Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners

Abstract

Private vehicle travel is the most basic mode of transportation, so that an effective way to control the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic growth as well as achieving a green low-carbon society. Therefore, the factors impacting individual carbon emissions must be elucidated. This study builds five different models to estimate the real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the light gradient boosting machine (LightGBM) model performs better than the linear regression, naïve Bayes regression, neural network regression, and decision tree regression models, with a mean absolute error of 0.911 L/100 km, a mean absolute percentage error of 10.4%, a mean square error of 1.536, and an R-squared (R2) value of 0.642. This study also assesses a large pool of potential factors affecting real-world fuel consumption, from which the three most important factors are extracted, namely, reference fuel-consumption-rate value, engine power, and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with the greatest impact are the vehicle brand, engine power, and engine displacement. The average air pressure, average temperature, and sunshine time are the three most important climate factors.

A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption

“…Zhu et al [36] proposed a prediction model based on the improved C4.5 decision tree and verified the effectiveness of the model by relying on a set of test data under the expressway scenario. In addition, the application of gradient boosting algorithms [37,38], LightGBM [39], and linear regression (LR) [40] in fuel consumption prediction models has also achieved good results. In order to give full play to the advantages of traditional machine learning methods, Li [28] and Mahzad [41] et al developed multiple hybrid models, including the Aquila optimizer and extreme gradient boosting (AO-XGB), black widow optimization algorithm and extreme gradient boosting (BWOA-XGB), AO-SVM, AO-RF, etc.…”

Trip Based Modeling of Fuel Consumption in Modern Heavy-Duty Vehicles Using Artificial Intelligence

“…While the current approaches determine the fuel consumption of the vehicle, combining these techniques with data helps to identify parameters that may cause anomalies, such as malfunctions due to wear and tear of the engine, improper maintenance, engine failure, exhaust after-treatment system, and external factors like climate, traffic, road conditions, etc. Most studies in the literature have been limited to passenger cars [21,27], light-duty vehicles [28], heavy-duty vehicles [29], or were based on a huge number of parameters or limited dynamic data collected during on-road trips. The relative importance of various factors influencing fuel consumption was reviewed in the past [30,31].…”