Research Project

Estimation of Emissions and Fuel Consumption of in-use Vehicles in Different Driving Conditions

Geetam Tiwari, S.R.Kale, Kalaga Ramchandra Rao and Dinesh Mohan

Project Details

Objective:

The project involves determination of driving cycle of different types of vehicles in varying traffic mix conditions representative of urban traffic in India. The driving cycles developed for car has been simulated on a chassis dynamometer and fuel consumption and emissions have been measured. Driving cycle developed for bus, three wheelers and two wheelers are proposed to be simulated in a similar experiment. These data will be used to estimate fuel savings in alternate road plans, such as, creating exclusive bus lanes, bicycle lanes, flyovers, alternate signal cycle etc. because the impact of alternate strategies are reflected in the driving cycle. This research is important because a large number of cities are planning strategies to improve traffic flow. The broad objectives of the project are:
a.Estimating the current status of Emissions and Fuel Consumption of in –use Vehicles in Different Driving Conditions
b.Impact of different traffic management strategies on vehicular emissions and fuel economy

Car, two wheeler, three wheeler and bus were selected to represent different vehicles types and signal free corridor, signalized corridor and exclusive bus lanes(BRT) were selected to represent different traffic management strategies. 

The specific objectives of the project are as follows: 

a)Development of appropriate methodology for construction of driving cycles. 
b)Construction of driving cycles of different types of vehicles in varying traffic mix conditions representative of urban traffic in Delhi. 
c)Construction of driving cycles of different types of vehicles for Delhi city. 
d)Estimation of emission and fuel consumption by different vehicles under different driving conditions.

2.    Methodology
2.1  Methodology for the development of Driving Cycle
2.1.1  Methodology for construction of Microtrip
Drive-cycle is synthesized by dividing speed time traces into smaller microtrips or trip snippets. While a microtrip has ends defined by zero speeds, a trip snippet can be bounded by even traffic facilities/conditions. Microtrip is an excursion between two successive time points at which the vehicle is stopped (Sharma, Matthew 2008 et al).
Microtrips were generated using a computer program from the base data collected for a particular road and vehicle type. Microtrips were defined to have a minimum duration of 2.5 seconds and a moving time of at least 1 second. This was to ensure that the readings due to vibrations at traffic signals were neglected.

2.1.2  Methodology for Construction of SAPD/SAFD:
Data is effectively analysed on dividing it into speed acceleration ranges in the matrix forms. The process converts the data into discrete steps of speed and acceleration and logs the time spent under a particular acceleration and speed. This matrix when converted to the normalized form shows the percentage of total time spent in with a given speed range and acceleration.
A normalised SAPD matrix using the complete base data for a particular mode is constructed. One of our final objectives is that our driving cycle SAPD matrix, which would be in the range of 1200-1800 seconds, must closely resemble the base SAPD matrix, which would contain data of around 12 hours of driving for each mode on a particular type of road.       
Base data was divided into numerous microtrips. 
We tried three different methodologies, all of which are based on the principle that microtrips having similar characteristics and recurring repeatedly in the base data need to be given sufficient weightage in the final driving cycle. These three methodologies are:
1)    Sorting Algorithm using Group Representative
2)    Sorting Algorithm using Group head
3)    K-means Clustering Methodology using SPSS software      
                                  
2.2 Methodology for simulation of the driving cycles on chassis dynamometer for emission and fuel consumption testing: 
The task includes (a) record driving pattern with different traffic conditions for different modes of vehicles (Car, 2-wheeler, 3-wheeler, Bus), generates of representative driving cycles for each followed by (b) simulation of these driving cycles on chassis dynamometer for emission and fuel consumption testing, in iCAT Manesar. 


3.Conclusion 
The methodology was based on the five important parameters of time-speed profile; the percentage acceleration, deceleration, idle, cruise, and the average speed. The uniqueness of the methodology was that the clustering of the micro-trips using k-means clustering algorithm based on five target parameters. Therefore, this approach expected to be a better representation of heterogeneous traffic behaviour. The driving cycle for various traffic management conditions for Delhi city is constructed using the proposed methodology. In this study we compared driving cycles mode wise and route wise to understand the variation in driving cycle when different vehicles operate under different traffic operating conditions. Chassis dynamometer tests of DC obtained for car on three different routes have given fuel consumption and emissions levels of CO, HC,NOx and  CO2.   Emission and fuel consumption measurements showed significant variations between MIDC and the IIT driving cycles, compared to  MIDC, IIT-1 (Ring Road) is 10.83 % better, and Khel Gaon is 6.48% worse. Compared to MIDC, HC emission on Ring Road 33.30 % lower, Khel Gaon 33.30 % higher.  Compared to MIDC, CO emission on Ring Road is 25 % greater and Khel Gaon is 91.6 % higher. It is therefore evident that the MIDC is not suitable for the emission and fuel consumption estimation for every type traffic plan. 

Future Work
1. Two wheeler, three wheeler and bus can be tested on chassis dynamometer for different driving cycles documented in this study. Fuel consumption and emissions of PM, HC, NOx, CO and CO2 can be measured. 
2. From the emission data we can observe the impact of different traffic management strategies on vehicular emissions and fuel economy. 
3. The study can be extended to other cities to understand the impact of prevailing driving conditions on fuel consumption and emissions in other cities
 

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