Research progress of vehicle fuel consumption prediction models
GUAN Peng
REN Shuojin
SHEN Yitao
ZHAO Jianfu
Abstract：To accurately predict the fuel consumption characteristics of vehicles under various operating conditions,assist researchers in gaining a deeper understanding of the patterns of fuel consumption changes in engines and further optimize engine performance,this paper aims to comprehensively summarize and analyze existing fuel consumption prediction models and categorize them into two main types:traditional fuel consumption prediction models and data-driven machine learning-based fuel consumption prediction models.For the latter category,this paper further divides it into four subcategories:multiple regression,shallow machine learning,deep learning,and hybrid fuel consumption models,detailing the application status,advantages,and limitations of each method and its variants.Through comparative analysis of these models,this paper not only clarifies their optimal application scenarios but also pointes out the main problems and challenges present in current research.When dealing with data that exhibits strong linear correlations,multiple regression methods perform well,offering high model transparency and ease of understanding.Machine learning approaches,especially deep learning,can effectively address more complex nonlinear relationships,fully exploiting features within the data to achieve precise predictions of fuel consumption,albeit with higher requirements for data quality and relatively complex models.Finally,based on the characteristics and applications of different models,this paper provides an outlook on the future development of fuel consumption prediction.
Keywords：fuel consumption predictiondata-drivenmachine learningdeep learninghybrid fuel consumption model
Publication Date：2025-03-30
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：15( 1-15 )