Open Access

ARTICLE

Application of ANFIS Model for Thailand’s Electric Vehicle Consumption

Narongkorn Uthathip^1,*, Pornrapeepat Bhasaputra¹, Woraratana Pattaraprakorn²
1 Electrical and Computer Engineering, Thammasat University, Pathum Thani, Thailand
2 Chemical Engineering, Thammasat University, Pathum Thani, Thailand
* Corresponding Author: Narongkorn Uthathip. Email:
(This article belongs to this Special Issue: Advances in Computational Intelligence and its Applications)

Computer Systems Science and Engineering 2022, 42(1), 69-86. https://doi.org/10.32604/csse.2022.020120

Received 09 May 2021; Accepted 10 June 2021; Issue published 02 December 2021

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.

---

Keywords

Fuel consumption; fuzzy logic; artificial neural network; ANFIS; internal combustion engines; electric vehicles; root mean square error

---

Citations