Tailpipe Emissions and Fuel Economy for 2WD Vehicles and AWD Vehicles Tested on a Double-Axle Chassis Dynamometer: A Comparative Study

2016 ◽  
Author(s):  
Aaron J. Conde ◽  
Martha Christenson ◽  
Brad Richard
Author(s):  
Peter Vasquez ◽  
Edwin Quiros ◽  
Gerald Jo Denoga ◽  
Robert Michael Corpus ◽  
Robert James Lomotan

Abstract Efforts to mitigate climate change include lowering of greenhouse gas emissions by reducing fuel consumption in the transport sector. Various vehicle technologies and interventions for better fuel economy eventually require chassis dynamometer testing using drive cycles for validation. As such, the methodology to generate these drive cycles from on-road data should produce drive cycles that closely represent actual on-road driving from the fuel economy standpoint. This study presents a comparison of the fuel economy measured from a drive cycle developed using road load energy as a major assessment criterion and the actual on-road fuel economy of a 2013 Isuzu Crosswind utility vehicle used in the UV Express transport fleet in Metro Manila, Philippines. In this approach to drive cycle construction from on-road data, the ratio of the total road load energy of the generated drive cycle to that of the on-road trip is made the same ratio as their respective durations. On-road velocity and fuel consumption were recorded as the test vehicle traversed the 42.5 km. Sucat to Lawton route and vice versa in Metro Manila. Gathered data were processed to generate drive cycles using the modified Markov Chain approach. Three drive cycles of decreasing duration, based on the practicality of testing on a chassis dynamometer, were generated using three arbitrary data compression ratios. These drive cycles were tested using the same vehicle on the chassis dynamometer and compared with the on-road data using road load energy, fuel economy, average speed, and maximum acceleration. For the 893-seconds drive cycle generated, the road load energy error was 3.93% and fuel economy difference of 1.14%. For the 774-seconds cycle generated, the road load energy error was 4.34% and fuel economy difference was 0.91%. For the 664-seconds drive cycle, the road load energy error was 3.68% and fuel economy difference was 0.91%. On-road fuel economy for the 42.5-km. route averaged over nine round trips was 8.785 km/L. Based on the results, the road load energy criterion approach of drive cycle construction methodology can generate drive cycles which can very closely estimate on-road fuel economy.


1993 ◽  
Author(s):  
Kenyu Akiyama ◽  
Fumio Ueda ◽  
Johji Miyake ◽  
Kazuyoshi Tasaka ◽  
Shinichi Sugiyama

2014 ◽  
Vol 136 (2) ◽  
Author(s):  
Chih-Hsien Yu ◽  
Chyuan-Yow Tseng ◽  
Shiunn-Cheng Chuang

In an attempt to improve the fuel economy and reduce the exhaust emissions of motorcycles, some manufactures have developed commercialized motorcycles equipped with automatic idling-stop and go (AISG) functionality. Even though research efforts devoted to the idling-stop strategy have demonstrated its effectiveness, motorcycles equipped with the AISG device are not popular because the general public still has some concerns about them. This paper aims to evaluate the benefits and feasibility of a commercialized motorcycle with AISG functionality with regard to the public's concerns about fuel economy and emission problems during engine restart transients. In order to verify the accuracy of the analytical results and control for variable driver characteristics, a motorcycle chassis dynamometer was used to recreate the urban driving pattern. Furthermore, the feasibility of fuel-saving and emissions improvement by adjusting fuel-injection signal of the engine control unit (ECU) during engine restart operation was also evaluated. The experimental results showed that the addition of the fuel-injection modulation plus idling-stop strategy can improve the fuel economy rate by up to 12.2% and reduce carbon monoxide (CO) emission by up to 36.95% in comparison with the non-idling stop case.


1992 ◽  
Vol 114 (3) ◽  
pp. 561-567 ◽  
Author(s):  
S. G. Fritz ◽  
R. I. Egbuonu

Emissions are reported for four heavy-duty trucks, which were converted to operate on compressed natural gas fuel. Two 1988 model year Ford F700 Series trucks equipped with 7.0 L gasoline engines and two 1986 model year GMC trucks equipped with DDC 8.2 L diesel engines were tested on a heavy-duty chassis dynamometer in a baseline condition and again after conversion to natural gas. The vehicles were tested over the EPA Urban Dynamometer Driving Schedule for Heavy-Duty Vehicles and at no-load curb idle. Regulated emissions of NOx, CO, HC, and diesel particulate, along with nonmethane hydrocarbons, are reported in grams/mile. Fuel economy is reported in energy-equivalent miles per gallon of gasoline or diesel fuel.


Author(s):  
Jose Alejandro M. Reyes ◽  
Edwin N. Quiros

Abstract Carmakers, regulatory agencies, and consumers share an interest in accurately determining a vehicle’s fuel efficiency under operating conditions that match the expected use. Previous studies have shown that a vehicle’s air conditioning (A/C) system is the most energy-intensive non-propulsive system and significantly reduces fuel economy. This study aims to design and validate a new method of improving fuel economy estimates obtained on non-climate-controlled chassis dynamometers, as such laboratories are limited to measuring fuel economy with the A/C system deactivated. The methodology proposed herein uses a chassis dynamometer to measure the fuel economy penalty caused by the A/C system at different steady-state conditions. The hypothesis is that these penalties can be imposed accordingly for a given drive cycle to obtain an additional fuel consumption due to A/C. To validate the proposed methodology, a vehicle was outfitted with a data acquisition system and was driven 50 times around a predefined route using varying A/C settings. The proposed method was then used to estimate the additional fuel consumption due to A/C usage for each of the runs. Comparing the calculated and actual fuel economies showed an average error of 1.924%. It was concluded that the proposed methodology is a viable alternative to existing procedures.


2019 ◽  
Author(s):  
Michael Akard ◽  
Nathan Gramlich ◽  
Tim Nevius ◽  
Scott Porter

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