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Vehicle Energy Consumption and Emissions in Intelligent and Sustainable Transportation Systems

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Special Issue Information
Keywords
Published Papers

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "B: Energy and Environment".

Deadline for manuscript submissions: closed (25 March 2022) | Viewed by 20453

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Special Issue Editors

Dr. Michail Makridis

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Guest Editor

Institute for Transport Planning and Systems (IVT), ETH, Zürich, Switzerland
Interests: traffic flow; connected and automated vehicles; modeling and simulation; intelligent transportation systems; traffic estimation and control; simulation of vehicle dynamics and driving behavior; vehicle energy consumption and emissions
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hesham A. Rakha

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Guest Editor

Charles E. Via, Jr. Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA
Interests: traffic flow theory; traveler and driver behavior modeling; dynamic traffic assignment; transportation network control; use of artificial intelligence and machine learning techniques in transportation; intelligent vehicle systems; connected and automated vehicles; transportation energy and environmental modeling; transportation safety modeling

Dr. Georgios Fontaras

E-Mail Website
Guest Editor

Joint Research Centre, European Commission, 21027 Ispra, Italy
Interests: vehicle energy efficiency; transport energy and environmental impact; environmental policy; vehicle emissions modeling; emissions inventories and projections; internal combustion engines; vehicle and component simulations

Special Issue Information

Dear Colleagues,

We invite submissions to a Special Issue of Energies entitled “Vehicle Energy Consumption and Emissions in Intelligent and Sustainable Transportation Systems”.

Global warming and other human-generated environmental pressures have reached a critical point. Transport emissions accounted for almost a quarter of global CO₂ emissions in 2016, 72% of which originated from road vehicles. An alarming issue is that while global emissions need to decrease, transport emissions continue to rise, with increased travel demand counterbalancing vehicle efficiency improvements.

At the same time, road transport faces an unprecedented change in the decade to come mostly due to the deployment of advanced vehicle automation and connectivity, and the rapid uptake of new powertrains such as battery-electric and plug-in hybrid vehicles. These technological advances offer novel opportunities on a whole family of topics ranging from vehicle simulation, driving behavior, emissions and energy consumption modeling, vehicle and fleet energy demand optimization, to traffic flow and traffic management, and vehicle or fleet-level control strategies.

The interest in new solutions for simulation, control, and optimization of vehicle energy consumption and emissions has increased substantially. Indicative examples include:

Energy-related optimal solutions for service operators and network users;
Optimal vehicle platooning and vehicle routing strategies, vehicle cooperative strategies;
Homogenization of traffic flow, driving behavior, advanced driver assistance systems, traffic congestion, and energy efficiency for electric and hybrid vehicles;
Simulation and modeling of vehicle dynamics for new vehicle technologies;
Real-world vehicle emissions and energy consumption, versus nominal or simulated ones.

This Special Issue aims to bring together innovative developments in the above and other relevant areas toward future intelligent and sustainable road transportation systems. The topics of interest include but are not limited to the following:

Vehicle energy efficiency
Road transport emissions
Traffic simulation for energy estimation/prediction
Simulation and modeling of vehicle dynamics
Enabling energy-efficient mobility and ECO routing
Advanced traffic management strategies for environmental purposes
Simulation of light-duty passenger vehicles and heavy-duty vehicles
Energy-efficient smart cities
Automation, connectivity, and energy consumption
Modeling and simulation of driving behavior
Environmental policy
Assessment of different mobility trends (car sharing, dial a ride, etc.) from an energy perspective

Dr. Michail Makridis
Prof. Dr. Hesham Rakha
Dr. Georgios Fontaras
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Vehicle energy efficiency
Energy-efficient mobility
Automation, connectivity, and energy consumption
Modeling of driving behavior
Traffic management, control, and optimization
Smart cities
Emissions and fuel consumption measurement and simulation

Published Papers (9 papers)

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Research

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19 pages, 2949 KiB

Open AccessArticle

Comparison of the Real-Driving Emissions (RDE) of a Gasoline Direct Injection (GDI) Vehicle at Different Routes in Europe

by Barouch Giechaskiel, Victor Valverde, Anastasios Melas, Michaël Clairotte, Pierre Bonnel and Panagiota Dilara

Energies 2024, 17(6), 1308; https://0-doi-org.brum.beds.ac.uk/10.3390/en17061308 - 08 Mar 2024

Viewed by 439

Abstract

On-road real-driving emissions (RDE) tests with portable emissions measurement systems (PEMS) are part of the vehicle emissions regulations in the European Union (EU). For a given vehicle, the final emission results depend on the influence of the ambient conditions and the trip characteristics (including the driver’s behaviour) on the vehicle performance and the instrument measurement uncertainty. However, there are not many studies that have examined the emissions variability of a single vehicle following different routes. In this study, a 1.2 L gasoline direct injection (GDI) Euro 5b passenger car without a particulate filter and a PEMS was circulated in seven European laboratories. At their premises, the laboratories performed two to five repetitions of on-road trips compliant with the EU RDE regulation. The ambient temperature ranged between 7 °C and 23 °C. The average emission levels of the vehicle were 135 g/km for CO₂, 77 mg/km for CO, 55 mg/km for NO_x, and 9.2 × 10¹¹ #/km for particle number. The coefficient of variance in the emissions following the same route was 2.9% for CO₂, 23.8% for CO, 23.0% for NO_x, and 5.8% for particle number. The coefficient of variance in the emissions following different routes in Europe was 6.9% for CO₂, 9.1% for CO, 0.0% for NO_x, and 9.1% for particle number. The previous values include the specific vehicle emissions variability under the narrow test conditions of this study, but only partly the PEMS measurement uncertainty because the same instrument was used in all the trips. The results of this study can be used by laboratories conducting RDE tests to assess their uncertainty budget when testing or comparing vehicles of similar technology. Full article

(This article belongs to the Special Issue Vehicle Energy Consumption and Emissions in Intelligent and Sustainable Transportation Systems)

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29 pages, 3852 KiB

Open AccessArticle

Impact of Active Diesel Particulate Filter Regeneration on Carbon Dioxide, Nitrogen Oxides and Particle Number Emissions from Euro 5 and 6 Vehicles under Laboratory Testing and Real-World Driving

by Athanasios Dimaratos, Barouch Giechaskiel, Michaël Clairotte and Georgios Fontaras

Energies 2022, 15(14), 5070; https://0-doi-org.brum.beds.ac.uk/10.3390/en15145070 - 12 Jul 2022

Cited by 5 | Viewed by 1975

Abstract

Particulate mass concentration is a crucial parameter for characterising air quality. The diesel particulate filter (DPF) is the primary technology used to limit vehicle particle emissions, but it needs periodic cleaning, a process called regeneration. This study aims to assess the impact of active DPF regeneration on the performance and emissions of Euro 5 and 6 vehicles. The study examined both carbon dioxide (CO₂) and pollutant (nitrogen oxides (NO_x) and particle number (PN)) emissions for eight vehicles tested in the laboratory and on the road. Apart from the DPF, a wide range of emission control systems was covered in this experimental campaign, including exhaust gas recirculation (EGR), diesel oxidation catalyst (DOC), lean NO_x trap (LNT) and selective catalytic reduction (SCR) catalyst, revealing the different impacts on NO_x emissions. The regeneration frequency and duration were also determined and used to calculate the K_i factor, which accounts for the emissions with and without regeneration, weighted over the distance driven between two consecutive regeneration events. Based on these outcomes, representative emission factors (EF) were proposed for the regeneration phase only and the complete regeneration interval. In addition, the effect of regeneration on efficiency was estimated and compared with other energy consumers. The results indicated a significant impact of DPF regeneration on CO₂, NO_x and PN emissions, higher in the case of driving cycle testing in the laboratory. The relevant mechanisms behind the elevated emission levels were analysed, focusing on the regeneration period and the test phase following immediately after. The calculation of the K_i factor and the comparison with the official values revealed some weaknesses in its application in real-world conditions; to overcome these, new NO_x EF values were calculated, depending on the emission control system. It was revealed that Euro 6 vehicles equipped with SCR could comply with the applicable limits when considering the complete regeneration interval. Finally, it was indicated that the DPF regeneration impact on vehicle efficiency is similar to that of driving with the air conditioning (A/C) system and headlights on. Full article

(This article belongs to the Special Issue Vehicle Energy Consumption and Emissions in Intelligent and Sustainable Transportation Systems)

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25 pages, 4928 KiB

Open AccessArticle

A Multi-Criteria Evaluation of Applications Supporting Public Transport Users

by Katarzyna Solecka and Marcin Kiciński

Energies 2022, 15(10), 3493; https://0-doi-org.brum.beds.ac.uk/10.3390/en15103493 - 10 May 2022

Cited by 3 | Viewed by 1640

Abstract

Reducing the energy consumption of transport in urban areas is possible if appropriate measures are taken to make public transport more attractive. These include all kinds of journey planners that are part of the passenger information system. Various applications available on the market allow passengers to evaluate their usability. This paper presents and compares nine of the most popular journey planners available to iOS and Android users travelling in Krakow. The comparison took into account all the information obtained from the surveys. In addition, using a multi-criteria approach, the final ranking of the set of journey planners was developed. The assessment was made on the basis of a set of nine criteria indicated by travellers as the most important ones. The obtained results showed disproportions in the functionality of particular solutions. They also indicated the apps that are most frequently and willingly used by local (urban city/agglomeration) travellers. Full article

(This article belongs to the Special Issue Vehicle Energy Consumption and Emissions in Intelligent and Sustainable Transportation Systems)

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17 pages, 2422 KiB

Open AccessArticle

Developing and Field Testing a Green Light Optimal Speed Advisory System for Buses

by Hao Chen and Hesham A. Rakha

Energies 2022, 15(4), 1491; https://0-doi-org.brum.beds.ac.uk/10.3390/en15041491 - 17 Feb 2022

Cited by 7 | Viewed by 1745

Abstract

In this study, a Green Light Optimal Speed Advisory (GLOSA) system for buses (B-GLOSA) was developed. The proposed B-GLOSA system was implemented on diesel buses, and field tested to validate and quantify the potential real-world benefits. The developed system includes a simple and easy-to-calibrate fuel consumption model that computes instantaneous diesel bus fuel consumption rates. The bus fuel consumption model, a vehicle dynamics model, the traffic signal timings, and the relationship between vehicle speed and distance to the intersection are used to construct an optimization problem. A moving-horizon dynamic programming problem solved using the A-star algorithm is used to compute the energy-optimized vehicle trajectory through signalized intersections. The Virginia Smart Road test facility was used to conduct the field test on 30 participants. Each participant drove three scenarios, including a base case uninformed drive, an informed drive with signal timing information communicated to the driver, and an informed drive with the recommended speed computed by the B-GLOSA system. The field test investigated the performance of using the developed B-GLOSA system considering different impact factors, including road grades and red indication offsets, using a split-split-plot experimental design. The test results demonstrated that the proposed B-GLOSA system can produce smoother bus trajectories through signalized intersections, thus producing fuel consumption and travel time savings. Specifically, compared to the uninformed drive, the B-GLOSA system produces fuel and travel time savings of 22.1% and 6.1%, on average, respectively. Full article

(This article belongs to the Special Issue Vehicle Energy Consumption and Emissions in Intelligent and Sustainable Transportation Systems)

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19 pages, 15214 KiB

Open AccessArticle

Impact of Signalized Intersections on CO₂ and NO_x Emissions of Heavy Duty Vehicles

by Nicolás Deschle, Ernst Jan van Ark, René van Gijlswijk and Robbert Janssen

Energies 2022, 15(3), 1242; https://0-doi-org.brum.beds.ac.uk/10.3390/en15031242 - 08 Feb 2022

Cited by 4 | Viewed by 1962

Abstract

Pollutant emissions have been a topic of interest in the last decades. Not only environmentalists but also governments are taking rapid action to reduce emissions. As one of the main contributors, the transport sector is being subjected to strict scrutiny to ensure it complies with the short and long-term regulations. The measures imposed by governments clearly involve all the stakeholders in the logistics sector, from road authorities and logistic operators to truck manufacturers. The improvement of traffic conditions is one of the perspectives in which the reduction of emissions is being addressed. Optimization of traffic flow, avoidance of unnecessary stops, control of the cruise speed, and coordination of trips in an energy-efficient way are necessary steps to remain compliant with the upcoming regulations. In this study, we have estimated the CO₂ and NO_x emissions in heavy-duty vehicles while traversing signalized intersections, and we examined the differences between various behavioral scenarios. We found a consistent trend indicating that avoiding a stop can potentially reduce CO₂ and NO_x emissions by up to

0.32

k g

and

1.8

g

, respectively. Furthermore, an upper bound for the yearly CO₂ savings is provided for the case of The Netherlands. A reduction of

3.2 %

of the total CO₂ emitted by heavy-duty vehicles is estimated. These results put traffic control in the main scene as a yet unexplored dimension to control pollutant emissions, enabling authorities to more accurately estimate cost–benefit plans for traffic control system investments. Full article

(This article belongs to the Special Issue Vehicle Energy Consumption and Emissions in Intelligent and Sustainable Transportation Systems)

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15 pages, 3642 KiB

Open AccessArticle

Developing a Hydrogen Fuel Cell Vehicle (HFCV) Energy Consumption Model for Transportation Applications

by Kyoungho Ahn and Hesham A. Rakha

Energies 2022, 15(2), 529; https://0-doi-org.brum.beds.ac.uk/10.3390/en15020529 - 12 Jan 2022

Cited by 12 | Viewed by 4043

Abstract

This paper presents a simple hydrogen fuel cell vehicle (HFCV) energy consumption model. Simple fuel/energy consumption models have been developed and employed to estimate the energy and environmental impacts of various transportation projects for internal combustion engine vehicles (ICEVs), battery electric vehicles (BEVs), and hybrid electric vehicles (HEVs). However, there are few published results on HFCV energy models that can be simply implemented in transportation applications. The proposed HFCV energy model computes instantaneous energy consumption utilizing instantaneous vehicle speed, acceleration, and roadway grade as input variables. The mode accurately estimates energy consumption, generating errors of 0.86% and 2.17% relative to laboratory data for the fuel cell estimation and the total energy estimation, respectively. Furthermore, this work validated the proposed model against independent data and found that the new model accurately estimated the energy consumption, producing an error of 1.9% and 1.0% relative to empirical data for the fuel cell and the total energy estimation, respectively. The results demonstrate that transportation engineers, policy makers, automakers, and environmental engineers can use the proposed model to evaluate the energy consumption effects of transportation projects and connected and automated vehicle (CAV) transportation applications within microscopic traffic simulation models. Full article

(This article belongs to the Special Issue Vehicle Energy Consumption and Emissions in Intelligent and Sustainable Transportation Systems)

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23 pages, 5707 KiB

Open AccessArticle

Field-Based Prediction Models for Stop Penalty in Traffic Signal Timing Optimization

by Suhaib Alshayeb, Aleksandar Stevanovic and B. Brian Park

Energies 2021, 14(21), 7431; https://0-doi-org.brum.beds.ac.uk/10.3390/en14217431 - 08 Nov 2021

Cited by 7 | Viewed by 1843

Abstract

Transportation agencies optimize signals to improve safety, mobility, and the environment. One commonly used objective function to optimize signals is the Performance Index (PI), a linear combination of delays and stops that can be balanced to minimize fuel consumption (FC). The critical component of the PI is the stop penalty “K”, which expresses an FC stop equivalency estimated in seconds of pure delay. This study applies vehicular trajectory and FC data collected in the field, for a large fleet of modern vehicles, to compute the K-factor. The tested vehicles were classified into seven homogenous groups by using the k-prototype algorithm. Furthermore, multigene genetic programming (MGGP) is utilized to develop prediction models for the K-factor. The proposed K-factor models are expressed as functions of various parameters that impact its value, including vehicle type, cruising speed, road gradient, driving behavior, idling FC, and the deceleration duration. A parametric analysis is carried out to check the developed models’ quality in capturing the individual impact of the included parameters on the K-factor. The developed models showed an excellent performance in estimating the K-factor under multiple conditions. Future research shall evaluate the findings by using field-based K-values in optimizing signals to reduce FC. Full article

(This article belongs to the Special Issue Vehicle Energy Consumption and Emissions in Intelligent and Sustainable Transportation Systems)

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20 pages, 4777 KiB

Open AccessArticle

Impacts of Extreme Ambient Temperatures and Road Gradient on Energy Consumption and CO₂ Emissions of a Euro 6d-Temp Gasoline Vehicle

by Barouch Giechaskiel, Dimitrios Komnos and Georgios Fontaras

Energies 2021, 14(19), 6195; https://0-doi-org.brum.beds.ac.uk/10.3390/en14196195 - 28 Sep 2021

Cited by 11 | Viewed by 2418

Abstract

The EU aims to substantially reduce its greenhouse gas emissions in the following decades and achieve climate neutrality by 2050. Better CO₂ estimates, particularly in urban conditions, are necessary for assessing the effectiveness of various regional policy strategies. In this study, we measured the CO₂ emissions of a Euro 6d-temp gasoline direct injection (GDI) vehicle with a three-way catalyst (TWC) and a gasoline particulate filter (GPF) at ambient temperatures from −30 °C up to 50 °C with the air-conditioning on. The tests took place both on the road and in the laboratory, over cycles simulating congested urban traffic, dynamic driving, and uphill driving towing a trailer at 85% of the maximum payloads of both the car and the trailer. The CO₂ values varied over a wide range depending on the temperature and driving conditions. Vehicle simulation was used to quantify the effect of ambient temperature, vehicle weight and road grade on the CO₂ emissions. The results showed that vehicle energy demand was significantly increased under the test conditions. In urban trips, compared to the baseline at 23 °C, the CO₂ emissions were 9–20% higher at −10 °C, 30–44% higher at −30 °C, and 37–43% higher at 50 °C. Uphill driving with a trailer had 2–3 times higher CO₂ emissions. In motorway trips at 50 °C, CO₂ emissions increased by 13–19%. The results of this study can help in better quantification of CO₂ and fuel consumption under extreme conditions. Additional analysis on the occurrence of such conditions in real-world operation is advisable. Full article

(This article belongs to the Special Issue Vehicle Energy Consumption and Emissions in Intelligent and Sustainable Transportation Systems)

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Review

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24 pages, 4005 KiB

Open AccessReview

Traffic Signal Optimization to Improve Sustainability: A Literature Review

by Suhaib Alshayeb, Aleksandar Stevanovic, Nikola Mitrovic and Elio Espino

Energies 2022, 15(22), 8452; https://0-doi-org.brum.beds.ac.uk/10.3390/en15228452 - 11 Nov 2022

Cited by 3 | Viewed by 2951

Abstract

Optimizing traffic signals to improve traffic progression relies on minimizing mobility performance measures (e.g., delays and stops). However, delay and stop minimizations do not necessarily lead to minimal sustainability measures (e.g., fuel consumption and emissions). For that reason, researchers have focused, for decades, on integrating traffic models, signal optimization models, and fuel consumption and emissions models to minimize sustainability metrics while keeping acceptable levels of mobility metrics. Therefore, this paper reviews, classifies, and analyzes studies found in the literature regarding optimizing sustainable traffic signals. This paper provides researchers with a good starting point to further develop solutions which can address sustainable traffic control. To achieve that, this study details the most notable sustainable signal timing optimization studies from six perspectives: traffic models, fuel consumption and emissions models, optimization methods, objective functions, operating conditions, and reported sustainability savings. Outcomes of this research show that the previous studies deployed many combinations of elements from the six-perspective mentioned above, leading to a wide range of fuel consumption and emissions savings. The study also concludes that the available fuel consumption and emissions models are relatively old. Hence, future research is needed to develop new fuel consumption and emissions models based on recently collected data. Full article

(This article belongs to the Special Issue Vehicle Energy Consumption and Emissions in Intelligent and Sustainable Transportation Systems)

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