Abstract
Generally, road transport is a major energy-consuming sector. Fuel consumption of each vehicle is an important factor that affects the overall energy consumption, driving behavior and vehicle characteristic are the main factors affecting the change of vehicle fuel consumption. It is difficult to analyze the influence of fuel consumption with multiple and complex factors. The Adaptive Neuro-Fuzzy Inference System (ANFIS) approach was employed to develop a vehicle fuel consumption model based on multivariate input. The ANFIS network was constructed by various experiments based on the ANFIS Parameter setting. The performance of the ANFIS network was validated using Root Mean Square Error (RMSE) and Mean Average Error (MAE) which related to the setting of ANFIS parameters. The experimental results indicated that the training data sample, number, and type of membership functions are the most important factor affecting the performance of the ANFIS network. However, the number of epochs does not necessarily significantly improve the system performance, too many the number of epochs setting may not provide the best results and lead to excessive responding time. The results also demonstrate that three factors, consisted of the engine size, driving speed, and the number of passengers, are important factors that influence the change of vehicle fuel consumption. The selected ANFIS models with minimum error can be properly and efficiently used to predict vehicle fuel consumption for Thailand’s road transport sector.
Highlights
The concerns of energy consumption and environmental impact caused many countries to attempt to improve energy efficiency and promoting an alternative technology
This paper introduced the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach as a learning and decision tool to predict the vehicle fuel consumption of internal combustion engine vehicles (ICEVs) and electric vehicles (EVs) in Thailand’s road transport
The vehicle fuel consumption model was developed based on the driving behavior and vehicle characteristic data
Summary
The concerns of energy consumption and environmental impact caused many countries to attempt to improve energy efficiency and promoting an alternative technology. According to the energy and environmental crises, the government of Thailand has promoted energy efficiency plans in all sectors. In the energy efficiency plan of Thailand, the target is to reduce the energy consumption of the transportation sector by 40% of the total target in 2030. One of which is the use of electric vehicles (EVs) technology, accounting for 4% of the total energy consumption reduction of the transport sector, and it is expected that 1.2 million EVs will be used by 2030 [2]. The effective assessment of load demand from EVs charging has become one of the most important challenges in the transport section. Investigation of factors, especially reliability and accuracy prediction of fuel consumption affecting the remaining battery level of each EV before the recharging is a very important factor
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