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Driving cycles are extremely important in establishing compliance of emission control
norms for vehicles. Internationally, it has been observed that there are considerable
differences between the driving conditions of type-approval cycles and those of real-world
vehicle use. This leads to real-world emissions being higher than expected, and
hence,...
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The project team selected eight typical buses (CNG, LNG, gasoline- electric hybrid, gas-electricity hybrid) at different emission stages in the built-up area of Tangshan City to test the emission of gaseous pollutants and particulate matter under actual road conditions, systematically evaluated, analyzed and compared the test results, and establish...
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... The Pearson correlation between the number of sharp acceleration events and the number of PN spikes was 0.45. This finding is consistent with previous on-road studies that found a high correlation between the high particle concentrations and accelerations (Gallus et al., 2017b(Gallus et al., , 2016Sharma et al., 2013). ...
This study explores the generation of ultrafine particle emissions, measured in particle number (PN), based on a portable emissions measurement system (PEMS) in the City of Toronto between October and December 2019. Two driving routes were designated to include busy arterial roads and highways. All measurements were conducted between 10 am and 4 pm. Altogether, emissions from 31 drives were collected, leading to approximately 200,000 seconds of data. A spike detection algorithm was employed to isolate PN spikes in time series data. A sensitivity analysis was conducted to identify the most optimum method for spike detection. The results indicate that the average emission rate during a PN spike is approximately 8 times the emission rate along the rest of the drive. In each test trip, about 25% of the duration was attributed to spike events, contributing 75% of total PN emissions. A Pearson correlation of 0.45 was estimated between the number of PN spikes and the number of sharp accelerations (above 8.5 km/h/s). The Pearson correlation between the occurrence of high engine torque (above 65.0 Nm) and the number of PN spikes was estimated at 0.80. The number of PN spikes was highest on arterial roads where the vehicle speed was relatively low, but with high variability, and including a high number of sharp accelerations. This pattern of UFP emissions leads to a spatial distribution of high UFP concentrations along arterial roads in the inner city core.
... The Pearson correlation between the number of sharp acceleration events and the number of PN spikes was 0.45. This finding is consistent with previous on-road studies that found a high correlation between the high particle concentrations and accelerations (Gallus et al., 2017b(Gallus et al., , 2016Sharma et al., 2013). ...
This study explores the generation of ultrafine particle emissions, measured in particle number (PN), based on a portable emissions measurement system (PEMS) in the City of Toronto between October and December 2019. Two driving routes were designed to include busy arterial roads and highways. All measurements were conducted between 10 a.m. and 4 p.m. Altogether, emissions from 31 drives were collected, leading to approximately 200,000 s of data. A spike detection algorithm was employed to isolate PN spikes in time series data. A sensitivity analysis was also conducted to identify the most optimum method for spike detection. The results indicate that the average emission rate during a PN spike is approximately 8 times the emission rate along the rest of the drive. In each test trip, about 25% of the duration was attributed to spike events, contributing 75% of total PN emissions. A Pearson correlation of 0.45 was estimated between the number of PN spikes and the number of sharp accelerations (above 8.5 km/h/s). The Pearson correlation between the occurrence of high engine torque (above 65.0 Nm) and the number of PN spikes was estimated at 0.80. The number of PN spikes was highest on arterial roads where the vehicle speed was relatively low, but with high variability, and including a high number of sharp accelerations. This pattern of UFP emissions leads to high UFP concentrations along arterial roads in the inner city core.
... The Pearson correlation between the number of sharp acceleration events and the number of PN spikes was 0.45. This finding is consistent with previous on-road studies that found a high correlation between the high particle concentrations and accelerations (Gallus et al., 2017b(Gallus et al., , 2016Sharma et al., 2013). ...
This study explores the generation of ultrafine particle emissions, measured in particle number (PN), based on a portable emissions measurement system (PEMS) in the City of Toronto between October and December 2019. Two driving routes were designed to include busy arterial roads and highways. All measurements were conducted between 10 a.m. and 4 p.m. Altogether, emissions from 31 drives were collected, leading to approximately 200,000 s of data. A spike detection algorithm was employed to isolate PN spikes in time series data. A sensitivity analysis was also conducted to identify the most optimum method for spike detection. The results indicate that the average emission rate during a PN spike is approximately 8 times the emission rate along the rest of the drive. In each test trip, about 25% of the duration was attributed to spike events, contributing 75% of total PN emissions. A Pearson correlation of 0.45 was estimated between the number of PN spikes and the number of sharp accelerations (above 8.5 km/h/s). The Pearson correlation between the occurrence of high engine torque (above 65.0 Nm) and the number of PN spikes was estimated at 0.80. The number of PN spikes was highest on arterial roads where the vehicle speed was relatively low, but with high variability, and including a high number of sharp accelerations. This pattern of UFP emissions leads to high UFP concentrations along arterial roads in the inner city core.
... The Pearson correlation between the number of sharp acceleration events and the number of PN spikes was 0.45. This finding is consistent with previous on-road studies that found a high correlation between the high particle concentrations and accelerations (Gallus et al., 2017b(Gallus et al., , 2016Sharma et al., 2013). ...
This study explores the generation of ultrafine particle emissions, measured in particle number (PN), based on a portable emissions measurement system (PEMS) in the City of Toronto between October and December 2019. Two driving routes were designed to include busy arterial roads and highways. All measurements were conducted between 10 a.m. and 4 p.m. Altogether, emissions from 31 drives were
collected, leading to approximately 200,000 s of data. A spike detection algorithm was employed to isolate PN spikes in time series data. A sensitivity analysis was also conducted to identify the most
optimum method for spike detection. The results indicate that the average emission rate during a PN spike is approximately 8 times the emission rate along the rest of the drive. In each test trip, about 25% of
the duration was attributed to spike events, contributing 75% of total PN emissions. A Pearson correlation of 0.45 was estimated between the number of PN spikes and the number of sharp accelerations (above 8.5 km/h/s). The Pearson correlation between the occurrence of high engine torque (above 65.0 Nm) and the number of PN spikes was estimated at 0.80. The number of PN spikes was highest on arterial roads where the vehicle speed was relatively low, but with high variability, and including a high number of sharp accelerations. This pattern of UFP emissions leads to high UFP concentrations along arterial roads in the inner city core.
... This means that the vehicles may comply with emission regulations, but may emit much more under real-world conditions, hence making the policy ineffective. The second issue highlighted in the TERI study of 10 cities is of very high congestion levels, which could lead to loss of time, fuel, and enhanced exposure to very high emission levels ( Sharma et al. 2013Sharma et al. , 2016. The driving patterns show very low driving speeds mainly on account of congestion. ...
... This means that the vehicles may comply with emission regulations, but may emit much more under real-world conditions, hence making the policy ineffective. The second issue highlighted in the TERI study of 10 cities is of very high congestion levels, which could lead to loss of time, fuel, and enhanced exposure to very high emission levels ( Sharma et al. 2013Sharma et al. , 2016. The driving patterns show very low driving speeds mainly on account of congestion. ...
The book presents and document air pollutant emission inventories for different sources in India for a base
and future years. Energy use information from the MARKAL model results is fed into the emission modelling equations to derive emission estimates for India. The main sectors considered in the analysis are residential,
transport, industries, power, and non-energy under the fuel categories of coal, diesel, gasoline, natural
gas, liquefied petroleum gas, and biomass. The emission inventory was prepared for pollutants, such
as PM, NOx, SO2, CO, and NMVOC. PM emissions are also speciated into different fraction of PM10, PM2.5,
black carbon (BC), and organic carbon (OC).
This article presents a performance investigation of the range and state of electric charge vehicles based on different drive cycles and analyzed. Range anxiety is the major concern in electric vehicles globally. To mitigate the range anxiety issue by reducing the energy consumption in electric vehicles further reduces the range-related problem, and the state of charge reduces the demand for driver's power. The drive cycles predict, optimize, and simulate the performance range of Ev's real-world driving conditions. This research thoroughly focuses on the performance range of electric vehicles incorporated with the state of charge based on various standard drive cycles. A drive cycle plays a vital role in estimating energy consumption (EC) per km. There are many standard drive cycles World Wide Harmonized Light Vehicles Test Procedure (WLTP Class-3), the New European Drive Cycle (NEDC), the Modified Indian Drive Cycle (MIDC), the Indian Drive Cycle (IDC) used to investigate and evaluate Electric vehicle (EVs) performance range and state of charge (SoC). Range obtained from above drive WLTP Class-3, NEDC, MIDC, and IDC drive cycles 135.1123.8115.7 and 101.5 km, respectively. Due to improved road conditions, the WLTP Class-3 drive cycle offers more than the others. Due to road conditions, the IDC drive cycle offers a shorter range of 101.5 km.
Electric vehicle (EV) is at borne stage and facing lot of challenges at design and development stage. The performance of EV is solely depending on distance travelled by vehicle means range of vehicle and electrical consumed by the vehicle. These two performance parameters affects on sizing of EV component. The electric motor and electric battery are power and electrical source of the vehicle. For sizing, this component electrical consumption (EC) per kilometer (km) plays major role. The correct estimation of EC per km is very much necessary at the design stage for sizing of components. Drive cycles play major role for accurate estimation of EC per km. Many drive cycles (Indian drive cycle [IDC], modified Indian drive cycle [MIDC], urban drive cycle [UDC], worldwide harmonized light vehicles test procedure [WLTP], new European drive cycle [NEDC], and Artemis drive cycle) are currently used by many countries to predict the performance of vehicle. Various standard cycles are studies the effect of drive cycle on energy consumption of an electric four‐wheeler through analytical methods which results the optimal performance of vehicle. Current study investigates the analytical approach for accurate prediction of energy consumption per km to enhance the range of EV, which is major concern of EV buyers. Present work compares the range and electric consumption of electric four‐wheeler by using NEDC, highway fuel economy test cycle (HWFET), UDC, WLTP, and MIDC drive cycle. The range obtained from NEDC, HWFET, UDC, WLTP, and MIDC drive cycles are 114.20, 115.18, 78.16, 103.33, and 93.73 km, respectively. HWFET drive cycle provides higher than other drive cycle due to better road condition. Urban drive cycle provides low range of 64 km because of road condition.
div class="section abstract"> The closed loop air fuel ratio control is one of the essential algorithm to control emissions in internal combustion engine-based vehicle fleets. In order to address this, system and component level diagnostics need to explore extensively. In the presented work, a data-driven method is proposed and implemented to monitor the health of switch type oxygen sensor (lambda sensor). As the response of the lambda sensor present in a vehicle varies over the mile-age which will directly affect the tailpipe emission of the vehicle. Lambda sensor monitoring is the key challenge in OBD-II regulations. A simplified linear model for lambda sensor is derived which includes the lambda sensor gain KL . The recursive least square estimation algorithm is employed to search the value of the lambda sensor gain KL for which the model can estimate the lambda sensor output signal. The lambda gain has the correlation with state of health of the lambda sensor. That will be used as an index for age monitoring of the sensor. Proposed algorithm is tested and validated for real city drive condition data set and the results show that the gain KL converges at different values for different sensor responses. This is an ECU implementable method for real time estimation of sensor gain KL which lead us to objective determination of degradation in lambda sensor. The estimated value of KL is validated with three differently aged lambda sensors and mapped to objectively diagnose the health of the sensor.
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