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

2021/10/25 by Masoud Aliramezani, Charles Robert Koch, Mahdi Shahbakhti · 4 citations
Chemical Engineering · Engineering · #Advanced Combustion Engine Technologies #Combustion and flame dynamics #Vehicle emissions and performance

paper · doi:10.1016/j.pecs.2021.100967

openalex publication_date 2021/10/25 · crossref created 2021/10/25 · crossref issued 2022/01/01 · crossref published 2022/01/01 · crossref published-print 2022/01/01 · crossref deposited 2025/10/06 · openalex created_date 2025/10/10 · crossref indexed 2026/07/25 · openalex updated_date 2026/08/01

Citations

• Sparse Principal Component Analysis
• A genetic algorithm tutorial
• The global k-means clustering algorithm
• An overview of statistical learning theory
• System identification—A survey
• The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms
• Bridging the Gap Between Value and Policy Based Reinforcement Learning
• Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

Cited by

• Grid-Edge Energy-Flexible Technologies: A Comparative Analysis Across Generators, Loads, and Energy Storage Systems
• A model-agnostic and data-independent tabu search algorithm to generate counterfactuals for tabular, image, and text data
• Future of internal combustion engines using sustainable, scalable, and storable E-fuels and biofuels for decarbonizing transport and enabling advanced combustion technologies
• Design and Experimental Test of Datatic Approximate Optimal Filter in Nonlinear Dynamic Systems