Simulation of Fuel Consumption and Emissions for Passenger Cars and Urban Buses in Real-World Driving Cycles

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


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.


2021 ◽  
pp. 146808742110387
Author(s):  
Stylianos Doulgeris ◽  
Zisimos Toumasatos ◽  
Maria Vittoria Prati ◽  
Carlo Beatrice ◽  
Zissis Samaras

Vehicles’ powertrain electrification is one of the key measures adopted by manufacturers in order to develop low emissions vehicles and reduce the CO2 emissions from passenger cars. High complexity of electrified powertrains increases the demand of cost-effective tools that can be used during the design of such powertrain architectures. Objective of the study is the proposal of a series of real-world velocity profiles that can be used during virtual design. To that aim, using three state of the art plug-in hybrid vehicles, a combined experimental, and simulation approach is followed to derive generic real-world cycles that can be used for the evaluation of the overall energy efficiency of electrified powertrains. The vehicles were tested under standard real driving emissions routes, real-world routes with reversed order (compared to a standard real driving emissions route) of urban, rural, motorway, and routes with high slope variation. To enhance the experimental activities, additional virtual mission profiles simulated using vehicle simulation models. Outcome of the study consists of specific driving cycles, designed based on standard real-world route, and a methodology for real-world data analysis and evaluation, along with the results from the assessment of the impact of different operational parameters on the total electrified powertrain.


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.


Fuel ◽  
2009 ◽  
Vol 88 (9) ◽  
pp. 1608-1617 ◽  
Author(s):  
Georgios Fontaras ◽  
Georgios Karavalakis ◽  
Marina Kousoulidou ◽  
Theodoros Tzamkiozis ◽  
Leonidas Ntziachristos ◽  
...  

2019 ◽  
Vol 11 (23) ◽  
pp. 6819
Author(s):  
Sangjun Park ◽  
Kyoungho Ahn ◽  
Hesham A. Rakha

Traffic signal priority is an operational technique employed for the smooth progression of a specific type of vehicle at signalized intersections. Transit signal priority is the most common type of traffic signal priority, and it has been researched extensively. Conversely, the impacts of freight signal priority (FSP) has not been widely investigated. Hence, this study aims to evaluate the energy and environmental impacts of FSP under connected vehicle environment by utilizing a simulation testbed developed for the multi-modal intelligent transportation signal system. The simulation platform consists of VISSIM microscopic traffic simulation software, a signal request messages distributor program, an RSE module, and an Econolite ASC/3 traffic controller emulator. The MOVES model was employed to estimate the vehicle fuel consumption and emissions. The simulation study revealed that the implementation of FSP significantly reduced the fuel consumption and emissions of connected trucks and general passenger cars; the network-wide fuel consumption was reduced by 11.8%, and the CO2, HC, CO, and NOX emissions by 11.8%, 28.3%, 24.8%, and 25.9%, respectively. However, the fuel consumption and emissions of the side-street vehicles increased substantially due to the reduced green signal times on the side streets, especially in the high truck composition scenario.


Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4529
Author(s):  
S. N. Shivappriya ◽  
S. Karthikeyan ◽  
S. Prabu ◽  
R. Pérez de Pérez de Prado ◽  
B. D. Parameshachari

In this paper, an improved fuel consumption and emissions control strategy based on a mathematical and heuristic approach is presented to optimize Parallel Hybrid Electric Vehicles (HEVs). The well-known Sequential Quadratic Programming mathematical method (SQP-Hessian approach) presents some limitations to achieve fuel consumption and emissions control optimization, as it is not able to find the global minimum, and it generally shows efficient results in local exploitation searches. The usage of a combined Modified Artificial Bee Colony algorithm (MABC) with the SQP approach is proposed in this work to obtain better optimal solutions and overcome these limitations. The optimization is performed with boundary conditions, considering that the optimized vehicle performance has to satisfy Partnership for a New Generation of Vehicles (PNGV) constraints. The weighting factor of the vehicle’s performance parameters in the objective function is varied, and optimization is carried out for two different driving cycles, namely Federal Test Procedure (FTP) and Economic commission Europe—Extra Urban Driving Cycle (ECE-EUDC), using the MABC and MABC with SQP approaches. The MABC with SQP approach shows better performance in terms of fuel consumption and emissions than the pure heuristic approach for the considered vehicle with similar boundary conditions. Moreover, it does not present significant penalties for final battery charging and it offers an optimized size of the key vehicle’s components for different driving cycles.


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