scholarly journals Comparison of Three Methods for Constructing Real Driving Cycles

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 665 ◽  
Author(s):  
José Huertas ◽  
Luis Quirama ◽  
Michael Giraldo ◽  
Jenny Díaz
Keyword(s):  
Fuel Consumption ◽  
Normal Operation ◽  
Mcmc Methods ◽  
Characteristic Parameters ◽  
The Real ◽  
Stochastic Nature ◽  
Driving Patterns ◽  
Driving Cycles ◽  
Relative Differences ◽  
Fuel Consumption And Emissions

This work compares the Micro-trips (MT), Markov chains–Monte Carlo (MCMC) and Fuel-based (FB) methods in their ability of constructing driving cycles (DC) that: (i) describe the real driving patterns of a given region and (ii) reproduce the real fuel consumption and emissions exhibited by the vehicles in that region. To that end, we selected four regions and monitored simultaneously the speed, fuel consumption and emissions of CO2, CO and NOx from a fleet of 15 buses of the same technology during eight months of normal operation. The driving patterns exhibited by drivers in each region were described in terms of 23 characteristic parameters (CPs) such as average speed and average positive kinetic energy. Then, for each region, we constructed their DC using the MT method and evaluated how close it describes the observed driving pattern in each region. We repeated the process using the MCMC and FB methods. Given the stochastic nature of MT and MCMC methods, the DCs obtained changed every time the methods were applied. Hence, we repeated the process of constructing the DCs up to 1000 times and reported their average relative differences and dispersion. We observed that the FB method exhibited the best performance producing DCs that describe the observed driving patterns. In all the regions considered in this study, the DCs produced by this method showed average relative differences smaller than 20% for all the CPs considered. A similar performance was observed for the case of fuel consumption and emission of pollutants.

scholarly journals Driving Cycles Based on Fuel Consumption

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3064 ◽  
Author(s):  
José Huertas ◽  
Michael Giraldo ◽  
Luis Quirama ◽  
Jenny Díaz
Keyword(s):  
Fuel Consumption ◽  
Test Procedure ◽  
Region Of Interest ◽  
Driving Cycle ◽  
Normal Operation ◽  
Characteristic Parameters ◽  
Driving Cycles ◽  
Driving Pattern ◽  
Relative Differences ◽  
Fuel Consumption And Emissions

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.

scholarly journals The Effect of Driving Cycle Duration on its Representativeness

2021 ◽  
Vol 12 (4) ◽  
pp. 212
Author(s):  
Michael Giraldo ◽  
Luis F. Quirama ◽  
José I. Huertas ◽  
Juan E. Tibaquirá
Keyword(s):  
Time Series ◽  
Energy Consumption ◽  
Fuel Consumption ◽  
Driving Cycle ◽  
Cycle Duration ◽  
Characteristic Parameters ◽  
Traffic Conditions ◽  
Driving Patterns ◽  
Driving Pattern ◽  
Fuel Consumption And Emissions

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.

Main characteristic parameters to describe driving patterns and construct driving cycles

2021 ◽  
Vol 97 ◽  
pp. 102959
Author(s):  
Luis F. Quirama ◽  
Michael Giraldo ◽  
José I. Huertas ◽  
Juan E. Tibaquirá ◽  
Daniel Cordero-Moreno
Keyword(s):  
Characteristic Parameters ◽  
Driving Patterns ◽  
Driving Cycles

Comparative study of different types of PHEV optimal control strategies in real-world conditions

2017 ◽  
Vol 232 (12) ◽  
pp. 1597-1610 ◽  
Author(s):  
Morteza Montazeri-Gh ◽  
Zeinab Pourbafarani ◽  
Mehdi Mahmoodi-k
Keyword(s):  
Optimal Control ◽  
Fuel Consumption ◽  
Energy Management ◽  
Real World ◽  
Hybrid Electric Vehicle ◽  
Control Strategies ◽  
Minimum Principle ◽  
Driving Cycles ◽  
Different Types ◽  
Fuel Consumption And Emissions

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.

scholarly journals A Modified ABC-SQP-Based Combined Approach for the Optimization of a Parallel Hybrid Electric Vehicle

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
Keyword(s):  
Boundary Conditions ◽  
Fuel Consumption ◽  
Hybrid Electric Vehicle ◽  
Test Procedure ◽  
Heuristic Approach ◽  
Emissions Control ◽  
Driving Cycles ◽  
Parallel Hybrid ◽  
Hybrid Electric ◽  
Fuel Consumption And Emissions

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.

scholarly journals myBas driving cycle for Kuala Terengganu city

2021 ◽  
Vol 11 (3) ◽  
pp. 2054
Author(s):  
J. S. Norbakyah ◽  
M. I. Nordiyana ◽  
I. N. Anida ◽  
A. F. Ayob ◽  
A. R. Salisa
Keyword(s):  
Fuel Consumption ◽  
Driving Cycle ◽  
Vehicle Speed ◽  
Clustering Method ◽  
Time Data ◽  
Driving Cycles ◽  
Data Points ◽  
Vehicle Simulator ◽  
Trunk Routes ◽  
Fuel Consumption And Emissions

Driving cycles are series of data points that represent vehicle speed versus time sequenced profile developed for specific road, route, city or certain location. It is widely utilized in the application of vehicle manufacturers, environmentalists and traffic engineers. Since the vehicles are one of the higher air pollution sources, driving cycle is needed to evaluate the fuel consumption and exhaust emissions. The main objectives in this study are to develop and characterize the driving cycle for myBAS in Kuala Terengganu city using established k-means clustering method and to analyse the fuel consumption and emissions using advanced vehicle simulator (ADVISOR). Operation of myBAS offers 7 trunk routes and one feeder route. The research covered on two operation routes of myBAS which is Kuala Terengganu city-feeder and from Kuala Terengganu to Jeti Merang where the speed-time data is collected using on-board measurement method. In general, driving cycle is made up of a few micro-trips, defined as the trip made between two idling periods. These micro-trips cluster by using the k-means clustering method and matrix laboratory software (MATLAB) is used in developing myBAS driving cycle. Typically, developing the driving cycle based on the real-world in resulting improved the fuel economy and emissions of myBAS.

scholarly journals Taxi fuel consumption and emissions estimation model based on the reconstruction of driving trajectory

2017 ◽  
Vol 9 (7) ◽  
pp. 168781401770870 ◽  
Author(s):  
Jiancheng Weng ◽  
Quan Liang ◽  
Guoliang Qiao ◽  
Zhihong Chen ◽  
Jian Rong
Keyword(s):  
Fuel Consumption ◽  
Urban Areas ◽  
Operating Conditions ◽  
Bench Test ◽  
Trajectory Data ◽  
Estimation Model ◽  
Driving Cycles ◽  
Gps Trajectory Data ◽  
Gps Trajectory ◽  
Fuel Consumption And Emissions

Monitoring operating vehicles’ fuel consumption and emissions is necessity for evaluating fuel saving and emissions reduction. Taxis are one of the key objects needed energy consumption monitoring in passenger transport system. However, the traditional data collection methods for vehicle fuel consumption and emissions had high cost and inconvenient maintenance. This study aims at proposing an approach to estimate taxi fuel consumption and emissions based on the global position system (GPS) trajectory data. The bench test experiment was first conducted with three different driving cycles: cruising, acceleration and deceleration, and the composite driving cycle including these two. Then, models to calculate fuel consumption and emission based on the driving trajectory reconstruction were proposed. Therefore, the taxis’ fuel consumption and emissions could be got through GPS trajectory data corresponding to these three driving cycles. The model accuracy were verified that fuel consumption (92%) and CO2 emission (95%) fit the measurements much better than CO, NOx, and HC emission models (60%–70%). Furthermore, taking fuel consumption per 100 km as dependent variable, the relative errors between the model’s outputs and field measurements were 1.9% in urban areas and 11.2% in comprehensive operating conditions (i.e. both urban and suburb areas).

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