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
Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 665 ◽  
Author(s):  
José Huertas ◽  
Luis Quirama ◽  
Michael Giraldo ◽  
Jenny Díaz

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.


2020 ◽  
Vol 82 ◽  
pp. 102294 ◽  
Author(s):  
Luis F. Quirama ◽  
Michael Giraldo ◽  
José I. Huertas ◽  
Miguel Jaller

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):  
A Esteves-Booth ◽  
T Muneer ◽  
J Kubie ◽  
H Kirby

This article reviews the latest and relevant work on both vehicular emission models and driving cycles. The three main types of emission models, namely emission factor models, average speed models and modal models, are covered. Each project is analysed regarding its characteristic parameters, such as data collection technique, methodology, statistical analysis and pollutants covered, where appropriate. Other parameters were taken into account, such as the project objectives, results and relevance regarding the wider spectrum of the road traffic situation.


2008 ◽  
Vol 13 (5) ◽  
pp. 289-297 ◽  
Author(s):  
Qidong Wang ◽  
Hong Huo ◽  
Kebin He ◽  
Zhiliang Yao ◽  
Qiang Zhang

2012 ◽  
Vol 253-255 ◽  
pp. 2113-2116
Author(s):  
Shi Jing Xu

In order to establish a real-time hybrid electric vehicle energy management strategy, a LVQ neural network based driving cycles recognizer is established. Selecting 6 typical driving cycles, and the characteristic parameters of the typical driving cycles are extracted and is used to train the LVQ neural network by LVQ2 algorithm. The trained LVQ neural network is employed to recognize the other driving cycle. The result shows that the recognition result reflects the character of the real driving cycle very well.


2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Xuan Zhao ◽  
Qiang Yu ◽  
Jian Ma ◽  
Yan Wu ◽  
Man Yu ◽  
...  

This paper proposes a scientific and systematic methodology for the development of a representative electric vehicle (EV) urban driving cycle. The methodology mainly includes three tasks: test route selection and data collection, data processing, and driving cycle construction. A test route is designed according to the overall topological structure of the urban roads and traffic flow survey results. The driving pattern data are collected using a hybrid method of on-board measurement method and chase car method. Principal component analysis (PCA) is used to reduce the dimensionality of the characteristic parameters. The driving segments are classified using a hybrid k-means and support vector machine (SVM) clustering algorithm. Scientific assessment criteria are studied to select the most representative driving cycle from multiple candidate driving cycles. Finally, the characteristic parameters of the Xi’an EV urban driving cycle, international standard driving cycles, and other city driving cycles are compared and analyzed. The results indicate that the Xi’an EV urban driving cycle reflects more aggressive driving characteristics than the other cycles.


2021 ◽  
Vol 12 (4) ◽  
pp. 212
Author(s):  
Michael Giraldo ◽  
Luis F. Quirama ◽  
José I. Huertas ◽  
Juan E. Tibaquirá

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.


2012 ◽  
Vol 462 ◽  
pp. 271-276 ◽  
Author(s):  
Nan Zhou ◽  
Qing Nian Wang ◽  
Peng Yu Wang

The study on standard driving cycles is of great significance on design and control algorithms for HEV. This article applies the theory of uniform design on the driving cycle parameters research. According to the uniform design scheme for driving cycle parameters, not only greatly reduce the number of simulation experiments, by analyzing the experiment results, but also efficiently and intuitively identify the primary and secondary factors of driving cycle experiment parameters. Through the relevant energy management algorithm, simulation proves that the driving cycle parameters research on HEV is significance to improve the fuel economy.


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