Work

Fuel Control System on CNG Fueled Vehicles using Machine Learning: A Case Study on the Downhill

Year: 2023

Type: article

Abstract: Compressed Natural Gas (CNG) is an affordable fuel with a higher octane number. However, older CNG kits without electronic controls have the potential to supply more fuel when driving downhill due to the vacuum in the intake manifold. Therefore, this article presents a development of a CNG control system that accommodates road inclination angles to improve fuel efficiency. Machine learning is involved in this work to process engine speed, throttle valve position, and road slope angle. The control system is designed to ensure reduced fuel consumption when the vehicle is operating downhill. The results showed that the control system increases fuel consumption by 25.7% when driving downhill which an inclination of 5ᵒ. The AFR increased from 17.5 to 22 and the CNG flow rate decreased from 17.7 liters/min to 13.8 liters/min which is promising for applying to CNG vehicles.

Source: Automotive Experiences

DOI: 10.31603/ae.8107

Authors: Suroto Munahar, Muji Setiyo, Ray Adhan Brieghtera, Madihah Mohd Saudi, Azuan Ahmad, Dori Yuvenda

Institutions: Universitas Muhammadiyah Magelang, Universiti Sains Islam Malaysia, Universitas Negeri Padang

Language: English

Topic: Engine and Fuel Emissions

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $275