Estimating Effect of Vehicle Characteristics on Fuel Consumption

Biggs, D C; Akçelik, R

doi:10.1061/(asce)0733-947x(1987)113:1(101)

Cited by 14 publications

(5 citation statements)

References 2 publications

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“…About 85% of the vehicles were operated in an acceleration mode between 1 and 6 km h -1 s -1 , less than 7% were in a deceleration mode, and less than 7% were in cruising mode. A 0.36 km h -1 s -1 acceleration rate has been used extensively in defining driving modes ( − ).…”

Section: Resultsmentioning

confidence: 99%

Remote Sensing Study of Emissions from Motor Vehicles in the Metropolitan Area of Mexico City

Schifter

Díaz

Duran

et al. 2002

Environ. Sci. Technol.

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Remote sensing was employed for the first time to measure nitric oxide (NO) levels of on-road light-duty motor vehicles of the Metropolitan Area of Mexico City (MAMC). The sensor placed at 12 different sites also measured the concentration of CO2, CO, and total hydrocarbons (THC) in the exhaust emissions. A database was compiled containing 122 800 readings, of which 84 650 (69%) records were valid emissions measurements. CO, HC, and NO valid readings were 68.9, 63.4, and 62.9%, respectively, of the total attempted measurements. Furthermore, 42 822 vehicles were number-plate-matched to model year with the information provided by the Inspection/Maintenance Program. The mean emissions of total valid readings for CO, HC, and NO were determined to be 1.31 vol %, 440 ppm (propane), and 914 ppm, respectively. In 1991 and 1994, remote sensing measurements of CO and HC tailpipe emissions were performed in the MAMC in five different locations (30 000 valid readings). Large drops in both pollutants were observed for the intervening years, but sufficient vehicle information was not available at that time to fully explain the observed trends. Compared with those reports, our results point out to a steady decrease in CO and HC exhaust emissions with vehicle model year. The fleet emissions measured exhibit a gamma-distribution, with 10% of the most polluting fleet studied being responsible for 45%, 25%, and 29% of the CO, HC, and NO emissions, respectively. NO emissions in taxis are the highest among the vintage of vehicles, a matter of concern since according to the distance traveled per year, they represent 22% of the total activity in the MAMC.

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Section: Resultsmentioning

confidence: 99%

Remote Sensing Study of Emissions from Motor Vehicles in the Metropolitan Area of Mexico City

Schifter

Díaz

Duran

et al. 2002

Environ. Sci. Technol.

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“…In all these factors, vehicle acceleration and velocity are the main contributors. Following the idea of [24,25], the following fuel consumption model is adopted, which has been studied in [26].…”

Section: Evaluation Indicatorsmentioning

confidence: 99%

Evaluating Efficiency of Connected and Autonomous Vehicles with Different Communication Topologies

Liu,

Gong,

Chen

et al. 2023

Electronics

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Connected and autonomous vehicles (CAV) employ vehicle-to-vehicle communications to safely drive in a platoon with short inter-vehicle distances, which can improve traffic throughput and reduce fuel consumption. With the development of wireless communication technology, more and more information can be used for vehicle controllers. However, is more information better? In this paper, a fuel economy-based performance evaluation index is established for evaluating the efficiency of CAVs driving in a platoon with different communication topologies. Four typical communication topologies that describe the CAV with different amounts of information are studied by the linear controller. The differential evolution algorithm is used to solve the parameters. Due to the increase in information, more control parameters need to be computed, and it is hard to find an optimal solution. So, the simulation results show that CAV with more information did not obtain a better fuel economy.

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“…Since eco-driving for internal combustion engine (ICE) vehicles has been extensively studied in the literature, most of the available models for energy consumption estimation are focused on fuel consumption. In [25], the fuel consumption rate model introduced in [26] is employed that estimates fuel usage based on driving inputs. Xiang et al employed a quasi-static engine model based on power and used the vehicle dynamics and model parameters obtained through the curve fitting of mapped engine data [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Pro-Active Eco-Driving Control Framework for Energy-Efficient Autonomous Electric Mobility

Hesami,

Vafaeipour,

De Cauwer

et al. 2023

Energies

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As autonomous vehicle technology advances, the development of energy-efficient control methodologies emerges as a critical area in the literature. This includes the behavior control of vehicles near signalized intersections, which still needs comprehensive exploration. Through connectivity, the adoption of promising eco-driving approaches can manage a vehicle’s speed profile to improve energy consumption. This study focuses on controlling the speed of an autonomous electric vehicle (AEV) both up and downstream of a signalized intersection in the presence of preceding vehicles. In order to achieve this, a dynamic pro-active predictive cruise control eco-driving (eco-PPCC) framework is developed that, instead of merely reacting to the preceding vehicle’s speed changes, uses the preceding vehicle’s upcoming data to actively adjust and optimize the speed profile of the AEV. The proposed algorithm is compared to the conventional Gipps and eco-PCC models for benchmarking and performance analysis through numerous scenarios. Additionally, real-world measurements are performed and taken to consider practical use cases. The results demonstrate that when compared to the two baseline methods, the proposed framework can add significant value to reducing energy consumption, preventing unnecessary stops at intersections, and improving travel time.

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Estimating Effect of Vehicle Characteristics on Fuel Consumption

Cited by 14 publications

References 2 publications

Remote Sensing Study of Emissions from Motor Vehicles in the Metropolitan Area of Mexico City

Remote Sensing Study of Emissions from Motor Vehicles in the Metropolitan Area of Mexico City

Evaluating Efficiency of Connected and Autonomous Vehicles with Different Communication Topologies

Dynamic Pro-Active Eco-Driving Control Framework for Energy-Efficient Autonomous Electric Mobility

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