Fuel saving index assessment on driving behavior control system of prototype model using neural network

Suroto Munahar; Aris Triwiyatno; M. Munadi; Joga Dharma Setiavan

doi:10.5604/01.3001.0016.0019

Authors

Suroto Munahar Department of Mechanical Engineering, Diponegoro University, Semarang, Indonesia Department of Automotive Engineering, Universitas Muhammadiyah Magelang, Magelang, Author https://orcid.org/0000-0002-8236-0741
Aris Triwiyatno Department of Electronics Engineering, Diponegoro University, Semarang Author https://orcid.org/0000-0001-8666-2678
M. Munadi Department of Mechanical Engineering, Diponegoro University, Semarang Author https://orcid.org/0000-0002-0766-0955
Joga Dharma Setiavan Department of Mechanical Engineering, Diponegoro University, Semarang Author https://orcid.org/0000-0002-2948-9204

DOI:

https://doi.org/10.5604/01.3001.0016.0019

Keywords:

Fuel Saving Index, fuel consumption, FSI, stoichiometry, driving behaviour, neural network

Abstract

Efficient fuel consumption in the world is essential in automotive technology development due to the increase in vehicle usage and the decrease in global oil production. Several studies have been conducted to increase fuel consumption savings, Fuel Cells (FCs), the application of alternative energy vehicles and the Engine Control Unit (ECU) system. FCs do not require oil energy to propel the vehicle, so this technology promises to reduce energy consumption and emissions. However, this research still leaves problems. FCs are susceptible to short circuit hazards, and ownership costs are very high. Alternative energy applications produce less power, less responsive acceleration, and insufficient energy sources to enter mass production. The ECU application still has an orientation toward achieving stoichiometry values, so the increase in fuel efficiency has the potential to be improved. Driving behavior is a variable that has a close relationship with fuel consumption efficiency. However, research on driving behavior is only studied for implementation in autonomous car-following technologies, safety systems, charging needs characteristic of electric vehicles, emission controls, and display images on invehicle information systems. Meanwhile, research on driving behavior as a control system to improve fuel efficiency has not been carried out. To that end, this study proposes the use of driving behavior for a newly designed control system to improve fuel efficiency. The control system in this research is a prototype model to be assessed using the Fuel Saving Index (FSI) analysis. An artificial neural network is used to help the recognition of driving behavior. The results showed that the newly designed control system was categorized on scale IV of FSI. On this scale, the power generated by the engine is quite optimal when it is in the eco-scheme driving behavior. The driving behavior control system can significantly improve the efficiency of fuel consumption. Air to Fuel Ratio (AFR) is achieved above the stoichiometric value.

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