Road Transport Sector Research Articles

Impact of Electrification in the Road Transport Sector on the Level of Electricity Consumption and Daily Load Curve in the UES of Russia

The article assesses the impact of potential motor transport sector electrification on the value of the aggregate demand for electricity and capacity in the UES of Russia. Despite official documents suppose quite modest quantitative targets for the electric transport until 2030, in the longer term, a large-scale replacement of oil fuel with electricity will have a significant effect on reducing greenhouse gas emissions, but will also require a more intensive development of generating and network capacities in the electric power industry. The scenaric forecast for the development of electric transport is made with the allocation of three segments - cars, trucks and buses, which allows taking into account the characteristics of electricity consumption characteristic of each of them. Another important result is an assessment of the possible extent of the influence of electric transport on the configuration of a typical daily load curve. The paper considers various modes of using infrastructure for electric transport charging, which result in different daily load curves. It is shown that, depending on the scales of electric transport development, their annual electricity consumption in the country will lay in the diapason from 168 to 460 TWh. At the same time, depending on the charging mode of electric vehicles, the additional power demand will vary from 26 to 74 million kW, and the amplitude of fluctuations in the hourly loads of electric vehicles will vary from two to four times.

Review of Decadal Changes in ASEAN Emissions Based on Regional and Global Emission Inventory Datasets

In Asia, anthropogenic emissions have increased substantially over the last decade from various sectors, including power generation (PG), industries, road transportation (RT), and residential. This study analyzed different regional (REAS, MIX-Asia) and global (EDGAR) emission inventory (EI) datasets to provide insight into ASEAN's comprehensive emission status (emission trend, sectoral and country-specific emissions, changes in spatial distribution) during 2000–2015. The study observed a considerable increase in SO2, NOx, CO, CO2, and particulate matter (PM) emissions in ASEAN during this period. Results analyzed from the EDGAR EI dataset (2015) show that among the pollutants, SO2, CO2, and N2O were substantially contributed by the PG sector (43.4–56%), while CO, NOx, NMVOC, and CH4 were from the RT sector (35.6–61.5%), and PM and NH3 emissions were from the residential sector (50–80.6%). Similar contributions were also observed in 2000 and 2010. It is apparent that these sectors contributed noticeably to the total Asian emission (i.e., 14–34% in 2010, based on the MIX-Asian dataset). We have observed increasing annual emission trends for most pollutants in ASEAN countries, with more significant emission growth in Vietnam (e.g., SO2 and NOx emissions increased by 232% and 145%, respectively). Considerable changes in spatial emission distributions over the ASEAN between that period were also observed caused by the shifting of sparse development into concentrated urban expansion surrounding large metropolitan clusters. The information from this study will be vital for the ASEAN governments to review and update their approved/planned regulations on emission control with prioritizing the sectors aimed at air quality management and environmental sustainability.

An Improved Rank Order Centroid Method (IROC) for Criteria Weight Estimation: An Application in the Engine/Vehicle Selection Problem

The focus of this paper is on the criteria weight approximation in Multiple Criteria Decision Making (MCDM). An approximate weighting method produces the weights that are surrogates for the exact values that cannot be elicited directly from the DM. In this field, a very famous model is Rank Order Centroid (ROC). The paper shows that there is a drawback to the ROC method that could be resolved. The paper gives an idea to develop a revised version of the ROC method called Improved ROC (IROC). The behaviour of the IROC method is investigated using a set of simulation experiments. The IROC method could be employed in situations of time pressure, imprecise information, etc. The paper also proposes a methodology including the application of the IROC method in a group decision making mode, to estimate the weights of the criteria in a tree-shaped structure. The proposed methodology is useful for academics/managers/decision makers who want to deal with MCDM problem. A study case is examined to show applicability of the proposed methodology in a real-world situation. This case is engine/vehicle selection problem, that is one of the fundamental challenges of road transport sector of any country.

Quasi-static failure analysis of lithium-ion battery (LIB) used in electric vehicle

Safety and reliability are two essential factors for extensive electrification of the road transport sector. Lithium Ion Battery (LIB) packs are vulnerable to failure due to mechanical vibrations, impact forces, and thermal runaway. The present work explores quasi-static failure mechanisms of the multi-layered structure of LIB cells subjected to mechanical loading conditions like tensile loading and three-point bending, mainly predicting the beginning of the fracture of the cell. Also, an in-depth computational model for quasi-static loading was subsequently established in Abaqus, which effectively indicated the mechanical deformation of the cell. The ultimate tensile strength for the cell casing was 400 MPa and 480 MPa for experimental and simulation, respectively. Similarly, the peak deflection of the cell was: experimental 13.8 mm and numerical 13.65 mm for the three-point bending test. During the axial compression test, buckles were observed for 450Mpa stress, and the strain was around 0.0918. The mechanical behavior of the cell casing becomes one of the important parameters for characterizing the LIB cells.

A Stochastic Fuzzy Multicriteria Methodology for Energy Planning Decision Support: Case Study of the Electrification of the Greek Road Transport Sector

A Stochastic Fuzzy Multicriteria Methodology for Energy Planning Decision Support: Case Study of the Electrification of the Greek Road Transport Sector

Machine learning for activity-based road transportation emissions estimation

Abstract Measuring and attributing greenhouse gas (GHG) emissions remains a challenging problem as the world strives toward meeting emissions reductions targets. As a significant portion of total global emissions, the road transportation sector represents an enormous challenge for estimating and tracking emissions at a global scale. To meet this challenge, we have developed a hybrid approach for estimating road transportation emissions that combines the strengths of machine learning and satellite imagery with localized emissions factors data to create an accurate, globally scalable, and easily configurable GHG monitoring framework.

Reducing the environmental impacts of passenger cars: a comparison between electricity and biofuels

This study assessed the potential for decreasing the environmental impact of the European road transport sector by exploiting biofuels. After processing available data on the actual trends of biofuels utilisation in the sector, laboratory and on-road tests were conducted to compare the emissions from a C-segment Euro 6d-TEMP passenger car fed with commercial reference petrol and three experimental petrol blends containing components of renewable origin. Through the Life Cycle Assessment methodology based on measurement data, the environmental impacts of the four fuels were compared with an average C-segment battery electric vehicle. Concerning a fossil petrol car, all the fuels slightly reduced the impact on climate change, while the battery electric vehicle performed best. For the remaining environmental impact categories, the picture was less straightforward.

Analysis of the Impact of Transportation Infrastructure Development on Traffic Performance in NTB Province

Transportation development is carried out with the construction of various infrastructures. In order to support the realization of safe and peaceful conditions, it is necessary to provide transportation infrastructure and facilities to support the acceleration of regional development, including underdeveloped areas and border areas. then the role of transportation is needed to bridge the gap and encourage the distribution of development results. Development of transportation infrastructure is also being carried out in West Nusa Tenggara Province, which is experiencing transportation development. It was noted that from 2016 to 2020 there were 49 infrastructure developments and developments in both the traffic and road transport sector as well as the ASDP sector. The purpose of this study was to evaluate and determine the impact of existing transportation infrastructure development in West Nusa Tenggara Province in the period 2016 to 2020 through aspects of benefits and traffic performance. The method used is quantitative by obtaining data which is then analyzed by gap analysis. The results of the study show that in the administrative aspect, the usability aspect, and the road performance aspect, it has a good impact on the community, even though in the road performance aspect there are several national roads that do not meet the network speed target.

Economic, Energy and Environmental Efficiency of Road Freight Transportation Sector in the EU

The proper development of transportation constitutes the basis for an effectively functioning economy at the national and global levels. On the other hand, transportation significantly impacts the environment and climate. Sustainable transportation management should therefore include both economic, social and environmental aspects. The article aims to comprehensively assess the economic–energy–environmental efficiency of the 27-road freight transport sector in EU countries in 2019. The research was conducted using the non-parametric Data Envelopment Analysis (DEA) method. The Slacks-Based Measure–Data Envelopment Analysis (SBM-DEA) model was used, taking into account unwanted (undesirable) effects. As non-energy inputs in the DEA model used the labor in the road freight transport sector, stock of registered goods vehicles, and the length of the road network. Moreover, the energy consumption by the road freight transport sector was used as energy inputs in the DEA model. Desirable outputs were taken as road freight transport sector revenues and freight work performed by the sector. GHG emissions expressed in CO2 equivalent were treated as undesirable outputs. The research also adopts energy productivity and GHG emission efficiency indicators. The eco-efficiency of the road freight transport sector in EU countries varies. Ten countries have efficient road freight transport sectors. The efficient road freight transport group included Denmark, Germany, Belgium, Lithuania, Luxembourg, the Netherlands, Poland, Portugal, Slovenia and Bulgaria. They efficiently transformed the inputs into outputs. Five countries were recognized as eco-efficiency followers, including Italy, Finland, Slovakia, Sweden and Romania, and 12 countries were characterized by an inefficient road freight transport sector. Based on benchmarking principles for inefficient road freight transport sectors, the changes in input and output levels were proposed to improve efficiency. The relationship between the economic development of EU countries and the eco-efficiency of the road freight transport sector was also analyzed, indicating a positive relationship between the variables but with weak strength. The main contributions of this article are an extension of previous DEA works that assesses the efficiency of the road freight transport sector, also considering undesirable variables. Research conclusions are particularly important for policymakers in the context of management sustainable transportation development in the EU.

Evaluating major anthropogenic VOC emission sources in densely populated Vietnamese cities.

Volatile organic compounds (VOCs) play an important role in urban air pollution, both as primary pollutants and through their contribution to the formation of secondary pollutants, such as tropospheric ozone and secondary organic aerosols. In this study, more than 30 VOC species were continuously monitored in the two most populous cities in Vietnam, namely Ho Chi Minh City (HCMC, September-October 2018 and March 2019) and Hanoi (March 2019). In parallel with ambient VOC sampling, grab sampling was used to target the most prevalent regional-specific emission sources and estimate their emission factors (EFs).Emission ratios (ERs) obtained from ambient sampling were compared between Vietnamese cities and other cities across the globe. No significant differences were observed between HCMC and Hanoi, suggesting the presence of similar sources. Moreover, a good global agreement was obtained in the spatial comparison within a factor of 2, with greater ER for aromatics and pentanes obtained in the Vietnamese cities.The detailed analysis of sources included the evaluation of EF from passenger cars, buses, trucks, motorcycles, 3-wheeled motorcycles, waste burning, and coal-burning emissions. Our comparisons between ambient and near-source concentration profiles show that road transport sources are the main contributors to VOC concentrations in Vietnamese cities.VOC emissions were calculated from measured EF and consumption data available in Hanoi and compared with those estimated by a global emission inventory (EDGAR v4.3.2). The total VOC emissions from the road transport sector estimated by the inventory do not agree with those calculated from our observations which showed higher total emissions by a factor of 3. Furthermore, the inventory misrepresented the VOCs speciation, mainly for isoprene, monoterpenes, aromatics, and oxygenated compounds. Accounting for these differences in regional air quality models would lead to improved predictions of their impacts and help to prioritise pollution reduction strategies in the region.

From coal to variable renewables: Impact of flexible electric vehicle charging on the future Indian electricity sector

India ranks third globally in overall greenhouse gas (GHG) emissions, with power and transportation first and third, respectively, in terms of India's sectoral GHG contributions. Motivated by India's Nationally Determined Contribution goals and stated intent to drive renewables growth and vehicle electrification, this paper explores the connections between India's power and road transportation sectors by investigating the benefits of flexible electric vehicle (EV) charging on a set of hypothetical 2037 Indian power grid infrastructures. Using two production cost models, one simulating optimal unit commitment and hourly dispatch of available generation, the other simulating subhourly real-time dispatch operations, we examine the impact of flexible and inflexible EV charging regimes and vehicle-to-grid integration on generation profiles, peak load shaving, energy storage utilization, and overall emissions. Our techno-economic analysis shows that a number of future grid infrastructure scenarios, despite not specifically accounting for EV growth scenarios, may meet annual and hourly peak demand when combined with flexible EV charging; in contrast, inflexible EV charging, particularly during the early morning, will lead to substantial need for additional generation capacity. Coupled sectoral decarbonization remains tied to the grid generation mix, highlighting the need for continued policy focus on decarbonization of the rapidly growing power sector.

Economic and environmental impacts of EVs promotion under the 2060 carbon neutrality target—A CGE based study in Shaanxi Province of China

As the world's largest emitter of CO2, China has committed to achieving carbon neutrality by 2060. For the high emission of the transport sector, the promotion of electric vehicles (EVs) is an important way to reduce CO2 emissions in the road transport sector. Due to the current high proportion of coal-fired power in Shaanxi province, the indirect emission of EVs is greater than the direct emission of fuel vehicles. In this study, two regional dynamic CGE models were constructed to evaluate the economic and environmental impact of EVs promotion and power generation structure optimization under the carbon neutral target in Shaanxi province. The results show that the promotion of EVs and optimization of the power generation structure will reduce the carbon emissions of electricity used in the transport sector from 4.5 million tons to 0.1 million tons. Compared with the baseline scenario, the NOX, SO2, and PM emission in the carbon neutrality scenarios has decreased by 50%, 65%, and 32%, respectively in 2060. Further, the GDP loss of carbon neutrality scenarios compared to the baseline scenario in 2060 is between 7.9% and 12.3%. Fortunately, the promotion of EVs and the promotion of the power generation structure can help to optimize the allocation of carbon emission allowance, which would alleviate the GDP loss caused by the carbon neutrality target. So it is necessary to encourage the development of EVs and vigorously develop renewable energy at the same time in other provinces with similar power structure like Shaanxi.

Promoting economic and environmental resilience in the post-COVID-19 era through the city and regional on-road fuel sustainability development

How to control the global temperature rise within 1.5 °C in the post-COVID-19 era has attracted attention. Road transport accounts for nearly a quarter of global CO2 emissions, and the related sulfur dioxide (SO2) emissions also trigger air pollution issues in population-intensive cities and areas. Many cities and states have announced a timetable for phasing out urban-based fossil fuel vehicles. By combining a Markov-chain model with a dynamic stochastic general equilibrium (DSGE) model, the impacts of on-road energy structural change led by phasing out fossil fuel vehicles in the road transportation sector are evaluated. The impact of automobile emissions (both CO2 and SO2) on the environment is evaluated, taking into consideration of variation between cities, regions, and countries. Two other major driving forces in addition to CO2 emissions reduction in promoting fossil fuel vehicles’ transition toward net-zero carbon are identified and analyzed with multiple different indicators. Under the framework of the DSGE model, climate policy instruments’ effects on economic development, energy consumption, and their link to economic and environmental resilience are evaluated under exogenous shocks as well.

Modeling the potential impacts of automated vehicles on pollutant emissions under different scenarios of a test track

One of the significant sources of air pollution and greenhouse gas emissions is the road transportation sector. These emissions are worsened by driving behaviors and network conditions. It is common knowledge that experienced and inexperienced drivers behave differently when operating vehicles. Given the same vehicle in a different timeframe, the drivers’ reactions to similar situations vary, which has a significant influence on the emissions and fuel consumption as their use of acceleration and speed differ. Because the driving patterns of automated vehicles are programmable and provide a platform for smooth driving situations, it is predicted that deploying them might potentially reduce fuel consumption, particularly in urban areas with given traffic situations. This study’s goal is to examine how different degrees of automated vehicles behave when it comes to emissions and how accelerations affect that behavior. Furthermore, the total aggregated emissions on the synthesized urban network are evaluated and compared to legacy vehicles. The emission measuring model is based on the Handbook Emission Factors for Road Transport (HBEFA)3 and is utilized with the Simulation of Urban Mobility (SUMO) microscopic simulation software. The results demonstrate that acceleration value is strongly correlated with individual vehicle emissions. Although the ability of automated vehicles (AVs) to swiftly achieve higher acceleration values has an adverse effect on emissions reduction, it was compensated by the rate of accelerations, which decreases as the automation level increases. According to the simulation results, automated vehicles can reduce carbon monoxide (CO) emissions by 38.56%, carbon dioxide (CO2) emissions by 17.09%, hydrocarbons (HC) emissions by 36.3%, particulate matter (PMx) emissions by 28.12%, nitrogen oxides (NOx) emissions by 19.78% in the most optimistic scenario (that is, when all vehicles are replaced by the upper bound automated vehicles) in the network level.

Performance Metric Development to Measure Overall Vehicle Effectiveness in Mining Transportation

Several performance metrics to measure the effectiveness of manufacturing equipment have been studied, and one of the most used is overall equipment effectiveness (OEE). However, its application and development in the road transportation sector remains little studied. The purpose of this paper is to establish a new metric derived from OEE to measure the overall effectiveness of coal mining transportation and apply this metric to actual operation. This study employed in-depth interviews and Delphi techniques to explore the specific metrics. Three rounds of Delphi involving mining experts, academia, and consultants identified six potential metrics to measure coal mining transportation operations that contributed to a new metric to measure mining transportation overall vehicle effectiveness. This paper also discusses a case study of how this newly developed metric was tested and applied to the actual operation of the truck fleet at a coal-hauling company in Indonesia. According to the case study, this metric accommodated the important factors in coal mining transportation operations and reflected operational performance. This study contributes to measuring the effectiveness of coal-hauling transportation by providing an effective metric that will help the managerial team make better decisions for process improvement.

Energy Demand of the Road Transport Sector of Saudi Arabia—Application of a Causality-Based Machine Learning Model to Ensure Sustainable Environment

The road transportation sector in Saudi Arabia has been observing a surging growth of demand trends for the last couple of decades. The main objective of this article is to extract insightful information for the country’s policymakers through a comprehensive investigation of the rising energy trends. In the first phase, it employs econometric analysis to provide the causal relationship between the energy demand of the road transportation sector and different socio-economic elements, including the gross domestic product (GDP), number of registered vehicles, total population, the population in the urban agglomeration, and fuel price. Then, it estimates future energy demand for the sector using two machine-learning models, i.e., artificial neural network (ANN) and support vector regression (SVR). The core features of the future demand model include: (i) removal of the linear trend, (ii) input data projection using a double exponential smoothing technique, and (iii) energy demand prediction using the machine learning models. The findings of the study show that the GDP and urban population have a significant causal relationship with energy demand in the road transportation sector in both the short and long run. The greenhouse gas emissions from the road transportation in Saudi Arabia are directly proportional to energy consumption because the demand is solely met by fossil fuels. Therefore, appropriate policy measures should be taken to reduce energy intensity without compromising the country’s development. In addition, the SVR model outperformed the ANN model in predicting the future energy demand of the sector based on the achieved performance indices. For instance, the correlation coefficients of the SVR and the ANN models were 0.8932 and 0.9925, respectively, for the test datasets. The results show that the SVR is better for predicting energy consumption than the ANN. It is expected that the findings of the study will assist the decision-makers of the country in achieving environmental sustainability goals by initiating appropriate policies.

A review on best practices using hydrogen fuel-cell electric vehicles in public transport

Road transportation sector is responsible for 21.8% of EU’s CO2eq emissions forcing the shift to a de-carbonised mobility scheme, whilst maintaining competitiveness and satisfying the increasing demand mobility needs. Thus, the development of electric vehicles (EVs) and the investigation of alternative fuels by the research community have emerged. Hydrogen is considered a good substitute fuel for the road transport sector if it is produced from renewable energy sources. Several cities have already started to replace a percentage of their conventional public transport fleet with fuel-cell electric vehicles (FCEV). This paper provides a detailed presentation of several best practices applied in small-medium scale European cities and regions. Best practices are investigated from a city-centred point of view to provide a clearer understanding of the hydrogen initiatives performed in the specific areas. The selected scenarios have demonstrated and implemented different state-of-the-art hydrogen applications in the hydrogen mobility supply chain, which could be potentially tested in other regions with similar geographical, economical and transport activities. Finally, the outcomes of this review study identify different challenges, barriers and opportunities of hydrogen mobility applications which other cities should take into consideration before investing in this technology.

European Green Deal Implications on Country Level Energy Consumption

Abstract Research background: The European Green deal set by the European Commission has launched new business models in sustainable development. Major contributions are expected in the road transport sector; as far as conventional internal combustion creates significant input in Green House Gas emission inventories. Each EU member state has an obligation to reduce GhG emission by accelerating Electric Vehicle development. In order to foster growth of EVs, there is the need of significant investment into charging infrastructures. The article propose the model of forecasting of investment based on the forecast of the growth of the amount of electric vehicles and their demand on energy. The model includes the behaviouristic approach based on the total cost of ownership model as well as calculations of efficient usage of EV charging points. The model takes into account all types of vehicles including personal and commercial, freight and passenger. Purpose: The aim of this article is to present a complex model for forecasting the required investments based on the fore-cast of the increase in the number of electric vehicles and their demand on energy and investments. Research methodology: The general algorithm of forecasting consists of several consecutive phases: (1) Forecasting the number of electric vehicles, (2) Forecasting the energy needed for electric vehicles, based on the forecast (1) and the predicted usage level of these vehicles. (3) Forecasting the charging station number with the expected technical capacities and characteristics of these charging stations based on the forecasts (1) and (2). (4) Forecasting the need to upgrade the low-voltage grid based on the forecast (3). (5) Calculating the total investment needed based on the results of the forecasts (3) and (4). The main limitations of the study are related to the statistics available for modelling and human behaviour uncertainty, especially in the evaluation impact of measures to foster use of electric vehicles. Results: The findings of the Lithuanian case analysis, which is expressed in three scenarios, focuses on two trends. The most promising scenario projects 319,470 electric vehicles by 2030 which will demand for 1.09 TWh of electricity, representing 8.4–9.9 percent of the total energy consumption in the country. It requires EUR 230, million in the low-voltage grid and EUR 209, million in the charging stations. Novelty: The scientific problem is that the current approach on the forecasting of electric vehicles is too abstract, forecast models cannot be transferred from country to country. This article proposes a model of forecasting investments based on the forecast of the increase in the number of electric vehicles and their demand on energy. The model includes the behaviouristic approach based on the total cost of ownership model as well as calculations of efficient usage of EV charging points. The model takes into account all types of vehicles including personal and commercial, freight and passenger. The article has proven that statistics-based forecasting gives very different results compared to the objective function and to the evaluation of the effects of measures. This has not been compared in previous studies.

Eco-Driving Control Architecture for Platoons of Uncertain Heterogeneous Nonlinear Connected Autonomous Electric Vehicles

The improvement of energy performance for platoons of autonomous connected vehicles is one of the major challenges the road transport sector is facing with. To this aim, this work addresses and solves the energy-consumption problem for uncertain heterogeneous electric nonlinear autonomous vehicles platoon via a novel Eco-Driving Control Architecture able to optimize its energy consumption performance while ensuring the fulfillment of the optimal leader tracking trajectory. Specifically, it consists of a Nonlinear Model Predictive Control (NMPC) strategy, driving the leader motion and computing the optimal ecological trajectory to be imposed on the whole platoon, and a novel distributed exponentially-stable robust PID-like protocol, driving the follower vehicles motion for achieving a precise leader-tracking with a desired transient behavior as required for the accurate implementation of the energy-saving control. The exponential stability of the overall vehicular network is analytically proven with the Lyapunov theory and the derived robust stability conditions allow the proper tuning of the control gains on the basis of the desired decay rate. The efficiency of the proposed approach is corroborated via the high-fidelity Mixed Traffic Simulator (MiTraS) co-simulation platform under different operative scenarios and a wide uncertainty range for the vehicles parameters. Simulation results confirm how the proposed architecture ensures the eco-driving behaviour for the whole vehicles platoon.

Prediction of Pollutant Emissions in Various Cases in Road Transport

The road transport sector is a key source of carbon dioxide and air pollutants. Mathematical modeling is frequently used to assess the sector’s contribution to the total national emissions budget (inventory). The present article focuses on studying the impacts of the fuel used (LPG, CNG, gasoline, diesel, and biofuel), the Euro standard, and the structure of vehicles on CO2, NOX, and PM2.5 emissions. This paper presents the results of mathematical simulations of the influence of the fuel type and Euro standards on pollutant emissions. Two scenarios were considered in terms of the effect on CO2, NOX, and PM2.5 emissions: one focused on changing the current fleet and introducing Euro 6/VI vehicles, and the second scenario focused on cities adding low-pollution zones (only Euro 6 vehicles and PHEVs, HEVs, and BEVs entering the city). The results of the simulations showed that Euro 6/IV vehicles emit significantly less PM2.5 and NOX, and biofuels can substantially contribute to reducing emissions.