Analysis and Prediction Model of Fuel Consumption and Carbon Dioxide Emissions of Light-Duty Vehicles

Due to the alarming rate of climate change, fuel consumption and emission estimates are critical in determining the effects of materials and stringent emission control strategies. In this research, an analytical and predictive study has been conducted using the Government of Canada dataset, containing 4973 light-duty vehicles observed from 2017 to 2021, delivering a comparative view of different brands and vehicle models by their fuel consumption and carbon dioxide emissions. Based on the findings of the statistical data analysis, this study makes evidence-based recommendations to both vehicle users and producers to reduce their environmental impacts. Additionally, Convolutional Neural Networks (CNN) and various regression models have been built to estimate fuel consumption and carbon dioxide emissions for future vehicle designs. This study reveals that the Univariate Polynomial Regression model is the best model for predictions from one vehicle feature input, with up to 98.6% accuracy. Multiple Linear Regression and Multivariate Polynomial Regression are good models for predictions from multiple vehicle feature inputs, with approximately 75% accuracy. Convolutional Neural Network is also a promising method for prediction because of its stable and high accuracy of around 70%. The results contribute to the quantifying process of energy cost and air pollution caused by transportation, followed by proposing relevant recommendations for both vehicle users and producers. Future research should aim towards developing higher performance models and larger datasets for building APIs and applications.

Implementation of a Multi-Zone Numerical Blow-by Model and Its Integration with CFD Simulations for Estimating Collateral Mass and Heat Fluxes in Optical Engines

Nowadays reducing green-house gas emissions and pushing the fossil fuel savings in the field of light-duty vehicles is compulsory to slow down climate change. To this aim, the use of new combustion modes and dilution strategies to increase the stability of operations rich in diluent is an effective technique to reduce combustion temperatures and heat losses in throttled operations. Since the combustion behavior in those solutions highly differs from that of typical market systems, fundamental analyses in optical engines are mandatory in order to gain a deep understanding of those and to tune new models for improving the mutual support between experiments and simulations. However, it is known that optical accessible engines suffer from significant blow-by collateral flow due to the installation of the optical measure line. Thus, a reliable custom blow-by model capable of being integrated with both mono-dimensional and three-dimensional simulations was developed and validated against experimental data. The model can work for two different configurations: (a) stand-alone, aiming at providing macroscopic data on the ignitable mixture mass loss/recover through the piston rings; (b) combined, in which it is integrated in CFD engine simulations for the local analysis of likely collateral heat release induced by blow-by. Furthermore, once the model was validated, the effect of the engine speed and charge dilution on the blow-by phenomenon in the optical engine were simulated and discussed in the stand-alone mode.

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

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.

Current experimental developments in 48 V-based CI-driven SUVs in response to expected future EU7 legislation

In this paper, we describe experimental developments in an Exhaust Aftertreatment System (EAS) used in a four-cylinder Compression Ignition (CI) engine. To meet the carbon dioxide (CO$$_\mathrm {2}$$ 2 ) fleet limit values and to demonstrate a clean emission concept, the CI engine needs to be further developed in a hybridized, modern form before it can be included in the future fleet. In this work, the existing EAS was replaced by an Electrically Heated Catalyst (EHC) and a Selective Catalytic Reduction (SCR) double-dosing system. We focused specifically on calibrating the heating modes in tandem with the electric exhaust heating, which enabled us to develop an ultra-fast light-off concept. The paper first outlines the development steps, which were subsequently validated using the Worldwide harmonized Light-duty vehicles Test Cycle (WLTC). Then, based on the defined calibration, a sensitivity analysis was conducted by performing various dynamic driving cycles. In particular, we identified emission species that may be limited in the future, such as laughing gas (N$$_\mathrm {2}$$ 2 O), ammonia (NH$$_\mathrm {3}$$ 3 ), or formaldehyde (HCHO), and examined the effects of a general, additional decrease in the limit values, which may occur in the near future. This advanced emission concept can be applied when considering overall internal engine and external exhaust system measures. In our study, we demonstrate impressively low tailpipe (TP) emissions, but also clarify the system limits and the necessary framework conditions that ensure the applicability of this drivetrain concept in this sector.

Influence of Charging Losses on Energy Consumption and CO2 Emissions of Battery-Electric Vehicles

Due to increasing sales figures, the energy consumption of battery-electric vehicles is moving further into focus. In addition to efficient driving, it is also important that the energy losses during AC charging are as low as possible for a sustainable operation. In many situations it is not possible or necessary to charge the vehicle with the maximum charging power e.g., in apartment buildings. The influence of the charging mode (number of phases used, in-cable-control-box or used wallbox, charging current) on the charging efficiency is often unknown. In this work, the energy consumption of two electric vehicles in the Worldwide Harmonized Light-Duty Vehicles Test Cycle is presented. In-house developed measurement technology and vehicle CAN data are used. A detailed breakdown of charging losses, drivetrain efficiency, and overall energy consumption for one of the vehicles is provided. Finally, the results are discussed with reference to avoidable CO2 emissions. The charging losses of the tested vehicles range from 12.79 to 20.42%. Maximum charging power with three phases and 16 A charging current delivers the best efficiencies. Single-phase charging was considered down to 10 A, where the losses are greatest. The drivetrain efficiency while driving is 63.88% on average for the WLTC, 77.12% in the “extra high” section and 23.12% in the “low” section. The resulting energy consumption for both vehicles is higher than the OEM data given (21.6 to 44.9%). Possible origins for the surplus on energy consumption are detailed. Over 100,000 km, unfavorable charging results in additional CO2 emissions of 1.24 t. The emissions for an assumed annual mileage of 20,000 km are three times larger than for a class A+ refrigerator. A classification of charging modes and chargers thus appears to make sense. In the following work, efficiency improvements in the charger as well as DC charging will be proposed.

Estimation of Real-world Fuel Consumption Rate of Light-duty Vehicles Based on Big Data

Private vehicle travel is the most basic mode of transportation, and the effective control of the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic development as well as achieving a green low-carbon society. Therefore, the impact factors of individual carbon emission must be elucidated. This study builds five different models to estimate 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 mean absolute error of 0.911 L/100 km, mean absolute percentage error of 10.4%, mean square error of 1.536, and R squared (R2) of 0.642. This study also assesses a large number of factors, from which 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 greater impact on real-world fuel consumption rate are vehicle brand, engine power, and engine displacement. Average air pressure, average temperature, and sunshine time are the three most important climate factors.

Effect of Butanol on the Performance of DeNOx Aftertreatment Systems of a Diesel Vehicle Under WLTC Driving Conditions

Advanced biofuels and electrofuels, among which are medium-long chain alcohols, have gained importance in the transport sector with the enforcement of the EU Renewable Energy Directive (2018/2001). In parallel, last European emission regulations have become much more restrictive regarding NOx, so vehicle manufacturers have been forced to incorporate lean NOx trap (LNT) and/or selective catalytic reduction (SCR). Thus, the combination of modern DeNOx devices and the upcoming higher contribution of sustainable biofuels is a new challenge. In this work, two Euro 6 diesel vehicles, one equipped with LNT and the other with ammonia-SCR, have been tested following the Worldwide harmonized Light-duty vehicles Test Cycle (WLTC) at warm (24°C) and cold (−7°C) conditions using conventional diesel fuel and a diesel-butanol (90/10% vol.) blend. While the effect of butanol on the LNT efficiency was not significant, its influence on the SCR performance was notable during the low and medium-speed phases of the driving cycle, mainly under warm climatic conditions. Despite of the lower NOx concentration at the catalyst inlet, the worst efficiency of the SCR with butanol could be attributed to hydrocarbons deposition on the catalyst surface, which inhibits the NOx reduction reactions with ammonia. Moreover, the LNT was not sensitive to the ambient temperature while the SCR performance greatly depended on this parameter.