Segment-Based CO₂ Emission Evaluations From Passenger Cars Based on Deep Learning Techniques

Abstract

The overall level of emissions from the Swiss passenger cars is strongly dependent on the fleet composition. Despite technology improvements, the Swiss passenger cars fleet remains emissions intensive. To analyze the root of this problem and evaluate potential solutions, this paper applies deep learning techniques to evaluate the inter-class (namely micro, small, middle, upper middle, large and luxury class) and intra-class (namely sport utility vehicle and non-sport utility vehicle) differences in carbon dioxide (CO2) emissions. This paper takes full use of novel semi-supervised fuzzy C-means (SSFCM), random forest and AdaBoost models as well as model fusion to successfully classify passenger vehicles and enable segmentbased CO2 emission evaluations.

A science-based approach to classifying light vehicles in Europe: methodology and case studies

“…After the cleaning, the dataset included vehicle production years ranging from 1980 to 2023. However, literature indicates that the evolution of vehicle features over time can substantially hinder classification efforts 5,28,35,36 . Our analysis (see the Discussion section), supports this view, suggesting that a broad temporal range may negatively impact classification accuracy.…”

“…As a result, the definition of a passenger car covers a spectrum of vehicle types, sizes, and uses: from small, 2-seat bubble cars to large family vans. The variation of passenger cars' characteristics translates into variability in energy consumption and related Greenhouse Gas (GHG) emissions 5,6 . Consequently, several studies have identified fleet composition as one of the key drivers of the overall emissions and fuel consumption performance of the sector [7][8][9][10][11] .…”

“…Some of them 6,[27][28][29] have investigated various schemes using statistical and exploratory data analysis, aiming to describe typical characteristics of each segment. However, due to the complexity of the classification problem, most of these studies 5,[30][31][32][33][34] have tackled the classification and dimensionality reduction task with advanced Machine Learning (ML) algorithms and principal component analysis techniques, respectively. These studies have demonstrated that ML techniques can successfully address the complex problem of cars classification.…”

“…These studies have demonstrated that ML techniques can successfully address the complex problem of cars classification. Among them, the series of works by Niroomand et al 5,32,35 proved able to reproduce the EU classification scheme with an accuracy of up to 85%. Nevertheless, ML models obscure the understanding of vehicles classification, so that the criteria behind the assignation of a vehicle to a certain segment remain opaque.…”

A Comprehensive Review of Various Topologies and Control Techniques for DC-DC Converter-Based Lithium-Ion Battery Charge Equalization

“…Furthermore, the global depletion of fossil resources and, as a result, rising crude oil costs pose a signifcant challenge to the vehicle industry. Electric vehicles (EVs) have already been widely adopted in the automotive industry as the most promising substitutes for decreasing CO 2 emissions and global warming concerns [2]. Te most popular EVs, such as hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and pure electric vehicles or battery electric vehicles (BEVs), have become popular options due to their ability to address the issues that fossil fuel vehicles pose.…”