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.

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.

Traffic Condition-Based Gain Scheduling of Electric Assist Control Strategy for Parallel Hybrid Electric Vehicles

2007 ◽  
Author(s):  
Amir Poursamad
Keyword(s):  
Fuel Consumption ◽  
Electric Vehicles ◽  
Control Strategy ◽  
Hybrid Electric Vehicles ◽  
Gain Scheduling ◽  
Driving Cycle ◽  
Traffic Condition ◽  
Traffic Conditions ◽  
Parallel Hybrid ◽  
Fuel Consumption And Emissions

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.

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.

Analysis of energy consumption and emission of the heterogeneous traffic flow consisting of traditional vehicles and electric vehicles

2017 ◽  
Vol 31 (34) ◽  
pp. 1750324 ◽  
Author(s):  
Hong Xiao ◽  
Hai-Jun Huang ◽  
Tie-Qiao Tang
Keyword(s):  
Energy Consumption ◽  
Fuel Consumption ◽  
Electric Vehicle ◽  
Traffic Flow ◽  
Small Perturbation ◽  
Electric Vehicles ◽  
Electricity Consumption ◽  
Heterogeneous Traffic ◽  
Rarefaction Waves ◽  
Fuel Consumption And Emissions

Electric vehicle (EV) has become a potential traffic tool, which has attracted researchers to explore various traffic phenomena caused by EV (e.g. congestion, electricity consumption, etc.). In this paper, we study the energy consumption (including the fuel consumption and the electricity consumption) and emissions of heterogeneous traffic flow (that consists of the traditional vehicle (TV) and EV) under three traffic situations (i.e. uniform flow, shock and rarefaction waves, and a small perturbation) from the perspective of macro traffic flow. The numerical results show that the proportion of electric vehicular flow has great effects on the TV’s fuel consumption and emissions and the EV’s electricity consumption, i.e. the fuel consumption and emissions decrease while the electricity consumption increases with the increase of the proportion of electric vehicular flow. The results can help us better understand the energy consumption and emissions of the heterogeneous traffic flow consisting of TV and EV.

The development of a driving cycle for fuel consumption and emissions evaluation

1986 ◽  
Vol 20 (6) ◽  
pp. 447-462 ◽  
Author(s):  
T.J. Lyons ◽  
J.R. Kenworthy ◽  
P.I. Austin ◽  
P.W.G. Newman
Keyword(s):  
Fuel Consumption ◽  
Driving Cycle ◽  
Fuel Consumption And Emissions

scholarly journals Idling Fuel Consumption and Emissions of Public Vehicles run by Bhaktapur Minibus Sewa Samiti and its Reduction Possibilities

2019 ◽  
Vol 15 (2) ◽  
pp. 155-164
Author(s):  
Ravi Suwal ◽  
Bhakta Bahadur Ale
Keyword(s):  
Air Pollution ◽  
Energy Consumption ◽  
Fuel Consumption ◽  
Ghg Emissions ◽  
Research Paper ◽  
Kathmandu Valley ◽  
Low Carbon ◽  
Passenger Vehicle ◽  
Financial Loss ◽  
Fuel Consumption And Emissions

This research paper is carried out to analyze the idling fuel consumption and emissions of public vehicles of Bhaktapur Minibus Sewa Samiti (BMSS) and quantify the reduction possibilities of idling emission. In order to reduce the air pollution in Kathmandu Valley, low carbon technology like idling reduction has become necessity. Idling of passenger vehicle is mainly due to waiting for passengers at the bus stops and stopped by traffic. Idling contributes significantly to energy consumption without any useful output. Many researchers found that idling is harmful to engine and increases maintenance. For that, idling data of different routes were collected and fuel consumption were tested using pseudo method. These data were used to calculate the total idling time, fuel consumption, GHG emissions and financial loss in the routes. Idling time was as high as an hour in a trip. About 18% to 32 % of idling time was due to traffic only and the traffic were high at day (11 am – 5 pm) and evening. The idling fuel consumption and GHG emissions were 117.56 ml to 181.16 ml and 0.31 kg to 0.478 kg per trip in the routes of BMSS respectively. Using 3 minutes of limit, the fuel consumption and emissions could be reduced by 22.48 ml to 59.45 ml and 0.059 kg to 0.157 kg per trip respectively. Rs. 943,783 out of Rs. 3,214,872 of financial loss could be saved in selected routes annually.

A driving-cycle predictive control approach for energy consumption optimization of hybrid electric vehicle

2021 ◽  
pp. 095440702110674
Author(s):  
Bingjiao Liu ◽  
Qin Shi ◽  
Zejia He ◽  
Yujiang Wei ◽  
Duoyang Qiu ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Fuel Consumption ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Driving Cycle ◽  
Electric System ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Hybrid Electric

This paper proposes an adaptive control strategy of fuel consumption optimization for hybrid electric vehicles (HEVs). The strategy combines a moving-horizon-based nonlinear autoregressive (NAR) algorithm, a backpropagation (BP) neural network algorithm, and an equivalent consumption minimization strategy (ECMS) method to reduce energy consumption. The moving-horizon-based NAR algorithm is applied to predict the short future driving cycle. The BP neural network algorithm is employed to recognize the driving cycle types, which provides the basis for the adaptive ECMS. Based on the abovementioned approach, the power split of the fuel and electric system is determined in advance, and the optimal control of energy efficiency is achieved. A driving experiment platform is established, taking a synthetic driving cycle composed of several standard driving cycles as the target cycle, and the control strategy is tested by the driver’s real operation. The results indicate that, compared with the basic ECMS, the A-ECMS with moving-horizon-based driving cycle prediction and recognition has better SOC (state of charge) retention and reduces the fuel consumption of the engine by 3.31%, the equivalent fuel consumption of the electric system by 0.9 L/100 km and the total energy consumption by 1 L/100 km. Adaptive ECMS based on driving cycle prediction and recognition is an effective method for the energy management of HEVs.

Investigation of the Contribution of Deceleration Fuel Cut-off and Start/Stop Technologies to Fuel Economy by Considering New European Driving Cycle

2022 ◽  
pp. 036119812110669
Author(s):  
Merve Tekin ◽  
M. İhsan Karamangil
Keyword(s):  
Fuel Consumption ◽  
Fuel Economy ◽  
Fossil Fuels ◽  
Combustion Engine ◽  
Ghg Emissions ◽  
Driving Cycle ◽  
Consumption Values ◽  
Transportation Sector ◽  
Light Commercial Vehicle ◽  
Fuel Consumption And Emissions

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.

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