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Air pollution poses a major threat to health and climate, yet cities lack simple tools to quantify the costs and effects of their measures and assess those that are most effective in improving air quality. In this work, a complete modeling framework to estimate road traffic microscopic pollutant emissions from common macroscopic road and traffic information is proposed. A machine learning model to estimate driving behavior as a function of traffic conditions and road infrastructure is coupled with a physics-based microscopic emissions model. The up-scaling of the individual vehicle emissions to the traffic-level contribution is simply performed via a meta-model using both statistical vehicles fleet composition and traffic volume data. Validation results with real-world driving data show that: the driving behavior model is able to maintain an estimation error below 10% for relevant boundary parameter of the speed profiles (i.e., mean, initial, and final speed) on any road segment; the traffic microscopic emissions model is able to reduce the estimation error by more than 50% with respect to reference macroscopic models for major pollutants such as NOx and CO2. Such a high-resolution road traffic emissions model at the scale of every road segment in the network proves to be highly beneficial as a source for air quality models and as a monitoring tool for cities.

As tailpipe emissions have decreased, there is a growing focus on the relative contribution of non-exhaust sources of vehicle emissions. Addressing these emissions is key to better evaluating and reducing vehicles’ impact on air quality and public health. Tailoring solutions for different non-exhaust sources, including brake emissions, is essential for achieving sustainable mobility. Studying emissions from vehicles in real-world scenarios provides a better understanding of their environmental impact compared to laboratory testing alone. This study presents findings on the direct measurement of brake particles and the characterization of this source of particulate matter in real-world conditions using a mobile laboratory. In situ measurements of particle concentration and size distribution showed good agreement with previous laboratory studies, indicating the suitability of the approach to investigate break particle emissions during real-world operation. The study demonstrates that particle size distributions can vary based on the temperature of the brake disk, which is influenced by the initial braking speed, with significant variations observed between speeds of 60, 80, 100, and 120 km/h. Particles with sizes between 6 and 523 nm were released into the air from the brake system, although it is likely that larger particles were also emitted but not captured due to the upper detection limit of the Engine Exhaust Particle Sizer. During harsh braking events, such as decelerations of 4.2 m/s 2 from 120 km/h, a concentration of up 10 6 (#/cm 3 ) was measured for particles under 8 nm. Moreover, scanning electron microscope analysis revealed that nanoparticles are present in the form of agglomerates, whose shape can change depending on the formation process. Elements present in the particles comprised mainly iron, copper, and aluminium, indicating wear of the brake pad materials and disk components.

This paper is focused on the determination of real driving ranges for electric vehicles (EV’s) and how it influences fuel consumption and carbon emissions. A precise method to evaluate the driving range of an EV can establish the correct reduction in GEI amount, mainly CO and CO2, ejected to the environment. The comparison of the daily driving range between an internal combustion engine (ICE) vehicle and an EV provides a useful tool for determining actual fuel saved during a daily trip and a method to compute carbon emissions saved depending on the type of ICE vehicle. Real driving range has been estimated on the basis of a daily trip consisting of five different segments, acceleration, deceleration, constant speed, ascent and descent, which reproduce the different types of driving. The modelling has been developed for urban routes since they are the most common and the most polluted environment where the use of electric vehicles is applied. The effects of types of driving have been taken into account for the calculation of the driving range by considering three main types of driving: aggressive, normal and moderate. The types of vehicle in terms of shape and size as well as dynamic conditions and the types of roads have also been considered for the determination of the driving range. Specific software has been developed to predict electric vehicle range under real driving conditions as a function of the characteristic parameters of a daily trip.

Electric heavy-duty trucks, fully powered by batteries, are nowadays a reality in cities across Europe, North America, and China. This movement is driven by stringent CO 2 emission regulations, which advocate for zero greenhouse gas emissions in the road transportation sector, thus promoting the development of battery-powered electric trucks. From the point of view of operational cost and technology adoption by fleet owners, it is of great relevance to assess energy efficiency of battery electric heavy-duty trucks under real-world driving conditions. In this regard, an experimental evaluation of the seasonal energy variation of a battery electric heavy-duty truck is presented in this paper. The chosen battery electric truck ran daily on a predefined route in the São Paulo metropolitan region during nearly eight months to embrace different operational and weather situations. Energy consumption parameters were obtained from different Electronic Control Units of the battery electric heavy-duty truck, which allowed us to estimate, e.g., the actual energy consumption and the energy consumed by the drivetrain along with electric machine losses under varying weather situations, as well as to calculate, e.g., the energy efficiency for the propulsive and regenerative operation modes. In sum, the road test results indicate that energy consumption reduces as the ambient temperature falls, energy supplied to the electric machine and mechanical parts decreases as the ambient temperature drops, and energy efficiency in both modes reduces as the temperature rises.

The long-standing need for a modern public transportation system in Lebanon, a developing country of the Middle East with an almost exclusive dependence on costly and polluting passenger cars, has become more pressing in recent years due to the worsening economic crisis and the onset of hyperinflation. This study investigates the potential reductions in energy use, emissions, and costs from the possible introduction of natural gas, hybrid, and battery-electric buses compared to traditional diesel buses in local real driving conditions. Four operating conditions were considered including severe congestion, peak, off-peak, and bus rapid transit (BRT) operation. Battery-electric buses are found to be the best performers in any traffic operation, conditional on having clean energy supply at the power plant and significant subsidy of bus purchase cost. Natural gas buses do not provide significant greenhouse gas emission savings compared to diesel buses but offer substantial reductions in the emission of all major pollutants harmful to human health. Results also show that accounting for additional energy consumption from the use of climate-control auxiliaries in hot and cold weather can significantly impact the performance of all bus technologies by up to 44.7% for electric buses on average. Performance of all considered bus technologies improves considerably in free-flowing traffic conditions, making BRT operation the most beneficial. A vehicle mix of diesel, natural gas, and hybrid bus technologies is found most feasible for the case of Lebanon and similar developing countries lacking necessary infrastructure for a near-term transition to battery-electric technology.

Based on the demand of vehicle emission research and control, this paper presents the development of a portable vehicle measurement system (PEMS) based on SEMTECH-DS and ELPI+, the vehicle emission tests carried out on actual roads, and the data obtained for the establishment and validation of a vehicle emission model. Based on the results of the vehicle emission test, it was found that vehicle driving conditions (speed, acceleration, vehicle specific power (VSP), etc.) had a significant impact on the pollutant emission rate. In addition, local driving cycles were generated and the frequency distribution of VSP-bin under different cycles was analyzed. Then, through the establishment of an emission rate database, calculation of emission factors and validation of the emission model, a vehicle emission model based on actual road driving conditions was developed by taking VSP as the “surrogate variables”. It showed that the emission factor model established in this study could better reflect the vehicle transient emissions on the actual road with high accuracy and local adaptability. Through this study, it could be found that due to the great differences in traffic development modes and vehicle driving conditions in different cities in China, the emission model based on driving conditions was a better choice to carry out the research on vehicle emission in Chinese cities. Compared with directly applying international models or quoting the recommended values of relevant macroscopic guidelines, the emission factor model established in this study, using actual driving conditions, could better reflect the vehicle transient emissions on the actual road with high accuracy and local adaptability. In addition, due to the rapid development of China’s urban traffic and the rapid change of driving conditions, it was of great significance to regularly update China’s urban conditions to improve the accuracy of the model, no matter which model was chosen.

The thermal decay of the brake has a great impact on the long downhill braking stability of pure electric commercial vehicles. Based on the road slope and using the fuzzy control method, the motor regenerative braking force and friction braking force distribution strategies were designed to reduce the friction braking force, improve the braking stability and recover the braking energy. By establishing road driving conditions with different slopes, numerical analysis methods are used to verify the proposed control strategy. The results show that the vehicle maintains a constant speed downhill at 30 km/h under the condition of 6% constant slope driving, and the braking energy recovery rate reaches 50.93% under 60% initial battery SOC, 50.89% under 70% initial battery SOC, and 50.81% under 80% initial battery SOC. The speed of the vehicle fluctuates slightly under the driving condition of an 18 km long variable slope distance, but the power torque of the electric mechanism can still be maintained at a constant speed of 30 km/h by adjusting the electric mechanism, and the braking energy recovery rate reaches 49.96%. During the downhill driving at a constant speed, the friction braking force does not participate in braking, and the recuperation rate of braking is determined by the slope and the magnitude of braking deceleration.

Technological advancements have positively impacted the automotive industry, leading to the development of autonomous cars, which aim to minimize human intervention during driving, and thus reduce the likelihood of human error and accidents. These cars are distinguished by their advanced driving systems and environmental benefits due to their integration of cutting-edge autonomous technology and electric powertrains. This combination of safety, efficiency, and sustainability positions autonomous vehicles as a transformational solution for modern transportation challenges. Optimizing vehicle speed is essential in the development of these vehicles, particularly in minimizing energy consumption. Thus, in this paper, a method to generate the maximum velocity profile of a vehicle on a real road, extracted using online mapping platforms while ensuring compliance with maximum legal speed limits, is proposed. A nonlinear model, closely aligned with real-world conditions, captures and describes vehicle dynamics. Further, a nonlinear model predictive control strategy is proposed for optimizing the vehicle’s performance and safety in dynamic driving conditions, yielding satisfactory results that demonstrate the effectiveness of the method.

It is different for the liquid tank semi-trailer to keep roll stability during turning or emergency voidance, and that may cause serious accidents. Although the scholars did lots of research about the roll stability of liquid tank semi-trailer in theory by calculating and simulation, how to make an effective early warning of rollover is still unsolved in practice. The reasons include the complex driving condition and the difficulty of the vehicle parameter obtaining. The feasible method used currently is evaluating the roll stability of a liquid tank semi-trailer by the lateral acceleration or the attitude of the vehicle. Unfortunately, the lateral acceleration is more useful for sideslip rather than rollover, and the attitude is a kind of posterior way, which means it is hard to take measures to cope with the rollover accident when the attitude exceeds the safety threshold. Considering the movement of the vehicle is totally caused by the wheel force, the rollover could also be predicted by the changing of the wheel force. Therefore, in this paper, we developed a method to analyze the roll stability by the vertical wheel force. A thorough experiment environment is established, and the effectiveness of the proposed method is verified in real driving conditions.

Nowadays, the need for more sustainable mobility is fostering powertrain electrification as a way of reducing the carbon footprint of conventional vehicles. On the other side, the presence of multiple energy sources significantly increases the powertrain complexity and requires the development of a suitable Energy Management System (EMS) whose performance can strongly affect the fuel economy potential of the vehicle. In such a framework, this article proposes a novel methodology to reverse engineer the control strategy of a test case P2 Plug-in Hybrid Electric Vehicle (PHEV) through the analysis of experimental data acquired in a wide range of driving conditions. In particular, a combination of data obtained from On-Board Diagnostic system (OBD), Controller Area Network (CAN)-bus protocol, and additional sensors installed on the High Voltage (HV) electric net of the vehicle is used to point out any dependency of the EMS decisions on the powertrain main operating variables. Furthermore, the impact that Vehicle-to-Infrastructure (V2I) connections have on the control law is assessed on several tests performing the same real-world route with the vehicle navigation system alternatively switched on and of. Finally, a virtual test rig of the tested vehicle, developed in the GT– SUITE environment, is used to validate the set of extracted rules against the experimental data. An error of about 1-2% on the prediction of the vehicle CO₂ emissions and good matching of the State of Charge (SoC) profile in both Charge Depleting (CD) and Charge Sustaining (CS) phases prove the effectiveness of the proposed methodology.