Enhancing HCCI Engine Performance through AI Integration: Addressing Ignition Timing and Emission Challenge
Year: 2024
Type: article
Abstract: This article explores the integration of artificial intelligence (AI) with Homogeneous Charge Compression Ignition (HCCI) engines to address the inherent drawbacks of traditional spark-ignition (SI) and compression-ignition (CI) engines. It highlights how HCCI technology mitigates issues such as higher emissions and lower efficiency associated with SI and CI engines. However, HCCI also faces challenges, particularly in ignition timing control. The article delves into the detrimental impact of diesel emissions on engines and underscores the critical role of precise ignition timing in HCCI performance. To overcome these challenges, the potential of combining AI, specifically through the Internet of Things (IoT) and deep learning within the realm of machine learning, is examined. The integration of AI technologies with HCCI engines promises significant improvements in thermal efficiency and fuel economy by optimizing ignition timing. This synergy between AI and HCCI engines represents a promising avenue for enhancing engine performance while reducing environmental impact.
DOI: 10.54097/cg7yw860
Author: Tianming Liu
Institution: North Carolina State University
Language: English
Subfield: Fluid Flow and Transfer Processes
Field: Chemical Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405409807