Evaluation of a Real-World Driving Cycle and its Impacts on Fuel Consumption and Emissions

Author(s):  
Vinícius Rückert Roso ◽  
Mario Eduardo Santos Martins
Author(s):  
Amir Poursamad

This paper presents gain scheduling of control strategy for parallel hybrid electric vehicles based on the traffic condition. Electric assist control strategy (EACS) is employed with different parameters for different traffic conditions. The parameters of the EACS are optimized and scheduled for different traffic conditions of TEH-CAR driving cycle. TEH-CAR is a driving cycle which is developed based on the experimental data collected from the real traffic condition in the city of Tehran. The objective of the optimization is to minimize the fuel consumption and emissions over the driving cycle, while enhancing or maintaining the driving performance characteristics of the vehicle. Genetic algorithm (GA) is used to solve the optimization problem and the constraints are handled by using penalty functions. The results from the computer simulation show the effectiveness of the approach and reduction in fuel consumption and emissions, while ensuring that the vehicle performance is not sacrificed.


1986 ◽  
Vol 20 (6) ◽  
pp. 447-462 ◽  
Author(s):  
T.J. Lyons ◽  
J.R. Kenworthy ◽  
P.I. Austin ◽  
P.W.G. Newman

Author(s):  
Konstantin Weller ◽  
Silke Lipp ◽  
Martin Röck ◽  
Claus Matzer ◽  
Andreas Bittermann ◽  
...  

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3064 ◽  
Author(s):  
José Huertas ◽  
Michael Giraldo ◽  
Luis Quirama ◽  
Jenny Díaz

Type-approval driving cycles currently available, such as the Federal Test Procedure (FTP) and the Worldwide harmonized Light vehicles Test Cycle (WLTC), cannot be used to estimate real fuel consumption nor emissions from vehicles in a region of interest because they do not describe its local driving pattern. We defined a driving cycle (DC) as the time series of speeds that when reproduced by a vehicle, the resulting fuel consumption and emissions are similar to the average fuel consumption and emissions of all vehicles of the same technology driven in that region. We also declared that the driving pattern can be described by a set of characteristic parameters (CPs) such as mean speed, positive kinetic energy and percentage of idling time. Then, we proposed a method to construct those local DC that use fuel consumption as criterion. We hypothesized that by using this criterion, the resulting DC describes, implicitly, the driving pattern in that region. Aiming to demonstrate this hypothesis, we monitored the location, speed, altitude, and fuel consumption of a fleet of 15 vehicles of similar technology, during 8 months of normal operation, in four regions with diverse topography, traveling on roads with diverse level of service. In every region, we considered 1000 instances of samples made of m trips, where m varied from 4 to 40. We found that the CPs of the local driving cycle constructed using the fuel-based method exhibit small relative differences (<15%) with respect to the CPs that describe the driving patterns in that region. This result demonstrates the hypothesis that using the fuel based method the resulting local DC exhibits CPs similar to the CPs that describe the driving pattern of the region under study.


Author(s):  
Morteza Montazeri-Gh ◽  
Zeinab Pourbafarani ◽  
Mehdi Mahmoodi-k

With increasingly serious global environmental issues and energy shortages, energy conservation in transportation has become a significant, fundamental objective. The objective of the current research is to investigate the impacts of different types of optimal control strategies on the plug-in hybrid electric vehicle (PHEV) performance in real-world conditions. The optimal control strategies according to Pontryagin’s minimum principle (PMP) and optimized rule-based approaches are developed for the optimal pattern of a PHEV energy management system to reduce fuel consumption and emissions simultaneously, without sacrificing the vehicle performance. For this purpose, first, using test data for engine and battery, an experimental map-based model of the parallel PHEV is developed. Then, the powertrain components are sized by using a genetic algorithm (GA), over the real-world driving cycles. Subsequently, GA-fuzzy and PMP controllers are developed for energy management of the PHEV. Simulation results show the significant effectiveness of the proposed optimal control approaches on the fuel consumption and emissions reduction in various driving cycles. The convergence speed and global searching ability of PMP are significantly better than GA-fuzzy for the design of control strategy parameters. The sensitivity of battery initial state of charge, driving cycle, and road grade are analyzed on vehicle emissions and fuel consumption. The findings reveal that PMP could be adapted to different conditions by tuning co-state in a short time. This advantage makes it more adaptable to variation of real-world conditions. On the other hand, a fuzzy controller needs less computational effort and so is more appropriate for a certain condition.


Author(s):  
Merve Tekin ◽  
M. İhsan Karamangil

Greenhouse gas (GHG) emissions released into the atmosphere cause climate change and air pollution. One of the main causes of GHG emissions is the transportation sector. The use of fossil fuels in internal combustion engine vehicles leads to the release of these harmful gases. For this reason, since 1992, several standards have been introduced to limit emissions from vehicles. Technologies such as reducing engine sizes, advanced compression-ignition or start/stop, and fuel cut-off have been developed to reduce fuel consumption and emissions. In this study, the contribution of deceleration fuel cut-off and start/stop technologies to fuel economy has been examined considering the New European Driving Cycle. Therefore, the fuel consumption values were calculated by creating a longitudinal vehicle model for a light commercial vehicle with a diesel engine. At the end of the study, by using the two strategies together, fuel economies of 17.5% in the urban driving cycle, 3.7% in the extra-urban cycle, and 10% in total were achieved. CO2 emissions decreased in parallel with fuel consumption, by 10.1% in total.


2020 ◽  
Vol 13 (4) ◽  
pp. 102-113
Author(s):  
Loay M. Mubarak ◽  
Ahmed Al-Samari

This manuscript instrumented two light-duty passenger cars to construct real-world driving cycles for the Baghdad-Basrah highway road in Iraq using a data logger. The recorded data is conducted to obtain typical speed profiles for each vehicle. Each of the recruited vehicles is modelized using Advanced Vehicle Simulator and conducted on the associated created driving cycle to investigate fuel economy and analyze performance. Moreover, to inspect the influence of driving behavior on fuel consumption and emissions, the simulation process is re-implemented by substituting the conducted real-world driving cycle. The analyses are done for the first and second stages of simulation predictions to explore the fuel-penalty of aggressive driving behavior. The analysis for substitution predictions showed that fuel consumption could be reduced by 12.8% due to conducting vehicle under the more consistent real-world driving cycle. However, conducting vehicle under the more aggressive one would increase fuel consumption by 14.6%. The associated emissions change prediction due to the substitution is also achieved and presented.


Sign in / Sign up

Export Citation Format

Share Document