, and control challenges and the promising state-of-the-art Machine Learning (ML) solutions for them is provided in this paper. Some of the major challenges include Real Driving Emission (RDE) modeling and control, combustion knock detection and control, combustion mode transition in multi-mode engines, combustion noise modeling and control, combustion instability and cyclic variability control, costly and time-consuming engine calibration, and fault diagnostics of some ICE components. In this paper, conventional ICE modeling approaches are discussed along with their limitations for realtime ICE optimization and control. Promising ML approaches to address ICE challenges are then classified into three main groups of unsupervised learning, supervised learning, and reinforcement learning. The working principles of each approach along with their advantages and disadvantages in addressing ICE challenges are discussed. ML-based grey-box approach is proposed as a solution that combines the benefits from physics-based and ML-based models to provide robust and high fidelity solutions for ICE modeling and control challenges. This review provides in-depth insight into the applications of ML for ICEs and provides recommendations for future directions to address ICE challenges.

Automated summary

This paper provides a critical review of challenges in Internal Combustion Engine (ICE) modeling, optimization, diagnosis, and control, and discusses promising Machine Learning (ML) solutions. Major challenges include RDE modeling and control, combustion knock detection and control, and combustion mode transition in multi-mode engines. ML approaches are categorized into unsupervised, supervised, and reinforcement learning to address these challenges.