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WEVJ
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Fuel Consumption and Emissions from Vehicles
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Fuel Consumption and Emissions from Vehicles

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 23905

Special Issue Editor

Dr. Jose Ignacio Huertas

E-Mail Website
Guest Editor
School of Engineering and Sciences,Tecnologico de Monterrey, Monterrey 32070, Mexico
Interests: energy; combustion; vehicular emissions; air pollution and smart mobility
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ground vehicles are the main source of air pollutants in the majority of urban centers. Governmental authorities, leading carmakers, fleet administrators, and the scientific community continue looking for alternatives to reduce fuel consumption and tailpipe emissions from brand new and in-use vehicles.

Currently, the progressive replacement of fuel-powered vehicles by electric vehicles is the most well-accepted solution to reduce air pollution in cities. However, the higher total operative costs (TOC) of electric vehicles are slowing down the penetration of these vehicles for the case of public transport and freight distribution in urban centers.

Assuming that the strategies to reduce energy consumption in fuel-powered vehicles can be extended to the incoming electric vehicles, the information gathered will allow the identification of new strategies to address the issues described above.  Thus, work must be conducted to assess the energy consumption from the actual in-use vehicles, at low cost, under real conditions of use.

This special issue in actual fuel consumption and emissions from vehicles looks for contributions to achieve such an objective.  Topics include:

  • Measurements of driving patterns, fuel or energy consumption, and tailpipe emissions
  • Local driving cycles
  • Methodologies to estimate real fuel consumption and tailpipe emissions at low cost.
  • The use of information technology to assess driving patterns, fuel consumption, and tailpipe emissions.

Dr. Jose Ignacio Huertas
Guest Editor

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. World Electric Vehicle Journal is an international peer-reviewed open access monthly 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 1400 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

  • Driving cycles
  • Driving patterns
  • MOVES
  • COPERT
  • PEMS
  • RSD
  • low cost sensors
  • telemetry
  • OBD
  • I/M programs
  • environmental regulations
  • vehicle dynamics
  • energy
  • fuel economy

Published Papers (7 papers)

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Research

21 pages, 4860 KiB  
Article
Adaptive Model Predictive Control Including Battery Thermal Limitations for Fuel Consumption Reduction in P2 Hybrid Electric Vehicles
by Ethelbert Ezemobi, Gulnora Yakhshilikova, Sanjarbek Ruzimov, Luis Miguel Castellanos and Andrea Tonoli
World Electr. Veh. J. 2022, 13(2), 33; https://0-doi-org.brum.beds.ac.uk/10.3390/wevj13020033 - 01 Feb 2022
Cited by 4 | Viewed by 2775
Abstract
The primary objective of a hybrid electric vehicle (HEV) is to optimize the energy consumption of the automotive powertrain. This optimization has to be applied while respecting the operating conditions of the battery. Otherwise, there is a risk of compromising the battery life [...] Read more.
The primary objective of a hybrid electric vehicle (HEV) is to optimize the energy consumption of the automotive powertrain. This optimization has to be applied while respecting the operating conditions of the battery. Otherwise, there is a risk of compromising the battery life and thermal runaway that may result from excessive power transfer across the battery. Such considerations are critical if factoring in the low battery capacity and the passive battery cooling technology that is commonly associated with HEVs. The literature has proposed many solutions to HEV energy optimization. However, only a few of the solutions have addressed this optimization in the presence of thermal constraints. In this paper, a strategy for energy optimization in the presence of thermal constraints is developed for P2 HEVs based on battery sizing and the application of model predictive control (MPC) strategy. To analyse this approach, an electro-thermal battery pack model is integrated with an off-axis P2 HEV powertrain. The battery pack is properly sized to prevent thermal runaway while improving the energy consumption. The power splitting, thermal enhancement and energy optimization of the complex and nonlinear system are handled in this work with an adaptive MPC operated within a moving finite prediction horizon. The simulation results of the HEV SUV demonstrate that, by applying thermal constraints, energy consumption for a 0.9 kWh battery capacity can be reduced by 11.3% relative to the conventional vehicle. This corresponds to about a 1.5% energy increase when there is no thermal constraint. However, by increasing the battery capacity to 1.5 kWh (14s10p), it is possible to reduce the energy consumption by 15.7%. Additional benefits associated with the predictive capability of MPC are reported in terms of energy minimization and thermal improvement. Full article
(This article belongs to the Special Issue Fuel Consumption and Emissions from Vehicles)
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24 pages, 8237 KiB  
Article
Configuration of Electric Vehicles for Specific Applications from a Holistic Perspective
by José I. Huertas, Antonio E. Mogro and Juan P. Jiménez
World Electr. Veh. J. 2022, 13(2), 29; https://0-doi-org.brum.beds.ac.uk/10.3390/wevj13020029 - 28 Jan 2022
Cited by 3 | Viewed by 5075
Abstract
Electrification of heavy-duty vehicles (HDVs) used for passengers and goods transportation is a key strategy to reduce the high levels of air pollution in large urban centers. However, the high investment cost of the commercially available electrified HDVs has limited their adoption. We [...] Read more.
Electrification of heavy-duty vehicles (HDVs) used for passengers and goods transportation is a key strategy to reduce the high levels of air pollution in large urban centers. However, the high investment cost of the commercially available electrified HDVs has limited their adoption. We hypothesized that there are applications where the operation with tailored electrified HDVs results in a lower total cost of ownership and lower well-to-wheel emissions of air pollutants, with higher acceleration capacity and energy efficiency than the fossil-fueled counterparts. The road transportation services running on fixed routes with short span distances (<50 km), such as the last mile cargo distribution and the passenger shuttle services, is a clear example with a high possibility of cost reduction through tailored electric HDVs. In this work, we present a methodology to define the most appropriate configuration of the powertrain of an electric vehicle for any given application. As a case study, this work aimed to define an electric powertrain configuration tailored for a university shuttle service application. A multi-objective weighted-sum optimization was performed to define the best geometrical gearbox ratios, energy management strategy, size of the motor, and batteries required. Based on three different driving profiles and five battery technologies, the results showed that, based on a 50 km autonomy, the obtained powertrain configuration satisfies the current vehicle operation with a reduced cost in every driving profile and battery technology compared. Furthermore, by using lithium-based batteries, the vehicle’s acceleration capacity is improved by 33% while reducing energy consumption by 37%, CO2 emissions by 31%, and the total cost of ownership by 29% when compared to the current diesel-fueled buses. Full article
(This article belongs to the Special Issue Fuel Consumption and Emissions from Vehicles)
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12 pages, 606 KiB  
Article
Quantifying the Impact of Traffic on Electric Vehicle Efficiency
by Tim Jonas, Christopher D. Hunter and Gretchen A. Macht
World Electr. Veh. J. 2022, 13(1), 15; https://0-doi-org.brum.beds.ac.uk/10.3390/wevj13010015 - 01 Jan 2022
Cited by 12 | Viewed by 3264
Abstract
While the influence of several factors on battery electric vehicle (BEV) efficiency has been investigated in the past, their impact on traffic is not yet fully understood, especially when driving in a natural environment. This paper investigates the influence of driving in intense [...] Read more.
While the influence of several factors on battery electric vehicle (BEV) efficiency has been investigated in the past, their impact on traffic is not yet fully understood, especially when driving in a natural environment. This paper investigates the influence of driving in intense traffic conditions while considering the ambient temperature and driving behavior on BEV energy efficiency in a field study. A total of 30 BEV inexperienced drivers test drove a 2017 Volkswagen eGolf on a route with various road types in two different traffic intensity scenarios: During morning commute hours with higher traffic congestion and lower congestion hours throughout the middle of the day. Results support the hypothesis that traffic conditions significantly impact the vehicle’s efficiency, with additional consumption of approximately 4–5% in the high traffic scenario. By creating and comparing driving in traffic to an underlying base case scenario, the additional range potential by avoiding traffic for this particular vehicle can be quantified as up to seven miles. New patterns of BEV efficiencies emerged, which can help stakeholders understand how eco-driving can be strategically improved by selecting trip times and routes that avoid high traffic intensity. Full article
(This article belongs to the Special Issue Fuel Consumption and Emissions from Vehicles)
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13 pages, 3409 KiB  
Article
Research on Hydrogen Consumption and Driving Range of Hydrogen Fuel Cell Vehicle under the CLTC-P Condition
by Zhijie Duan, Nan Mei, Lili Feng, Shuguang Yu, Zengyou Jiang, Dongfang Chen, Xiaoming Xu and Jichao Hong
World Electr. Veh. J. 2022, 13(1), 9; https://0-doi-org.brum.beds.ac.uk/10.3390/wevj13010009 - 29 Dec 2021
Cited by 12 | Viewed by 4648
Abstract
Hydrogen consumption and mileage are important economic indicators of fuel cell vehicles. Hydrogen consumption is the fundamental reason that restricts mileage. Since there are few quantitative studies on hydrogen consumption during actual vehicle operation, the high cost of hydrogen consumption in outdoor testing [...] Read more.
Hydrogen consumption and mileage are important economic indicators of fuel cell vehicles. Hydrogen consumption is the fundamental reason that restricts mileage. Since there are few quantitative studies on hydrogen consumption during actual vehicle operation, the high cost of hydrogen consumption in outdoor testing makes it impossible to guarantee the accuracy of the test. Therefore, this study puts forward a test method based on the hydrogen consumption of fuel cell vehicles under CLTC-P operating conditions to test the hydrogen consumption of fuel cell vehicles per 100 km. Finally, the experiment shows that the mileage calculated by hydrogen consumption has a higher consistency with the actual mileage. Based on this hydrogen consumption test method, the hydrogen consumption can be accurately measured, and the test time and cost can be effectively reduced. Full article
(This article belongs to the Special Issue Fuel Consumption and Emissions from Vehicles)
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13 pages, 2689 KiB  
Article
The Effect of Driving Cycle Duration on Its Representativeness
by Michael Giraldo, Luis F. Quirama, José I. Huertas and Juan E. Tibaquirá
World Electr. Veh. J. 2021, 12(4), 212; https://0-doi-org.brum.beds.ac.uk/10.3390/wevj12040212 - 27 Oct 2021
Cited by 4 | Viewed by 1530
Abstract
There is an increasing interest in properly representing local driving patterns. The most frequent alternative to describe driving patterns is through a representative time series of speed, denominated driving cycle (DC). However, the DC duration is an important factor in achieving DC representativeness. [...] Read more.
There is an increasing interest in properly representing local driving patterns. The most frequent alternative to describe driving patterns is through a representative time series of speed, denominated driving cycle (DC). However, the DC duration is an important factor in achieving DC representativeness. Long DCs involve high testing costs, while short DCs tend to increase the uncertainty of the fuel consumption and tailpipe emissions results. There is not a defined methodology to establish the DC duration. This study aims to study the effect of different durations of the DCs on their representativeness. We used data of speed, time, fuel consumption, and emissions obtained by monitoring for two months the regular operation of a fleet of 15 buses running in two flat urban regions with different traffic conditions. Using the micro-trips method, we constructed DCs with a duration of 5, 10, 15, 20, 25, 30, 45, 60, and 120 min for each region. For each duration, we repeated the process 500 times in order to establish the trend and dispersion of the DC characteristic parameters. The results indicate that to obtain driving pattern representativeness, the DCs must last at least 25 min. This duration also guarantees the DC representativeness in terms of energy consumption and tailpipe emissions. Full article
(This article belongs to the Special Issue Fuel Consumption and Emissions from Vehicles)
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21 pages, 2863 KiB  
Article
Real Driving Range in Electric Vehicles: Influence on Fuel Consumption and Carbon Emissions
by Carlos Armenta-Déu and Erwan Cattin
World Electr. Veh. J. 2021, 12(4), 166; https://0-doi-org.brum.beds.ac.uk/10.3390/wevj12040166 - 26 Sep 2021
Cited by 9 | Viewed by 3435
Abstract
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, [...] Read more.
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. Full article
(This article belongs to the Special Issue Fuel Consumption and Emissions from Vehicles)
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19 pages, 7401 KiB  
Article
Fuel Consumption and Emissions Analysis of a Light Vehicle Fuelled with Two Ethanol–Gasoline Blends in Urban Driving Conditions of Lima Metropolitana
by Andrea Rondón, Rolando Aliaga and Julio Cuisano
World Electr. Veh. J. 2021, 12(3), 99; https://0-doi-org.brum.beds.ac.uk/10.3390/wevj12030099 - 15 Jul 2021
Cited by 2 | Viewed by 1846
Abstract
We present a comparative study of fuel consumption, emissions factors, and vehicle-specific power of a light vehicle operating with two gasoline–ethanol blends as fuel: commercial gasohol (E7.8) and an alternative mix with 10% v/v of ethanol (E10). For this purpose, a vehicle in [...] Read more.
We present a comparative study of fuel consumption, emissions factors, and vehicle-specific power of a light vehicle operating with two gasoline–ethanol blends as fuel: commercial gasohol (E7.8) and an alternative mix with 10% v/v of ethanol (E10). For this purpose, a vehicle in the city’s fleet was equipped with a central system of data acquisition, whose main function was to capture second-by-second data of the air intake of the engine, the emissions concentration levels in the exhaust, the distance traveled, vehicle speed, and environmental conditions during testing. The measuring campaign was carried out in the city of Lima Metropolitana. Fuel consumption was calculated indirectly, using air intake measurements. The vehicle’s engine emissions were analysed using the mass flow rates of CO2, CO, HC, and NOx, as well as the vehicle-specific power. The results show that, in traffic conditions, the change in fuels does not affect the consumption. On the other hand, a correlation was found between the vehicle-specific power and the emissions mass flow. During the comparison between fuels, the results showed an increase in the mass flow standard deviation when using E10. Full article
(This article belongs to the Special Issue Fuel Consumption and Emissions from Vehicles)
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